Industry executives and experts share their predictions for 2025. Read them in this 17th annual VMblog.com series exclusive.
By Devavrat Shah, CEO and co-Founder, Ikigai Labs
Artificial intelligence is the most exciting technology of our time. Over the past two years, we’ve seen massive leaps in AI. And in 2025, things will accelerate even faster – from both a technical and business perspective.
There are a dozen or so major AI trends that will take off next year. But two in particular stand out to me: 1) The evolution of generative AI for tabular data, and 2) Growing company-wide efforts to drive AI upskilling.
A numbers game: GenAI moves beyond text
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. These things are useful for some enterprises, depending on the sector. However, they have limited value for organizations hoping that AI can help them make better overall business decisions.
LLMs are not effective in scenarios that require deep numerical predictive and statistical modeling to predict how a given variable will change over time based on one or more input variables (aka regression tasks). Gartner recently broke it down: “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.”
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.
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 enable companies to improve demand forecasting, business planning, fraud detection, financial reconciliation, supply chain management, operations optimization, and many other applications. It’ll be especially beneficial for verticals that have tons of tabular data, like retail, consumer goods, finance, and manufacturing.
Ultimately, though, most enterprises will benefit by leveraging the tech to drive better decisions, minimize risk, and boost efficiencies.
AI education becomes C-level priority
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.
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.
We’ve seen this trend firsthand at Ikigai Labs. The company runs Ikigai Academy, which offers interactive educational content covering everything from the fundamentals of AI to more advanced classes teaching users how to leverage tabular and time series data and develop AI Apps to solve complex forecasting, planning, and reconciliation problems. The courses require no coding whatsoever.
Ikigai Academy’s entire purpose is to help people build AI skills, no matter their starting point. There’s been booming interest in the Academy across a diverse cross section of workers, from higher level business analysts to seasoned data
scientists. And we’re seeing more and more organizations and companies that are sending teams of workers to Ikigai Academy to help build their workforce’s AI skillset.
Organizations look for real ROI from AI in 2025
Plenty of enterprises have been experimenting with AI over the last few years. Next year, though, these organizations will look to move beyond experimentation and drive real, measurable value from artificial intelligence. That’s why the rise of genAI for tabular, time-series data is such an important development. It will enable companies that have been struggling to find a major business case for LLMs a vehicle to transform their operations.
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ABOUT THE AUTHOR
Devavrat successfully combines academia and entrepreneurship. He is a professor and a director of Statistics and Data Science Center at MIT. He co-founded Celect, a predictive analytics platform for retailers, which he sold to Nike. Devavrat
holds a Bachelor and PhD in Computer Science from Indian Institute of Technology and Stanford University, respectively.
