Enterprises do not have an AI problem; they have an AI governance problem.
In my recent conversation with David Trier, VP of product at ModelOp, he described the current state inside large organizations as “the Wild West of AI”—dozens of teams, hundreds of tools, and no shared way to get models safely into production.
The reality is that many enterprises are staring at portfolios of 50 to 100 generative AI use cases, but only a handful ever make it into production, often taking six to 18 months to ship.
What clicked for me in this episode was David’s analogy: ModelOp is essentially ServiceNow for AI.
ServiceNow gave IT leaders a consistent, auditable way to turn messy tickets into reliable service management. ModelOp does the same for AI initiatives: it sits at the enterprise layer, orchestrating 10 to 12 teams and systems—data, security, legal, risk, compliance, infrastructure—so AI projects move through a repeatable playbook instead of one-off review cycles.
David walked through a financial-services case where this approach cut time-to-production in half, turning 18‑month science projects into AI services that ship in weeks and generate business value before models degrade.
For product leaders and CTOs, the takeaway is simple: if AI is a C‑suite–sized investment, it needs C‑suite–grade governance, not grassroots experimentation scattered across the org.
If you are thinking about how to move from proof‑of‑concept chaos to an enterprise AI operating model, this episode is worth your time.
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