Exclusives

MicroStrategy and Enterprise Strategy Group on March 27 provided an update on how artificial intelligence (AI) is being infused into analytics and business intelligence (BI) platforms.

The update was provided during the MicroStrategy webinar “Fireside Chat: Infusing AI into Analytics,” which focused on research completed by Mike Leone, principal analyst at Enterprise Strategy Group, and his team at that company.

The discussion touched on several issues and themes, including: Why AI integration into BI and analytics remains a priority to businesses; the challenges of integrating AI into BI and analytics effectively; the realities of how AI is revolutionizing data and analytics to bring pervasive insights to all employees; and what steps organizations should be taking to properly integrate AI into their BI and analytics.

With Leone on the webinar were Saurabh Abhyankar, EVP and chief product officer at MicroStrategy, and PeggySue Werthessen, VP of product marketing at MicroStrategy.

“If you think about the evolution of AI and BI, there’s these kind of three steps [and] I do think we’re somewhere between step one and two, but people expect that we’re in step three,” according to Abhyankar.

The first step is that AI can certainly make us more productive, Abhyankar told viewers. “It can make it easier for you to do things that you already do. In some cases, in the second [step], it can do for you things that you could do but maybe didn’t have the skills to do [such as] ask questions, get answers, look at things in slightly different ways [and] self-service. And I think the technology is excellent for those two scenarios…. AI with BI gets you that stuff way faster, [makes you] more productive [and] makes it much easier,” Abhyankar said.

“But then the third [step] is one that concerns me because, when I talk to customers, they’re like, ‘Hey, now we can predict the future and we can do this, and we can do that.’ It’s like, ‘Whoa, eventually, but I do not think that we are there yet as an industry,” said Abhyankar.

However, Abhyankar said: “The good news is we have accomplished, I think, step one and step two, which is an enormous productivity boost. And it really just makes people’s lives a lot easier and gives them skills that they maybe didn’t have before.”

The examples provided by Abhyankar were, however, “not generative AI … not at all; that’s predictive AI,” Leone pointed out.

“There are folks and organizations that are looking at generative AI as something that unlocks” their minds and lead to them saying, ‘Alright, I’m really going to lean into AI now because I think this is really cool.’”

Leone said: “That’s fantastic, but it’s about bridging the gap between predictive and generative AI” that is crucial. “In analytics, and this is really important, generative AI is, for the most part, non-deterministic…. If you ask it a question, it’s going to give you a bunch of different answers. But when it comes to analytics, there needs to be very specific guardrails in place. So it’s a matter of interacting with definitive insights and definitive outcomes, as opposed to just actually doing the analysis.”

Leone “wanted to call that out because I think it’s really important,” he said. “I’d love to do a compare and contrast between traditional conversational AI and generative AI and almost kind of create a funny use case and ask, ‘Hey, which one do you think is generative AI and which one’s predictive AI?’”

Leone added: “I think it’s important that folks recognize whether generative AI is your initial soiree into AI or you’ve been doing it before. I think it’s really both of them together that are going to really add the most value and hopefully generative AI kind of lit the fire in a lot of organizations to recognize the power that they may be missing out on from a predictive AI standpoint.”

According to Leone’s research, 97% of organizations increased their analytics and BI budgets last year and 89% of organizations agreed they’re allocating more of their budgets to tools that enable them to better integrate, access and analyze data. It is also not just one tool that they’re investing in, as 73% of organizations have at least three BI tools today, he said.

Also, diverse AI platform challenges are clear across the market, according to Leone, who pointed to data showing 97% of organizations had challenges with their AI platforms that spanned several aspects of the technology, he said.

While security is a top concern, data quality is also crucial because precise results depend on high-quality data, Leone said. There is also the inherent complexity of AI platforms that can be daunting for some personnel, especially those who lack a deep technical understanding according to Leone.

Meanwhile, 93% of organizations polled indicated that integrating AI and machine learning into analytics and BI has increased end-user adoption, and 94% of organizations believe generative AI will impact AI, Leone said.

Organizations also recognize that “none of this is inexpensive,” Leone said, adding generative AI is “costly [and] it’s hard to train, hard to maintain [and] it’s hard to predict.”