Rebuilding the data stack for AI
MIT Technology Review / 4/27/2026
💬 OpinionDeveloper Stack & InfrastructureIdeas & Deep AnalysisIndustry & Market Moves
Key Points
- Many enterprises are finding that successful AI adoption is constrained less by AI models and more by the quality and readiness of their underlying data infrastructure.
- Consumer AI experiences have emphasized speed and usability, but scaling AI in enterprise settings demands significant, often unglamorous work on data foundations.
- Rebuilding or modernizing the data stack is positioned as a prerequisite for meaningful, large-scale AI deployment.
- Leadership focus is shifting from AI experimentation to data strategy and engineering efforts that enable reliable AI outcomes across the organization.
Artificial intelligence may be dominating boardroom agendas, but many enterprises are discovering that the biggest obstacle to meaningful adoption is the state of their data. While consumer-facing AI tools have dazzled users with speed and ease, enterprise leaders are discovering that deploying AI at scale requires something far less glamorous but far more consequential: data…
