The twin pillars of AI and cloud computing are propelling businesses forward in multiple ways beyond IT. While the cloud computing market is projected to double from its current size to $947 billion by 2026, the AI market is slated to grow more than 5 times to $309 billion.
Rather than viewing the two as competing markets, however, enterprise leaders should strive to understand how the rapidly expanding field of AI can build a relationship with cloud computing technology that spurs ever-greater innovation.
The symbiotic relationship between AI and cloud computing lies in automation. The implementation of AI streamlines simple processes, thereby increasing efficiency and allowing IT talent to focus on more innovative development.
Needless to say, both these technologies are impacting each other in umpteen ways. Investment in the cloud is driving faster adoption and higher spending on AI, resulting in full-scale deployments of AI – in fact, a Deloitte study found that 70% of companies get their AI capabilities through cloud-based software, while 65% create AI applications using cloud services.
“The cloud has turned out to be an amazing distribution mechanism for algorithms – all three of the leading cloud providers have made available a set of algorithms that make AI much easier to do,” explained Tom Davenport, President's Distinguished Professor of IT & Management at Babson College.
The Unification of AI and Cloud Computing
AI and cloud computing converge in automating processes such as data analysis, data management, security, and decision-making. The ability of AI to exercise machine learning and to derive impartial interpretations of data-driven insights fuels efficiency in these processes and can lead to significant cost savings on numerous fronts within the enterprise.
The application of AI software based on machine learning algorithms in cloud environments delivers intuitive and connected experiences for customers and users. Alexa and Siri are but two examples of this seamless combination that enables a variety of operations, from conducting a search to playing a song to making a purchase.
In ML models, large sets of data are used to train the algorithm. This data could be structured, unstructured, or raw and needs powerful CPUs and GPUs to process. Only an ideal combination of public, private, or hybrid cloud systems (based on security and compliance requirements) can provide such huge amounts of compute power today. Further, AI cloud computing also enables services that are used in ML, such as serverless computing, batch processing, and container orchestration.
The Applications of AI in Cloud Computing
With public cloud services, developers do not need to build and manage a separate infrastructure for hosting AI platforms. They can use ready configurations and models to test and deploy AI applications.
Further, generic services based on AI but not necessarily requiring an ML model – such as speech-to-text, analytics, and visualization – can be improved by running them from the cloud using first-party data generated by the organization.
Some of the more common AI-based applications in the cloud include:
- IoT – Cloud architectures and services that power the internet of things can store and process data generated by AI platforms on IoT devices.
- Chatbots – Chatbots are ubiquitous AI-based software that use natural language processing to carry out conversations with users – a boon for customer service in the age of instant gratification. Cloud platforms store and process the data captured by chatbots and cloud services connect them to the appropriate applications for further processing. Customer data is also fed back into the chatbot application that resides in the cloud.
- Business Intelligence – BI is another mainstream application where AI cloud computing can gather data on the market, target audience, and competitors of customers. The cloud again facilitates the storage and transfer of data while the AI runs it through predictive analytics models.
- AI as a Service (AIaaS) – Public cloud vendors now offer AI outsourcing services, allowing companies to test out software and ML algorithms without risking their primary infrastructure. They can deploy off-the-shelf AI applications at a fraction of the cost of in-house AI with significant CAPEX savings.
- Cognitive cloud computing – Cognitive computing is the use of AI models to replicate and simulate human thought processes in complex situations. Players such as IBM and Google have built cognitive cloud platforms that provide cognitive insights-as-a-service to enterprises and facilitate the application of this technology in finance, retail, healthcare, and other industries.
Advantages of Deploying AI in Cloud Environments
AI is the proverbial cherry on the cloud cake — and also the frosting, ganache, strawberries, and sprinkles combined! Here’s why AI and cloud computing form a winning team.
Cost Savings
Traditionally ML-based models ran on expensive machines with multiple GPUs in enterprise datacenters. With advances in virtualization in both public and private clouds, the cost of building, testing, and deploying these models has come down drastically. This has leveled the playing field for many small-to-medium businesses.
“I can start up my AI skillsets with just a credit card these days,” said David Linthicum, Chief Cloud Strategy Officer at Deloitte Consulting.
“Back when I first got out of college, we were building things that literally cost $100 million of data center space just to get simple questions answered,” he said.
Productivity
AI-based algorithms required significant admin time and effort in terms of building testing and production environments, software management, and provisioning hardware resources for compute operations and storage. A centrally managed hybrid cloud or a public cloud does away with this, leaving IT staff to focus on non-repetitive tasks.
Automation
AI cloud computing is also being embedded right into the infrastructure to help automate routine processes and streamline workloads. In a hybrid cloud environment, AI tools can be used to monitor, manage, and self-heal individual public and private cloud components.
Analytics
Data residing in most cloud workloads needs to be analyzed for more insights. AI-based models make it easy to mine this data in real time and develop native analytics and dashboards for each of these applications.
Data Management
AI helps boost cloud workloads in customer service, marketing, ERP, and supply chain management by processing and generating data in real time. For example, AI tools embedded in Dataflow, the streaming analytics platform in Google Cloud, can enable functions as varied as programmatic bidding in media advertising, fraud prevention in financial services, threat detection in IT security, and personalized shopping recommendations in retail.
Better SaaS Tools
Perhaps the most obvious and popular use of algorithms in AI cloud computing is their integration in mainstream SaaS tools to help these deliver more functionality and value to end users.
For example, Salesforce added “Einstein,” an AI-based algorithm, to its flagship CRM system to help customers make sense of the immense volumes of data generated, find patterns in this data, and derive actionable insights to improve their sales strategies. This is but one example in a landscape of literally hundreds of AI-enabled SaaS tools.
Challenges in Deploying AI in Cloud Environments
Merging AI and the cloud isn’t always cakes and ale. The main concerns are data privacy and connectivity.
Data Privacy
The pay-as-you-go model of SaaS technology allows thousands of companies across the world to make sense of data, find efficiencies in routine processes, develop new products, and even expand into new verticals. However, they often run their customer, vendor, and market data through cloud applications with little to no appreciation of the security risks of the public cloud.
When AI processes data fed into a SaaS tool in a public cloud environment, it amplifies these risks on an exponential scale. Sensitive company data could be exposed to a security breach or unauthorized access when the processes and perimeters for AI cloud computing algorithms are not clearly defined.
Connectivity
Any algorithm or data processing system in the cloud depends on one thing to keep it running: a steady internet connection. Poor network connectivity can slow down ML processes and defeat the purpose where real-time transactions and analytics are involved.
The Future of AI Cloud Computing
As cloud computing itself finds a comfortable place in every sector of the IT industry, revenue growth inevitably slows. Investors therefore expect the boom of AI to reinvigorate cloud computing as major tech companies increasingly seek to harness artificial intelligence in the cloud.
Amazon’s new Bedrock cloud service is an example of a notable initiative regarding generative AI in the cloud. Through this service, developers could efficiently enhance their software with AI-generated text.
Even as companies of all sizes are placing big bets on AI, the respective IT teams at those companies must race to keep up with the knowledge and skill requirements necessary to implement and scale AI cloud computing solutions effectively. Adopting AI technology sooner rather than later is key in ensuring that the IT department has time to implement the technology properly before other businesses soar ahead.
Hybrid Cloud – The New Home of AI Cloud Computing
Cloud-based enterprises are looking to AI for more and more real-time insights that drive innovation and give a competitive advantage. This calls for a robust infrastructure that can handle vast amounts of data while guaranteeing security and functionality for end users.
Nutanix has partnered with NVIDIA and Mellanox Technologies to create an AI-ready hybrid cloud infrastructure that enables companies in retail, healthcare, finance, aerospace, and other industries to develop turnkey AI-based solutions and applications. With this level of AI cloud computing, one can say that AI has finally found its castle in the cloud.
Editor’s note: Learn more about how AI and automated self-service is fueling the shift to hybrid work as well as the potential risks of relying on AI-generated text.
This is an update to the article originally publish January 4, 2022.
Michael Brenner is a keynote speaker, author and CEO of Marketing Insider Group. Michael has written hundreds of articles on sites such as Forbes, Entrepreneur Magazine, and The Guardian and he speaks at dozens of leadership conferences each year covering topics such as marketing, leadership, technology, and business strategy.
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