Mastering AI Governance in the Enterprise: Insights from ModelOp’s VP of Product, Dave Trier

Artificial intelligence (AI) is no longer optional for enterprises—it’s a key driver of innovation, efficiency, and growth. Yet integrating AI across large organizations introduces complexity, risk, and governance challenges. In this episode of the Product Led Growth Leaders podcast, Thomas Watkins speaks with Dave Trier, VP of Product at ModelOp, about actionable strategies for governing AI at scale. Dave shares practical insights for enterprise leaders, product managers, and AI practitioners to balance innovation with oversight, ensuring AI initiatives deliver value safely and effectively.

Building Robust AI Governance Without Slowing Innovation

AI adoption often creates a “wild west” environment in enterprises, where different departments independently deploy models, tools, or experiments. Dave Trier emphasizes that centralizing AI visibility is the first step toward effective governance. Organizations need a unified registry to track all AI initiatives, capturing critical details like business objectives, data sources, and performance expectations. ModelOp’s platform offers this enterprise-level oversight, providing leaders and governance teams the clarity needed to make informed decisions.

Standardizing governance is equally critical. Too rigid, and innovation stalls; too loose, and enterprises face operational and compliance risks. Dave advises creating flexible guardrails tailored to each department’s risk profile, allowing teams to innovate while maintaining oversight. Automating workflows—such as intake, review, approval, and continuous monitoring—ensures governance scales without bottlenecks. Treating AI models, vendor systems, and even spreadsheets as critical business assets reinforces a culture of accountability and reliability.

Finally, enterprises must support the full model lifecycle. AI models require continuous monitoring, updates, and retraining to remain effective and compliant. ModelOp provides templates and automation to manage models end-to-end, balancing customer success support with self-service options for mature users. Dave highlights that a well-implemented AI governance strategy enables organizations to innovate confidently, without exposing the business to unnecessary risk.

About Dave Trier

Dave Trier is the VP of Product at ModelOp, specializing in AI governance, product strategy, and enterprise-scale implementation. With deep experience in guiding organizations through AI adoption, Dave helps businesses manage models as first-class assets while fostering innovation. Connect with him on LinkedIn.

About ModelOp

ModelOp is a leading provider of AI governance software that enables enterprises to manage AI initiatives at scale. Their platform centralizes model oversight, automates workflows, and supports full lifecycle management, helping organizations reduce risk, maintain compliance, and drive measurable business impact.

Links Mentioned in This Episode

• ModelOp Website: https://www.modelop.com/

• Dave Trier LinkedIn: https://www.linkedin.com/in/artofai/

• Podcast Guest Application: Apply here

Key Episode Highlights

• Centralizing AI initiatives to maintain enterprise-wide visibility.

• Standardizing governance with flexible guardrails that don’t hinder innovation.

• Automating workflows for intake, review, monitoring, and retraining of models.

• Treating all models—including spreadsheets and vendor systems—as critical business assets.

• Supporting the full AI lifecycle while balancing hands-on guidance and self-service.

Conclusion

Effective AI governance doesn’t mean slowing innovation—it’s about creating clarity, accountability, and trust across the organization. Dave Trier’s insights from ModelOp provide a practical roadmap for enterprises to scale AI initiatives safely while maximizing value.

Ready to explore AI strategies for your business? Visit 3Leaf Consulting for expert insights and resources.

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