Artificial intelligence has become a core element of business strategy across industries. As AI evolves, experts highlight the powerful synergy between high-profile generative AI and established analytical AI, driving industry transformation and boosting operational efficiency.

But how do these AI technologies complement each other, and what industries stand to gain the most from their advancement?

“I think [analytical AI] is at least as important as generative AI, despite all the publicity about generative AI,” said Tom Davenport (pictured), distinguished professor at Babson College. “In many cases, I think it’s a bit more likely to make money for organizations, because you use that type of AI in areas like pricing, in personalizing marketing content, [ad targeting], fraud elimination in financial services and figuring out who to give a credit card to. It’s been around for a while, but it certainly has legs, and I think we’ll see combinations of analytical and generative AI in many use cases.”

Davenport spoke with theCUBE’s Dave Vellante at the AI & Analytics: Shaping the Future With Alteryx CEO & Tom Davenport event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed analytical AI as the foundation of data-driven business strategies, as companies embrace real-time analytics, predictive modeling and AI-powered automation. (* Disclosure below.)

The coexistence of gen AI and Analytical AI

While generative AI grabs the headlines, analytical AI remains the workhorse behind key business decisions. The latter encompasses traditional machine learning techniques that predict numbers rather than generate content. Unlike gen AI, which focuses on text and image creation, analytical AI is critical in pricing strategies, marketing personalization, fraud detection and credit risk assessments. In a likely future where analytical and generative AI work hand-in-hand, analytical AI will determine the best audience for an ad while gen AI will tailor the ad’s message to each viewer, according to Davenport.

Traditional data analytics processes are cumbersome, requiring extensive computing power and long processing times. However, the rise of AI-driven automation is accelerating real-time decision-making. Automated machine learning has made sophisticated analytics accessible to professionals lacking extensive data science backgrounds. By simply selecting a dataset and defining the variable they wish to predict, business users can generate insights in minutes rather than weeks, Davenport added.

“With some of these tools, you don’t need to know very much about the ins and outs of statistics and you don’t have to write Python code to do the analysis,” he said. “You just say, ‘Here’s the data set I want, and here’s the variable that I want to predict,’ and it’s off to the races. It’s much faster and makes it possible for a whole different group of people to do sophisticated analytical work.”

This shift to real-time analytics is breaking down long-standing barriers. With cloud computing and AI-enhanced data integration, organizations can connect their backend systems seamlessly. The reduction in processing time has significant implications for business agility, allowing companies to react faster to market shifts and customer behaviors.

The adoption challenges and ROI concerns with enterprise AI

Despite AI’s transformative potential, many organizations struggle to move beyond proof-of-concept experiments. Operationalizing AI requires training employees, integrating AI with existing technology stacks, and modifying business processes. Historically, as much as 87% of machine learning models never make it to production, according to Davenport.

“The big issue, whether it’s generative or analytical AI, has always been how to we get to production deployments,” he said. “It’s easy to do a proof of concept, a pilot or a little experiment — but putting something into production means you have to train the people who will be using this system. You have to integrate it with your existing technology architecture; you have to change the business process into which it fits. It’s getting better, I think, with analytical AI.”

One of the biggest hurdles in justifying AI strategies is securing a return on investment. While AI promises efficiency gains, companies often find it challenging to quantify its impact. A recent survey indicated that most organizations prioritize revenue generation over productivity gains when evaluating AI’s value proposition. Analytical AI, with its ability to drive targeted marketing and pricing strategies, aligns well with revenue-focused objectives, according to Davenport.

“If [revenue] is your objective, then analytical AI is probably gonna get you there more easily than generative AI, because you can target the right customers, you can figure out what’s the best price to charge, all those sorts of things,” he said. “I think generative AI has been more oriented to productivity kinds of improvements, but most organizations haven’t seriously measured the productivity gains.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of the “AI & Analytics: Shaping the Future With Alteryx CEO & Tom Davenport“ event: 

(* Disclosure: TheCUBE is a paid media partner for the “AI & Analytics: Shaping the Future With Alteryx CEO & Tom Davenport” event. Neither Alteryx Inc., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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