The Role and Benefits of AI in Cloud Computing

There are two ways to think about AI in relation to cloud computing. One is that cloud computing providers are working furiously to make ever more sophisticated AI-backed services and applications available on their platforms. The other is that AI—and the automation and lightning-quick decision-making it facilitates—is increasingly what makes those hyperscale cloud platforms possible.

Both are true, and that makes the future of cloud computing and AI both intertwined and exciting. Here’s what that future may hold.

What Is AI?

AI, or artificial intelligence, refers to computer systems that use algorithms and data to perform tasks that would typically require human intelligence, such as recognizing speech or creating an image in response to a prompt. In some cases, AI can do things humans can’t, such as perform complex calculations and analysis involving massive amounts of data in seconds, with extremely high precision, to identify anomalies.

AI technology is improving rapidly and finding many uses, including improving communications with customers, creating digital media, making diagnostics more accurate, enhancing cybersecurity, and even advising on business decisions.

The term “artificial intelligence” is often used interchangeably with related technologies such as machine learning (ML) and deep learning. The difference is that AI broadly describes the field of study, while machine learning systems more narrowly focus on learning-like improvement in performing a specific, defined task based on training data they ingest. Deep learning is a similar process that’s built on complex neural networks designed to simulate the architecture of the human brain. This structure allows deep learning systems to detect complex, nonlinear relationships and derive meaning from complicated or imprecise data. Large language models (LLMs), like those from ChatGPT or Cohere, train using deep learning and large amounts of curated data. Once trained, the LLM becomes the core of a generative AI system that can answer questions by inferring or predicting the correct response. The result: uncanny, human-like linguistic responses to questions.

To get full value from AI, many companies are making investments in data science teams and seeking sophisticated AI models and services that they can build on for their own applications.

What Is Cloud Computing?

In simple terms, cloud computing lets you rent IT services instead of buying them. Rather than investing in databases, software, facilities, and hardware, companies can opt to access their compute power via the internet and pay for it as they use it. Key characteristics of cloud computing include that it’s metered, scalable, and available on demand.

Cloud offerings include infrastructure, such as servers, storage, and databases, as well as services built on that infrastructure, such as data analytics, artificial intelligence, and applications for business functions, such as enterprise resource planning, or ERP, and human capital management. More and more, these applications contain functionality powered by AI. An example is the ability to convert printed documents to digital form and then classify those documents in functions like accounts payable and accounts receivable.

Key Takeaways

• AI is integral to cloud computing, enhancing cloud services' automation, decision-making, and scalability.
• Cloud computing provides the necessary infrastructure for AI, enabling businesses to leverage AI technologies without significant investments in hardware and software.
• AI in cloud computing boosts business efficiency by automating repetitive tasks, enhancing data analytics, and improving cybersecurity.
• The synergy between AI and cloud computing drives innovative applications like generative AI, IoT, and AI-assisted business intelligence.
• Adopting AI in cloud computing can be challenging, as it involves data privacy concerns, integration issues, and a talent gap in AI expertise.

AI and Cloud Computing Explained

AI and cloud computing are deeply intertwined. One reason: Cloud computing providers were early to the game in figuring out how to use AI to deliver better services. AI systems are very good at making decisions in the confined world of an IT architecture, and that lets cloud computing providers automate a range of operations in their massive data centers. AI can provision and scale technology services, detect potential errors, monitor for signs of a cyberattack, and detect hints of fraud in a range of use cases. These are just a few entries on a growing list of capabilities that help cloud computing companies economically offer hyperscale technology services to thousands or millions of customers.

Just as important, the cloud is becoming the go-to way to embed AI into business applications. Providers are baking AI into their own offerings, such as software-as-a-service (SaaS) applications enhanced by a variety of AI technologies and more recently with embedded LLM capabilities. Cloud providers also work with businesses that want to embed generative AI into their operations. With sophisticated LLMs in cloud architectures, businesses can use their own data to train and deploy AI models specific to their operations, or more commonly, augment the training of an existing model, whether that’s in healthcare, logistics, law, government, or any other field. Cloud customers even include AI model developers, who need large amounts of compute and storage capacity to train their models on vast amounts of data.

Increasingly, cloud providers will offer highly sophisticated AI-assisted services, such as application development platforms where developers describe the application functions they want and let the AI platform quickly write the first draft of code.

Why Is AI Important to Cloud Computing?

Cloud computing providers rely on AI to power the automated systems that deliver IT services and SaaS applications reliably and at the lowest possible cost. AI helps with provisioning, batching, and tuning hyperscale cloud systems, relieving humans of those tasks. Furthermore, as more companies look to take advantage of a broad range of AI services as well as generative AI’s burgeoning abilities, cloud computing companies are keen to accommodate them. In short, the path of least resistance to leveraging AI capabilities goes directly through the cloud.

It’s also true that cloud computing is important to AI. That’s because training generative AI systems such as LLMs is extremely compute-intensive, leading to competition for the world’s available computing power. Hyperscale cloud providers offer this power on demand, allowing AI companies to rent the GPU clusters they need to run AI workloads with high performance and at a reasonable cost.

Learn more about Oracle’s high performance cloud for AI.

How Does AI in Cloud Computing Change Business?

The availability of AI-backed services in the cloud has been key to growing business use of AI. That’s because building, training, and securely deploying AI models is too technically challenging and expensive for all but the largest organizations to attempt on their own. With AI-backed infrastructure services, AI-infused SaaS, and a growing menu of diverse technologies available through APIs, more companies are able to use AI to automate processes, gain a competitive edge, and take advantage of new business opportunities.

The benefits to business come along two tangents. In the first, AI assistants offload repetitive tasks, such as entering and classifying invoices and requisitions or matching expenses with receipts and policies, improving the efficiency and accuracy of teams that used to do those tasks manually. Second, AI-driven analytics can recommend and advise business professionals based on the patterns detected in company data. Advice can range from when to order more of certain products to recommending changes in supply chains based on complex analysis of seller behavior and company need.

Benefits of AI in Cloud Computing

Cloud computing providers that apply AI in their data centers are reaping benefits well beyond the immediate efficiency gains and cost savings. By taking what they’ve developed and offering it as branded AI services to customers, they can help increase loyalty and profitability.

Benefits of AI in cloud computing include the following:

• Automation: With AI, cloud providers and their customers can automate many of the IT processes necessary for delivering their services, including patching, securing, and scaling compute capacity. Businesses are also using AI processes, including intelligent automation, to increase speed and accuracy in functions such as document management and factory operations.
• Cost savings: AI can help lower costs by performing complex tasks faster, with fewer people involved. Tasks can include IT and data security operations as well as business functions such as help desk automation. For customers, AI can help them scale their cloud use up and down in response to their own changing needs. As customers see their own cost savings, loyalty may improve—particularly for cloud vendors that strive to keep their billing models simple and transparent. When current customers use more cloud services, that can deliver a higher margin for providers versus finding, winning, and onboarding new customers.
• Cloud management: AI lets cloud services run at scale by automating many aspects of IT management. For example, AI can provision and scale services, detect and anticipate failures, and repel cyberattacks, often with little or no human participation. Tracking service use and billing as well as providing customers with complex workload monitoring and management systems all can be simpler and more scalable with AI support.
• Data management: AI can help any organization manage its vital data better and more cost effectively. Tasks that AI can take on include moving data as required, cleansing data, and scanning networks to detect issues with data security or collection.
• Predictive analytics: AI is particularly good at this popular form of data analytics. Businesses use predictive analytics to identify trends, find correlations, and link causation, allowing them to make more informed decisions much more quickly.
• Personalization: AI’s keen observational and pattern matching abilities help companies, including cloud providers, better understand their customers’ behavior and desires. This lets firms offer personalized services and more accurate suggestions and thus increase retention and revenue.
• Increased productivity and efficiency: AI excels at jobs that people find difficult or tedious. With AI-backed processes, businesses can accomplish tasks such as managing documents, sorting packages, matching invoices, and even summarizing documents such as legal briefs faster and more accurately than humans. The added benefit of this efficiency is that it frees employees to do more complex tasks that require human experience and relationships.
• Enhanced security and threat detection: When included in a well-formed data security framework, AI can step in to monitor networks and users as it scans for patterns that signal trouble. Unlike humans, AI can closely monitor masses of data as it quickly analyzes streams that the network creates.
• Improved scalability: Because AI can help automate so many processes, it lets cloud providers run large data centers with a level of elastic scalability that would be impossible to achieve if people had to manually provision and manage services. This same efficiency works for cloud customers, who may scale up work in marketing, logistics, healthcare, and other fields faster and with fewer people involved.

Challenges of AI in Cloud Computing

Although cloud computing providers are working to lower the barriers to using AI, challenges remain, mostly around managing data and hiring personnel with the right expertise.

• Data privacy: When it comes to data security and privacy, AI brings its own set of challenges. For example, there’s the potential for AI models to “leak” details from sensitive data sets used to train them. Customers will prioritize cloud vendors who offer strong controls over data governance and security.
• Integration: AI runs on clean, well-organized data. And it gets smarter when data originates from many different sources. The challenge, then, is to integrate and standardize data from different internal business units or partner sites as well as external sources, such as weather feeds or open government data.
• Talent gap: People with the expertise needed to design, train, and deploy AI models take time to find and are expensive to hire and retain. Even with AI model developers and cloud services providing the building blocks, companies often need experienced data managers and data scientists to make AI work for them.

Applications of AI in Cloud Computing

Motivation to overcome the abovementioned challenges comes from the wide range of ways that AI and the cloud can be used in tandem to make organizations run better and free up time for more creative tasks. Popular and exciting applications include the following:

• Chatbots: Chatbots were one of the first software programs designed to simulate human communication and step into customer service roles. Unfortunately, before AI, they didn’t do a very good job and were more likely to annoy customers than solve problems. 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 and detect the intent of a user’s query.
• Business intelligence (BI): Cloud computing providers have long offered BI solutions, which gather, analyze, and interpret data from internal and external sources to help businesses make decisions. These cloud services as well as earlier packaged software have depended on machine learning algorithms to parse and analyze data. With newer AI models, however, businesses can go from understanding what happened in the past to better predicting future outcomes.
• The Internet of Things (IoT): The IoT is made up of devices connected to the internet and to one another. IoT devices are used in many industries, including trucking, farming, and manufacturing, and require “edge computing” systems that place some key decision-making capabilities close to the devices under management, not back at the cloud data center. Now, businesses using sophisticated AI cloud services or AI built into SaaS applications can enable their IoT devices to learn from their data and their experiences over time, improving or suggesting improvements as they go.
• Generative AI: With cloud computing, powerful generative AI models with LLMs at their core are now just an API away. Businesses can use generative AI to expedite research, brainstorm creative ideas, enhance customer service or help desk applications, and many other tasks. RAG helps cloud providers make generative AI even more useful by augmenting the LLM’s knowledge base with a customer’s recent and securely stored enterprise data.

Future of AI in Cloud Computing

Artificial intelligence is quickly finding its place in a wide range of human endeavors. Much of that growth is driven by the availability of AI on powerful cloud computing platforms. Internally, over time, cloud providers can expand past using AI to automate and monitor IT infrastructure and begin offering AI-driven services that help write and debug applications, evaluate and improve business processes, and even provide the back-end computing and edge services for highly autonomous robots and drones. Further in the future, services built on the cloud can use AI to think deeply and inventively about business challenges and social issues.

Get Started with AI in Cloud Computing with Oracle

When it’s time to explore how artificial intelligence can help your business, consider Oracle Cloud Infrastructure (OCI). Oracle delivers a comprehensive AI portfolio to help you take advantage of AI in the way that makes most sense for you, and OCI provides a broad set of deployment options for AI using OCI’s distributed cloud. For example, OCI makes it easy to bring AI-generated insights to your key business functions by embedding AI in Oracle Fusion Applications. For building AI into your own applications, OCI features a broad array of AI services with models that can be customized using your own business data.

For data scientists, OCI offers machine learning services that help teams collaboratively build, train, deploy, and manage machine learning models using their own favorite open source frameworks. When it’s time for the compute-hungry training of sophisticated models, OCI performs well in custom on-premises compute clusters while providing the elasticity and consumption-based cost benefits of the cloud.

AI wouldn’t be where it is today without cloud computing. Cloud providers offer the compute architectures needed to train a thriving mix of AI models, and they build onramps for more businesses to take advantage of AI’s growing capabilities. As AI finds more uses in business and human affairs, it will likely run on, or be accessed through, cloud computing platforms.

AI in Cloud Computing FAQs

Will AI replace cybersecurity?

Cybersecurity encompasses many disciplines, including user access management, network monitoring, and data analytics. AI can be a key component in all of these efforts. There’s no doubt that it will eventually take over more responsibilities. But rather than replace cybersecurity or cybersecurity professionals, AI will be a cornerstone technology in cybersecurity programs.

How are edge services related to AI?

Edge infrastructure places cloud services very close to or within devices where data is generated, letting it also be consumed in managing these devices. This lets IoT devices run AI that reacts quickly to its environment, even with intermittent or no internet connectivity. Picture an autonomous drone or car with no time to ping a data center before making its next decision.

What’s the difference between machine learning and AI?

Machine learning is a subdiscipline of artificial intelligence. Machine learning algorithms learn and improve how they perform a task based on the mix of data that’s presented to them over time. AI models often use machine learning algorithms in their work.

What’s the difference between AI training and AI inference?

An AI model has two parts to its life. One is training and the other is inference. Training is where the AI model is presented with a large amount of curated data, which it ingests to learn how to accurately recognize and predict based on that data. Then the model is moved to a different type of IT infrastructure, where it begins the inference phase of its life. Here it’s presented with new data on which to draw inferences and predict outcomes.