The Emergence of AI-as-a-Service
Software-as-a-service (SaaS) has become part of the tech lexicon since emerging as a delivery model, shifting how enterprises purchase and implement technology. A new “_” as a service model is aspiring to become just as widely adopted based on its potential to drive business outcomes with unmatched efficiency: artificial intelligence as a service (AIaaS).
According to recent research, AI-based software revenue is expected to climb from $9.5 billion in 2018 to $118.6 billion in 2025 as companies seek insights into their respective businesses that can give them a competitive edge. Organizations recognize that their systems hold virtual treasure troves of data but don’t know what to do with it or how to harness it. They do understand, however, that machines can complete a level of analysis in seconds that teams of dedicated researchers couldn’t attain even over weeks.
But there is tremendous complexity involved in developing AI and machine learning solutions that meet a business’ actual needs. Developing the right algorithms requires data scientists who know what they are looking for and why, to cull useful information and predictions that deliver on the promise of AI. However, it is not feasible or cost-effective for every organization to arm itself with enough domain knowledge and data scientists to build solutions in-house.
AIaaS is gaining momentum precisely because AI-based solutions can be economically used as a service by many companies for many purposes. Those companies that deliver AI-based solutions targeting specific needs understand vertical industries and build sophisticated models to find actionable information with remarkable efficiency. Thanks to the cloud, providers can deliver AI solutions as a service that can be accessed, refined and expanded in ways that were unfathomable in the past.
One of the biggest signals of the AIaaS trend is the recent spike in funding for AI startups. Q2 fundraising numbers show that AI startups collected $7.4 billion — the single highest funding total ever seen in a quarter. The number of deals also grew to the second-highest quarter on record. Perhaps what is most impressive, however, is the percentage increase in funding for AI technologies — 592% growth in only four years. As these companies continue to grow and mature, expect to see AIaaS surge, particularly as vertical markets become more comfortable with the AI value proposition.
Organizations that operate within vertical markets are often the last to adopt new technologies. AI, in particular, fosters a heightened degree of apprehension. Fears of machines overtaking workers’ jobs, a loss of control (i.e., how do we know if the findings are “right”?) and concerns over compliance with industry regulations can slow adoption. Another key factor is where organizations are in their digitization journey. For example, McKinsey found that 67% of the most digitized companies have embedded AI into standard business processes, compared with 43% at all other companies. These digitized companies are also the most likely to integrate machine learning, with 39% indicating it is embedded in their processes. Machine learning adoption is only at 16% elsewhere.
