Five Trends in AI and Data Science for 2025 From MIT Sloan Management Review
From agentic AI to unstructured data, these 2025 AI trends deserve close attention from leaders.
CAMBRIDGE, Mass., Jan. 8, 2025 /PRNewswire/ -- Artificial intelligence and data science remained front-page news in 2024. MIT Sloan Management Review reveals five big AI and Data Science trends for 2025: the promise and hype around agentic AI; the push to measure results from generative AI experiments; an emerging vision of what a data-driven culture really means; a renewed focus on unstructured data; and a continued struggle over which C-suite role will oversee data and AI responsibilities.
In "Five Trends in AI and Data Science for 2025," (a part of its AI in Action series), Tom Davenport, President's Distinguished Professor of Information Technology and Management at Babson College, and Randy Bean, adviser to Fortune 1000 organizations on data and AI leadership, incorporate the latest AI research along with the 2025 AI & Data Leadership Executive Benchmark Survey, an annual survey of data, analytics, and AI executives, conducted by Bean's educational firm, Data & AI Leadership Exchange, as well as Davenport's surveys on generative AI and data, technology leadership structures, and most recently, agentic AI, to compile their prediction on AI trends.
Leaders will grapple with both the promise and hype around agentic AI. Agentic AI, the kind of AI that does tasks independently, is on an inevitable rise. Most technology executives believe that these autonomous and collaborative AI programs will be primarily based on focused generative AI bots that will perform specific tasks. "There will be (and in some cases, already are) generative AI bots that will do people's bidding on specific content creation tasks, but it will require more than one of these agentic AI tools to do something significant, such as make a travel reservation or conduct a banking transaction," said Davenport.
The time has come to measure results from generative AI experiments. Very few companies are actually measuring productivity gains carefully or figuring out what the liberated knowledge workers are doing with their freed-up time. "Sadly, if companies are really going to see and profit from GenAI, they're going to need to measure and experiment to see the benefits," commented Davenport.
The reality about data-driven culture sets in. Ninety-two percent of survey respondents said they feel that cultural and change management challenges are the primary barrier to becoming data- and AI-driven. This suggests that any technology alone is insufficient. "It's still a good thing that data and AI leaders feel that their organizations have improved in this regard over the distant past, but our long-term prediction is that generative AI alone is not enough to make organizations and cultures data-driven," Bean added.
Unstructured data is important again. Generative AI has made unstructured data important again. To get unstructured data into shape is still work that is human-intensive. Davenport says, "At some point, perhaps, we'll be able to just load tons of our internal documents into a GenAI prompt window, but 2025 is unlikely to be that time. Even when that's possible, there will still be a need for considerable human curation of the data."
Who should run data and AI? Expect continued struggle. These roles continue to evolve and organizations continue to wrestle with their mandates, responsibilities, and reporting structures. Bean asserts that the role of chief data and AI officer should be a business role reporting to business leadership. Davenport agrees that tech leaders need to be more focused on business value, but he would prefer to see "supertech leaders" with all of the tech roles reporting to them. Whatever the right answer is, it's clear that organizations must make some interventions and make those who lead data as respected as the data itself.
Read the full article with data and statistics in the MIT Sloan Management Review article "Five Trends in AI and Data Science for 2025," which publishes at 8 a.m. ET on Jan. 8, 2025. This column is part of the series AI in Action.
About the Authors
Thomas H. Davenport is the President's Distinguished Professor of Information Technology and Management at Babson College, the Bodily Bicentennial Professor of Analytics at the University of Virginia Darden School of Business, a fellow of the MIT Initiative on the Digital Economy, and senior adviser to the Deloitte Chief Data and Analytics Officer Program. His latest book is All Hands on Tech: The AI-Powered Citizen Revolution (Wiley 2024). Randy Bean has been an adviser to Fortune 1000 organizations on data and AI leadership for nearly four decades. He is the author of Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI (Wiley, 2021), and is a contributing author to MIT Sloan Management Review, Harvard Business Review, and Forbes.
About MIT Sloan Management Review
MIT Sloan Management Review is an independent, research-based magazine and digital platform for business leaders published at the MIT Sloan School of Management. MIT SMR explores how leadership and management are transforming in a disruptive world. We help thoughtful leaders capture the exciting opportunities — and face down the challenges — created as technological, societal, and environmental forces reshape how organizations operate, compete, and create value.
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SOURCE MIT Sloan Management Review
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