In this episode of Exponential, Zohar Bronfman, co-founder and CEO of Pecan AI, unpacks the role of predictive AI in today’s business landscape — and why it’s quickly becoming indispensable.
At its core, predictive AI estimates the likelihood of future events using massive amounts of data. Zohar explains: “In very general terms, it’s the ability to make some form of a statistical likelihood estimation of any type of future event.”
He contrasts this with the limitations of human decision-making: “Our mind can take into consideration three or four pieces of information for a given task… Predictive AI would basically take 1500 data points.”
Zohar draws a line between generative AI, which mimics human-like reasoning, and predictive AI, which is optimized for decision-making at scale.
This capability makes predictive AI critical for business operations, particularly when it comes to modeling customer behavior, forecasting, and operational decisions.
As Zohar puts it: “The most successful businesses… Amazon, Meta, Google… they all use predictive AI extensively.”
But predictive AI still faces major hurdles. One is data normalization — unlike standardized text used in language models, proprietary business data is highly idiosyncratic. “It’s like different languages… it’s extremely hard to transfer learning that happened on one business’s data.”
Zohar also points to an under-discussed challenge: causality. “AI today… is correlational analysis, not causal analysis. The fact that something is going to happen doesn’t mean that you can affect it.”
As businesses increasingly rely on predictive systems, these challenges — both technical and philosophical — will become more urgent. But for now, predictive AI is already reshaping how companies anticipate and act on the future.
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