The Emergence of AI-as-a-Service

Thanks to the cloud, providers can deliver AI solutions as a service that can be accessed, refined and expanded in ways that were unfathomable.

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).

The AIaaS Opportunity

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.

See also: Predictions for AI Adoption in 2020  

Vertical Adoption

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.

These numbers will likely balance out once verticals realize the areas in which AI and machine learning technologies can practically influence their business and day-to-day operations. Three key ways are:

Empowering Data

Data that can be most useful within organizations is often difficult to spot. There is simply too much for humans to handle. The data becomes overwhelming and thus incapacitating, leaving powerful insights lurking in plain sight. Most companies don’t have the tools in their arsenal to leverage data effectively, which is where AIaaS comes into play.

An AIaaS provider with knowledge of a specific vertical understands how to leverage the data to get to those meaningful insights, making data far more manageable for people like claims adjusters, case managers or financial advisers. A claims adjuster, for example, could use an AI-based solution to run a query to predict claim costs or perform text mining on the vast amount of claim notes.

Layering Insights for Better Outcomes

Machine learning technologies, when integrated into systems in ways that match an organization’s needs, can reveal progressively insightful information. A claims adjuster, for example, could use AIaaS for much more than predictive analysis. The adjuster might need to determine the right provider to send a claimant to based not only on traditional provider scores but also on categories that assess for things like fraudulent claims or network optimization that can affect the cost and duration of a claim. With AIaaS, that information is at the adjuster’s fingertips in seconds. 

In the case of text mining, an adjuster could leverage machine learning to constantly monitor unstructured data, using natural language processing to, for example, conduct sentiment analysis. Machine learning models would look for signals of a claimant’s dissatisfaction — an early indicator of potential attorney involvement. Once a claim is flagged, the adjuster could take immediate action, as guided by an AI system, to intervene and prevent the claim from heading off the rails. While these examples are specific to insurance claims, it’s not hard to see how AIaaS could be tailored to meet other verticals’ needs by applying specific information to solve for a defined need.

Assisting Humans at a Moment’s Notice

Data is power, but it takes a human a tremendous amount of manual processing to effectively use it. By efficiently delivering multilayer insights, AIaaS provides people the capability to obtain panoramic views in an instant. Particularly in insurance, adjusters, managers and executives get access to a panoramic view of one or more claims, the whole claim life cycle, the trend, etc. derived from many data resources, essentially by a click of a button.

See also: How to Use AI in Customer Service  

The Place for AIaaS

AIaaS models will be essential for AI adoption. By delivering analytical behavior persistently learned and refined by a machine, AIaaS significantly improves business processes. Knowledge gleaned from specifically designed algorithms helps companies operate in increasingly efficient ways based on deeply granular insights produced in real time. Thanks to the cloud, these insights are delivered, updated and expanded upon without resource drain.

AIaaS is how AI’s potential will be fulfilled and how industries transform for the better. What was once a pipe dream has arrived. It is time to embrace it.

As first published in The Next Web.

Ji Li

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Ji Li

Ji Li, Ph.D., data science director at Clara Analytics, has leadership responsibility for organizing and directing the Clara data science team in building optimized machine learning solutions, creating artificial intelligence applications and driving innovation.


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