AiThority Interview with Pete Foley, CEO of ModelOp

What does it take to effectively manage AI lifecycles? Pete Foley, CEO of ModelOp weighs in with some pointers in this interview by AiThority.com:

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Hi Pete, what inspired you to co-found ModelOp, and how has the company evolved since its inception?

Nothing compares to the transformation AI is bringing, but when we started ModelOp, we saw a struggle in how enterprises brought AI to market at Fortune 500 enterprises. Everyone was focused on building ML and AI models—but few had figured out how to operationalize it at scale, govern it across business lines, or measure its impact. Our background in enterprise IT and data science told us this wasn’t a tooling problem—it was a lifecycle and accountability problem.

Since then, ModelOp has evolved into the AI control tower for the enterprise. We help the world’s most complex and regulated organizations manage the full AI lifecycle—from traditional models to GenAI, LLMs, and agents—with the structure, oversight, and speed they need to drive real business value.

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AI governance is now a boardroom priority—how does ModelOp help enterprises operationalize governance without slowing down innovation?

We make governance feel like acceleration, not bureaucracy. Our platform automates model lifecycle and governance workflows—risk reviews, policy enforcement, traceability—so teams can launch AI initiatives faster, with fewer manual bottlenecks and more confidence. Think of us as the layer that lets innovation thrive safely at enterprise scale. The key is aligning governance to business risk—not applying one-size-fits-all rules that kill momentum.

In today’s LLM-driven environment, what are the most overlooked risks enterprises face when scaling GenAI initiatives?

The biggest blind spot is ownership and oversight. Who owns a chatbot that’s built with a third-party LLM? Who’s accountable when it hallucinates or violates policy? Enterprises are also underestimating the challenge of managing embedded AI—GenAI is now showing up inside SaaS tools, partner models, and software agents. If you don’t have a system to inventory, risk-rank, and govern all of that, you’re not scaling innovation—you’re scaling risk.

Can you elaborate on how automation and integration in ModelOp’s platform contribute to AI lifecycle management at scale?

Enterprises can’t govern AI manually—not when they’re managing hundreds or thousands of use cases across business lines. ModelOp automates the critical steps: onboarding use cases, assessing risk, mapping to regulations like the EU AI Act or Colorado Consumer AI Act, enforcing controls, and generating audit-ready evidence. And because we integrate with the tools enterprises already use— data management, ITSM, AI development and execution systems—we slot right into existing processes. It’s full-lifecycle AI automation without disruption.

What are the biggest blockers for enterprises when it comes to scaling AI responsibly, and how can they overcome them?

The top blockers are fragmentation, accountability, and visibility. Most enterprises have no central source of truth for what AI exists, who owns it, or whether it’s compliant. This creates risk, slows down time to market, and undermines trust. The solution isn’t more point tools—it’s a unified system of record and control for AI. That’s what ModelOp delivers: centralized governance across a decentralized AI landscape.

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Have you noticed any industry-specific adoption patterns, for instance, in finance, healthcare, or manufacturing, where AI governance is either leading or lagging?

Financial services and healthcare are leading the way—they’ve felt the regulatory pressure and operational risk for years. But we’re now seeing a surge in interest from manufacturers and global CPG companies, especially as they adopt GenAI for customer service, advertising optimization, and other high-priority use cases. What’s universal is the need for governance that’s agile enough to keep up with innovation, and robust enough to satisfy regulators and auditors.

Finally, what’s your vision for ModelOp in the next 3–5 years, especially as AI becomes increasingly decentralized and democratized?

We believe the future of AI is autonomous, agentic, and deeply embedded—and that future demands control. Our vision is to be the de facto AI governance layer for the enterprise. As AI gets more powerful and decentralized, enterprises will need guardrails they can trust. ModelOp will be there to provide the automation, oversight, and visibility that allows AI to move fast without breaking the business.

[To share your insights with us, please write to psen@itechseries.com]