Tag: Cloud Computing

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

Artificial Intelligence in Smart Food Supply Chains

Nehatrendy
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A Taster to Smart Food Supply Chains

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

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

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

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

Demand for Smart Food Supply Chains

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

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

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

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

Digitization of Food Supply Chains

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

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

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

A Data-Driven Approach for Smart Food Supply Chains

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

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

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

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

Benefits of Smart Food Supply Chain

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

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

Industry Applications of Smart Food Supply Chains

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

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

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

Conclusion

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

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

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

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

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

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

Enhancing Business Operations with AI-based Cloud Computing Services

Nehatrendy
Photo by Pixabay on Pexels.com

A foreword to Artificial Intelligence in Cloud Computing

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

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

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

Interlink between Artificial Intelligence and Cloud Computing

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

Pre-built AI-based Cloud Computing Services

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

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

Case in Point: Recommendation AI model

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

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

Benefits of AI in Cloud Computing

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

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

Challenges with AI in Cloud Computing

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

Applications of AI in Cloud Computing

There are several popular AI applications in cloud computing:

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

A Closure to AI in Cloud Computing

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

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

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

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

  1. https://www.gartner.com/en/newsroom/press-releases/2023-04-19-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-reach-nearly-600-billion-in-2023 ↩︎