AI Governance as an Accelerator with David Trier, CEO at ModelOp

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Apple: https://podcasts.apple.com/us/podcast/ai-governance-as-an-accelerator-with-david-trier-ceo/id1525571613?i=1000799769911
Spotify: https://open.spotify.com/episode/4VC6IkKLKAxcg0hmA6GvoX

Enterprise AI spending keeps rising, yet most pilots never reach production and ROI stays hard to prove.

Our host, Ray Rike talks with David Trier, CEO of ModelOp, about why AI governance speeds up production deployment when it is built as a repeatable, automated process. Dave has spent the last decade helping large enterprises put AI into production.

Dave explains how leading companies define business impact before deployment, govern multi-agent systems at several levels, and manage AI spend as a portfolio of investments a CFO can track.

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Key Highlights
• Why pilots stall before production: Each team builds its own path to production, so projects run into “enterprise friction” from legal, risk, compliance, security, data, and IT reviews. Dave’s fix is a set of enterprise-wide standards that every business unit follows, which still leaves departments free to choose their own tools and use cases. One customer used this approach with automated workflows and cut time from idea to production from 9 to 12 months to less than half that.
• Governance runs for the life of the system: AI is probabilistic and depends on its data, so governance continues after launch. That means continuous risk monitoring, ongoing testing against verified data sets, traceability into execution logs, and a clear record of every model, tool, skill, and orchestrator version running.
• Measure value before you deploy: Companies that get the most from AI answer three questions up front. What business impact are we targeting? Which metrics will show it? Do we have the instrumentation to track those metrics in production?
• An AI system of record with one accountable owner: A typical AI solution involves 8 to 10 stakeholders. Dave argues for a single system of record that sits above every vendor’s agent platform (ERP, CRM, supply chain) and shows usage, risk, cost, and value in one place. Each business use case has one named AI owner who is accountable for it, even when 50 agents run underneath.
• Manage AI as a portfolio, and drop token maxing: Dave’s advice to CFOs is to manage AI investments like a stock portfolio, using consistent impact metrics and a central view of cost and benefit. ROI ownership sits with the head of AI and the CIO, working with the CFO, who decides whether to continue or kill each initiative. He calls tracking each employee’s token consumption a vanity metric that boards should stop accepting.

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