Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

Understanding Agentic AI: The Future of Autonomous Customer Service

Nehatrendy
Generated by Gen AI

What is Agentic AI?

Gartner1 predicts Agentic AI will autonomously resolve 80% of common customer service issues without human Intervention by 2029. Despite AI’s rise, 89% of customers emphasize the need to combine human connection with AI efficiency to optimize experiences.

Agentic AI, also known as autonomous AI, is a form of artificial intelligence that operates independently to design, execute, and optimize workflows. This empowers enterprises to make decisions and complete tasks more effectively. These AI agents can plan, adapt, and achieve predefined goals without human intervention.

Agentic AI leverages advanced reasoning and iterative planning to solve complex, multi-step problems autonomously. In contrast, AI chatbots typically use Generative AI to respond to single queries, processing one interaction at a time using Natural Language Processing (NLP).

The next frontier in AI is Agentic AI, poised to enhance productivity and operational efficiency across industries. These systems can process vast amounts of data from various sources and third-party applications, independently analyzing challenges, developing strategies, and executing tasks. Businesses are adopting Agentic AI to personalize customer service, streamline software development, and facilitate patient interactions.

For example, an AI agent in customer service could go beyond answering questions. It could check a user’s outstanding balance, recommend ways to pay it off, and complete the transaction after user approval.

How does Agentic AI work?

Define your objectives, and Agentic AI takes action. If a suitable workflow exists, the agent executes it; otherwise, agents may design their own workflows on the fly, always consulting with humans when needed. If circumstances change, AI agents adapt their strategies to achieve optimal results. Agentic AI proactively anticipates needs, predicts outcomes, and responds to opportunities.

Agentic AI follows a four-step process for problem-solving:

  • Perceive: Agents gather and process data from various sources—sensors, databases, and digital interfaces—extracting features, recognizing objects, or identifying relevant entities in the environment
  • Reason: A large language model (LLM) orchestrates tasks, generates solutions, and coordinates specialized models for functions like content creation, visual processing, or recommendations. Techniques such as retrieval-augmented generation (RAG) enable access to proprietary data for accurate, relevant outputs
  • Act: Agentic AI swiftly executes its plans by connecting with external tools and software via APIs. Built-in guardrails ensure tasks are performed correctly; for instance, a customer service AI may handle claims up to a set amount, with larger claims requiring human approval
  • Learn: Agentic AI continuously improves through a feedback loop (data flywheel), feeding interaction-generated data into the system to enhance models. This adaptive capability helps businesses drive better decision-making and operational efficiency

Capabilities of Agentic AI

  • Workflow Design: Use AI agents to implement best practices and rapidly create new workflows
  • AI-Powered Decisioning: Automate decision-making with context-rich data to determine the next best action or enable straight-through processing
  • Adaptability: Enhance business agility as Agentic AI adapts to change and predicts future scenarios, ensuring optimal outcomes
  • Workflow Execution: AI agents identify and execute the appropriate workflows to resolve user requests automatically
  • Workflow Optimization: Leverage AI agents to optimize and execute complex, multi-step workflows

Spot the Difference: Generative AI Vs. Agentic AI

Artificial intelligence (AI) has become a buzzword in today’s world. The term often brings to mind robots and self-learning machines, but in reality, AI encompasses a wide range of subfields. Two of the most discussed advancements are Generative AI and Agentic AI. It is crucial to understand that they operate in fundamentally different ways. Grasping these distinctions is essential for understanding how AI is shaping and will continue to shape our world.

Generative AI

Generative AI is focused on creation—think of it as the inventive branch of artificial intelligence. These systems are designed to produce a wide range of content, including text, images, music, code, and even videos. By learning from existing data, generative AI generates original outputs that emulate human creativity. The advent of tools like ChatGPT, DALL·E, and MidJourney has brought Generative AI into the mainstream. These platforms rely on advanced machine learning models, especially neural networks, to analyse patterns from their training data and replicate them.

However, Generative AI is not without flaws. Its outputs are constrained by the quality of its training data: if the data is biased or incomplete, the results will reflect those limitations. Moreover, Generative AI does not truly “understand” the content it creates; it predicts subsequent elements based on recognized patterns. Despite these drawbacks, Generative AI is rapidly transforming industries ranging from marketing to entertainment.

Agentic AI

Agentic AI systems do more than generate outputs—they make decisions, take action, and adapt to changing environments. Operating as intelligent agents, they make real-time decisions, dynamically adapt, and integrate into various systems or workflows. Agentic AI powers technologies such as autonomous vehicles, robotic process automation, and smart assistants capable of scheduling meetings or ordering groceries. These systems use sensors, algorithms, and actuators to perceive their environment, process information, and execute actions.

What sets Agentic AI apart is its purposeful decision-making. Instead of merely responding to inputs, it acts based on objectives, choosing strategies to achieve its goals. For instance, an autonomous drone making deliveries must avoid obstacles, optimize its route, and adapt to unexpected events—all without human intervention.

However, granting these systems autonomy raises important ethical and accountability concerns. Who is responsible if an autonomous system makes an error? How do we ensure such systems align with human values? Addressing these challenges is crucial as Agentic AI becomes more widespread.

Summarizing the Core Differences

The simplest way to distinguish Generative AI from Agentic AI is to consider their main functions. Generative AI focuses on creating new content, whereas Agentic AI is designed to accomplish specific goals. In essence, one generates, while the other acts.

Generative AI is generally static; it produces outputs based on previously learned data but does not interact with or adapt to its environment in real time. Its activities are confined within preset boundaries. Agentic AI, by contrast, is dynamic: it continually processes new information, learns from its surroundings, and adjusts its actions to achieve objectives.

Another important difference is in the complexity of their tasks. Generative AI is best suited for narrow, well-defined problems—such as writing a paragraph or creating digital art. Agentic AI typically handles more complex, multi-step tasks that require ongoing decision-making and adaptation.

Benefits of Agentic AI

  • Increased efficiency: By automating workflows and enabling real-time decision-making, AI agents support straight-through processing, transforming your enterprise into an operations powerhouse
  • Enhanced self-service: When interacting with a chatbot, Agentic AI understands the customer’s intent and guides them to resolve issues or workflows on their own, without needing escalation to a live agent
  • Maximized employee performance: AI agents can assess complex situations and provide employees with step-by-step guidance based on your organization’s policies and best practices
  • Better and faster workflow design: Agentic AI, such as Pega Blueprint™, recommends workflow designs that follow industry best practices—accelerating speed to market and serving as a creative partner for developers
  • Hyper-personalized engagement: By analyzing vast data, AI agents understand customer needs and determine the best course of action in real time, delivering highly personalized experiences

Challenges and Considerations of Agentic AI

  • Cost of Implementation: Integrating Agentic AI can be expensive, especially for mid-sized businesses. Costs may include upgrading infrastructure, licensing software, and providing training for staff. Careful planning and phased implementation can help manage these expenses. And over time, the long-term ROI from time and efficiency savings may balance out the upfront costs. 
  • Implementation Barriers: Adopting Agentic AI requires investment in technology and integration. You may need to upgrade legacy systems and provide training to ensure agents can use the tools effectively
  • Training and Adoption: Agents might initially struggle with adapting to this new way of working. Proper onboarding and ongoing support are vital to building trust in the technology and maximizing its benefits
  • Ethical and Transparency Concerns: These are a major issue in AI deployment. People are especially worried about bias in AI models, and their personal privacy. So, ensure you are transparent with your customers when AI is used
  • Balancing AI and Human Touch: While Agentic AI does improve efficiency, we need to keep the human touch. This builds trust and rapport. Striking the right balance ensures customers feel valued and heard, not just processed

What are some Use Cases for Agentic AI?

Customer Service

By 2028, 68% of all customer service and support interactions with technology vendors are expected to be handled by Agentic AI

In self-service situations, AI agents can understand customer intent and determine the right workflow to guide customers in resolving their inquiries. Agentic AI can also assist customer service reps with their work by suggesting actions and automating tasks and processes. AI agents are improving customer support by enhancing self-service capabilities and automating routine communications. Over half of service professionals report significant improvements in customer interactions, reducing response times and boosting satisfaction. There’s also growing interest in digital humans — AI-powered agents that embody a company’s brand and offer lifelike, real-time interactions to help sales representatives answer customer queries or solve issues directly when call volumes are high.

Content Creation

Agentic AI enables rapid creation of high-quality, personalized marketing content. Generative AI agents save marketers an average of three hours per content piece, freeing them to focus on strategy and innovation. Streamlining content creation helps businesses remain competitive and boost customer engagement.

On the performance side, AI agents can scan hundreds of millions of pages, analyze metrics, flag optimization issues and fine-tune content. Previously, this could take months or even years. Teams no longer need to wait for underperformance to show up in a dashboard—now they can identify issues before taking a hit on traffic or conversions.

Healthcare

Key applications of Agentic AI in healthcare include:

  • Workflow automation: Agentic AI automates tasks like staff scheduling, patient intake, documentation, and many other routine manual tasks to free up clinical teams to focus on patient care.
  • Real-time decision-making support: AI Agents can support clinicians by providing data-informed choices in the moment and taking actions without human guidance. AI agents can also provide 24/7 support, offering information about prescribed medication usage, appointment scheduling and reminders, and additional resources to help patients adhere to treatment plans.
  • Adaptive learning: Agentic systems can adapt to changing circumstances and continuously improve their performance by interacting with APIs, digital tools, and evolving data sets in real-world healthcare environments. Agentic AI brings significant value to healthcare by analyzing patient health data for proactive care, scheduling appointments, and guiding both patients and providers through the complexities of healthcare journeys.

Software Engineering

AI agents are enhancing developer productivity by automating repetitive coding tasks. Projections suggest that by 2030, AI could automate up to 30% of work hours, enabling developers to focus on more complex challenges and drive innovation. With growing IT complexity, 92% of organizations say the support and services they receive are more important than ever.

Video Analytics

Enterprises and public sector organizations around the world are developing Video Analytics AI Agent to boost the capabilities of workforce that rely on visual information from a growing number of devices — including cameras, IoT sensors and vehicles. Video analytics AI agents can analyze large amounts of live or archived videos, request tasks via natural language and perform complex operations like video search, summarization and visual question-answering. These agents can also be used to deliver anomaly alerts, draft incident reports, improve quality control through visual inspection and enhance predictive maintenance. 

Unlike conventional rule-based systems, Agentic AI video analytics adapts dynamically to context, learns from ongoing interactions, and collaborates across enterprise systems. The ability to process video streams in real time with semantic accuracy not only enhances situational awareness but also improves predictive capabilities—driving faster, more informed business outcomes.

Financial Services

Imagine a trading AI agent that analyzes market data and autonomously monitors market trends, deciphers trading signals, adjusts strategies and mitigates risks in real time. Agentic AI will make this increasingly possible, integrating tools via APIs, sensors and advanced reasoning, to respond to new information and data, and automate and enhance financial tasks beyond Gen AI capabilities. Agentic AI’s increased autonomy, relative to Gen AI, enables it to handle repetitive, data-intensive processes. This gives financial institutions and market participants an increased capability to optimize workflows, enhance compliance and improve decision-making, transforming how financial services conduct business and interact with customers in the age of Agentic AI.

Conclusion: The Future of Agentic AI

Agentic AI holds tremendous potential to transform enterprises. Because it operates autonomously, companies will need frameworks that support autonomous workflows, provide seamless data integration, and continually optimize processes. This new technology will drive enterprises to undergo broader transformations to fully harness Agentic AI’s capabilities.

Agentic AI is still in its early stages. However, it has the potential to really transform customer service and other aligned avenues. Here’s a glimpse into what the future might hold:

  • Hyper-Personalization: As Agentic AI matures, its ability to analyze and predict customer behaviour might improve, though accuracy may vary depending on the data and context. This may lead to hyper-personalized customer experiences, where interactions are tailored not just to past behaviour but to real-time needs and preferences
  • Enhanced Multi-Channel Support: Agentic AI appears ready to make omnichannel customer service truly consistent. Whether a customer reaches out via messaging, chat, email, voice, or video, the AI could provide consistent, informed support and ensure smooth transitions between channels

The distinction between Generative and Agentic AI will blur as both evolve. Advancements may yield systems that merge creation and action, offering the best of both worlds. Imagine an AI that not only generates innovative ideas but also implements them autonomously—a true game-changer across industries like healthcare and manufacturing.

However, this potential comes with responsibility. These technologies must be developed and deployed ethically, with transparency and accountability. Understanding the differences between Generative and Agentic AI will help us appreciate their strengths and guide us toward a future where AI meaningfully benefits humanity.

References

  1. https://www.gartner.com/en/newsroom/press-releases/2025-03-05-gartner-predicts-agentic-ai-will-autonomously-resolve-80-percent-of-common-customer-service-issues-without-human-intervention-by-20290 ↩︎
Generative AI, Industry Perspectives, Technology & Digital

How Generative AI is Reshaping the Media & Entertainment Industry

Nehatrendy

Setting-Up Generative AI in Media & Entertainment

The global Media and Entertainment (M&E) industry has experienced a 5% revenue increase over the past decade. Conversely, advertising revenues are projected to nearly double in the next era. As the M&E industry continues to expand, market players encounter both risks and opportunities. Changes in consumer preferences, along with the ongoing impact of digital transformation and emerging technologies like Generative AI, are driving a wave of business model reinvention.

One of the most significant developments in the digital age is the rise of individuals creating their brands and cultivating online audiences. This ecosystem is growing for several reasons, including the increase in digital media consumption and advancements in technology that have lowered the barriers to content creation.

New platforms such as TikTok have emerged, while legacy platforms like Facebook and YouTube have also introduced new formats for sharing short-form videos, live streaming channels, and other forms of user-generated content

This growth aligns with estimates for global digital advertising spending during the same period. Analysts expect that spending on influencer marketing, along with platform payouts fueled by the monetization of short-form video platforms through advertising, will be the main drivers of the creator economy. 

The Creator Economy

Goldman Sachs Research expects the 50 million global creators to grow at a 10-20% compound annual growth rate during the next five years. The creator economy earns income primarily through direct branding deals to pitch products as an influencer; via a share of advertising revenues with the host platform; and through subscriptions, donations and other forms of direct payment from followers. Brand deals are the main source of revenue at about 70%, according to survey data.

But the analysts also cite six key enablers for creating a “flywheel effect” in which small gains build on each other over time and create further growth momentum. These enablers are scale, large pools of capital, strong AI-powered recommendation engines, effective monetization tools, robust data and analytics, and e-commerce options. 

Leveraging Generative AI in Media & Entertainment

The global entertainment and media industry has always thrived on technological disruption. To capitalize on various growth opportunities, it must harness the power of new and emerging technologies, such as Generative AI, to reshape its business operations, build new creative models, and enhance advertising efforts.

Many organizations are currently using Generative AI to transform their business models and create new opportunities. However, these deployment strategies come with significant costs. According to Gartner, Inc., at least 30% of Generative AI (GenAI) projects are expected to be abandoned after the proof of concept phase due to issues such as poor data quality, inadequate risk controls, rising costs, or unclear business value. A major challenge for organizations is justifying the substantial investment required for GenAI, particularly in enhancing productivity, as the financial benefits can be difficult to quantify directly.

Marketing Trends in Media & Entertainment

More than half of marketers are successfully personalizing their content across various channels: 57% for mobile messaging, 54% for email marketing, and 52% for social media. The channels that allow for easy testing and quick iteration, such as mobile messaging, email marketing, and social media, tend to see the most advanced personalization strategies.

Marketers’ biggest concern regarding generative AI is data leaks, followed closely by the lack of adequate quality data. To gather enough valuable information, marketers are primarily using customer service and transaction data. This approach demonstrates their efforts to collaborate with colleagues in sales and commerce. However, integrating other types of data, such as unstructured data from emails, remains a significant challenge.        

Industry Applications

Marketing and Advertising

  • AI in Targeted Advertising: Artificial Intelligence (AI) enables advertisers to analyze consumer behavior patterns, allowing for highly targeted advertising campaigns. Machine learning algorithms process large volumes of data to identify trends and predict which products or services a customer is most likely to purchase. For instance, Facebook’s AI-driven advertising platform assists businesses in targeting users based on their interests, demographic information, and even browsing habits.
  • AI in Content Creation for Ads: AI is also enhancing the creation of engaging ad content. Tools like Copy.ai and Jasper utilize AI to generate ad copy that resonates with specific audiences. These tools analyze extensive data sets to create content that has a high likelihood of converting viewers into customers. Furthermore, AI facilitates dynamic ad creation; for example, Google Ads employs AI to automatically produce different versions of ads and optimize them in real-time for maximum performance. This feature significantly boosts the return on investment (ROI) for advertising campaigns.
  • AI in Ad Analytics & Performance Tracking: AI-powered analytics platforms provide advertisers with real-time insights into the performance of their campaigns. These tools track user interactions, predict future trends, and suggest adjustments to optimize campaigns—all of which help maximize the effectiveness of advertising expenditures.

AI-Driven Content Creation

  • AI in Content Writing and Journalism: AI tools like GPT-3 and Jasper have opened up new possibilities for content creators. These tools can automatically generate articles, blog posts, and even long-form content in just a matter of seconds. In journalism, AI can analyze vast amounts of data, quickly identifying trends and news stories that may interest readers. While AI cannot replace human creativity and critical thinking, it can assist writers by providing a solid foundation for content creation, allowing them to focus on higher-level tasks such as research and analysis.
  • AI in Video and Visual Content Creation: AI has also made its way into video and visual content creation. Several available tools utilize AI to generate videos, animations, and visual effects with minimal human input. These platforms enable creators to produce high-quality content without requiring advanced skills in video editing or animation.

Future of AI in Media & Entertainment

Hyper-Personalized Content Experiences

AI-driven recommendation engines are becoming increasingly sophisticated, enabling hyper-personalized content experiences for consumers. In the future, these systems will leverage predictive analytics to anticipate user needs even before they begin their searches, thereby enhancing viewer satisfaction.

AI-Generated Content

AI in media is set to redefine content creation. Tools like OpenAI’s ChatGPT and DALL·E are being utilized to create text, music, and visual art. This trend is expected to grow, with AI assisting filmmakers, authors, and musicians in developing ideas, generating scripts, or creating concept art.

AI in Media Localization and Accessibility

As global demand for localized content rises, AI in media will play a larger role in subtitling, dubbing, and translating media into multiple languages. AI-powered tools are already speeding up these processes, making them more accurate and cost-effective. By 2030, it is anticipated that AI will power 90% of all media localization efforts, making global content more accessible than ever.

Challenges and Ethical Considerations

Deepfakes

Deepfakes are created with such precision that distinguishing them from real footage can be extremely difficult. This highlights both the potential benefits and the risks associated with this advanced technology. Utilizing the latest generative AI techniques, deepfakes represent a new form of deception. By harnessing artificial intelligence, they produce hyper-realistic videos, audio, and text that can deceive even the most discerning individuals. This has the potential to lead to multimillion-dollar losses for businesses. 

As the capabilities of deepfakes continue to advance, organizations must invest in proactive cybersecurity measures. Doing so is not only a strategic decision but also a cost-effective one. The financial toll of restoring an organization’s reputation and rebuilding customer trust following a deepfake attack far exceeds the costs of implementing strong cybersecurity protocols in advance.

Data Privacy and Security

  • Data Collection: Many AI systems collect large amounts of data, which raises concerns about how user data is gathered, stored, and processed.
  • Security Breaches: Protecting user data is crucial, which is why it is important to implement measures to prevent hacking and misuse.
  • Content Moderation, Filtering, and Monitoring: AI applications in the industry help social networking sites identify and remove inappropriate content, making the online environment safer for users.

Bias and Fairness

  • Algorithmic Bias: AI systems can reproduce the biases present in their training data, leading to unfair treatment or representation.
  • Diverse Representation: It is essential to ensure that AI-generated content fairly and accurately represents different cultures, incorporating various perspectives and voices.

Intellectual Property

  • Content Ownership: Determining the ownership of text created by AI can be challenging, particularly when the content is jointly produced or derived from existing works.
  • Copyright Infringement: There are legal concerns regarding the use of AI, as the techniques used may generate content that could potentially violate existing copyright laws.

Wrapping Up

Artificial intelligence is transforming the media and entertainment industry by making content creation faster, enhancing personalization, and enabling new ways to interact with media. While these technological advancements are exciting, they also bring challenges, including ethical concerns and the potential for job displacement. As AI continues to evolve, it will reshape the industry by creating more immersive and personalized media experiences, pushing the boundaries of creativity, and driving the future of entertainment.

The most significant impact of generative AI will be in simplifying complex, skilled processes for a broader audience, especially in areas where outputs can be immediately verified. Media companies should focus on applications that empower creatives to produce more high-quality work rather than replacing them with AI-generated content.

Currently, AI tools are not suitable for completely replacing human-made content. Media companies should maintain strong relationships with creators and have a deep understanding of the creative process and the market for media products. Generative AI systems are less effective for tasks that require precision, involve critical system dependencies, or where the appropriateness of the output is not immediately clear. In many cases, traditional software approaches, human input, or a combination of both will provide the most effective solution for business challenges. 

Generative AI, Industry Perspectives, Technology & Digital

Generative AI: Driving Cost Reductions in Oil & Gas Industry

Nehatrendy

Artificial Intelligence (AI) is fundamentally transforming the Oil and Gas industry, bringing innovation and efficiency that’s reshaping the entire sector. The traditional exploration process, which was often lengthy and costly, is now being revolutionized by AI’s advanced algorithms, this not only saves time and resources but also reduces the environmental impact. Furthermore, AI-powered predictive maintenance is a game-changer in drilling operations, continuously monitoring equipment to anticipate failures and minimize downtime.

With recent emerging technology developments, it is evident that companies in the Oil and Gas sector should strategically invest in AI technologies. For instance, all of the top 20 global Oil and Gas producers, be they state-owned entities or public-listed ones, have a clear AI strategy for their upstream (exploration and production), downstream (processing and refining), and where applicable, midstream (pipeline and logistics) businesses. 

In a recent consulting survey, Oil and Gas executives reported the two highest opportunities for creating value from Artificial Intelligence: predictive maintenance for heavy equipment & assets and intelligent optimization of operations performance. These include AI-led raw materials procurement, inventory, logistics, operational decision making, back-office management, and AI-premised cybersecurity, based on the strategic importance of energy infrastructure.

Benefits Modeling through Generative AI

Leading consulting firms like Deloitte emphasize that the value of Generative AI for the Oil and Gas industry can be categorized into four dimensions: immediate cost reduction, enhanced process efficiency, generating new revenue streams, and accelerating innovative AI-Driven solutions.

  • Cost Reduction: Generative AI-driven solutions can help companies in cutting operational costs, especially in addressing challenges related to unplanned downtime
  • Process Efficiency: Generative AI can enhance efficiency by integrating and analyzing diverse data sources. It can help process vast quantities of data, including geological and subsurface information such as seismic surveys, well logs, and historical drilling records, leading to optimized drilling processes
  • Revenue Expansion: Generative AI can help pave the way for increased revenue generation. It can optimize the exploration of high-yield reserves and enhance recovery from the existing ones
  • Accelerating Innovation: Generative AI can help expedite the development of new solutions by enabling rapid testing of new ideas and concepts

Industry Trends

Demand Forecasting

By leveraging vast amounts of historical data and current market trends, Oil and Gas companies can improve demand forecasting for their products. This allows companies to not only optimize production schedules but also ensure they produce the right quantities to meet market needs. By minimizing overproduction and underproduction, businesses can reduce waste, manage inventory more efficiently, and improve profitability. 

Predictive Maintenance

Monitors the health of equipment through sensors and data analytics. These systems can detect anomalies and predict potential failures before they happen, allowing for timely maintenance interventions. This proactive approach reduces unplanned downtime and extends critical machinery’s lifespan, leading to significant cost savings. 

Robotic Process Automation

Streamlines back-office operations by automating repetitive and time-consuming tasks such as data entry, invoice processing, and report generation. This not only improves accuracy but also significantly reduces operational costs and enhances productivity. Employees are freed from manual tasks, allowing them to focus on more strategic and value-added activities. Additionally, Robotic Process Automation enhances data integrity and compliance by reducing the likelihood of human error. 

AI-Driven Supply Chain Management

Identifies potential disruptions in the supply chain, enabling companies to implement contingency plans and maintain continuity of operations. Predictive analytics can anticipate demand fluctuations, allowing companies to adjust their supply chain operations leading to improved efficiency, reduced costs, and better customer satisfaction. Enhanced supply chain visibility and agility ultimately contribute to a more resilient and responsive supply chain network. 

Dynamic Pricing

Allows companies to navigate the volatile nature of Oil and Gas prices by analyzing real-time market data and historical trends. Predictive models can forecast price movements, enabling businesses to make informed pricing strategies, hedging, and inventory management decisions. This ability to anticipate price fluctuations allows companies to mitigate risks and capitalize on favorable market conditions. 

Digital Twins

The fundamental technology behind a Digital Twin are various models – First principles, machine learning, and dynamic process response models. Often the term used for a combination of first principles and machine learning models is hybrid model. The back-bone of Digital Twins are various modeling technologies, which continue to undergo improvements as the industry has always done. Advances in Computing Power, Big Data, Machine Learning and Sensors, combined with lower technology costs, are driving increased adoption of Digital Twins. 

The holy grail of management is the deployment of “digital twins”, or virtual cloud-based replicas of an operational piece of upstream, midstream or downstream process or equipment. It is underpinned by first principles schematics / models / workflow charts, machine learning and process response software. Combining one or more of these aspects, digital twins generate simulations that can anticipate operational needs and prevent mishaps. 

Case Studies

British Petroleum

Leverages AI to transform its operational efficiency and cost-effectiveness. Their usage of AI in geological data analysis streamlines the identification of potential drilling sites, ensuring higher accuracy and better resource allocation. 

ExxonMobil

Employs AI to revolutionize reservoir management, predictive maintenance, and safety monitoring. By applying AI, they can simulate reservoir behavior to maximize extraction efficiency and recovery rates. Real-time AI analytics enhance safety measures by identifying and mitigating potential hazards, showcasing ExxonMobil’s dedication to innovative operational excellence. 

Chevron

Integrates AI in existing processes to enhance its exploration and production activities. With machine learning algorithms, they process seismic data with unparalleled accuracy, improving subsurface imaging and oil reserve identification. Chevron’s predictive analytics for equipment maintenance reduce operational downtime and lower costs, demonstrating their innovative approach to utilizing AI in the Oil and Gas industry. Using Generative AI, Oil and Gas companies and logistics providers automate mission-critical supply chain processes such as: 

  • Procurement: Materials demand forecasting, identifying the most suitable suppliers
  • On-shore and Off-shore inventory management: Improving asset tracking
  • Route Planning: Identifying current traffic conditions, tuning optimal delivery timing, vehicle tracking, and fuel-efficient routing
  • Contingency Planning: Running what-if scenarios in a digital twin environment to develop custom multi-purpose mitigation strategies

Generative AI in Oil and Gas

Generative AI is sent to be a linchpin in the ongoing transformation of the Energy Utilities, Oil and Gas sectors. Furthermore, the convergence of Generative AI and IoT technologies will create a more interconnected and data-rich ecosystem, fostering intelligent decision-making and reducing operational costs. 

Streamlining Transportation and Distribution

Timely delivery of Oil and Gas products to their designated markets is imperative. The complexities of transportation and distribution, involving numerous stakeholders, pose significant challenges. Generative AI refines these processes by analyzing logistics data, leading to optimized distribution channels and transportation networks.

This application of Generative AI enhances resource distribution and supplier management and ensures optimal shipping schedules and routes. Adopting Generative AI in these areas yields substantial cost reductions, boosts operational efficiency, and reinforces a company’s ability to satisfy customer demands, driving competitiveness and value creation. 

Driving Operational Efficiency

Utility companies benefit from improved operational efficiencies through Generative AI, which streamlines workflows, automates routine tasks and enhances overall performance. In addition, AI-generated insights can help customers optimize their energy consumption by recommending personalized energy-saving strategies based on usage patterns, weather data, and smart meter readings.

Improving Customer Experience and Energy Services

The Oil and Energy sector is increasingly focused on enhancing the customer experience, particularly in retail energy services and distributed energy resources (DERs). Generative AI can help create personalized energy services, recommend energy-saving strategies, and improve customer engagement through predictive insights.

Future of AI in Oil and Gas

The future of Generative AI in the Oil and Gas sector will continue to reshape the industry, driving innovations in exploration, production, and sustainability. As companies increasingly embrace AI, the following trends are expected to shape the future:

  • AI-Driven Decarbonization: Generative AI will play a pivotal role in helping the energy sector achieve its decarbonization goals by optimizing renewable energy integration, reducing emissions in extraction processes, and improving energy efficiency
  • Collaborative AI Ecosystems: Energy companies will likely collaborate more with AI providers, universities, and startups to co-develop Generative AI solutions, creating an ecosystem of shared knowledge and innovation
  • Autonomous Energy Systems: AI-powered autonomous systems will be able to revolutionize operations, enabling fully automated rigs, smart grids, and self-healing power networks that generate, distribute, and manage energy with minimal human intervention

Generative AI has the potential to revolutionize downstream Oil and Gas operations. With its ability to optimize refinery processes, predict maintenance needs, manage risks, and monitor operations in real-time, this technology can help companies increase efficiency, reduce downtime, and save costs. 

Wrapping Up

The energy industry has embraced AI as a lever for enhancing the productivity and decision-making, and companies of all sizes are investing resources significantly to develop and deploy AI tools. Successful adoption of AI in Oil and Gas requires leadership buy-in, culture change and constant feedback. Embracing AI can enable Oil and Gas companies to optimize their operations, enhance exploration and production process, improve safety measures, and lower costs. While AI offers numerous benefits to the Oil and Gas sector, the path to realizing these advantages is not without its obstacles. Some of the most common challenges of implementing AI in the oil and gas industry are as follows. 

Regulatory Compliance

It’s difficult to navigate complex regulatory environments encompassing data privacy, environmental regulations, and safety standards. For this reason, it’s imperative to engage early with regulatory bodies to understand and comply with requirements, conduct regular compliance audits, stay updated on regulatory changes, collaborate with legal experts, and maintain comprehensive documentation and reporting processes. 

Talent Shortage

There has always been a shortage of skilled professionals with expertise in AI, data science, and specific domain knowledge of oil and gas. Leveraging the expertise of consultants can give access to specialized knowledge and innovative solutions, enhancing your organizations capabilities.

Data Quality

Ensuring data accuracy and reliability from diverse sources such as sensors and operational records is crucial for AI application in the Oil and Gas industry. Implementing robust data governance frameworks, conducting regular validation and cleansing, utilizing advanced analytics for anomaly detection, and fostering collaboration between data scientist and domain experts. 

The energy sector is increasingly adopting AI to boost productivity and decision-making. While initial efforts focus on establishing data foundations for AI deployment, the challenge now is scaling AI across enterprises for significant impact. Success requires executive buy-in, viewing AI as a continuous process needing constant monitoring, and fostering a culture that embraces AI.  

Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

AI-Powered Solutions for Smart Transportation Systems

Nehatrendy

Perspective on AI in Transportation

The transportation industry is undergoing a major transformation as artificial intelligence (AI) takes center stage. From self-driving cars to drone taxis, AI has the potential to significantly change how we move, promising greater efficiency, improved safety, and a more sustainable future.

According to a Deloitte Consulting report, Intelligent automation is paving the way for a reality filled with autonomous trucks and ships, automated digital fulfillment centres, and last-mile delivery drones. These three interconnected pillars are crucial for advancing the future of the transportation ecosystem. While many organizations are making progress in developing connected communities—with 49% of respondents actively pursuing strategies in this area—holistic decision-making follows at 39%. In contrast, intelligent automation is more challenging to implement, with only 35% successfully integrating it due to its inherent complexities.

The rise of interconnected technologies such as the Internet of Things (IoT), electric vehicles, geolocation, and mobile technology has enabled us to better manage the flow of people and goods, particularly in densely populated urban areas. By harnessing AI in transportation, the industry can enhance passenger safety, reduce traffic congestion and accidents, decrease carbon emissions, and lower overall costs. Recognizing the unique potential of AI, businesses in the transportation sector are making substantial investments to boost revenue and maintain a competitive edge.

An IBM study indicates the crown jewel of any smart city is the potential of smart transportation to transform urban operations and facilitate the movement of people within those cities. Known as smart mobility, this concept has gained traction as local governments increasingly adopt data collection and automation technologies. The widespread use of smartphones, which can easily transmit and receive messages and data, has made this transition possible. Additionally, the affordability and availability of public Wi-Fi networks present new opportunities for governments to implement innovative transportation initiatives effectively.

Industry Applications

Artificial Intelligence combined with other emerging tech is making the industry more user-centered and automated. Here are some of the applications of implementing AI in Transportation. 

Autonomous Vehicles

Self-driving cars are one of the most exciting advancements in artificial intelligence for transportation. By utilizing a combination of cameras, sensors, geolocation, and AI, these vehicles can either drive themselves or provide advanced driver-assist features that require minimal to no human intervention. However, the true potential of AI in transportation goes beyond personal use; it also has significant applications in commercial and public transport. In public transportation, AI can optimize schedules to reduce wait times, enhance passenger safety, and manage passenger loads to prevent overcrowding.

The manufacturer of this autonomous taxi claims that the technology will lower operational costs for taxi services, which could help expand public transportation options in remote areas. Similarly, the logistics industry in the U.S. is adopting autonomous trucks to benefit from various advantages.

According to a leading consulting report, 65% of goods are transported by trucks worldwide. With the introduction of autonomous trucks, maintenance and operational costs are expected to decrease by approximately 45%. Currently, most companies are still conducting pilot projects, working to ensure that self-driving vehicles are both safe and reliable for passengers.

Traffic Flow Optimization

Traffic congestion is a daily issue that many people face, but AI is now poised to tackle this problem as well. AI can analyze traffic patterns in real-time, providing alternate route suggestions, predicting bottlenecks, and optimizing traffic light synchronization. This leads to reduced travel times, lower emissions, and a more enjoyable driving experience.

Sensors and cameras placed throughout roadways collect vast amounts of traffic data. This information is then sent to the cloud, where big data analytics and AI systems analyze the traffic patterns. Commuters can receive important updates, such as traffic predictions, information about accidents, and road blockages. Additionally, they can be alerted to the shortest routes to their destinations, allowing for hassle-free travel. In this way, AI can help decrease traffic congestion, enhance road safety, and minimize wait times.

AI in Logistics and Supply Chain Management

Transportation logistics firms and retailers strive to maintain seamless supply chains, and with the integration of AI in logistics and transportation, this goal is both achievable and effective. Artificial intelligence can enhance various aspects, such as optimizing delivery routes, reducing fuel consumption, and sequencing deliveries. Additionally, AI enables more accurate predictions of product demand, inventory needs, and customer requirements.

It’s no surprise that, according to Gartner, by 2024, 50% of all supply chain companies are expected to invest in AI-driven applications featuring advanced analytics capabilities.

Ride-Sharing and Mobility as a Service (MaaS)

Intended to find the best ride options for on-demand mobility, whether it’s a carpool, a bike, or a public transit, AI in transportation makes the MaaS model incredibly efficient and user-oriented. By bringing together several transportation operators into a single mobility ecosystem, AI helps people get the most convenient and time-saving travel options based on their needs. It analyzes passenger demand and traffic trends to forecast demand and dispatch vehicles to areas with anticipated high demand to pick up users just in time. This way, detour routes and wait times are minimized.

In a single mobility ecosystem, AI assists individuals in finding the most convenient and time-saving travel options tailored to their needs. It analyzes passenger demand and traffic patterns to predict where demand will be high, allowing for timely vehicle dispatch to those areas. This approach minimizes detours and reduces wait times for users.

Logistics Route Optimization

AI optimizes delivery routes by analyzing real-time traffic, weather conditions, and demand patterns. This optimization ensures faster and more efficient deliveries while minimizing delays throughout supply chains. Companies can reduce fuel consumption and improve delivery times, thereby enhancing overall logistics efficiency. AI systems can predict potential disruptions, such as traffic congestion or weather delays, and reroute deliveries as needed. This flexibility allows companies to maintain smooth operations and meet customer expectations by minimizing delays and ensuring timely deliveries.

Predictive Fleet Maintenance

AI-powered systems monitor vehicle health and predict maintenance needs by analyzing data from sensors. This proactive approach prevents breakdowns and enhances fleet uptime, ensuring vehicles remain operational for longer periods. Predictive maintenance helps reduce the costs associated with unexpected repairs and maximizes the productivity of the fleet. With AI, companies can schedule maintenance only when necessary, which avoids unnecessary downtime. This strategy not only improves vehicle performance but also enhances safety and significantly reduces long-term operational costs.

Real-World Examples

Tesla Autopilot

Tesla’s Autopilot system utilizes artificial intelligence to assist cars in navigating roads with minimal human intervention. It uses a combination of sensors, cameras, and radar to detect road conditions and obstacles. The system can change lanes, adjust speed, and even take control during emergencies to help prevent accidents. Tesla regularly updates Autopilot through over-the-air improvements, allowing the system to evolve and become safer over time. Autopilot is one of the most recognized examples of how AI is contributing to the development of fully autonomous vehicles.

Waymo’s Autonomous Vehicle

Waymo, a subsidiary of Alphabet, is a leader in self-driving technology. Its autonomous vehicles can navigate public roads without human intervention by using artificial intelligence to understand their surroundings. The system analyzes data from sensors and LiDAR to make driving decisions. Waymo has already launched a commercial ride-hailing service in select cities, allowing riders to book fully autonomous vehicles for their trips. This innovation demonstrates how AI is transforming daily transportation.

Amazon’s Delivery Drones

Amazon has developed AI-powered drones to enhance package delivery. These drones utilize artificial intelligence to navigate, avoid obstacles, and determine the optimal routes. They can adapt in real-time to changing weather conditions, ensuring safe package delivery. Amazon’s goal is to reduce delivery times and lower costs, particularly for last-mile logistics. By utilizing electric-powered drones, Amazon also aims to minimize its environmental impact, making deliveries more efficient and sustainable.

UPS Route Optimization

UPS utilizes AI to optimize its delivery routes, enhancing logistics efficiency. The AI analyzes real-time traffic data, weather conditions, and delivery locations to determine the best routes. This approach helps UPS save fuel, reduce delivery times, and improve customer satisfaction. By implementing AI, UPS has minimized millions of miles from its delivery routes. The system also helps avoid delays caused by traffic congestion or road closures. This AI-powered strategy ensures that UPS can operate smoothly, even during peak delivery seasons.

Potential Roadblocks

High Implementation Costs

Implementing AI in transportation involves substantial upfront investments in advanced sensors, software, and hardware. However, the long-term benefits—such as increased efficiency, enhanced safety, and cost savings—often surpass these initial expenses. Although the initial costs can be high, the long-term gains typically result in reduced fuel consumption, improved operational efficiency, and minimized downtime.

Integration with Legacy Systems

Many transportation networks continue to depend on outdated legacy systems that were not designed to accommodate the complexities of AI technology. Integrating AI into these older systems can be difficult, resulting in technical incompatibilities, increased costs, and delays in implementation. Companies can begin by adopting hybrid systems where AI functions alongside legacy infrastructure, gradually transitioning to fully AI-driven systems over time.

Data Privacy and Cybersecurity

AI systems collect large amounts of data, including sensitive personal and operational information. This raises significant concerns about data privacy and security since these systems can be vulnerable to cyberattacks. A data breach could jeopardize passenger safety, disrupt transportation networks, and erode public trust. To mitigate these risks, companies must implement robust cybersecurity measures, such as encryption, regular system audits, and compliance with regulations like GDPR.

Regulatory and Compliance Issues

Regulations are essential for ensuring safety and privacy, but they can also hinder innovation and implementation. Public trust in autonomous vehicles continues to be a concern. To promote widespread adoption, it is vital to address issues related to safety and reliability through thorough testing and transparent communication. Additionally, collaborating early with regulators can help minimize delays in implementation and ensure that AI systems comply with all necessary safety and legal standards.

Skilled Workforce Gap

The use of AI in transportation necessitates highly skilled professionals to develop, implement, and maintain these advanced systems. Currently, there is a shortage of AI talent, which makes it challenging for companies to find qualified individuals to manage AI-driven transportation networks. This talent gap can hinder the adoption and effective utilization of AI technologies. To address this issue, companies can promote cross-training for their existing employees, helping them build AI expertise in-house and reducing the need to rely solely on external hires.

Future of AI & The Road Ahead

The Rise of Drone Taxis is a futuristic concept that is now becoming a reality. AI-powered drone taxis offer a fast, convenient, and potentially less congested mode of transportation. Although still in the early stages, this technology has immense potential to reshape urban mobility.

Personalized Experiences leveraging Generative AI

Allows creating more refined personalized routes for drivers and riders while optimizing movement on the network, insurance and the way we communicate about our travels. This has shown a potential to reduce travel time and fuel consumption, and reduce operating and insurance costs while increasing the safety performance of the network. Generative AI can personalize out and in-vehicle experiences, suggesting next steps based on your preferences, recommending eco-friendly routes with scenic or experiential detours, or even tailoring traffic and surrounding cultural context information to individual driving, riding and walking styles.

Customized Logistics Solutions

AI plays a crucial role in personalization by customizing services and experiences to meet individual business needs. It can analyze various data sources to understand customer preferences and behaviours.

According to a 2021 McKinsey report, over 90% of consumers view two- to three-day delivery as the standard, with 30% of shoppers expecting same-day delivery. From personalized routing and delivery schedules to dynamic pricing, AI can help businesses understand return patterns and customer feedback. This enhances customer satisfaction, builds loyalty, and allows companies to differentiate themselves in a competitive market.

Chatbots and Virtual Assistants

According to an IBM study, customer service has surpassed other functions to become the top priority for CEOs regarding generative AI. The integration of generative AI can enhance interactions between supply chain decision-makers and virtual assistants, enabling quick and informed actions. AI-powered chatbots and virtual assistants can streamline communication by providing real-time updates, answering questions, and improving the overall customer experience. Additionally, these tools can serve as research instruments to collect and analyze sentiment-based metrics for each customer service interaction. 

As we look towards the future of work, companies are increasingly reimagining the roles and capabilities of both humans and machines. Businesses should plan for these future skill requirements, assess the evolution of existing roles, and consider the applicability of new, value-added roles that may emerge. 81% of survey respondents report that they are actively redesigning work or planning to do so in the future to better harmonize the strengths of machines and humans.

In the transportation sector, despite its challenges, AI holds tremendous potential for creating a more efficient, safer, and sustainable future. By embracing innovation and tackling existing challenges, we can pave the way for a transportation landscape that benefits individuals, businesses, and the environment alike. The applications of AI in transportation offer just a glimpse of the possibilities and opportunities that this technology can present. Imagine how incredible and exciting a future shaped by AI could be!

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Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

Artificial Intelligence in Sustainability

Nehatrendy

AI in Sustainability Initiatives

Climate-related disasters are costing the global economy over $500 billion annually, prompting business leaders to prioritize climate action.

According to recent surveys, 37% more CEOs rank sustainability as a top concern compared to the previous year. Additionally, 64% of CEOs believe that combining digitalization, such as AI adoption, with environmental sustainability presents a significant growth opportunity. This should encourage CIOs to take proactive steps in establishing their leadership by implementing sustainability transformation strategies.

Gartner predicts that by the next decade, AI could consume up to 3.5% of the world’s electricity, resulting in a considerable environmental impact. Therefore, executives need to be aware of AI’s growing carbon footprint and adopt measures to mitigate it. One approach is to prioritize data centers powered by renewable energy. Public cloud providers can generate 70% to 90% fewer greenhouse gas emissions compared to traditional server rooms, owned data centers, and midsize data center facilities.

By scaling proven applications and technologies, artificial intelligence has the potential to reduce global greenhouse gas emissions by 5% to 10% by the next decade, while also enhancing climate resilience and adaptation initiatives.

While there are various ways to address sustainability, most organizations focus on three primary Sustainable Development Goals (SDGs):

  • Monitor and predict climate and weather change trends, such as global warming
  • Manage waste and optimize recycling processes and operations
  • Enhance transportation efficiency to improve fuel efficiency and reduce carbon footprints

Applications

Machine Learning

Google Cloud Document AI automates the workflow involved in searching for documents that contain relevant ESG (Environmental, Social, and Governance) information. It parses these documents for important data, extracts that information, and populates structured datasets that can be sold to investors, helping them make informed investment decisions. By using AI to streamline this process, the efficiency of manual data collection efforts can be improved by approximately 50%.

Additionally, a Google news sentiment analysis tool employs AI to collate, summarize, and analyze the sentiment of millions of news articles. This provides insights into companies’ sustainability and environmental practices that will not be fully captured in their own reports. The AI workflow generates an augmented rating for each company by converting sentiment into a score. This offers an outside perspective, enabling financial markets to invest in the most sustainable businesses.

Advanced Analytics

Advanced mathematical and statistical techniques are utilized to extract insights from both structured and unstructured data. Through advanced analytics, energy consumption can be optimized, thereby reducing a building’s carbon footprint. This is achieved by adjusting heating, cooling, and lighting systems based on real-time data gathered from sensors and weather forecasts.

Large Language Models (LLMs)

Generative AI plays a crucial role in transforming vast amounts of unstructured, text-based data within corporate supply chains into a format suitable for modeling, thereby enhancing the efficiency of internal processes. Additionally, by allowing customers to leverage large language models (LLMs) trained on relevant sustainability datasets, businesses can streamline the process of writing their ESG reports, saving significant time that can be redirected towards achieving sustainability targets.

Furthermore, ESG Book aims to empower customers to use unstructured, natural language questions to explore its data limitlessly. This capability will allow users to uncover insights such as trend analyses, heatmaps, correlations, and precise inquiries that are currently challenging to scale.

Case Studies

Invest in AI

Microsoft Research’s AI for Science team has developed an AI foundation model called Aurora, which predicts weather with unprecedented accuracy. Utilizing 1.3 billion parameters, Aurora forecasts global weather patterns and atmospheric processes, including air pollution. The model was trained on over 1 million hours of weather and climate simulations, enabling it to understand complex atmospheric dynamics.

In addition, the Microsoft AI for Science initiative aims to apply advanced AI capabilities to accelerate scientific discovery. By leveraging deep learning and machine learning, the initiative seeks to transform fields such as materials discovery and green energy solutions. It also enhances our ability to model and predict natural phenomena across various scales of space and time.

Reimagining greener urban cities

Deloitte is committed to creating a more sustainable future and is utilizing a new integration with Google Earth to develop AI-enabled digital twins of urban communities and land parcels. This technology allows for the rapid generation of scenarios that incorporate metrics related to sustainability, carbon efficiency, and community quality of life.

To achieve this, Deloitte has specialized teams of computational designers and urban planners who use up-to-date real estate market data. This data-driven approach allows for innovative and dynamic scenario planning for future urban environments. By doing so, Deloitte can assess how cities will evolve over time, considering the long-term risks associated with climate change. This insight helps inform decisions regarding infrastructure planning, service delivery, population density, and zoning, ultimately aiming to enhance resilience and community well-being.

Develop Digital and Data Infrastructure

AI models depend on high-quality, representative data and the infrastructure needed to process it. However, access to data can limit the full potential of AI’s transformative capabilities. For instance, satellites are collecting increasing amounts of data each year, resulting in a growing repository of valuable information for managing climate risks and facilitating the discovery of new insights to tackle various sustainability challenges. Unfortunately, accessing this data can be difficult, and if it is not easily obtainable, it cannot be effectively used for sustainability solutions.

Minimize resource use, expand access to carbon-free electricity, and support local communities

Datacenters currently account for approximately 1.0–1.5% of global electricity demand, with most of them being used for non-AI applications. Microsoft is actively redesigning the construction and management of datacenters to enhance resource efficiency and promote circularity. The company’s engineers have developed a hybrid datacenter construction model that is expected to reduce the carbon footprint of two new datacenters by 35%.

Microsoft’s global network of advanced datacenters relies on support from local communities, including suppliers, officials, stakeholders, and residents, to plan, build, and operate these facilities. The company is developing Energy Transition Programs in collaboration with communities to align their goals with broader sustainability objectives. This initiative will also create job opportunities within the community, allowing residents to acquire new skills and gain employment.

Advance AI policy principles and governance

Policies and governance are crucial for accelerating progress in sustainability through AI. Government policies are vital for facilitating the decarbonization of electricity grids and promoting the responsible use of AI in sustainability initiatives. AI demonstrates significant value across various sustainability-related areas, including energy system management, water resource management, and supply chain optimization. With effective policies in place, AI can enhance its sustainability impacts across different industries by optimizing systems, increasing efficiencies, and improving operations from manufacturing to electric grid management. Ensuring transparency in AI operations allows grid operators to understand and validate the recommendations made by these systems, thereby reducing the risk of system failures or inefficiencies.

Build workforce capacity to use AI

Microsoft Philanthropies’ Skills for Social Impact program has trained over 14 million people in digital and AI skills to create a workforce ready to implement AI for sustainability. Bridging the gap in the sustainability workforce requires investment in training, skill development, and capacity-building programs to ensure broad access to AI’s transformative capabilities.

In the age of generative AI, building AI capacity is less about programming and more about fostering a general fluency in AI. This involves teaching individuals to effectively use AI-enabled tools, such as Copilot, to enhance innovation, develop sustainability solutions, and scale their impact. Microsoft has partnered with AI and technology leaders, along with sustainability experts, to create targeted training programs focused on AI for sustainability. Additionally, Microsoft supports collaborative networks and innovation hubs to help entrepreneurs advance scalable sustainability solutions.

IBM is also actively working to close the skills gap in the workforce related to AI and sustainability. Last year, IBM SkillsBuild® introduced a new range of generative AI courses as part of its commitment to AI training. IBM also launched a new sustainability curriculum aimed at equipping the next generation of leaders with skills for the green economy.

Deloitte is integrating generative AI capabilities throughout its enterprise by deploying purpose-specific large language models (LLMs) and chatbots to support specialized teams across the organization. These tools are implemented within Deloitte’s Trustworthy AI™ framework to manage AI risks and enhance user confidence. Moreover, Deloitte is increasing AI fluency by training over 120,000 professionals through the Deloitte AI Academy™ and investing more than $2 billion in global technology learning and development initiatives to boost skills in AI and other fields.

Industry Best Practices

Making AI more environmentally friendly is essential for any sustainable technology initiative. Here are five strategies for developing more sustainable AI.

Enhance AI Efficiency

Consider adopting composite AI, which organizes and learns through network structures similar to the efficient human brain. This approach utilizes knowledge graphs, causal networks, and other “symbolic” representations to effectively solve a broader range of business challenges.

Implement a Health Regimen for AI

Monitor energy consumption during machine learning processes and halt training once improvements plateau, as continued training may no longer justify the costs. Keep training data local while sharing improvements at a central level. This strategy, known as “federated machine learning,” helps reduce electricity usage and enhances data privacy. Reuse already trained models and contextualize them when necessary. Opt for more energy-efficient hardware and networking equipment.

Optimize AI Workload Timing and Location

Manage when and where AI workloads are processed. The carbon intensity of local energy supplies fluctuates based on factors like country, generating authority, time of day, weather conditions, transfer agreements, and fuel supply. Balance workloads across data centers to coincide with cleaner energy production and implement energy-aware job scheduling alongside carbon tracking and forecasting services to minimize emissions.

Invest in Clean Power Where you Operate

Where possible, procure power purchase agreements (PPAs) or renewable energy certificates (RECs) that help reduce or offset greenhouse gas emissions and contribute new renewable energy to the grid at your point of consumption. Prepare for future regulations as PPAs and RECs may not always be available. Build a comprehensive clean power plan considering location and time to develop a sustainable energy strategy.

Consider Environmental Impact in AI Use Cases

When developing your AI strategy, assess both environmental impacts and business benefits. Pursue use cases that deliver more value than they detract. Before launching new AI initiatives, improve the energy efficiency of existing ones and minimize risks to intellectual property and proprietary data. Avoid investing in AI projects that will harm business value or the environment.

Conclusion

Policymakers play a crucial role in maximizing the benefits of AI-driven climate action while minimizing its risks. To enable AI for climate progress, we must encourage data sharing, ensure affordable access to technology, raise awareness, and invest in skill development.

Sustainability is not a journey that can be undertaken alone. To unlock the full potential of AI for climate progress, we need ongoing partnerships that combine expertise, technology, and innovation. Partnerships are central to this vision of innovation. Microsoft continues to collaborate with researchers to accelerate breakthroughs in sustainability solutions across sectors such as energy and agriculture. They are also partnering with governments and nonprofits to address data gaps and build infrastructure that supports inclusive AI-enabled solutions.

Collaborations with educational institutions and entrepreneurial organizations are vital for equipping the workforce with the necessary skills and knowledge to use AI for sustainability. These efforts highlight the power of partnerships in driving progress, although much work still lies ahead.

Evidence-based scenarios provide structured pathways to explore how AI innovation can influence global sustainability efforts. They help assess trade-offs, anticipate challenges, and inform strategic decisions. By aligning efforts and prioritizing actions based on scenarios that promote positive outcomes, stakeholders can ensure that AI innovation accelerates sustainability in an impactful and equitable manner.

Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

AI Innovations Transforming Financial Services

Nehatrendy

A Primer to Artificial Intelligence in Finance

Artificial Intelligence (AI) in finance refers to fintech innovations that enable financial services organizations to enhance the efficiency, accuracy, and speed of tasks such as data analytics, forecasting, investment management, risk management, fraud detection, and customer service. AI is modernizing the financial industry by automating traditionally manual banking processes, improving the understanding of financial markets, and creating customer engagement methods that emulate human intelligence and interaction.

AI models execute trades with unparalleled speed and precision, leveraging real-time market data to uncover deeper insights and inform investment decisions. By analyzing complex patterns in transaction data, AI solutions help financial organizations enhance their risk management efforts, including security, fraud prevention, anti-money laundering (AML), know your customer (KYC) compliance, and other regulatory initiatives. 

Furthermore, AI is changing how financial organizations interact with customers by predicting their behaviors and understanding their purchasing preferences. This leads to more personalized interactions, faster and more accurate customer support, improved credit scoring, and the development of innovative banking products and services.

In summary, Artificial Intelligence is a crucial tool for the banking and finance industry, allowing financial institutions to process large volumes of data more quickly and accurately. This capability to analyze extensive data in real time is essential for identifying patterns and trends, detecting fraud and financial crimes, and making reliable predictions.

Benefits of AI in Finance

AI technology has the potential to significantly enhance accuracy and precision in decision-making and risk management, particularly within the financial industry. One of the main advantages of using AI in this context is its ability to make unbiased decisions, leading to quicker and more precise loan eligibility determinations.

Enhancing Customer Experience

One of the most noticeable impacts of AI in financial services is the transformation of customer experience. Chatbots and virtual assistants powered by large language models provide customers with 24/7 support, addressing queries and solving problems in real time. AI-driven chatbots can manage a wide range of customer interactions, from answering basic questions about account balances to providing detailed information about complex financial products. This level of service availability ensures customers receive timely assistance, improving their overall experience and fostering loyalty. Additionally, AI tools can handle multiple interactions simultaneously, significantly increasing efficiency compared to human-operated customer service centers. Personalized banking experiences are another important benefit; AI algorithms analyze vast amounts of customer data to offer tailored financial advice, product recommendations, and investment strategies.

Strengthening Security and Fraud Detection

Many financial institutions are leveraging AI for fraud detection by using sophisticated algorithms that identify unusual patterns in customer behavior. These models continuously learn from the large datasets generated by users, enhancing their ability to flag fraudulent activities with high accuracy—something humans could not achieve at scale. AI systems can flag suspicious transactions and interactions in real time, allowing institutions to act swiftly and prevent potential losses. 

Data-Driven Decision Making

Historically, the financial sector has relied heavily on human expertise for decision-making processes. However, the enormous volume of data generated in today’s digital age necessitates more advanced analytical tools. AI, with its ability to process and analyze massive datasets at incredible speeds, provides a solution that is both efficient and highly accurate. By leveraging AI, financial institutions can uncover valuable insights from their data, enabling them to forecast market trends, customer behaviors, and potential risks with unprecedented accuracy.

Streamline Operations

Many back-office functions in financial institutions, such as data entry, reconciliation, report generation, and data backups, can be automated using AI. Automating these mundane and repetitive tasks not only enhances efficiency but also reduces the likelihood of human error. This allows employees to focus on product development and strategic activities. Financial institutions that effectively implement automation can operate with leaner teams, thereby reducing overhead expenses. 

Industry Applications of AI in Finance

Consumer Finance

Artificial Intelligence (AI) is playing a crucial role in preventing fraud and cyberattacks in the finance industry, addressing a major concern for consumers who seek secure accounts. For instance, JPMorgan Chase has adopted AI to enhance the security of its account holders by using a proprietary algorithm that detects fraud patterns. When a credit card transaction is processed, the transaction details are sent to central computers that assess whether the transaction is fraudulent.

Personal Finance

AI is gaining traction as consumers desire more control over their financial health. To remain competitive, financial institutions are implementing AI technologies that provide services like personalized wealth management and 24/7 financial guidance through chatbots powered by natural language processing. For example, Eno utilizes over 12 proactive capabilities to generate insights and anticipate customer needs, such as alerting customers to suspected fraud or notifying them of price increases in subscription services.

Corporate Finance

In the realm of corporate finance, AI proves to be valuable for predicting and assessing loan risks, reducing financial crimes, and enhancing loan underwriting processes. AI can analyze data to identify anomalies that may be missed by human analysts, thereby assisting companies in making informed decisions and mitigating financial risks. For instance, banks are leveraging deep learning techniques to analyze customer data and detect potential fraudulent activities like money laundering. 

FinTech

Banks are under pressure to enhance their digital services due to the growing demand from consumers and competition from tech-savvy startups. To cater to the needs of younger customers, banks are boosting their IT and AI budgets. Notably, Millennials and Gen Z prefer digital banking channels over visiting physical branches, with 78% choosing to avoid branch visits altogether.

Role of Intelligent Finance

Trading and Investment Management

  • Investment Software for Recommendations: Banks constantly seek profitable investment opportunities beyond just earning interest income. They utilize investment software to provide recommendations that align with their risk appetite. This enables them to make informed investment decisions and achieve healthy returns.
  • Accurate Evaluation of Client Proposals: Understanding industry-specific information can be challenging, but investment software can accurately evaluate client funding proposals. It takes into account various factors and accommodates more variables, simplifying the process for human analysts.
  • Accelerating the Assessment of New Environments: When institutions consider investing outside their national borders, accessing relevant information can be time-consuming. However, advanced AI software can expedite this process by quickly assessing new environments and providing valuable insights for investment decisions.
  • Algorithmic Trading: AI can be utilized to develop trading algorithms that analyze market trends and historical data, allowing for faster decision-making and execution of trades compared to human traders.

Risk Management and Fraud Detection

  • Decision Management System and Audit Documentation: Implementing a Decision Management System can facilitate early detection of fraudulent activities and improve audit documentation. Third-party audits can disrupt regular operations, especially when employees need to provide missing information.
  • Importance of Artificial Intelligence in Fraud Detection: As financial institutions enhance their vigilance against fraudulent activities, fraudsters are adapting their tactics. They have learned to conduct transactions just below detection thresholds to avoid triggering investigations. Without proper analysis, criminal activities may go undetected, even when they comply with prescribed regulations. This is where artificial intelligence (AI) excels, by processing vast amounts of data, AI can identify suspicious transactions more effectively, whereas manual analysis is often prone to error.
  • Risk Management: AI can analyze data to assist financial organizations in assessing and managing risks more effectively, creating a more secure and stable financial environment.
  • Fraud Detection: AI algorithms play a crucial role in preventing financial crimes, such as fraud and cyberattacks, by identifying unusual patterns in financial transactions. This capability enhances security in activities like online banking and credit card transactions.

Future of AI in Financial Services

AI will play a crucial role in driving growth in the financial services sector. Many organizations have transitioned to digital platforms, discovering new ways to sell, improve efficiencies, and utilize their data effectively. Moving forward, it will be essential for these organizations to personalize customer engagement on a large scale. AI is instrumental in delivering tailored customer responses, making safer and more accountable product and service recommendations.

Additionally, financial institutions will need to create strong and unique permission-based digital customer profiles. However, the necessary data may often be trapped in silos. By breaking down these silos, implementing an AI layer, and incorporating human engagement seamlessly, financial institutions can develop experiences that cater to the unique needs of their customers while operating efficiently.

Generative AI in Finance

Finance teams are exploring how generative AI can enhance existing processes by creating text and conducting research. In the future, integrating generative AI will transform essential processes, redefine business partnerships, and help mitigate risks. Generative AI will eventually work alongside traditional AI forecasting tools to generate reports, explain variances, and provide recommendations. These improvements will empower finance professionals to make more informed strategic decisions, ultimately boosting operational efficiency and effectiveness.

  • Anomaly Detection: AI can detect anomalies such as fraudulent transactions, financial crimes, spoofing in trading, and cyber threats.
  • Sentiment Analysis: AI can identify sentiment in various texts to gauge emotional opinions on topics like investment research and chat data
  • Market Sentiment Analysis: AI can analyze news sources, social media, and other information to assess market sentiment, which helps predict market trends and influences decision-making
  • Document Processing: AI can extract structured and unstructured data from documents and analyze, search, and store this data for document-intensive processes such as loan servicing and investment opportunity discovery.
  • Data Science and Analytics: Organizations can access a comprehensive suite of data management, analytics, and machine learning tools to generate insights and unlock value from data for business intelligence and decision-making.
  • Predictive Modeling: AI can utilize customer, risk, transaction, trading, and other data insights to predict specific future outcomes with a high degree of precision. These capabilities are beneficial for fraud detection, risk reduction, and anticipating customer needs.
  • Predictive Analytics: AI enables predictive modeling, helping financial organizations forecast market trends, potential risks, and customer behavior.

For the finance and controllership workforce, finance leaders and accounting professionals can also use Generative AI tools targeted specifically for their role in the function or professional motivations.

  • Finance leaders can use Generative AI to maintain a pulse on the business and adapt to market conditions, predict and preempt strategic macroeconomic blockers, enhance organizational structure, and provide quick answers to evolving questions and real-time information.
  • Directors and managers that need to focus time on strategic process improvements; improve budget efficiencies; provide key, timely insights to enable business decisions; synthesize information to understand problems; and streamline processes can use Generative AI to conduct trend analysis, proactively manage organizational spending, generate insights from emails and reports, and perform tasks that can help drive management efficiencies.
  • Experts can use Generative AI to run intelligent searches of knowledge bases, standard operational procedures, and regulatory documents; generate control compliance reports to provide domain-specific expertise to business decisions; and monitor compliance, ethics, and control across the business.
  • Finance and accounting analysts can use Generative AI tools like virtual assistants and intelligent bots to deliver vital operational tasks faster and more efficiently and respond to ad hoc reporting requests. AI applications such as virtual finance analysts can also help analysts drive operational excellence and value-added strategic tasks.

Wrapping Up

In today’s fast-paced world, artificial intelligence (AI) has emerged as a key driver of innovation in finance. Its capabilities include enhancing customer experience, detecting fraudulent transactions, assessing risks, and developing trading strategies. As the AI ecosystem evolves, financial institutions must stay updated on the latest AI applications and customer needs by continuously improving their AI systems.

AI is set to transform various aspects of the financial sector, including banking, investments, and insurance. However, like all rapidly evolving technologies, AI also presents new challenges and concerns. Key issues such as regulation, ethics, and workforce changes will need to be addressed moving forward. Governments and regulators must find a balance between fostering innovation and protecting consumers from potential abuses or unintended consequences. 

Implementing AI systems in the highly regulated finance industry involves significant logistical and compliance challenges. Financial institutions must carefully manage AI projects to ensure data quality, security, and adherence to regulations. A major hurdle is obtaining clean, representative data to train AI models. Since models are only as effective as the data they are built upon, financial institutions must establish robust data governance processes. However, many banks have complex, fragmented data architectures that span decades-old mainframe systems. Connecting and preparing this data for AI projects requires considerable effort.

Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

Artificial Intelligence in Retail: Enhancing Shopping Experience

Nehatrendy

Introduction

Retail is on the cusp of a significant transformation driven by artificial intelligence (AI). This technology is being rapidly adopted, disrupting traditional practices and redefining customer experiences. AI is set to revolutionize sales and marketing while also optimizing supply chain processes and operations, fundamentally changing how consumers buy and sell.

Within the next decade, 20% of the top global retailers are expected to achieve holistic results by utilizing distributed AI systems. Retailers can leverage AI in various areas, including personalization, marketing, supply chain management, and operations. Personalization through AI allows brands to enjoy numerous benefits, such as 18% lower customer acquisition costs, 20% increased customer spending, and 30% higher customer satisfaction rates.

In the retail sector, AI encompasses applying AI technologies to enhance different aspects of the industry, including customer experience, business operations, and decision-making. AI-driven systems analyze vast amounts of data, automate processes, and create more personalized and efficient experiences for both customers and retailers. These technologies are utilized in both online and physical stores, affecting everything from product recommendations and pricing to inventory management and customer service.

With capabilities to process and understand large volumes of information, AI reformulates the retail shopping management process and the way consumers shop. In recent years, advancements in generative AI technologies have begun to transform the retail sector by offering new opportunities for content generation and enabling real-time customer engagement through natural language.

To remain competitive in today’s global market, retail businesses must prioritize profit and productivity. It is crucial to act quickly and effectively to ensure success and stay ahead of competitors. AI can support retail operations by increasing profits and optimizing business processes. It enables companies to make informed decisions regarding staffing and inventory replenishment, which minimizes labor costs and stock-outs while maximizing sales. As AI continues to advance, it will redefine retail roles and enhance overall business efficiency.

Advantages of using AI in Retail Business

While we are just beginning to explore the full potential of AI in business, more than 80% of retail executives expect their companies to fully adopt AI automation within the next year.

Personalized Shopping Experiences

AI algorithms analyze customer behavior, preferences, and past purchases to deliver personalized recommendations and targeted marketing. This enhances the shopping experience, making it more engaging and relevant, which in turn increases customer loyalty and conversion rates. For example, some businesses utilize recommendation engines to suggest products based on a user’s browsing and purchase history, while others may implement dynamic pricing. This strategy adjusts prices in real-time based on demand, competitor pricing, and customer preferences, allowing retailers to offer discounts during low-traffic periods and optimize revenue. The trend in retail AI is moving towards hyper-personalization, where nearly every aspect of the omnichannel shopping experience is tailored to the individual user.

Enhanced Customer Experience

AI allows retailers to provide personalized offers and experiences by automatically analyzing customer preferences, behaviour, and purchase history. By offering tailored product recommendations, customized promotions, and 24/7 support through chatbots and virtual assistants, retailers can significantly improve customer satisfaction and loyalty. AI chatbot support agents, in particular, have shown substantial improvements in customer experience. Among marketing teams that utilize AI tools, 66% already use chatbots to strengthen their connection with customers.

Improved Operational Efficiency

AI helps retailers streamline their operations by automating tasks and processes, thereby reducing manual effort and minimizing errors. Businesses can greatly benefit from AI in areas such as automated inventory management, supply chain optimization, and logistics planning.

By automating routine tasks like inventory control, customer support, marketing material generation, and fraud detection, AI allows retailers to focus on more strategic and creative initiatives. AI-driven optimization processes—such as supply chain management and automated delivery planning—can reduce waste, improve accuracy, and lower operational costs.

Increased Sales and Revenue

AI-driven pricing algorithms and recommendation engines can help retailers boost sales by optimizing pricing strategies and identifying cross-selling and up-selling opportunities. By dynamically adjusting prices based on market trends and customer demand forecasting, retailers can increase revenue and capitalize on more sales opportunities. Organizations that leverage AI tools can maximize revenue by determining the most effective pricing strategies, thereby staying competitive in crowded markets.

Better decision-making

With powerful AI analytics tools, retailers can gain actionable insights and valuable predictions derived from data analysis, enabling informed decision-making across critical areas. Real-time analytics dashboards and predictive algorithms empower retailers to monitor performance, anticipate trends, and respond quickly to changing market conditions. These capabilities lead to a more informed decision-making process for executives, as AI tools provide access to real-time data and actionable insights for better decision-making in areas like pricing, inventory management, marketing, and product development.

A Retail Playbook – 6 Industry Applications of AI

There are many areas in business where retailers can use artificial intelligence (AI) to improve efficiency, reduce costs, and enhance customer experience.

Inventory Management

Maintaining adequate stock is a constant challenge for retailers. By combining customer purchase data with supply chain analytics, AI can predict future buying trends, help align stock levels, and identify inefficiencies that drain profits1. This approach reduces waste, optimizes space, enhances customer satisfaction, and increases profitability. Predictive replenishment enables retailers to optimize inventory levels and costs while maximizing product availability and profit margins.

Demand Forecasting

Demand forecasting employs advanced data analytics and machine learning models to predict future customer demand for products. By analyzing a combination of sales data, customer data, and third-party information, such as market trends, these tools help organizations plan more effectively. As AI models can analyze vast amounts of data and detect patterns that traditional methods might overlook, they tend to be more accurate than previous forecasting tools. More precise demand predictions allow retailers to manage inventory better and optimize logistics.

AI-Powered Retail Analytics

AI-driven analytics offer retailers valuable insights into customer behavior, facilitating data-driven decision-making and the analysis of customer interactions. This information enables retailers to optimize pricing strategies, marketing campaigns, and product placements. Dynamic pricing, the ability to adjust prices in real time based on consumer demand, competitor pricing, or inventory levels, provides a competitive advantage for many retailers. Applying AI allows retailers to leverage dynamic pricing to remain competitive and maximize profits.

Predictive Analytics for Demand Forecasting

The integration of artificial intelligence in retail extends to AI-powered predictive analytics, which enables retailers to forecast demand with unprecedented accuracy. Algorithms leverage their expertise across various categories of data, including historical sales records, market trends, and web signals from social media.

Route Planning

Delivery logistics significantly impact a retailer’s bottom line. Using complex algorithms and real-time data, AI can optimize delivery routes to minimize transit times, reduce fuel consumption, and enhance customer satisfaction. AI-based route planning helps companies adapt to changing conditions and avoid service disruptions.

Assortment Planning

Traditional retail assortment strategies and planning methods often struggle to keep pace with dynamic customer behavior. AI analyzes customer data, identifying patterns and relevant variables that might be difficult to spot otherwise. This leads to a more personalized product mix, whether it be regional or tailored to individual preferences. According to Gartner, all global multichannel fashion retailers will increasingly rely on AI and automation for targeted assortments.

5 Examples of AI in Retail Business

Retailers worldwide are employing AI in various ways to enhance their operations. This includes aiding store associates in increasing customer basket sizes, equipping service agents with relevant information for better service to returning customers and assisting back-office staff in making informed decisions related to staffing levels, inventory allocation, merchandising, and purchasing. 

In-store Navigation

Many large department stores utilize an AI-powered chatbot to assist customers in navigating their outlets. Customers can use a smartphone app to ask the chatbot for directions to specific items on store shelves or inquire about the availability of desired products. The bot can even detect customer frustration by analyzing their language and alert a human store associate to assist.

Smart Stores

Apparel retailers are using AI to help customers find the right clothing fits. For instance, one retailer employs AI to run touchscreen mirrors that allow customers to browse clothing items and visualize how they would fit without the need to try on multiple items. Another apparel retailer has developed an AI-powered app that enables customers to notify store associates when they require a different size in the dressing room and offers suggestions for additional items based on what customers have already tried on.

Smart Shelves

A grocer employs sensors on store shelves combined with AI in its app to suggest items to shoppers. For example, it can recommend gluten-free products to customers with dietary restrictions by analyzing real-time data collected on individual customers.

Merging Digital and Physical Experiences

One furniture and home decor retailer uses AI to provide in-store product recommendations tailored to customers’ design preferences, as indicated by their Pinterest boards. This approach helps convert casual browsers into buyers.

Cashierless Shopping

A warehouse shopping club utilizes an AI-powered app to help customers plan the most efficient route through the store to locate everything on their shopping list. The app also allows them to pay through the platform, enabling them to exit the store without waiting in line. 

Challenges and Limitations of AI in Retail

When we discuss the quality of available data, we focus on its accuracy, completeness, consistency, reliability, and relevance for specific use cases or applications. High-quality and readily accessible data is essential for the effective and accurate operation of AI tools. Organizations often face several key issues with their data, including:

Fragmented Data Sources

Retailers collect data from various sources, such as Point-Of-Sale (POS) systems, eCommerce platforms, Customer Relationship Management (CRM) systems, social media, and physical stores. These data sources can be siloed or fragmented, making it challenging to integrate, analyze, and utilize data effectively.

Legacy Systems

Many businesses rely on outdated systems that store data in different formats and structures. This lack of standardization can hinder data integration and interoperability. Legacy systems cannot often capture and process real-time data, which limits the availability of information for AI algorithms.

Advanced Security and Data Protection

One of the major challenges in retail and eCommerce development is ensuring data security. AI technologies can help enhance and automate security measures, protecting retailers’ data and assets from cyber threats and fraud. By implementing advanced AI-driven fraud detection and data encryption techniques, retailers can safeguard sensitive information, prevent financial losses, and maintain compliance with data privacy regulations.

Future Trends and Opportunities for AI in Retail

The future of AI in retail looks very promising, as emerging trends are taking center stage and becoming the focus of the industry.

Hyper-Personalization and Hyper-Automation

Hyper-personalization utilizes advanced data analytics and AI techniques to tailor products, services, and marketing efforts to the specific needs, preferences, and behaviours of customers. It goes beyond traditional personalization by leveraging real-time data and contextual information to deliver highly relevant and timely experiences to each customer. Expectations for hyper-personalization are already significantly high, as research shows that 71% of consumers expect companies to provide personalized interactions.

On the other hand, hyper-automation involves using AI, machine learning, robotics, and process automation to optimize various business processes within an organization. The goal is to streamline workflows, improve efficiency, and reduce human intervention by automating repetitive tasks and decision-making processes.

Rise of Omnichannel Retailing

The seamless integration of various online and offline channels—including brick-and-mortar stores, eCommerce websites, mobile apps, and social media platforms—will continue to evolve, providing customers with a unified and consistent shopping experience. Today, it’s not enough to have just an eCommerce website and a few physical stores; businesses must deliver a cohesive brand experience across all channels and touchpoints. Current top trends in omnichannel retailing include:

Unified Commerce Platforms: Integrating all channels and touchpoints to provide customers with a seamless shopping experience across online and offline mediums.

Click-and-Collect Services: Offering options like curbside pickup, locker pickup, and in-store pickup to give online shoppers convenience and flexibility in how they receive their orders.

Social Commerce: Transforming social media platforms into shopping destinations, where retailers leverage social commerce features to sell products directly to consumers.

Conclusion

The broad implementation of Artificial Intelligence (AI) in the retail sector is already transforming the industry and is expected to have an even greater impact shortly. Use cases such as personalized recommendations, dynamic pricing decisions, virtual try-on experiences, generative content creation, and predictive analytics are reshaping key aspects of retail.

For retailers striving for advanced AI capabilities—where technology shifts from making predictions to autonomous decision-making—investing in infrastructures like RFID and IoT, as well as fostering a unified data ecosystem, is essential. Strengthening an organization’s AI capabilities with the necessary skills and change management practices will enhance the effectiveness of AI initiatives. By leveraging artificial intelligence to refine their operations and engagement strategies, retailers can better position themselves to succeed in a digital-centric commerce environment.

The future of AI in retail will focus on further automating processes, enhancing customer personalization, and advancing autonomous technologies such as cashier-less stores. AI’s role in real-time decision-making will expand, enabling retailers to respond instantly to market changes. Technologies like virtual and augmented reality are expected to integrate more deeply with AI, providing immersive shopping experiences. Furthermore, AI-driven sustainability initiatives will continue to grow, aligning with consumer demand for more eco-friendly practices.

References

  1. https://www.forbes.com/sites/sap/2024/04/19/artificial-intelligence-in-retail-6-use-cases-and-examples/ ↩︎
Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

Transforming Smart Manufacturing and Industry 4.0

Nehatrendy

A Primer to Smart Manufacturing and Industry 4.0

The manufacturing industry is leading the way in digital transformation by utilizing technologies such as big data analytics, artificial intelligence (AI), and robotics. Research has shown significant benefits from these advancements, with machine downtime potentially reduced by 30% to 50% and quality-related costs cut by 10% to 20%.

AI is revolutionizing the manufacturing sector by improving efficiency, precision, and adaptability across various production processes, especially in the context of Industry 4.0. It can analyse large amounts of data from sensors, equipment, and production lines to optimize efficiency, enhance quality, and minimize downtime. By using algorithms to identify patterns in data, AI can predict potential issues, recommend improvements, and even adjust processes autonomously in real-time.

In today’s landscape, digital transformation is essential for business survival. From automated factories to AI-driven quality control, the primary goal of digital transformation is to gain a competitive edge through technology, leading to improved customer experiences and lower operational costs.

Latest Trends on Smart Manufacturing and Industry 4.0

AI in manufacturing goes beyond automation by facilitating real-time decision-making. This function is part of what is often referred to as “smart factories” or “smart manufacturing1,” both of which are key components of Industry 4.02.

Smart manufacturing, as a facet of the digital transformation under Industry 4.0, employs a combination of emerging technologies and diagnostic tools—such as artificial intelligence (AI) applications, the Internet of Things (IoT), robotics, and augmented reality—to optimize enterprise resource planning (ERP). This makes companies more agile and adaptable.

One of the most significant advantages of AI technology in smart manufacturing is its capability for efficient real-time data analysis. With IoT devices and sensors collecting data from machines, equipment, and assembly lines, AI-powered algorithms can quickly process and analyze this information to identify patterns and trends. This helps manufacturers gain insights into the performance of their production processes.

Benefits of Artificial Intelligence in Manufacturing

Increased Efficiency and Productivity: AI systems can automate many routine and repetitive tasks, allowing employees to focus on more complex and value-added activities. This shift can lead to increased efficiency and productivity in manufacturing processes. AI-driven automation accelerates production by taking over repetitive tasks, reducing human error, and optimizing workflows. With integrated systems, processes become more streamlined—from raw materials to finished products—minimizing manual intervention and enabling “hands-off” manufacturing.

Improved Decision-Making: AI processes data in real-time, empowering managers to make informed, data-driven decisions. Digital twins allow manufacturers to simulate production scenarios, minimizing risks and enhancing decision-making by testing outcomes before full implementation.

Innovation and Competitive Advantage: With faster prototyping, generative design, and digital twin simulations, AI enables manufacturers to innovate quickly and efficiently. By reducing time-to-market and supporting more advanced product designs, AI helps companies remain competitive and responsive in a rapidly evolving industry landscape.

Cost Reduction: AI improves cost-effectiveness through more than just automation. Digital twin technology and AI-driven predictive maintenance can extend the life of equipment, leading to long-term savings, as well as conserving energy, time, water, and other resources. Additionally, optimized supply chain management through AI-assisted data analysis enhances demand planning and inventory management, making them more cost-efficient and resilient to risks. Automation, predictive analytics, and improved quality control all contribute to significant cost savings. AI reduces labor and maintenance expenses, lowers waste, and optimizes energy consumption, creating a leaner, more cost-effective production environment.

Environmental Sustainability: By optimizing the management of resources, logistics, and warehouses, AI helps manufacturers reduce energy and material waste, thereby lessening their ecological footprint. This positive impact is crucial for sustainable manufacturing, which integrates product and process design with manufacturing planning and control to identify, quantify, assess, and manage the flow of environmental waste.

AI’s ability to optimize resource allocation, minimize energy use, and limit waste supports environmentally friendly manufacturing practices. Components with self-monitoring sensors also help reduce maintenance needs, contributing to a lower environmental impact.

Industry Applications of AI in Manufacturing

Factory In a Box

The “factory in a box” concept involves modular, self-contained manufacturing units that can be quickly deployed to various locations. These units are equipped with AI-driven automation, Internet of Things (IoT) sensors, and real-time data analytics, enabling flexible, localized production.

This approach allows companies to bring manufacturing closer to demand, reduce logistics costs, and swiftly respond to changing needs. Currently, industries such as electronics, automotive, and pharmaceuticals are experimenting with these portable units.

The full potential of this concept will be realized through future advancements in automation, modular design, and data integration, making it a scalable and mainstream solution. This attractiveness of the “factory in a box” approach encourages more enterprises, particularly small and medium-sized enterprises (SMEs), to adopt an end-to-end packaged process where software works seamlessly with tooling. The integration of sensors and analytics enhances efficiency. Additionally, the inclusion of digital twin technology enables engineers to simulate new manufacturing processes, reducing risk in decision-making.

Digital Twin Technology

AI is utilized to create virtual replicas of processes, production lines, factories, and supply chains. These digital twins simulate, analyze, and predict performance in real-time. By digitally mirroring the real world, digital twins enable manufacturers to monitor and optimize operations without needing to intervene directly in the physical asset.

Digital twins rely on data from Internet of Things (IoT) sensors, programmable logic controllers (PLCs), deep learning, and AI algorithms. These technologies continuously update the digital model with live data, providing an accurate and current virtual representation.

A digital twin is a virtual representation of a real-world product or asset. By integrating AI techniques with digital twins, manufacturers can enhance their understanding of products and explore future actions that may improve asset performance. There are typically four applications of digital twins in manufacturing, including predictive maintenance, shop floor performance improvement, self-driving car developments, and design customization.

Predictive Maintenance

A major focus for AI in manufacturing is predictive maintenance. This technology enables engineers to equip factory machines with pre-trained AI models that incorporate accumulated knowledge about the equipment. By analysing data from the machinery, these models can recognise new patterns of cause and effect that emerge on-site, helping to prevent issues before they occur.

AI examines sensor data from machines to predict failures in advance. By utilizing a digital twin to assess patterns in equipment behaviour and performance, these systems can notify operators about potential problems early on.

This proactive approach allows companies to avoid breakdowns before they escalate. For instance, automobile manufacturers often use predictive maintenance for assembly-line robots, which significantly reduces unplanned downtime and leads to substantial cost savings.

Additionally, this strategy enables manufacturing companies to schedule maintenance during non-peak hours, minimizing disruptions to production schedules. In industries like automotive and electronics manufacturing, precision is crucial due to the complexity of the components involved. AI plays a vital role in reducing production errors, improving product designs, and accelerating time-to-market.

Smart Factory

The increasing use of cameras, sensors, and other technologies that monitor manufacturing processes 24/7—originally initiated by smart factories and Industry 4.0 initiatives—enables manufacturers to provide AI with vast amounts of real-time data. This continuous data flow enhances the value that manufacturers can derive from their data and supports various applications of AI. Notably, several key applications of artificial intelligence in manufacturing, such as predictive maintenance, digital twin technology, and AI-assisted visual inspection, rely heavily on this data.

Another emerging trend is the use of smart components, which are parts equipped with embedded sensors. These include devices, machines, robots, or any objects with network-connected sensors that collect and upload data for analysis. Many of these sensors function as edge computing devices, featuring low-cost processors that perform initial computing tasks locally. This local processing enables preliminary data analysis or filtering at the source before the data is transmitted to the cloud, thus enhancing efficiency in data handling and transmission.

Generative AI

Product and Spare Parts Search: Generative AI assists customers in finding products even when they are unsure of the exact names or codes. Customers can describe the features they want, and AI translates these descriptions into effective search queries. Additionally, it can generate detailed product descriptions, improving search accuracy through a better understanding of the semantics involved.

Document Search and Summarization: Generative AI is revolutionizing document management in manufacturing by enabling efficient search and summarization. Rather than manually sorting through technical drawings, reports, and records, AI can process large volumes of documents to identify patterns and summarize key information. This technique accelerates retrieval and presents complex information in clear and accessible formats.

Manufacturing-Adjacent Areas: Generative AI is also beneficial in areas that support the manufacturing process, including ticket handling, call handling, market research, and the creation of product descriptions, maintenance schedules, and instructions.

Custom Manufacturing: AI allows manufacturers to offer mass customization, enabling products to be tailored to individual customer preferences without slowing down production. By integrating AI into the design process, companies can quickly adapt designs based on real-time consumer feedback. For example, clothing manufacturers utilize AI algorithms to personalize products, allowing customers to select designs that match their specific tastes. This flexibility enhances customer engagement and satisfaction.

Wrapping Up

A recent survey reveals that 63% of manufacturers plan to increase their spending on AI. The efficiency gains from integrating AI lead to significant cost and time savings, allowing resources to be redirected to more critical tasks and opportunities.

This insight aims to understand how manufacturers are benefiting from AI use cases, particularly in engineering, design, and factory operations. Many executives who responded to the survey indicate that they intend to significantly boost AI spending over the next two years. Those who have not yet implemented AI in production are progressing gradually. To develop and scale use cases effectively, these manufacturers must address challenges related to talent, skills, and data.

Talent, skills, and data are the primary constraints on scaling AI. In both engineering, design, and factory operations, manufacturers report that a lack of talent and skills is their biggest challenge in scaling AI use cases. This issue becomes more pronounced as these use cases move closer to production. Many respondents also indicate that inadequate data quality and governance hinder use-case development. Additionally, insufficient access to cloud-based computing power is frequently mentioned as a constraint in engineering and design.

The desired gains from AI are specific to various manufacturing functions. The most common use cases deployed by manufacturers involve product design, conversational AI, and content creation. Knowledge management and quality control are often cited as being in the pilot stage. In engineering and design, manufacturers primarily seek improvements in speed, efficiency, reduced failures, and enhanced security. In the factory, the top priorities are better innovation, improved safety, and a reduced carbon footprint.

To scale AI effectively, fragmentation must be addressed. Most manufacturers find that some modernization of data architecture, infrastructure, and processes is essential to support AI, along with other technology and business priorities. A modernization strategy that enhances the interoperability of data systems between engineering, design, and the factory, as well as between operational technology (OT) and information technology (IT), should be a key focus.

  1. https://www.gartner.com/smarterwithgartner/6-key-actions-for-a-successful-smart-manufacturing-strategy ↩︎
  2. https://www.analyticsinsight.net/artificial-intelligence/industry-40-unleashed-how-ai-will-transform-manufacturing-in-2025 ↩︎
Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

AI Trends in Healthcare

Nehatrendy
Photo by Google DeepMind on Pexels.com

A Foreword to Artificial Intelligence in Healthcare

Exciting developments are on the horizon for healthcare as leading research organizations and the CIO Agenda reveal a bold vision for digital technology investments! Imagine this: a staggering 91%1 of organizations are laser-focused on delivering an unparalleled customer experience, while 67% are determined to boost their operating margins. And that’s not all—48% are gearing up to tap into new revenue streams! The future of healthcare is vibrant and full of potential!

AI-based solutions can effectively streamline diagnostic and treatment processes by utilizing large amounts of structured and unstructured medical data across institutions. This support aids physicians at hospitals and health systems in clinical decision-making by providing real-time, data-driven insights, which physicians can modify and implement based on their expertise.

AI in healthcare manifests in various ways, including discovering new connections between genetic codes, powering surgery-assisting robots, automating administrative tasks, and personalizing treatment options. Additionally, AI enhances efficiency and improves customer experience in nonclinical operations. Its ability to quickly analyze vast amounts of information helps hospital and health plan administrators optimize performance, increase productivity, and improve resource utilization, leading to time and cost efficiencies. Moreover, AI-enabled solutions can accelerate and strengthen the insight generation process, allowing organizations to gain a comprehensive understanding necessary for making data-driven decisions. Finally, AI can provide personalized experiences by facilitating conversations with patients through virtual assistants.

Tech-infused tools, or Healthcare Technology, are being integrated into every step of the healthcare experience to address two key challenges: quality and efficiency. Healthcare technology refers to any IT tools or software designed to enhance hospital and administrative productivity, provide new insights into medicines and treatments, and improve the overall quality of care provided.

Modalities of AI in Healthcare

Generative AI has harnessed the capability to create new content based on existing data, simulating human-like creativity through increasingly user-friendly interfaces. This development has significantly expanded the potential for everyday users to utilize generative AI with minimal technical expertise. According to Gartner’s 2Hype Cycle for Emerging Technologies, which visually represents the typical maturity and adoption of technologies over time, Generative AI is currently at an early, yet rapidly evolving, stage in its journey. Generative AI has harnessed the capability to create new content based on existing data, simulating human-like creativity through increasingly user-friendly interfaces. This development has significantly expanded the potential for everyday users to utilize generative AI with minimal technical expertise.

As a sub-field of artificial intelligence (AI), Generative AI has gained immense popularity. While traditional AI systems primarily analyze data and make predictions, generative AI takes it a step further by creating new data from its training information across various modalities, including text, audio, visuals, video, and code. In essence, traditional AI excels at recognizing patterns, while generative AI is adept at creating new ones.

AI is a broad term that encompasses a range of interconnected processes. Some of the most common forms of AI utilized in healthcare include:

  • Machine Learning (ML): This involves training algorithms with data sets, such as health records, to create models capable of tasks like categorizing information or predicting outcomes.
  • Deep Learning: A subset of machine learning that uses larger data volumes, longer training times, and more layers of algorithms to produce neural networks capable of performing complex tasks.
  • Natural Language Processing (NLP): This technology uses machine learning to understand human language, whether spoken or written. In healthcare, NLP helps interpret documentation, notes, reports, and published research.
  • Robotic Process Automation (RPA): This involves using AI in computer programs to automate administrative and clinical workflows. Many healthcare organizations employ RPA to enhance patient experiences and improve facility operations.

How is AI being leveraged in Healthcare?

AI employs algorithms and machine learning (ML) to analyze and interpret data, deliver personalized experiences, and automate repetitive and costly healthcare operations. These capabilities have the potential to support both operational and clinical staff in decision-making, reduce time spent on administrative tasks, and allow humans to focus on more challenging, engaging, and impactful management and clinical work.

  • Healthtech Improves Efficiency: Patient waiting times are decreasing, and hospitals are being staffed more efficiently, due to artificial intelligence and predictive analytics. Even surgical procedures and recovery times have been reduced through the use of ultra-precise robots that assist in surgeries, making some procedures less invasive.
  • Healthtech Promotes Quality Care: Healthcare technology companies are injecting much-needed efficiency by personalizing experiences for individuals. They understand that there is no one-size-fits-all solution to proper care, making customization essential.
  • Administrative Healthtech: Artificial intelligence assists administrative teams in streamlining patient flow by accurately calculating wait times and predicting peak busy hours for staff scheduling.
  • Healthtech in Fitness: Fitness has developed hundreds of wearable, apps, and other tools that track workouts, monitor sleep patterns, and ultimately aim to enhance fitness while reducing preventable costs within the healthcare system.

Benefits, Challenges, and Risks

Top priorities in healthcare organizations include increasing process efficiency, enhancing customer offerings, and lowering costs. AI has the potential to create new efficiencies in administrative processes and provide precise and faster diagnoses and treatment plans for patients. This can lead to reduced lengths of stay, fewer subsequent re-admissions, and lower costs. When healthcare leaders were asked about the outcomes they aim to achieve through AI, the top priority cited was more efficient processes. Enhancing existing products and services and lowering costs were seen as secondary priorities. Other surveyed physicians reported that saving time and resources is expected to be the primary benefit of AI.

Top challenges in healthcare organizations include the cost of AI solutions, AI integration problems, and AI implementation and data management. Survey respondents highlighted issues such as poor-quality data, siloed data systems, high initial costs for AI solutions with low return on investment, and difficulties in integrating AI into legacy systems. The top challenges facing organizations include the cost of these systems, the integration of AI into their operations, and related implementation and risk issues. Moreover, when discussing ethical risks, leaders expressed particular concern about the safety of AI-powered systems.

AI algorithms carry inherent risks, such as variability in outputs for patient diagnosis and treatment, data bias, and traditional IT risks like change management. To mitigate these risks, healthcare organizations should verify the integrity and accuracy of their AI algorithms by developing a solid data strategy. Organizations need to conduct internal audits and testing of AI systems and ensure that AI vendors offer unbiased solutions. Understanding the data strategy and processes is crucial for minimizing bias in AI models.

Testing machine learning (ML) or cognitive algorithms differs from traditional test case methods, where actual results can be repeatedly compared to expected outcomes. Organizations should continuously monitor learning algorithms as they adapt to new data over time, ensuring their performance remains within acceptable control limits. This continuous monitoring is essential for maintaining validation and compliance in operational use.

The Future of AI in Healthcare

The future of healthcare will likely be driven by digital transformation, facilitated by radically interoperable data and open, secure platforms. It is essential for organizations to develop an enterprise-wide AI strategy, raise awareness about the upcoming wave of AI healthcare solutions, and establish an enterprise-level AI task force to guide the selection, acquisition, and implementation of AI technologies. Quick wins—those that are easiest to implement and lowest in cost—can demonstrate the value of AI and serve as stepping stones for future successes.

Healthcare organizations often face challenges with siloed, unstructured, and sometimes incomplete or inaccurate data when attempting to make integrated data more accessible. Additionally, patient-level data is sensitive and highly regulated, making it difficult for AI applications to access this information. The lack of access to clean, integrated datasets hampers the ability to train high-performance AI models and deploy them effectively at scale.

AI-enabled solutions can offer immediate benefits to healthcare organizations by reducing costs, assisting in new product development, and enhancing consumer engagement. In the short term, organizations may focus on investing in AI approaches that help achieve cost savings. Examples of such approaches include provider profiling (in supply chain management), detecting and preventing fraud, risk, and abuse, as well as automating healthcare operations.

In the long term, health systems can invest in more transformative AI applications to enhance their competitive positioning, achieve profitable growth, engage consumers, and deliver personalized customer experiences. By adopting a patient-centered approach and involving end users in designing the future patient experience, organizations can make informed choices that lead to positive outcomes for patients, healthcare practitioners, and providers.

References

  1. https://emt.gartnerweb.com/ngw/globalassets/en/information-technology/images/infographics/2024-cio-agenda/2024-cio-agenda-infographics/2024-cio-agenda-healthcar-payers-infographic.pdf?_gl=1*id6l0j*_gcl_au*OTQ0NzcyNDQzLjE3MzU4MDI4MDM.*_ga*MTY0NzMzNjIyMi4xNzM1ODAyODAy*_ga_R1W5CE5FEV*MTczNTgwMjgwMS4xLjEuMTczNTgwMjgzMy4yOC4wLjA. ↩︎
  2. https://www.gartner.com/en/industries/healthcare-providers-digital-transformation ↩︎

Artificial Intelligence, Business & Entrepreneurship, Generative AI, Industry Perspectives, Technology & Digital

Top AI Trends Transforming Digital Marketing

Nehatrendy

Kicking-off Digital Marketing Strategy

According to leading research reports, 35% of businesses adopting AI, indicating that more than one in three companies are investing in AI for transformation, innovation, and competitive advantage. In the ever-expanding digital marketing landscape, businesses face the challenge of navigating vast amounts of data, identifying trends, and delivering targeted content to their audiences. Artificial Intelligence (AI) has quickly become essential in reshaping the digital marketing field, fundamentally changing how businesses approach their promotional efforts. AI tackles these challenges by offering robust data analysis, predictive analytics, and automation capabilities. As a result, marketers can make data-driven decisions, refine their targeting, and optimize campaigns with unprecedented precision.

A global survey of early AI adopters shows that AI applications are focused on marketing-oriented goals. Beyond impressive statistics, the real impact of AI lies in the subtle transformations it brings to campaigns, customer experiences, and overall marketing strategies. Organizations leveraging AI are skillfully navigating the data landscape, strategically deploying AI to gain a competitive edge, and constantly innovating, and enhancing customer experiences.

AI in digital marketing works behind the scenes in two main ways. First, on the business side, it helps predict customer desires and optimizes ad buying. Second, on the customer side, AI enhances the experience—think personalized recommendations or quick responses to inquiries. AI acts as the brain behind marketing teams, meticulously analyzing vast datasets, which are the currency of the digital marketing realm. This data-driven approach allows AI to develop intelligent and effective marketing strategies.

Latest Trends on How Marketers are embracing AI

When we analyze the differing perspectives of various groups of marketers—such as freelancers, in-house marketers, and agency marketers—some interesting patterns emerge. This enthusiasm is likely driven by AI tools’ anticipated time-saving benefits and their relative ease of implementation for marketers. AI technology deserves attention in digital marketing due to its tangible benefits, which outweigh the associated risks. Some of these benefits include:

  • Cost Reduction: 54% of businesses reported a decrease in costs.
  • Enhanced IT and Network Performance: 53% experienced improvements in their IT and network performance.
  • Improved Customer Experiences: 48% noted that AI contributed to happier customers through enhanced experiences.

Emerging Themes in the Future of Digital Marketing

AI technology has the potential to revolutionize digital marketing by enhancing personalization, productivity, and overall effectiveness. As AI advances, new applications in digital advertising are likely to emerge. Here are some key areas where AI is significantly impacting the field:

Personalization: AI algorithms can adjust content based on customer browsing behavior, demographics, and preferences. This enables marketers to tailor content, offers, and advertisements. It improves user experience and allows for more targeted and effective marketing campaigns. Ultimately, this boosts conversion rates. The personalized approach ensures that each user receives messages suited to their specific interests.

User Journey Analysis: Understanding where users are in their journey is essential for effective content targeting. AI excels at analyzing user behavior and identifying the specific stage of the buying process a customer is in. Users can be in the research phase. They can also be ready to make a buy. AI provides tailored content that guides them seamlessly through the sales funnel.

Digital Marketing Campaigns: AI-driven chatbots play a crucial role in supporting digital marketing campaigns. They provide personalized customer support, gather data for lead generation and analysis, and enhance engagement and customer satisfaction. AI’s involvement in content marketing begins with predictive content optimization. By harnessing predictive analytics, AI can analyze extensive datasets to discern patterns and forecast user behavior.

Customer and Performance Analytics: AI algorithms help marketers gain a deeper understanding of their customers. Machine learning can analyze vast amounts of data from social media, website analytics, and customer feedback, providing valuable insights. AI’s analytical capabilities extend to measuring content performance. Algorithms track key metrics such as open rates, click-through rates, and social media engagement. With this data, marketers can refine their content strategies iteratively.

Automatic Content Generation: AI technology assists marketers in saving time and enhancing content quality. By utilizing data and user preferences, AI-powered content creation tools can generate more engaging and relevant blog posts and social media content, leading to improved campaign performance. AI addresses the previously time-consuming task of content creation by introducing automated tools that leverage natural language processing and machine learning algorithms to produce high-quality written content and visuals.

Images and Videos: In the realm of images and videos, AI can recognize objects, people, and other elements. This capability enables automated tagging, personalized search functionality, and creative inspiration, enhancing the visual aspects of digital marketing.

Risk Management: AI algorithms are proficient in detecting anomalies. By utilizing predictive modeling, analyzing text-based data, monitoring real-time activity, and assigning fraud scores, AI helps businesses identify and prevent fraud in their digital marketing activities.

AI in Advertisement

Artificial Intelligence (AI) is a transformative force that enhances the precision and effectiveness of targeted advertising. By thoroughly analyzing large datasets, AI identifies complex patterns in customer behavior, purchasing habits, and demographics.

Real-Time Optimization: In the fast-paced world of advertising, the ability to adapt in real-time is crucial. Real-time optimization is a key feature that allows continuous monitoring of campaign performance. AI can make instant adjustments to targeting or messaging based on changing dynamics, significantly improving the overall effectiveness of advertising strategies.

AI-Driven A/B Testing: AI also enhances A/B testing by introducing greater efficiency and depth to the process. AI algorithms manage the distribution of traffic across different web pages or ad variations during the testing phase. This automated approach not only saves valuable time but also ensures a randomized testing environment free from biases. By utilizing machine learning, AI can identify patterns from A/B tests, which informs strategic decisions for future experiments. Recommendations on aspects like color schemes and fonts, based on audience segmentation, highlight AI’s valuable contributions.

Efficiency Boost in Campaigns: Properly applying AI technology can significantly enhance the efficiency of digital advertising campaigns. By automating tasks such as audience segmentation, content creation, and performance analysis, AI allows advertisers to concentrate on strategic decision-making. These efficiency gains lead to campaigns with higher conversion rates, as resources are used optimally to achieve marketing goals.

AI in Customer Relationship Management (CRM)

AI in Customer Relationship Management (CRM) carefully processes and interprets user behavior data. Additionally, real-time optimization through access to marketing data enables marketing teams to quickly retrieve, analyze, and act on critical information. This faster pace enhances the agility of marketing campaigns, allowing businesses to respond promptly to market dynamics and consumer trends.

Accurate Sales Forecasting: AI-driven CRM systems significantly improve the accuracy of sales forecasts across various markets. By analyzing historical data, market trends, and consumer behavior, AI generates forecasts with remarkable precision. This enhanced accuracy allows businesses to optimize resource allocation, maintain an efficient supply chain, and make informed strategic decisions.

Intelligent Customer Record Organization: AI in CRM acts as a sophisticated organizer of customer records, managing a wealth of information with precision. This includes contact details, demographic insights, communication history, and purchase records. The result is a comprehensive view of each customer, enabling businesses to identify sales opportunities, tailor marketing strategies, and deliver personalized customer experiences.

Streamlined Customer Inquiry Response: The integration of AI-powered chatbots transforms the way customer inquiries are handled. These bots, enhanced by natural language processing, efficiently categorize incoming inquiries. Urgent questions receive immediate attention, reducing dissatisfaction and improving the overall customer support experience.

Conclusion

As the digital marketing landscape experiences a significant shift, the statistics highlight important trends. The diverse comfort levels expressed by freelancers, in-house marketers, and agency professionals provide valuable insights into the different approaches to AI adoption. Despite these differences, the overall sentiment is optimistic, with a majority feeling comfortable integrating AI into their campaigns.

However, AI in marketing also presents several challenges, particularly concerning data, that marketing teams should consider as the technology evolves. Firstly, there is the issue of transparency. How should content marketers disclose which materials, such as industry reports or blog articles, have been generated either partially or entirely by AI? What regulations exist to guide businesses in informing customers about data usage? Secondly, intellectual property poses challenges when AI systems utilize the intellectual property of third parties to generate new content without proper attribution. Thirdly, there are legal and ethical concerns. As AI systems often handle vast amounts of data, it’s crucial to consider how customers’ personal information is stored and used to prevent security breaches. Lastly, data quality concerns arise because some AI systems are trained on historical data, which may introduce biases or inaccuracies in their outputs.

Looking ahead, the path is clear: AI is not just a passing trend but a fundamental force shaping the present and future of digital marketing. Businesses that adapt to this changing landscape can gain a competitive edge by leveraging the efficiency and insights AI offers. As these tools evolve and become more sophisticated, we can expect widespread integration of AI throughout the digital marketing realm, leading to a new era of strategic optimization and audience engagement.

Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

Artificial Intelligence in Smart Food Supply Chains

Nehatrendy
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A Taster to Smart Food Supply Chains

Have you wondered how global food supply chains have become efficient and resilient over the years? It’s because of modern technologies such as Artificial Intelligence (AI) which plays a crucial role in nourishing the world’s growing population. AI optimizes logistics, and reduces waste and costs, while predictive analytics help forecast demand and respond to disruptions quickly. By leveraging data-driven insights, AI promotes sustainability and ensures a reliable food supply. It benefits all stakeholders in the value chain such as producers, distributors, retailers, and consumers, creating a more agile and responsive ecosystem.

This is where the concept of Smart Food Supply Chains comes into play, utilizing a blend of modern technologies such as Artificial Intelligence (AI) to address challenges within food supply chains. Smart Food Supply Chains integrate technology and data to improve efficiency, traceability, and responsiveness from farm to table. Their goals include minimizing waste, enhancing sustainability, and ensuring food safety.

Key objectives by leveraging these technologies involve real-time monitoring, predictive analytics, and agile logistics to meet consumer needs while optimizing resource use throughout the supply chain.

In a world of constant change, Artificial Intelligence (AI) optimizes all dimensions of the global supply chain such as production, distribution, and inventory management, lowering waste and costs. Furthermore, it strengthens supply chains against disruptions, ensuring food security, sustainability, and a more environmentally friendly global food distribution system.

Demand for Smart Food Supply Chains

Globalization and interconnected supply chains have led to complex disruptions in global value chains, caused by various factors including natural disasters, climate events, geopolitical conflicts, and economic crises. Recent advancements in AI, offer new opportunities for effectively forecasting and managing these disruptions.

These technologies allow organizations to effectively manage disruptions like COVID-19 on food supply chains, achieving food security in the face of climate change, leveraging predictive models in agriculture, and assessing how disruptions affect food price volatility. These insights offer valuable guidance for policymakers and researchers seeking to enhance food security amid uncertainties and disruptions in global value chains.

Food supply chains have traditionally relied on manual processes and outdated technologies, resulting in inefficiencies at various stages—including manufacturing, distribution, and stock management. These inefficiencies lead to increased costs and longer lead times, causing delays in delivering products to consumers. In today’s fast-paced world, these shortcomings can hinder a supply chain’s ability to meet consumer demands promptly.

Additionally, traditional supply chains are often plagued by significant food waste and spoilage. Insufficient monitoring and handling practices result in food products expiring before reaching consumers, leading to financial losses and contributing to the global food waste crisis. This situation has financial repercussions and poses environmental challenges, as resources used in food production are wasted. Addressing these issues is essential for developing more sustainable and environmentally friendly supply chains. Artificial Intelligence (AI) is increasingly being utilized to tackle these problems and optimize supply chains.

Digitization of Food Supply Chains

Supply Chain Management (SCM) involves the flow of goods, services, and information from points of origin to customers through a network of interconnected entities and activities. In typical SCM scenarios, it is assumed that factors such as capacity, demand, and cost are known. However, this assumption often does not hold in reality, as uncertainties arise from variations in customer demand, transportation issues, organizational risks, and lead times. Demand uncertainties, in particular, significantly impact supply chain performance, affecting production scheduling, inventory planning, and transportation.

In this context, demand forecasting becomes a crucial strategy to address the uncertainties faced in supply chains. A variety of statistical analysis techniques have been employed for demand forecasting in SCM, including time-series analysis and regression analysis. Traditional methods, such as time series analysis and regression models, are commonly employed to identify trends and seasonality in demand data. Additionally, advanced algorithms like random forests and neural networks can analyze complex datasets to uncover patterns that traditional methods overlook. These models can also adapt to changes in demand dynamics over time.

Recently, advancements in technologies like AI and predictive analytics have emerged as effective tools for generating more accurate predictions. These technologies enhance our understanding of customer needs, facilitate the evaluation of supply chain performance, improve efficiency, reduce response times, and support risk management in supply chains. The increasing need for customer behavior analysis and demand forecasting is driven by globalization, heightened market competition, and the rise of digitization practices in supply chains.

A Data-Driven Approach for Smart Food Supply Chains

Supply chain data is generated in high volumes across numerous points and serves various purposes, reflecting the complexity of interactions among multiple suppliers, products, and customers. This data is high-dimensional and processes many transactions continuously within supply chain networks. Given such complexities, there has been a shift away from conventional demand forecasting methods that rely on identifying statistically meaningful trends from historical data.

Instead, we are moving towards intelligent forecasting systems that learn from historical data and can adapt to predict the constantly changing demands in supply chains. This capability relies on AI, machine learning, and predictive analytics techniques that uncover forecasting rules by identifying the underlying relationships among demand data. By utilizing historical data and advanced statistical methods, organizations can gain insights into future demand patterns, which is essential for effective inventory management and resource allocation.

To maximize the benefits of Artificial Intelligence, it is crucial to integrate intelligent forecasting models with supply chain operations. Incorporating real-time data from IoT devices can enhance the accuracy of forecasts by providing up-to-date information on inventory levels and market conditions. Effective collaboration among all departments—including sales, marketing, and supply chain teams—ensures that these forecasts align with business objectives and market realities.

Leveraging Artificial Intelligence for demand forecasting not only enhances operational efficiency but also supports strategic decision-making. By adopting a data-driven approach, organizations can better navigate the complexities of supply chain management and respond proactively to changing market demands.

Benefits of Smart Food Supply Chain

The need for efficiency and resilience in food supply chains is paramount. Efficiency ensures that resources are used optimally at every stage of the supply chain, minimizing waste and lowering costs. In contrast, resilience is essential for facing the multitude of challenges that food supply chains encounter, such as natural disasters, pandemics, and geopolitical disruptions.

  • Enhanced Efficiency: A combination of AI and IoT devices tracks food items in real-time, reducing waste and improving delivery efficiency. By optimizing processes like stock management and logistics, AI significantly enhances the effectiveness of smart food supply chains. AI-powered sensors and monitoring systems track food products from farm to table, providing real-time information about their location, condition, and quality.
  • Improved Resilience: AI predicts and mitigates supply chain disruptions, reducing food waste and ensuring a more robust supply system even in challenging situations. It strengthens resilience in smart food supply chains by anticipating and addressing potential disruptions, such as supply chain bottlenecks or disasters. Additionally, AI improves stock management and spoilage prediction, helping to further reduce food waste.
  • Sustainability and Environmental Impact: AI minimizes the carbon footprint by optimizing transportation and energy usage, contributing to more sustainable and eco-friendly food supply systems. In smart food supply chains, AI promotes sustainability through environmentally friendly practices, aiding in the reduction of carbon emissions linked to transportation and energy consumption.

Industry Applications of Smart Food Supply Chains

Food supply chains are becoming increasingly digitized and automated through the use of modern technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain. These digitization efforts often depend on cloud computing, which can create challenges related to bandwidth overhead, high latency, and issues of security and privacy.

In contrast, Edge AI can enhance scalability and mitigate the security and privacy concerns related to centralized cloud computing. This shift aligns with the provenance and traceability requirements of food supply chains, while also supporting their digitization through these modern technologies. This proposed Edge AI approach to supply chain digitization paves the way for greater resilience in modern digital supply chains.

When used alongside AI, IoT plays a crucial role in smart food supply chains. It enables real-time monitoring and management by collecting information on temperature, humidity, and location. This data allows for effective management of stock and transportation, reducing spoilage and waste. Furthermore, AI coupled with Blockchain and IoT technologies improves supply chain transparency by enabling stakeholders and customers to trace a product’s journey from its source to its destination. This transparency not only ensures food safety and quality but also fosters customer trust in the supply chain, contributing to a more sustainable and responsible food distribution system.

Conclusion

AI is revolutionizing food supply chains, presenting significant opportunities for efficiency and resilience. It optimizes production, distribution, and inventory management, benefiting producers, distributors, retailers, and consumers. AI enhances traceability, transparency, and sustainability, promoting ethical and eco-friendly practices.

Despite the many advantages of digitizing the food supply chain, there are growing ethical concerns surrounding the use of AI in agriculture. These concerns include enforcing sustainable farming methods and promoting equitable access to AI technologies. Ethical AI emphasizes fairness, transparency, and environmental responsibility, ensuring that advancements in agricultural technology benefit all stakeholders while adhering to moral standards in food production.

To create an environmentally friendly and resilient future, stakeholders must prioritize AI integration. Governments should establish supportive regulatory frameworks, and industries should adopt practices that align with ethical and sustainable values. Additionally, funding for worker retraining and upskilling is essential to address concerns about job displacement.

The message is clear: Embrace Edge AI as a transformative force in food supply chains, ensuring that it serves not only economic interests but also ethical, environmental, and social objectives.

Furthermore, the food supply chain consumes substantial amounts of energy, significantly impacting the environment. AI-driven optimization can reduce energy consumption by facilitating information sharing, minimizing energy use, optimizing transportation routes, and lowering greenhouse gas emissions.

Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

The Future of AI and Robotics

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A Foreword to Artificial Intelligence (AI) and Robotics

Robotics is a field of engineering and computer science that involves designing, constructing, and operating machines capable of performing programmed tasks without additional human involvement. At its core, robotics aims to use technology to automate tasks, making them more efficient and safer. Robotics is a technology branch concerned with physical robots, which are programmable machines that usually carry out a series of actions autonomously or semi-autonomously.

The convergence of robotics and artificial intelligence (AI) is rapidly driving the creation of new industries, cutting-edge technologies, and increased productivity and efficiency in existing sectors. With AI in robotics advancing, its real-world applications are becoming increasingly apparent.

Confluence of AI & Robotics

Artificial Intelligence (AI) involves developing systems that can perform tasks typically requiring human intelligence, such as learning, problem-solving, and decision-making. These systems can work independently, without constant instructions, as they are programmed to learn and adapt on their own. On the other hand, Robotics involves developing robots that can carry out specific physical tasks. These robots can be programmed to perform simple, repetitive actions, like sorting items or assembling small parts.

Although AI and robotics are distinct fields, they complement each other and can work in tandem to bring about a wide range of benefits and advancements in various applications. AI algorithms are necessary when aiming to enable the robot to perform more complex tasks. Companies are employing AI-powered robots to bridge the gap between humans and technology, solve problems, and transform their business models to meet evolving demands.

Four Key Functions: AI & Robotics

AI is teaching robots four key functions to achieve human-like intelligence.

  • Vision: AI uses algorithms to improve a robot’s ability to interpret and understand what it sees by detecting unfamiliar patterns.
  • Grasping: AI enables robots to learn how to grasp objects based on distance and orientation information provided by vision.
  • Motion Control: AI utilizes various technologies, including vision, space detection, and edge detection, to provide precise motion control to robots.
  • Data: AI facilitates robots in becoming self-reliant through strong data processing and analysis capabilities.

Robotics Control Algorithms

Robots require specific control algorithms depending on their type and tasks. These algorithms include motion control, computer vision, and other functionalities such as controlling robotic arms, drones, obstacle avoidance, and grasping. Here are some of the recognized algorithms.

Motion Control

These algorithms determine how a robot moves and navigate its environment. Different algorithms are needed for tasks like path following, obstacle avoidance, and localization, depending on the robot’s mobility. Path Planning algorithms involve finding the best path for a robot to reach its destination while avoiding obstacles. Simultaneous Localization and Mapping (SLAM) algorithms enable a robot to create a map of its environment while simultaneously locating itself within that map. These are crucial for autonomous navigation in unknown or dynamic environments.

Computer Vision

This is a fundamental aspect of a robot’s perception, involving processing visual data from cameras to understand the robot’s surroundings. Object detection algorithms identify and locate objects within an image or video stream. This capability is essential for tasks such as identifying obstacles or recognizing specific objects. Image processing techniques enhance the quality of visual data and extract meaningful information. Commonly used techniques include image filtering, edge detection, and feature extraction.

Archetypes: Types of Robots

With the rise of industrial robots in manufacturing settings, there is a growing need for various types of industrial robots that can be tailored to specific applications and industries. These include Cartesian, SCARA, Articulated, Cylindrical, Delta, Polar, and Collaborative Robots. The most common types of AI-powered robots include.

Autonomous Mobile Robots (AMRs)

An Autonomous Mobile Robot (AMR) is a robot that can understand and move through its environment without direct oversight by an operator. AMRs are equipped with sophisticated sensors that enable them to interpret their environment and navigate around obstacles. One of the key applications of AMRs in warehousing and logistics is inventory management. They can autonomously scan and track inventory levels in real time, reducing the need for manual inventory checks and minimizing the risk of human error.

Articulated Robots (Robotic Arms)

Articulated robots are highly versatile industrial robots used in various industries to perform a wide range of tasks. These robots are designed with a series of interconnected segments, known as links, which are attached through movable joints. They are used to perform repetitive and dangerous tasks that are often too difficult or hazardous for humans to undertake. Typical applications for Articulated Robots include assembly, arc welding, material handling, machine tending, and packaging.

Cobots

A Cobot, or Collaborative Robot, is a robot designed to work alongside humans. They are often designed to take on menial, repetitive tasks, freeing up humans to focus on more important work. Tech companies use cobots for repetitive tasks that require high precision, such as dispensing, labeling, or insertion. Cobot arms are lightweight and easily programmable, allowing them to be assigned to different processes as per organizational needs. Small and mid-sized electronics manufacturers using collaborative robots often see a return on investment in under a year.

Industry Practices: AI & Robotics

A warehousing robot may use a pathfinding algorithm to navigate around the warehouse. A drone may utilize autonomous navigation to return home when it is about to run out of battery. A self-driving car may use a combination of AI algorithms to detect and avoid potential hazards on the road. These examples depict artificially intelligent robots, and AI has proven to be a valuable asset in various industries. Let’s examine some key areas where AI is being used alongside robotics today.

Warehousing & Logistics

Autonomous Mobile Robots (AMRs) are revolutionizing the warehousing and logistics industry by offering efficient and adaptable solutions for material handling and transportation tasks. These advanced robotic systems are equipped with sensors, cameras, and algorithms that enable them to navigate warehouse environments, pick and pack items, and transport goods with precision and accuracy. Moreover, articulated robots are extensively used in manufacturing industries for tasks such as assembly, welding, painting, packaging, and material handling. These robots can accomplish these tasks with high precision and speed, leading to improved productivity and efficiency.

Automobiles

AI in robotics was initially developed to meet the needs of the automobile sector. There are AI-controlled RC cars, dirt bikes, and go-carts used for entertainment, as well as AI-controlled trucks and heavy-duty vehicles for transporting materials. In automobile manufacturing hubs, various tasks such as assembly, screwing, and fitting are executed by AI-controlled robots. The built-in safety features of Cobots enable them to navigate human workspaces safely without causing harm.

Picking & Delivery Robots

Picking robots streamlines human-led order fulfillment, delivering items to workers when they’re needed, thereby enhancing productivity and efficiency. Delivery robots, which may include drones or compact vehicles with secure compartments, are commonly utilized for last-mile delivery or curbside pickup of items such as food orders, groceries, and packages.

Drones

Drones, or unmanned aerial vehicles (UAVs), excel in tasks requiring aerial mobility and remote sensing. They function as autonomous mobile robots for tasks such as inspecting hard-to-reach areas, conducting surveillance, or aiding in search and rescue operations. Additionally, drones are employed in agriculture, aerial photography, search and rescue operations, and various other applications.

Summing up: AI & Robotics

In the next decade, half of cloud data centers will incorporate advanced robots with artificial intelligence (AI) and machine learning (ML) capabilities, resulting in a 30% increase in operating efficiency1. Much of the work carried out in data centers, such as capacity planning, rightsizing virtual machine and container environments, and ensuring efficient resource utilization to avoid “cloud waste,” is tedious, complex, and repetitive. Therefore, integrating robots and AI in data centers can create a more secure, precise, and efficient environment that requires less human intervention.

While the initial integration of artificial intelligence into an existing business model may seem daunting, the benefits typically outweigh the challenges. There is a noticeable increase in productivity and efficiency when AI robots handle repetitive or time-consuming tasks, such as inventory checks and alerting on out-of-stock or misplaced items in retail environments. Furthermore, AI robots can improve quality and accuracy by seeing and understanding their environments, enabling them to complete complex tasks like quality control inspections. Additionally, AI robots contribute to enhanced workplace safety, as AI-enabled robots can learn from human gestures and speech and continuously improve their task performance while working safely alongside employees.

AI is playing a crucial role in transforming various industries, from self-driving cars, and customer service to industrial and service robots, thereby enhancing daily life. This growth presents an opportunity for workforce retraining, reskilling, and investing in knowledge that aligns with the latest technologies. Automating these processes with Artificial Intelligence and Robotics allows organizations to focus on more strategic tasks.

References

  1. https://www.gartner.com/en/newsroom/press-releases/2021-11-01-gartner-predicts-half-of-cloud-data-centers-will-deploy-robots-with-ai-capabilties-by-2025 ↩︎

Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

Enhancing Business Operations with AI-based Cloud Computing Services

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A foreword to Artificial Intelligence in Cloud Computing

Discover the future of technology with Artificial Intelligence (AI) and cloud computing! AI harnesses the power of algorithms and data to mimic human intelligence, enabling incredible capabilities such as speech recognition and image generation. Companies worldwide are investing in AI models and services to unleash the full potential of AI applications.

Step into the realm of cloud computing, where you can access computing power via the internet and pay for the resources you use, without the need to invest in databases, software, and hardware. Cloud offerings encompass infrastructure like servers, storage, and databases, as well as cutting-edge services such as data analytics, AI, and applications.

Experience the thrill as leading cloud providers develop advanced AI services and applications available on their platforms. Witness AI’s pivotal role in enabling hyper-scale cloud computing through automation and agility. Cloud computing lays the groundwork for AI, empowering businesses to embrace AI technologies without substantial investments in hardware and software. Join the exhilarating journey where AI and cloud computing converge to shape the future of technology!

Interlink between Artificial Intelligence and Cloud Computing

AI and cloud computing are closely interlaced. Cloud is becoming the preferred method for integrating AI into business applications. Providers are integrating AI into their offerings, including software-as-a-service (SaaS) applications that are enhanced with various AI technologies, as well as embedded Large Language Models (LLM) capabilities. With advanced LLMs in cloud architectures, businesses can utilize their data to train and deploy AI models tailored to their operations, or more commonly, enhance the training of an existing model. Cloud customers even include AI model developers, who require significant computing and storage capacity to train their models using extensive amounts of data. Additionally, cloud providers are likely to provide highly advanced AI-assisted services, such as application development platforms where developers outline the application functions they desire and enable the AI platform to swiftly generate the initial code.

Pre-built AI-based Cloud Computing Services

Many AI API services are available both on-premise and in cloud-native environments, such as Kubernetes. For example, in Google Cloud, containers are used to isolate applications from computing environments, and Kubernetes is an open-source system for automating the deployment, scaling, and management of containerized AI and ML applications. There are several pre-built AI and ML APIs available on the cloud platform, including the following:

  • Document AI: Creates document processors that automate tedious tasks, improve data extraction, and provide deeper insights from unstructured or structured document information.
  • Contact Center AI: Offers natural interactions to improve customer service and supports multichannel communications between customers and agents.
  • Pre-trained APIs: Allows leveraging pre-trained models, freeing users from developing core AI technologies from scratch such as the Speech API.
  • BigQuery ML: Enables creation and running of ML models using GoogleSQL queries, and also provides access to Vertex AI models and Cloud AI APIs for performing AI tasks like text generation or machine translation.
  • AutoML: Allows leveraging abstracted and low-code ML production without requiring deep ML expertise.
  • Vertex AI: A fully managed, unified AI development platform for building and using Generative AI, making it easy to prototype, customize, integrate, and deploy them into applications.

Case in Point: Recommendation AI model

Recommendations AI model as a solution utilizes machine learning to provide product recommendations to shoppers from the existing product catalog. This recommendation solution is fully managed, operates at a global scale, and is powered by deep learning, allowing the customer to concentrate on a great shopping experience. Compared to the baseline recommendation systems that customers use, Recommendations AI has shown a double-digit increase in conversion and click-through rates in A/B experiments controlled by customers. The model utilizes the existing product catalog and metadata about those products to gain a better understanding of the nuances in assortment, pricing, and variables such as size and style.

There are three business objectives that the Recommendations AI model helps optimize. Firstly, Click-Through Rate – How often did someone click on a recommended item? Secondly, Conversion Rate – How often did someone add a recommended item to their cart? Lastly, Revenue Per Session – How much money did the recommendations generate for us? Another similar example is the expansion of AI-driven recommendation systems frequently being used in cloud-based content streaming services.

Benefits of AI in Cloud Computing

The fusion of AI and cloud computing offers several benefits across different industries.

  • Process Automation: AI enables cloud providers and their customers to automate many IT processes required for delivering services and scaling compute capacity. Businesses use AI processes, such as intelligent automation, to enhance speed and accuracy in document management and factory operations.
  • Cost Savings: AI can reduce costs by performing complex tasks faster with fewer people involved. As current customers use more cloud services, this can deliver not only higher margins for providers but also build loyalty with existing customers instead of acquiring new customers.
  • Personalization: AI’s observant and pattern-matching abilities help companies, including cloud providers, better understand their customers’ behavior and preferences. This allows firms to offer personalized services and more accurate suggestions, thus improving retention and revenue.
  • Increased Productivity and Efficiency: AI excels at tasks that people find challenging or tedious. With AI-backed processes, businesses can handle document management, package sorting, invoice matching, and document summarization. The added benefit of this efficiency is that it frees employees to focus on more complex tasks requiring human experience and relationships.

Challenges with AI in Cloud Computing

Cloud computing providers are working to make it easier to use AI, but there are still challenges, especially related to managing data. Data security and privacy are major concerns when it comes to AI. Customers prefer cloud vendors that prioritize strong controls over data governance and security. Integration is also critical. AI functions best with clean, well-organized data, and becomes more effective with data from multiple sources. The challenge is integrating and standardizing data from various internal business units, partner sites, and external sources. The talent gap is also a growing problem. Finding and retaining people with the expertise to design, train, and deploy AI models is difficult and expensive. Even with AI model developers and cloud services available, companies often require experienced professionals to effectively implement AI.

Applications of AI in Cloud Computing

There are several popular AI applications in cloud computing:

  • Chatbots: One of the first software programs designed to simulate human communication and step into customer service roles. With AI and cloud computing, companies can now use APIs to tap into large language models, giving them powerful chatbots that can understand spoken or written human language.
  • Self-Driving Cars: Some of the most complex examples of AI in the world are self-driving cars and other autonomous vehicles. They use image recognition to identify signs, signals, traffic flow, and obstructions. They optimize the routes they take to reach their destinations. And they send and receive data in real-time to proactively diagnose issues and update model.
  • Smart Home Technology: Almost anything that automates your home uses AI. Examples include intelligent light bulbs that listen for commands, intelligent thermostats that learn your preferences and adjust themselves throughout the day, and intelligent vacuum cleaners that learn how to navigate the layout of your home without instruction.

A Closure to AI in Cloud Computing

According to leading research organizations, 75% of organizations1 are expected to adopt a digital transformation model based on cloud computing. Cloud computing is shaping the future of digital business as organizations are now focusing on emerging technologies such as generative artificial intelligence.

Integrating AI into cloud computing not only enhances operational efficiency by automating processes, optimizing resource allocation, and improving scalability but also leads to substantial cost savings, allowing IT teams to focus on other strategic initiatives. Cloud service providers can increase profitability by leveraging what they have developed and offering it as branded AI services to customers.

The emergence of AI-driven cybersecurity solutions enables real-time threat identification and mitigation. The collaboration between AI and cloud computing introduces transformative trends such as enhanced security through intelligent threat detection, real-time analytics personalization, and the implementation of edge computing for quicker on-site decision-making. Additionally, there is a development of AI-powered DevOps tools for continuous integration and delivery in the cloud.

AI-driven cloud solutions offer positive return on investment (ROI) impacts due to their scalability. The ability to adjust resources based on workload requirements allows businesses to effectively address the challenges of a dynamic market, driving business growth and performance without large initial infrastructure expenditures.

  1. https://www.gartner.com/en/newsroom/press-releases/2023-04-19-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-reach-nearly-600-billion-in-2023 ↩︎
Generative AI, Industry Perspectives, Technology & Digital

Unleashing the Power of Generative AI

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A Primer to Generative AI

Are you ready to revolutionize the way you work? Generative AI is the game-changing tool you’ve been waiting for! This cutting-edge technology can streamline your workflow, boost your productivity, and unleash your creativity like never before. Learn about Generative AI and its amazing applications in my startup RETRENDE – a cloud computing product that utilizes advanced Machine Learning (ML) and Artificial Intelligence (AI) in a retail, direct-to-consumer, and print-on-demand platform.

Generative AI is a type of artificial intelligence that refers to machine learning systems that are capable of generating text, images, or code in response to prompts entered by the user. These models are increasingly being incorporated into online tools and chatbots that allow users to ask questions or provide instructions, and receive human-like responses generated by AI.

While different generative AI tools can create new audio, image, and video content, text-based conversational AI has been the most popular and effective use case. Essentially, people can converse with and learn from text-based generative AI models in much the same way they would with humans. These models “learn” from existing online documents and artifacts, and can be used to assist with writing, research, and coding.

The main difference between Traditional AI and Generative AI lies in their capabilities and applications. Traditional AI systems are primarily used to analyze data and make predictions, while Generative AI goes a step further by creating new data based on its training data. In other words, Traditional AI excels at pattern recognition, while generative AI excels at pattern creation. Traditional AI can analyze data and provide insights, but generative AI can use the same data to create something entirely new.

Generative AI Disposition

Generative AI models are designed to identify patterns and structures in existing data. This is made possible through neural networks, which enable the models to generate new and original content. Deep Learning is the computing process that underpins Generative AI. While deep learning has fewer nodes than neural networks, both technologies identify patterns in datasets to derive insights. By analyzing patterns in large datasets, Generative AI models create new data that looks like it was generated by humans. One of the main advantages of Generative AI models is that they enable organizations to quickly create foundational models using large amounts of unlabeled data. ChatGPT uses a foundational model called GPT-3 to generate stories based on a prompt.

Benefits Vs. Limitations

Generative AI is a technology that can bring significant benefits to businesses. It offers three primary advantages: knowledge synthesis, human-AI collaboration, and speed.

  • Increased Productivity: By using this technology, knowledge workers can reduce the time they spend on routine tasks, which frees up more time for more important work.
  • Reduced Costs: By minimizing errors, eliminating downtime, and identifying inefficiencies, businesses can save money in the long run.
  • Better-Informed Decision-Making: By using scenario modeling and risk assessment tools, generative AI models can provide detailed insights into various business operations. This information can help managers make better decisions and reduce the risk of costly mistakes.

Generative AI tools have a known limitation – they can make up false information with the same confidence as truth. Businesses need to be aware of this and other limitations while implementing generative AI.

  • Requires oversight: It is important to have human oversight in the workflow to prevent flawed outputs from being disseminated or influencing company policy.
  • Computational power and initial investment: Generative AI models require a significant amount of computational power for both training and operation, which is why much of the development is done using cloud infrastructure.
  • Resistance from Employees: Some employees may resist the adoption of generative AI, particularly those who have been with the company for a long time and are accustomed to established protocols and methods. This can lead to a temporary decrease in productivity.

Generative AI Archetypes

There are various types of generative models, and by combining their positive attributes, we can build even more powerful models.

Diffusion Models

One such model is the Diffusion Model, also known as Denoising Diffusion Probabilistic Models (DDPM). During training, this model determines vectors in latent space through a two-step process. The first step is the Forward Diffusion process, which gradually adds random noise to training data. The second step is the Reverse Diffusion process, which reverses the noise to reconstruct the data samples. Diffusion Models are considered foundation models because they are large-scale, flexible, and offer high-quality outputs.

Some of the applications of Diffusion Models include generating realistic audio samples, denoising audio recordings, and enhancing audio quality. They have also been used in reinforcement learning tasks to aid in policy optimization and exploration. Diffusion Models provide a flexible framework for high-dimensional data modeling. However, their intricate processes can make them not only computationally expensive but also a slow and lengthy process.

Variational Autoencoders (VAEs) Models

Variational autoencoders leverage two networks to interpret and generate data which are an Encoder and a Decoder. The encoder compresses the input data into a simplified format, while the decoder reconstructs the compressed information into something new that resembles the original data but is not the same. One of the common applications of VAEs is teaching a computer program to generate new face images similar to that of training data. VAEs can provide a probabilistic interpretation of data, allowing uncertainty modeling. However, VAEs may struggle to capture complex data distributions, and generated samples may lack sharpness compared to other generative models.

Transformer Models

Transformers are a type of neural network architecture that can process sequential input data non-sequentially. They are particularly useful for text-based generative AI applications, thanks to two mechanisms: Self-Attention and Positional Encodings. The Self-Attention Layer assigns a weight to each part of an input, indicating its contextual importance. Meanwhile, Positional Encoding represents the order in which input words occur. Transformer-based models are trained on large sets of data to understand the relationships between sequential information in text, such as words and sentences. These models are highly proficient in natural language processing and can understand the structure and context of language, making them well-suited for text-generation tasks. Transformer models are beneficial in various applications, such as machine translation, text generation, sentiment analysis, and question-answering. They capture complex context and dependencies in data and work well with sequential text, image, or video data. However, transformer models may struggle with training large-scale transformers since it requires substantial computational resources.

Generative AI Applications

With generative AI models at your fingertips, you can effortlessly generate new content across multiple modalities – including text, images, audio, video, and code – faster than you ever thought possible. Whether you’re a creative, an engineer, a researcher, or a scientist, the possibilities are endless. So why wait? Below are some of the notable Generative AI applications in the marketplace today.

Gaming Experience

Generative AI in gaming is a technique that uses artificial intelligence to create unique content for gamers. It saves game developers time and effort by automating content creation. Game developers have embraced cutting-edge advancements in computing, graphics, and networking to create Immersive Gaming Experiences. AI enthusiasts are excited about AI in gaming that can generate realistic gaming environments, characters, and even entire game plots, enhancing the gaming experience. Procedural Generation is a technique used in game development that involves the use of algorithms to generate game content dynamically. This can lead to the creation of more realistic and immersive game environments that are tailored to each player’s preferences. Player Behavior Prediction is another technique that can be used to predict player behavior and adjust game mechanics accordingly. Dynamic Game Difficulty can also be used to dynamically adjust game difficulty based on player performance. This technique can help players be engaged without making the game too difficult or too easy.

Fashion Forward

Generative AI is a technology that can create unique and original fashion designs based on specific criteria, such as preferred aesthetics, materials, and target market. It can also apply Style Transfer, which allows designers to combine elements from different sources or create new variations of existing designs. Generative AI has many benefits for the fashion industry, such as Transforming Sketches into Fully Colored images, reducing human error, and generating countless variations for review. Another advantage of generative AI is the ability to Create Diverse and Representative Fashion Models, which can help fashion companies better serve a wide range of customers and showcase their products more accurately. Virtual Try-ons and 3D models are also possible with generative AI, where virtual representations of fashion products can be superimposed onto images of people, allowing customers to “try on” clothes virtually. These virtual models can be customized to represent various body types, colors, and sizes.

Marketing Optimization

Generative AI is becoming increasingly important in the field of marketing. It allows for Streamlined Workflow by consolidating siloed marketing workflows into unified generative AI systems and minimizing context switching. Generative AI chatbots and process automation reduce oversight by handling mundane monitoring of campaigns, platforms, and inventory. By analyzing information such as purchase history, browsing behavior, and demographics, generative AI systems can identify micro-segments of customers. AI tools can then generate personalized ads, content, and recommendations fine-tuned to each segment’s interests and needs. Generative AI plays a crucial role in dynamically targeting, segmenting audiences, and identifying high-quality leads, significantly improving the effectiveness of marketing strategies and outreach efforts.

Applications in RETRENDE

As the developer of RETRENDE, a cloud-based product that uses advanced machine learning (ML) and artificial intelligence (AI) in a retail, direct-to-consumer, and print-on-demand platform, I can provide insight into how I implemented Generative AI in RETRENDE.

RETRENDE uses Generative AI for targeted advertising and providing hyper-personalized experiences. With data-driven algorithms like Reinforcement Learning, each social media or web ad is optimized to specific user interests and characteristics. In A/B tests, AI-generated ads have achieved up to 2x higher clickthrough rates. With Generative AI, RETRENDE delivers the right message or experience to the right customer at the right time to scale perfectly. It uses artificial intelligence algorithms to analyze data and identifies the most relevant audience for products. This collaboration allows RETRENDE to focus on strategic tasks requiring creativity while AI handles data analysis and marketing optimization. In addition, RETRENDE also creates custom generative AI language generators such as AI-generated images, conversational AI for customer service, and SEO content optimization.

Wrapping up Generative AI

According to leading research reports, Generative AI is not expected to outperform humans anytime this decade. However, a significant leap in Generative AI capabilities is expected in the next decade. For businesses, operational efficiency is the most compelling benefit of Generative AI. It helps automate specific tasks and allows employees to focus on more important strategic objectives, resulting in lower labor costs, greater productivity, and insights into the performance of business processes.

The primary benefit of Generative AI for marketing is automating repetitive and manual tasks. This frees up employees’ time and resources to focus on high-value strategies and creativity. Marketers currently spend just 40%1 of their time on strategic work and the rest on repetitive tasks and oversight. AI automation can redirect those valuable human hours, resulting in a 20%2 reduction in costs and nearly doubling productivity. The efficiency gains allow teams to get more done.

Experts anticipate that bias will remain a persistent aspect of most generative AI models. This challenge is expected to give rise to new marketplaces centered around ethical data sets. As generative tools become more widespread in the workplace, they will bring changes to job roles and necessitate new skills. Navigating ethical considerations, maximizing data security, and adapting to evolving best practices are paramount.

  1. https://www.forbes.com/advisor/business/ai-statistics/ ↩︎
  2. https://www.forbes.com/advisor/business/ai-statistics/ ↩︎
Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

Unlock the Potential of Deep Learning

Nehatrendy
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Understanding Deep Learning Concepts

Get ready to be blown away as we dive into the fascinating world of Deep Learning and its amazing applications in RETRENDE. Deep learning is a cutting-edge subset of Artificial Intelligence that mimics the cognitive abilities of the human brain. With its ability to create patterns and make decisions like human, Deep Learning goes beyond traditional task-based algorithms and opens up a whole new realm of possibilities. Through neural networks with multiple hidden layers and nodes, Deep Learning can analyze unstructured and unlabelled data with unparalleled accuracy. So, buckle up and get ready to explore the exciting world of Deep Learning and its incredible potential!

Role of Deep Neural Networks (DNN)

Deep Neural Networks (DNN) play a critical role in identifying and classifying phenomena, recognizing patterns and relationships, evaluating possibilities, and making predictions and decisions. Unlike single-layered neural networks, DNNs have three or more layers, which help refine and optimize outcomes for greater accuracy.

Difference between AI Vs. ML Vs. DL

Although Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are sometimes used interchangeably, they are distinct terms. AI is the overarching system, with ML being a subet of AI. DL is a subset of ML that distinguishes itself from conventional machine learning by the type of data it works with and its learning methods. Machine Learning algorithms use structured, labeled data to make predictions, meaning specific features are defined from the input data and organized into labels. On the other hand, Deep Learning eliminates some of the data pre-processing typically involved with machine learning. These algorithms can ingest and process unstructured data, such as text and images, and automate feature extraction, removing some of the dependencies on human experts.

Advantages Vs. Disadvantages

Deep learning has become an industry standard because it can easily learn from massive amounts of unstructured, complex, and unlabelled data. Deep learning models are more powerful than traditional machine learning methods because they can learn from complex relationships between features, which leads to more accurate predictions. Thanks to Graphical Processing Units (GPUs), deep learning models are scalable and can learn from actively generated real-time data. However, deep learning models have similar limitations. For instance, deep learning require a large amount of data to learn from, making it difficult to apply these techniques to problems where there is not a lot of data available. Deep learning models can also be prone to overfitting, which means that they can learn the noise in the data rather than the underlying relationships. As a result, these models can potentially be biased and may lead to inaccurate predictions.

Embarking on Deep Learning Process

Deep learning is a machine learning technique that utilizes a neural network with three or more layers. Deep Neural Networks (DNN) consist of interconnected nodes in multiple layers, each one refining and optimizing the prediction of the previous layer. This progression of computations through the network is called Forward Propagation. The input and output layers of a deep neural network are called visible layers: the input layer ingests the data for processing, and the output layer makes the final prediction or classfication. To train the model, a process called Back Propagation is used to calculate errors in predictions and adjusts the weights and biases of the function by moving backward through the layers. Together, forward and backward propagation allow a Deep Neural Network (DNN) to make predictions, correct any errors, and gradually improve accuracy.

Categorizing Deep Learning Algorithms

Deep learning algorithms are complex and rely on Artificial Neural Networks (ANN) that mimic human information processing. There are different types of neural networks to address specific problems.

Convolutional Neural Networks (CNN)

Convolutional Neural Networks (CNN) are feed-forward neural networks capable of processing structured data arrays. They consist of a convolutional layer, a pooling layer, and an output layer. Image classification models usually have multiple convolutional layers, followed by pooling layers to improve accuracy. CNNs can automatically learn features from raw pixel data, making them ideal for processing large and complex datasets. However, training CNNs requires significant computational resources and extensive datasets. They excel at recognizing patterns, lines, and shapes, and are used for Image Recognition, Computer Vision, and Image Classification tasks.

Recurrent Neural Networks (RNN)

Recurrent Neural Networks (RNN) are different from feed-forward networks as they use the output from the previous phase as input to the current phase and can memorize previous inputs. RNNs are ideal for sequential data as they can store previous samples information to predict future samples. RNNs are based on sequential modeling that captures temporal dependencies and input sequences of varying lengths. However, RNNs struggle with long-term dependencies and can be slow and computationally intensive.

Generative Adversarial Networks (GAN)

Generative Adversarial Networks (GAN) are deep learning algorithms that generate new data instances similar to the input data. GANs consist of a generator, which learns to produce fake data, and a discriminator, which identifies fake data from sample data. During training, the generator creates fake data, and the discriminator quickly distinguishes it. The GAN then sends the results to both the generator and the discriminator to update the model. GANs can learn without labeled data and produce realistic synthetic data, including images, audio, and text. However, GANs can suffer from training instability and limited diversity in generated samples. GANs are used for Image Synthesis, Style Transfer, and Anomaly Detection tasks.

There is a lot of hype around “Deepfakes“, which are videos or audios created using AI to portray something that never happened in reality. “Deepfakes” are created using deep learning algorithms, which can teach themselves to solve problems with large datasets. These deepfakes use the face-swapping technique of Deep Neural Networks (DNN) and Generative Adversarial Networks (GAN) to detect and improve any flaws in the deepfake within multiple rounds, making it difficult for deepfake decoders to decode them.

Exhibiting Deep Learning Applications

Without deep learning, we wouldn’t have self-driving vehicles, chatbots, or personal assistants like Alexa and Siri. Deep learning is the driving force behind many applications that not only automate tasks but also perform analytical and physical tasks without human intervention. In this section, we will explore some of the most popular deep learning applications that will help you unlock the full potential of deep neural networks.

Self-Driving Vehicles

The development of self-driving vehicles has been significantly impacted by deep learning, leading to remarkable advancements in autonomous vehicle technology. Deep learning models, particularly Convolutional Neural Networks (CNN), are highly effective in tasks such as image classification, object detection, and anomaly detection. Computer Vision (CV) technology is utilized in self-driving vehicles to detect objects and avoid collisions through Obstacle Detection and Traffic Sign Recognition. A deep learning system steers the vehicle based on visual input, adapting to various road conditions for Lane Keeping. Self-driving technology is a global effort, with research and deployment taking place across continents. Deep learning plays a pivotal role in self-driving vehicles, making them safer, more efficient, and capable of navigating complex environments.

Smart Agriculture

Deep learning has made significant strides in transforming agriculture by leveraging AI techniques. Smart agriculture involves the application of digital technologies, including deep learning, to enhance agricultural practices and address food security challenges. Deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have been researched and applied in agriculture, enabling data-driven decision-making. Deep learning aids in Precision Agriculture by optimizing crop management by analyzing data from sensors, drones, and satellites. Additionally, RNNs help with Crop Yield Protection by analyzing historical data to project yields, allowing farmers to plan effectively. Deep learning in agriculture is a global phenomenon, with applications across continents. Researchers and practitioners collaborate to enhance food security, sustainability, and productivity.

Field of Finance

Deep learning has had significant impact on the field of finance, revolutionizing how financial institutions operate. Deep learning excels at processing complex data swiftly and handling high-dimensional functions, making it an ideal tool for various financial applications. For example, deep learning is used in Portfolio Management to optimize investment portfolios, Credit Scoring to predict creditworthiness based on historical data and customer behavior, Algorithmic Trading to guide trading decisions using predictive models, and Market Sentiment Analysis by analyzing social media and news sentiment to predict market movements. Researchers often apply hybrid models combining Convolutional Neural Networks (CNN) onto these financial datasets. However, there have been limitations with robust testing, interpretability, and theoretical understanding. Nevertheless, deep learning in finance is global reality, with applications across banking, insurance, and assest management.

Demonstrating Applications in RETRENDE

As the developer of RETRENDE, a cloud-based product that utilizes advanced Machine Learning (ML) and Artificial Intelligence (AI) in a retail, direct-to-consumer, and print-on-demand platform, I can provide some insight into the Deep learning applications used in this product. One of the significant advantages of RETRENDE is that it uses Deep learning models to optimize various aspects of the retail business. For instance:

  • Price Optimization: RETRENDE analyzes historical pricing data, competitor prices, and market trends to optimize pricing strategies dynamically. This helps the business to adjust prices for maximum profitability.
  • Supply Chain Optimization: RETRENDE uses Deep Learning models to optimize logistics route planning and delivery schedules, ensuring timely and cost-effective product distribution.
  • Ad Optimization: RETRENDE uses Deep Learning models to improve online Ad responses without increasing costs. This helps the business to deliver targeted ads that resonate with individual preferences.
  • Inventory Management: RETRENDE uses Deep learning models to predict demand patterns, optimize inventory levels, and reduce stockouts. This helps the business to maintain optimal stock quantities, minimize wastage, and enhance supply chain efficiency.

Winding up Deep Learning

Despite concerns about the usage of AI, a recent study found that 65% of consumers still trust businesses that employ AI technology.1 This indicates that when companies use AI responsibly and transparently, they can maintain the confidence of their customers and even leverage AI’s potential to enhance customer experiences. AI can improve accuracy and speed in complex tasks, such as computer vision and decision-making.

The demand for Edge AI is growing as it enables data processing at the point of creation, helping organizations gain real-time insights, detect new patterns, and meet strict data privacy requirements. Leading research reports predict that more than 55% of data analysis using deep neural networks will occur at the point of capture in an edge system by the next decade2. Therefore, organizations should identify the AI applications and training necessary to move to edge environments near IoT endpoints.

Deep learning is expected to converge with other disciplines, such as neuroscience and quantum computing, which will improve our understanding of algorithm performance. Deep learning plays a significant role in the realm of robotics and human-machine interaction, and its application will drive the development of robots and autonomous systems that can communicate and collaborate more intelligently with humans. This breakthrough will open up new possibilities in industrial automation, autonomous transport, and everyday task assistance.

In conclusion, the future of deep learning is exciting, with promising advances and developments. As progress continues in areas such as model architectures, training optimization, interpretability, knowledge transfer, and cross-disciplinary integration, deep learning will remain a driving force of innovation, revolutionizing our interactions with technology in every aspect of life.

References

  1. https://www.forbes.com/advisor/business/ai-statistics/ ↩︎
  2. https://www.gartner.com/en/newsroom/press-releases/2023-08-01-gartner-identifies-top-trends-shaping-future-of-data-science-and-machine-learning ↩︎
Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

Predictive Analytics Powered by Artificial Intelligence

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Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

Natural Language Processing (NLP): a Machine Learning and Artificial Intelligence perspective

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Search and Indexing: Artificial Intelligence (AI) and Machine Learning (ML) perspective

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Generative AI

Transforming M&A: The Role of Generative AI

Nehatrendy

Generative AI in M&A Deals

About one in five surveyed companies currently uses Generative AI in Mergers and Acquisitions (M&A) processes, and over half expect to integrate it into their dealmaking soon. Early adopters report that expanding generative AI tools across more M&A stages multiplies the benefits. Traditionally, M&As are strategies companies use for growth. Merging with another company in the same sector can boost market share and revenue, while acquisitions can involve purchasing a business in a different industry.

Generative AI is poised to become a permanent fixture in M&A. Its potential is vast, with a profound impact expected on dealmaking. Companies that master generative AI in M&A over the next five years are likely to identify targets faster, underwrite deal value with increased confidence, conduct diligence and integration more efficiently, and ultimately achieve higher shareholder returns.

Notably, in the latest survey, 36% of the most active acquirers use generative AI for M&A. This matters because long-term M&A studies show that companies making regular deals consistently outperform less active firms in total shareholder returns. Private equity is also an enthusiastic early adopter, with over 60% of surveyed firms leveraging at least one generative AI tool to improve sourcing, screening, or diligence.

Beyond accelerating sourcing, screening, and diligence, early adopters are now experimenting with generative AI for integration, divestiture planning, and program management.

How Generative AI is gaining traction in M&A deals

According to recent surveys, nearly 80% of companies leveraging Generative AI in their M&A processes report reduced manual effort. As a result, employees spend less time on the M&A program and can more easily balance demands from the core business. Key business cases for incorporating Gen AI into M&A deals include.

Advancing the Screening / Sourcing Process

While traditional AI has already proven effective in deal scanning, Gen AI enhances this by analyzing broader sets of both structured and unstructured data, synthesizing results for quantitative and qualitative insights, and highlighting key elements such as strategic, financial, and cultural fit of potential targets. This enables companies to identify and pursue targets that might otherwise go unnoticed.

Expediting Diligence and Negotiations

Generative AI accelerates the diligence and negotiation stages by summarising key documents, surfacing risks, drafting initial memoranda tailored to deal with specifics, sourcing relevant statutes and regulations, and pinpointing useful case law to ease negotiation friction. It can also generate highly accurate first drafts for deal announcements and regulatory filings. Collectively, these capabilities can significantly reduce the time required for various legal tasks associated with deal negotiations, signing, and closing.

Executing Integration or Separation Effectively

Experienced dealmakers understand the importance of quickly capturing deal synergies; delays can lead to missed value. Organizations engaged in significant M&A events are particularly prone to distractions that may result in a decline in organic momentum and an average decrease in excess revenue growth compared to peers.

Enabling Generative AI in M&A deals

74% percent of C-suite executives anticipate increasing their investments in data and generative AI technologies over the next decade, aiming to drive business transformation.

Task Standardization

Standardised tasks that are ideal for automation include designing Transition Service Agreements (TSAs) and integrating systems and infrastructures. Creative tasks benefiting from data augmentation include designing operating models, producing deal communications, and facilitating post-merger performance assessments.

Early adopters are using generative AI to draft integration workplans and TSAs in less than 20% of the time previously required, enabling faster team mobilization with better quality information

Generative AI can enhance internal capabilities by leveraging proprietary data from prior deals to assess performance patterns and identify new opportunities. For instance, AI can evaluate an acquisition portfolio and measure the impact of individual deals.

AI can efficiently analyse large volumes of data, such as reviewing material contracts and identifying deviations from model contracts, saving time and focusing attention on critical issues.

Value Creation

Generative AI tools allow access to specific company data to size realistic cost and revenue synergies and design value creation plans informed by historical acquisition performance.

A key use case is a generative AI “coach” trained on M&A best practices and the organisation’s unique playbook, providing rapid, insightful answers to integration and separation teams

Companies using generative AI can improve due diligence and value realization by identifying cost and revenue synergies and refining plans to achieve them. Tailored AI tools can leverage sales, pricing, customer relations, and catalog data to prioritize cross-selling targets and accelerate post-close value capture.

Leading research organizations recommend applying AI within internal M&A processes first by developing and testing diverse use cases. In particular, contract analytics is an impactful application for using AI right now to improve activities tied to letter of intent (LOI) negotiation, contract due diligence, definitive agreement and TSA negotiation, and contract renewals and integration.

Generative AI reduces manual efforts in M&A, allowing employees to focus on core business priorities. Nearly 80% of businesses report reduced manual work and improved efficiency and retention when using AI in their M&A processes.

Productivity and Efficiency

Generative AI-powered tools automate and accelerate a wide array of time- and resource-intensive tasks. Innovations like virtual data rooms, cloud-based integration tools, and advanced analytics have transformed due diligence, collaboration, risk assessment, and decision-making, resulting in smoother business integrations and improved deal outcomes.

One user described utilizing third-party tools for managing data rooms, including automated filing, advanced search, and streamlined Q&A processes.

Among surveyed companies, 78% report productivity gains from reduced manual effort, 54% saw faster timelines, 42% realized cost savings and better focus, and 85% of early users stated that AI met or exceeded expectations

Together, these post-deal applications of generative AI represent a significant and relatively untapped opportunity for dealmakers aiming to comprehensively reinvent their companies’ M&A functions. By developing and implementing a formal strategy, executives can simultaneously address related priorities, such as talent, technology maturity, and business value validation.

Adoption of Generative AI in M&A deals

Adoption of generative AI in M&A deal processes is currently low at 16%, but this is projected to rise to 80% over the next three years. Notably, 85% of current users report that generative AI meets or exceeds their expectations. Senior leaders should begin by honestly assessing their current M&A capabilities and identifying where technology can meaningfully improve their M&A processes.

Start Adopting Now

New technologies require testing, learning, and time to build expertise, identify valuable use cases, and drive user buy-in. Companies can’t simply purchase a generative AI–enabled solution later and expect to catch up with early adopters, as the learning curve will be steeper. While mastery will take time, businesses can start reducing manual process time within the first week of implementation. Prioritize generative AI use cases with the highest impact. For M&A strategies targeting many small acquisitions, generative AI will add the most value in opportunity scanning and assessment.

Build an AI Portfolio

A basic generative AI tool might be as simple as a well-designed prompt drawing upon high-quality data sources. At a minimum, companies can make substantial progress by securing an enterprise license and using these tools. When bringing prioritized use cases to life, carefully evaluate whether to build in-house or adopt off-the-shelf solutions. Recently, more turnkey solutions have entered the market and additional options are expected in the next one to two years.

Innovate with Purpose

Leading companies will move beyond simple automation, reimagining their end-to-end M&A processes to fully leverage generative AI. They’ll strategically invest in areas offering competitive advantages. As with any build-or-buy decision, leaders should weigh their team’s expertise, investment size, potential returns, and actions of peers.

Evolve the M&A Team

Generative AI is set to assume more of the time-intensive project management. This prompts a reevaluation of future skill needs for M&A. The most successful deals are delivered by professionals focused on strategic value rather than process management. To stay ahead, companies should revisit their talent strategies, aiming to build and sustain long-term value through generative AI.

Managing Risks in M&A deals

Mergers and acquisitions (M&A) can offer substantial benefits to both parties involved, but they may also present challenges, particularly when regulatory issues or cultural clashes arise. As more companies leverage advancements like generative AI, late adopters may struggle in three key areas:

Ensuring proper guardrails are in place. Generative AI differs from many existing technologies in that it amplifies certain risks, such as increased potential for security breaches due to its accessibility, reputational risks from quality control failures, and intellectual property infringement. Legal and regulatory environments are evolving rapidly, even as generative AI progresses.

Increased reliance on advanced AI models can raise the risk of human disengagement, potentially allowing critical issues to go unnoticed. It’s crucial for organizations to keep humans involved, work proactively with legal and technology teams to identify and mitigate risks, and uphold strong ethical standards.

Generative AI can drive significant process efficiencies in M&A, but it also brings risks such as data inaccuracies, privacy concerns, and cybersecurity threats

Generative AI accelerates data summarisation, reducing the time from a week to a day and enabling teams to focus on deeper analysis. This allows firms to make better-informed bids and quickly identify promising opportunities—or know when to walk away.

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