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By turning raw data into meaningful insights, organizations can make informed decisions at all times.
And now, with the inclusion of artificial intelligence, opportunities and capabilities to bridge the gap between available data and actionable insights are more accessible than ever.
This is especially true against the backdrop of today’s increasingly data-rich landscape, where digital transformation is turning enterprise data once trapped under technical debt into a tactical advantage.
“Large language models are generally very good at interacting with humans, collecting data, and providing access to knowledge and data.” pecans CEO and co-founder Zohar Bronfman spoke to PYMNTS about the “AI effect” during a series of conversations. “They are the best technology humans have ever created, and they help make knowledge accessible.”
However, he noted that these models are not specifically designed to make predictions, which has traditionally been a core aspect of AI.
But by combining the predictive and data processing capabilities of predictive AI with an intuitive, human-centered generative AI interface, prediction and accessibility can be achieved.
“Predictive AI helps estimate the likelihood of certain events in the future,” Bronfman says. “LLM makes semantic information, or language-related information, accessible in a very user-friendly way.”
He emphasized that it is important for companies to understand these differences and synergies to use AI effectively.
Data preparation underpins the success of every data activation
Yet, despite the benefits of enterprise AI, organizations’ readiness to integrate AI varies.
Bronfman explains that some companies have mature data practices and governance programs that allow them to seamlessly integrate AI output into existing business processes with minimal friction. However, many organizations still struggle with issues such as quality control, governance, and security, which can frequently cause problems when using AI.
“Interestingly, one of the biggest challenges in AI adoption is actually the talent gap,” he added.
“Often times, companies have use cases for AI and opportunities to leverage AI in meaningful ways, but the business doesn’t have enough access to the relevant talent to help them do that work,” Bronfman said. states. He explains that access to skilled data scientists who can effectively implement AI solutions is both valuable and scarce.
He suggested that addressing the talent gap requires a combination of improved technical skills and a broader understanding of business needs.
While technology can help bridge the technical gap, organizations also need to develop relevant business acumen to connect AI models to real-world business problems and effectively integrate them into existing processes. This requires a collaborative effort between engineering teams and executives.
“A model is only as good as the problem it solves,” Bronfman says. “And connecting models to business problems requires not only very technical precision, but also an understanding of effectiveness, how well the AI model is solving the problem, and how it should be integrated into business processes. You have to. That’s a more complicated question.”
The power of predictive GenAI in business intelligence
As technology evolves, so do the possibilities for its adoption.
Business intelligence is experiencing a paradigm shift driven by the immense potential of AI to analyze vast amounts of data, transforming the way businesses analyze and use the vast amounts of digital information they generate. .
Bronfman explained that industries with frequent and dense proprietary data are better suited for predictive-generating AI capabilities. Businesses that collect transaction data can use the platform to predict future events such as customer purchases, churn rates, and lifetime value.
“The moment you view the world through the lens of past trading behavior, you can leverage predictive-generating AI frameworks to say something about the likelihood of future trading,” Bronfman explained. “We’re evolving in terms of how we run our businesses.”
Although the range of use cases is expanding, customer behavior analytics remains a popular starting point for organizations looking to use predictive analytics, he added.
Bronfman emphasized the democratizing effect of combining predictive analytics and generative AI interfaces. The platform enables business analysts, marketing analysts, and other professionals to turn data into scientists, predicting future outcomes and making data-driven decisions. This change in value function strengthens the overall impact of predictive analytics within an organization.
Looking 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 shows potential, he stressed that AI must be used responsibly and with a clear understanding of the risks.
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