From pilot to platform: How companies turn AI experiments into results

For many companies, the early appeal of AI was its novelty. Teams were encouraged to run pilots, test generative models on isolated datasets, or explore new interfaces for internal search. These experiments helped increase familiarity, but they often took place outside of core systems and lacked the rigor required for enterprise-grade deployment.

Eland says these realities change expectations. “There’s a growing realization that you can’t just integrate AI into an existing system and expect it to work well. You need secure data pipelines, responsible model selection, testing frameworks and change management. When these pieces come together, AI becomes far more reliable and far more valuable,” she says.

Governance as an enabler

A common misconception in early experiments was that governance slows innovation. In practice, the lack of governance has slowed progress even further. Without clear guardrails, pilots falter, models drift and trust dwindles.

Regulated sectors offer…