AI is disrupting businesses of all types. AI can automate repetitive and time-consuming tasks, from data entry to customer service; AI can also provide valuable analytics and help to predict consumer behavior.
To obtain some 2025 predictions, Digital Journal asked Ikigai Labs CEO and longtime MIT AI professor Dr. Devavrat Shah. These predictions cover the evolution of GenAI to move beyond text applications and how corporations will approach AI reskilling.
A numbers game: GenAI moves beyond text
With generative artificial intelligence, Shah predicts: “Enterprises continue to leverage genAI in new ways to guide their businesses. But they’re finding out firsthand that the dominant genAI technology (LLMs) wasn’t built for that. LLMs were generally designed for text-based AI applications like content generation, chatbots, and knowledge bases. They are not effective in scenarios that require deep numerical predictive and statistical modelling to predict how a given variable will change over time based on one or more input variables (aka regression tasks).
Gartner recently broke the topicdown: “Use cases in the categories of prediction and forecasting, planning and optimization, decision intelligence, and autonomous systems are not currently a good fit for the use of GenAI models [LLMs] in isolation.”
Interpreting this, Shah observes: “At a high-level, this means that LLMs aren’t great at fundamental business planning use cases, which cover things like logistics, marketing, staffing, investing, product development, and all sorts of other areas. Those applications require modelling of enterprise-specific, tabular and time series data that span key areas of the business, including people, products, sales, and budgets.”
In terms of the expected business response, Shah predicts: “The industry will respond to this gap in 2025. Next year, more genAI technologies will emerge that are engineered specifically for modelling structured numerical and statistical data rather than just text. These technologies will allow enterprises to use their tabular business data to make better decisions, minimize risk, and boost efficiencies.”
AI education becomes C-level priority
How far should a deep understanding of AI stretch within the firm? Shah thinks: “In the era of AI, traditional white-collar workers worry about job security, while employees with strong tech and AI expertise are in major demand. It’s assumed that the former will be replaced by the latter. But reskilling workers is easier than replacing them: That’s why next year we’ll instead see enterprises invest aggressively in AI education for their existing employees.”
This also becomes part of a progressive trend, as Shah discovers: “LLMs have already shown how everyone can effectively wield AI. Rather than compete over a small pool of AI experts, major companies will re-train workers to leverage the technology. AI upskilling will soon become a C-level priority.”
