What You Need to Know about AI as a Service

Artificial intelligence as a service (AIaaS) has emerged as a way to access powerful AI tools at a fraction of the cost of a full, in-house AI deployment. The approach essentially outsources AI, allowing enterprises and their departments to explore and scale AI techniques at a minimal cost. 

Any software-as-a-service offering removes the burden of maintenance and administration, enabling the enterprise to focus on applying the technology, says David Menninger, executive director of ISG Ventana Research, in an email interview. "AIaaS service also addresses an issue many enterprises face -- a lack of AI skills." 

Menninger states that his organization's research shows that two-thirds of enterprises don’t have the skills they need to be successful with AI. "In addition, enterprises report that AI is the most challenging set of application skills to find and retain." AIaaS can help organizations address those skill challenges. 

AIaaS's future looks promising, with a significant surge in growth expected in the upcoming years, says Taylor Dolezal, head of ecosystem at the Cloud Native Computing Foundation. He notes in an email interview that the AIaaS market is predicted to experience a compound annual growth rate (CAGR) of 40.17% between 2022 and 2027, leading to a market size increase of $28.8 billion. "An increasing demand for predictive analytics, improved customer experiences, and a shift toward cloud-based solutions drive this growth," Dolezal says. "North America is expected to lead the market contribution, highlighting the region's active role in driving innovation and adoption in the AIaaS landscape." 

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Multiple Benefits 

AIaaS provides several benefits, which makes it an attractive option for a wide range of organizations, Dolezal says. "It eliminates the need for substantial initial investments in AI infrastructure and expertise, allowing businesses to access advanced AI functionalities on a subscription basis, which is cost-effective." 

With AIaaS, organizations can adjust their AI capabilities without managing the complexities of in-house AI solutions, providing scalability and flexibility. "AIaaS enables rapid deployment of AI solutions, accelerating the time-to-market for AI-driven products and services," Dolezal says. "AIaaS providers continuously update their offerings with the latest AI advancements, ensuring access to cutting-edge technologies." 

Initial Steps 

New adopters should start by understanding where to apply AI. Menninger says his organization's research shows that training on AI business applications has a higher correlation with success than basing training on AI techniques. "The next thing enterprises need to address is the skills issue," he advises. If in-house AI talent is absent or rudimentary, turn to third parties to find skilled resources on a contract basis. 

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The next step should be researching the major AIaaS providers to understand their services, technologies, and how they may align with your business objectives. "Once you have identified potential providers, implement pilot projects with selected AIaaS solutions to test their effectiveness and impact on your operations before a full-scale roll-out," Dolezal says. 

Top Providers 

All of the major hyperscalers, including Amazon, Google, and Microsoft now offer AIaaS, Menninger says. So do generative AI vendors, such as OpenAI and Anthropic, he notes. Pure-play AI vendors, including SAS, Dataiku, and DataRobot, also offer their products as a service. Meanwhile data platform players, such as Oracle, IBM, Databricks, Snowflake, and Teradata, also offer AI as a part of their platform services. 

The Downside 

While AIaaS presents many opportunities, it also has some drawbacks and challenges that potential adopters need to address, Dolezal says. "Data privacy and security concerns are top-of-mind, particularly when using a third-party provider," he notes. "Choosing a provider with strict data protection standards is essential." 

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Effective cost control is a challenge for many AIaaS adopters, Menninger says. Check provider pricing plans and policies carefully before taking the plunge, he advises. Other potential concerns include intellectual property rights violations and privacy protections. "Several AIaaS vendors have faced lawsuits alleging copyright infringements because they used content in their training process that they didn’t have rights to use." 

Integration complexities are another drawback for some adopters. "Integrating AIaaS solutions with existing systems can be complex and time-consuming," Dolezal says. "It requires detailed planning and execution." 

As with any AI platform, there's always a risk of encountering bias and ethical issues. "AI systems can inherit biases from their training data, leading to biased decision-making," Dolezal says. "Therefore, ethical concerns need to be addressed carefully." 

A Final Word 

AIaaS is here to stay, just as most SaaS offerings are here to stay, Menninger predicts. "Enterprises need to be aware of the potential issues so they can work to avoid them, but they are probably more likely to encounter issues if they attempt to do the entire AI stack on their own."