Tag: Business

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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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

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

Artificial Intelligence in Smart Food Supply Chains

Nehatrendy
Photo by Pexels.com

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.