Five Trends in AI and Data Science for 2026, say MIT SMR columnists

MIT Sloan Management Review columnists Thomas H. Davenport and Randy Bean identify five AI trends for 2026: an expected deflation of the AI bubble with economic effects, growth of internal "AI factories," a shift to enterprise-level generative AI, continued but overhyped progress toward agentic AI, and ongoing debate about who should manage data and AI.

Thomas H. Davenport and Randy Bean outline five AI and data science trends for 2026 and explain why these topics matter for large organizations. They note that organizations change more slowly than technology, which makes enterprise adoption patterns somewhat easier to forecast. The authors argue that AI has moved from a niche technology to a central economic force, and they draw on industry examples and a recent survey to support their points. Their column highlights both technical developments and organizational challenges. Five trends described: - The authors expect an AI bubble to deflate and warn this could have economic consequences; they express a preference for a gradual decline so markets and companies can adjust. - More all-in adopters are building internal "AI factories": combined platforms, methods, data, and reusable algorithms that speed AI development; they cite banks (BBVA, JPMorgan Chase) and firms such as Procter & Gamble and Intuit (GenOS) as examples. - Generative AI is shifting from individual productivity uses toward being treated as an enterprise resource for strategic functions, with examples including Johnson & Johnson’s focused projects and Sanofi’s internal competition to surface enterprise ideas. - Agentic AI remains overhyped in the authors’ view; experiments have shown reliability and security issues, but they anticipate meaningful business use within several years (they suggest around five years). - Debate continues over who should manage AI: a survey cited by the authors shows rising support for chief data officers, growth in chief AI officer roles (39%), mixed reporting lines (30% to CDOs, 27% to business, 34% to technology, 9% to transformation), and an increase in organizations running AI in production at scale (39% this year versus 24% last year). Summary: The column links potential market and economic effects from an anticipated AI bubble deflation with a shift in how firms organize for AI, including investment in internal infrastructure and more selective enterprise uses of generative AI. The authors also note that agentic AI is likely to progress toward practical business roles over several years while governance and reporting arrangements for AI remain unsettled; Undetermined at this time.