Predictive AI Models Will Make Every Business Smart, Says Pecan CEO

Transforming raw data into meaningful insights has always empowered organizations to make informed decisions.

And now, with artificial intelligence thrown into the mix, there have never been more opportunities — or capabilities — at hand to bridge the gap between available data and actionable insights.

That’s particularly true given the backdrop of today’s increasingly data-rich landscape, where digital transformations are turning company data once locked away under technical debt into tactical advantages.

“Large language models in general are extremely good at interacting with humans, gathering data, and making knowledge and data accessible,” Pecan CEO and Co-founder Zohar Bronfman told PYMNTS during a conversation for the series the “AI Effect.” “They are the best technology humanity has ever made that helps make knowledge accessible.”

However, he noted that these models are not specifically designed for making predictions, which has traditionally been a core aspect of AI.

But by pairing predictive AI’s forecasting and data crunching capabilities with intuitive, human-centric generative AI interfaces, prediction and accessibility can be achieved.

“Predictive AI helps you make estimations about the likelihood of certain future events,” Bronfman said. “LLMs make semantic, or language-related, information accessible in an extremely user-friendly manner.”

He emphasized the importance for businesses to understand these distinctions and synergies to use AI effectively.

Data Readiness Underpins All Successful Data Activations

Still, despite the benefits of enterprise AI, the readiness of organizations to integrate AI varies.

As Bronfman explained, some companies have mature data practices and governance programs, enabling them to integrate AI outputs seamlessly into existing business processes with minimal friction. However, many organizations still struggle with issues such as quality control, governance and security, and this can frequently cause hiccups when using AI.

“Interestingly enough, one of the biggest challenges in adopting AI is actually the talent gap,” he added.

“In many cases, even though firms have the AI use case, and they have the opportunity to leverage AI in a meaningful way, they don’t have sufficient access to relevant talent that can help their business do that work,” Bronfman said, explaining that access to skilled data scientists who can effectively implement AI solutions is both valuable and in short supply.

He suggested that addressing the talent gap requires a combination of technical upskilling and a broader understanding of business needs.

While technology can help close the technical gap, organizations also need to develop the relevant business acumen to tie AI models to their actual business problems and integrate them into existing processes effectively. This requires a collaborative effort between engineering teams and C-suite executives.

“A model is only as good as the problem it solves,” Bronfman said. “And to tie the model to the business problem requires an understanding of not only the accuracy, which is very technical, but also the efficacy, how well the AI model is solving the problem, and how it should be integrated into the business process, which is a more complex question.”

The Power of Predictive GenAI in Business Intelligence

As technology evolves, so do the possibilities of its deployment.

Business intelligence is undergoing a paradigm shift driven by the immense potential of AI to parse vast volumes of data, transforming the way businesses analyze and use the digital information they generate in troves.

Bronfman explained that industries with frequent and dense proprietary data are better suited for predictive generative AI capabilities. Companies that gather transactional data can use the platform to predict future events, such as customer purchases, churn rates and lifetime value.

“The moment you slice the world through the lens of historical transactional behavior, you can then leverage a predictive gen AI framework and say something about the likelihood of those future transactions,” Bronfman explained. “It’s evolutionary in terms of how businesses can operate.”

While the spectrum of use cases is broadening, customer behavior analysis remains a popular starting point for organizations looking to use predictive analytics, he added.

Bronfman emphasized the democratizing effect of combining predictive analytics with generative AI interfaces. The platform enables business analysts, marketing analysts and other professionals to transition into data scientists, empowering them to predict future outcomes and make data-driven decisions. This shift in value function enhances the overall impact of predictive analytics within organizations.

As for what’s ahead, Bronfman predicted that the future of AI lies in not only predicting future events but also prescribing actions based on those predictions. The goal is to automate decision-making processes and optimize business operations. While this vision presents possibilities, he emphasized the need for a clear understanding of risks and the responsible use of AI.

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