The future of enterprise AI that M&A should build towards (www.techradar.com)

Enterprise AI is stuck in a pilot purgatory: MIT found only ~5% of rollouts deliver meaningful value, and simply stitching together a “complete” AI stack or centralizing everything in a lakehouse isn’t solving it. The core problem is not compute or models but process and context — business logic and nuance live with frontline teams, not in IT or raw data lakes. Connecting models to vast stores of sensitive data also creates governance and risk headaches, so giving AI blanket access to the lakehouse is both unsafe and ineffective. The proposed fix is an “AI Data Clearinghouse”: a neutral software layer that selectively extracts the minimal, context-rich datasets needed per use case, embeds business logic, and exposes visual, drag-and-drop workflow builders so business users can design AI processes themselves. Built-in governance checks, auditable data flows, and executive-friendly visualizations speed approvals and reduce risk, enabling scaled deployment beyond proofs of concept. For M&A and product strategy, the implication is clear: acquisitions should prioritize tools that link disparate systems, operationalize process logic at the edge, and democratize AI for domain experts — not just centralize infrastructure for IT. That shift could turn stalled pilots into enterprise impact.