AI Quick Take
- AI adoption moved from experiments to everyday functions-half of companies used AI in three+ business areas by end‑2025.
- A stronger data fabric is framed as the bottleneck for turning deployed copilots, agents, and predictive systems into measurable business outcomes.
MIT Technology Review reports that enterprise AI has moved beyond experiments into routine use and that a stronger data fabric is now essential to capture business value. Organizations are rolling out copilots, agents, and predictive systems across finance, supply chains, HR, and customer operations, and a recent survey found that by the end of 2025 half of companies were using AI in at least three business functions.
The story emphasizes an infrastructure gap: deployed models and automation increasingly depend on connected data rather than isolated compute wins. Without reliable paths for data to flow between systems, governability and consistent access, AI tools will underdeliver even if the underlying models are capable. That shifts the operational burden to data engineering, platform teams, and procurement professionals who must integrate sources, enforce policies, and tie AI outputs into live workflows.
The practical consequence for infrastructure buyers and cloud teams is a reprioritization of spending and attention. Rather than treating AI success as solely a function of more powerful chips or larger model licenses, organizations will need to evaluate data-fabric capabilities - how easily tools connect across departments, how they handle lineage and governance, and how they support ongoing production operations. Watch for changing RFP criteria, hiring emphasis on platform engineering, and vendor positioning that foregrounds end‑to‑end data plumbing as a route to measurable business impact.