Post by Invisible Technologies

Consumers have moved faster than billion-dollar companies. 67% of them are already using AI weekly. But inside enterprises, only a small fraction of AI initiatives ever make it to production. Matthew Fitzpatrick spoke at Davos USA House during the World Economic Forum on why the enterprise AI adoption gap persists. Across conversations, three constraints consistently come up: 1. Data governance Enterprise data remains fragmented and inconsistent. Without a clean, harmonized data layer, even the best models struggle to deliver reliable outcomes. 2. Ownership and workflow redesign AI initiatives often lack clear business ownership. Without leadership driving change, they remain isolated pilots instead of being embedded into real workflows. 3. Evaluation and trust Unlike traditional systems, AI is harder to measure and verify. Many organizations lack the frameworks to assess performance, manage risk, and know when to trust outputs. The timelines for enterprise AI adoption are often underestimated. While expectations focus on near-term disruption, the reality is a longer-term shift that requires sustained changes to how organizations operate. The technology is ready, but the main challenge is building the infrastructure required to achieve true AI success.