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Report: Five trends in AI and data science for 2025

Companies will continue to grapple with the challenges and benefits of artificial intelligence in the year to come—from managing the hype to measuring progress.

Report: Five trends in AI and data science for 2025
DC Velocity Staff
By DC Velocity StaffJan 09, 2025
DC Velocity Staff
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Artificial intelligence (AI) and data science were hot business topics in 2024 and will remain on the front burner in 2025, according to recent research published in AI in Action, a series of technology-focused columns in the MIT Sloan Management Review.

In Five Trends in AI and Data Science for 2025, researchers Tom Davenport and Randy Bean outline ways in which AI and our data-driven culture will continue to shape the business landscape in the coming year. The information comes from a range of recent AI-focused research projects, including 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.

The five trends range from the promise of agentic AI to the struggle over which C-suite role should oversee data and AI responsibilities. At a glance, they reveal that:

Leaders will grapple with both the promise and hype around agentic AI. Agentic AI—which handles tasks independently—is on the rise, in the form of generative AI bots that can perform some content-creation tasks. But the authors say it will be a while before such tools can handle major tasks—like make a travel reservation or conduct a banking transaction.

The time has come to measure results from generative AI experiments. The authors say very few companies are carefully measuring productivity gains from AI projects—particularly when it comes to figuring out what their knowledge-based workers are doing with the freed-up time those projects provide. Doing so is vital to profiting from AI investments.

The reality about data-driven culture sets in. The authors found that 92% of survey respondents feel that cultural and change management challenges are the primary barriers to becoming data- and AI-driven—indicating that the shift to AI is about much more than just the technology.

Unstructured data is important again. The ability to apply Generative AI tools to manage unstructured data—such as text, images, and video—is putting a renewed focus on getting all that data into shape, which takes a whole lot of human effort. As the authors explain “organizations need to pick the best examples of each document type, tag or graph the content, and get it loaded into the system.” And many companies simply aren’t there yet.

Who should run data and AI? Expect continued struggle. Should these roles be concentrated on the business or tech side of the organization? Opinions differ, and as the roles themselves continue to evolve, the authors say companies should expect to continue to wrestle with responsibilities and reporting structures.

ArticleTechnologyTechnologyEditorialTech & Infrastructure
aitechnologydata sciencemit - massachusetts institute of technologyartificial intelligence
ArticleTechnologyTechnologyEditorialTech & Infrastructure
aitechnologydata sciencemit - massachusetts institute of technologyartificial intelligence

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