How Governance Helps Scale Enterprise AI

Article Origin Note:

This article emerged from a compelling conversation between Skip McCormick, CTO of Cornerstone Technologies, and Dave Trier, VP of Product at ModelUp, during an episode of the Entry Point podcast.

Their discussion revealed such valuable insights about the true relationship between AI governance and enterprise scalability that the key points needed to be formalized and expanded upon to help other organizations understand how governance actually accelerates rather than hinders AI deployment.

Why Governance Accelerates AI Deployment

During a recent ModelOp podcast, Skip McCormick, CTO of Cornerstone Technologies, offers a compelling sports analogy to explain how governance actually speeds up AI initiatives: "If you think about a football team, everybody thinks it's all about the quarterback—the data scientists and developers. But there are ten other people on your team that need to work together to move the ball down the field."

In AI development, these "other players" include:

• data engineering teams
• database management
• legal
• risk
• compliance
• IT
• production support

AI governance serves as the playbook that coordinates all these stakeholders effectively.

"If you don't have that playbook, it's like backyard football—every person for themselves, and it ends up being chaos," McCormick explains. "But if you have the right playbook in place, you allow all the different players to know exactly what they should be doing and when."

This coordinated approach transforms what many perceive as bureaucratic hurdles into streamlined processes that actually accelerate time-to-market for AI solutions.

From Obstacles to Enablers: Real-World Evidence

Dave Trier, VP of Product at ModelUp, has witnessed this transformation firsthand across Fortune 100 companies.

"Unfortunately, the reality is that governance and compliance can absolutely be an enabler to help you get solutions to market faster," he notes.

Before implementing proper governance frameworks, organizations often experience what Trier calls "cowboy coding"—ad hoc AI development that inevitably gets stopped by compliance and risk management teams. "They would do their best to push solutions through, and they would just get stopped," he recalls.

The turning point comes when organizations bring all stakeholders together to develop a unified governance framework. "It was an 'aha' moment to them," Trier explains. "Getting them in a room was a light bulb moment because they said, 'Okay, well, now I know what we need to do and when we need to be involved.'"

The Trust Factor: Building Confidence Across Teams

One of governance's most significant benefits is building trust among different organizational groups that might traditionally view each other with suspicion.

When legal, risk, IT, and development teams collaborate on establishing governance frameworks, they develop mutual understanding and confidence in shared processes.

"It helped to develop trust among all those different groups that might have put the brakes on in the past," Trier observes. "They trust that the process will work and will help keep us out of trouble for many different factors."

This trust doesn't just improve relationships—it fundamentally changes how quickly AI initiatives can move through organizational approval processes.

The Cost of Governance Neglect: A $3 Billion Lesson

The importance of proper AI governance becomes starkly clear when examining recent regulatory actions. TD Bank's $3+ billion fine for inadequate anti-money laundering models serves as a cautionary tale for the entire industry.

"They had models that took shortcuts on governance or skipped governance completely," McCormick explains.

"The models didn't work right and advised people to make decisions which were comfortable to blame the model on because the decision, even if it was unethical, was profitable."

What makes this case particularly sobering is that the fine applied to just one category of models—anti-money laundering—representing only a fraction of a major bank's total AI model inventory. The potential exposure for comprehensive governance failures could be exponentially higher.

Scaling Beyond Individual Models: The Factory Approach

Pete Foley, CEO of ModelOp, emphasizes that enterprise AI governance must address scale from the outset.

"One of our financial services customers has over 32 different model development technologies," he notes, highlighting the complexity modern enterprises face.

Rather than treating each AI model as a unique snowflake requiring individual governance reviews, successful organizations implement what experts call a "factory floor" approach.

This systematic methodology treats AI governance like manufacturing, with standardized processes, clear quality controls, and predictable timelines.

"Instead of the playbook, you have more of the bill of materials that goes into shop floor manufacturing equipment," Trier explains. "As you're getting AI solutions through, they go to the right step, with the right mechanisms involved and the right parts being placed at the right time."

The Complete Lifecycle: From Inception to Retirement

Effective AI governance extends far beyond getting models into production.

As Trier emphasizes, true lifecycle management runs "from inception to retirement"—not just "inception to production" as many organizations mistakenly believe.

This comprehensive approach addresses several critical needs:

Audit Readiness

When regulators come calling, organizations need complete documentation of model development, testing, validation, and ongoing monitoring. Without proper governance, this becomes what experts call "audit search parties"—frantic efforts to reconstruct missing documentation.

Continuous Monitoring

AI models require ongoing care and maintenance as data, context, and business environments change. Governance frameworks ensure this monitoring happens systematically rather than reactively.

Proper Decommissioning

When models are retired, organizations must prove to auditors that they're no longer in use, document their final usage date, and account for all associated data.

The Competitive Advantage: Happy Auditors Equal Faster ROI

Perhaps counterintuitively, organizations with robust AI governance often find that regulatory audits become collaborative rather than adversarial experiences.

"The first time I had to support a Fed audit for models, we had no findings," McCormick recalls. "Everybody was like, 'Wait, that can't be right. There's always findings.'"

When governance is done properly, auditors become advocates rather than obstacles. "The auditors believed that we were actually advocates for the same things they were advocates for," McCormick explains. "Once we earned their trust, the audits actually got pretty easy."

This shift from confrontational to collaborative auditing relationships creates significant business value, leading to what McCormick calls the fundamental equation: "Happy auditors equals faster ROI."

Building for the Future: Agents and Exponential Complexity

Looking ahead, the need for comprehensive AI governance will only intensify. The emergence of AI agents—autonomous systems that can interact with multiple external systems and partners—represents a new frontier of complexity and risk.

"Because there's this variety of different types of AI, they're doing different things, they're touching all your different systems now, they're opening you up to other external partners and systems via these agents and tools, you gotta do something," Trier warns. "You have to have a governance capability in place that's able to encompass all those different types of AI."

McCormick sees the problem growing exponentially: "My sense is that I barely have a sense of how fast it's growing, and I think it's growing exponentially, explosively."

Getting Started: Minimal Viable Governance

For organizations feeling overwhelmed by the scope of AI governance requirements, experts recommend starting with what Foley calls "minimal viable governance"—just the right level of oversight to begin showing value quickly.

This approach typically begins with AI portfolio management: understanding what AI assets exist across the organization, whether they're generative AI models, vendor-supported solutions, or embedded systems like ChatGPT integrations.

"Governance can feel overwhelming," Foley acknowledges, "but typically with our customers, we can get them up and running in 90 days."

The Bottom Line: Governance as Growth Enabler

The evidence is clear: rather than slowing down AI initiatives, proper governance accelerates them by creating predictable, efficient processes that build trust among stakeholders and satisfy regulatory requirements from the outset.

Organizations that view AI governance as an obstacle are setting themselves up for the kind of costly delays and compliance failures that can destroy AI ROI entirely. Those that embrace governance as a strategic enabler position themselves to scale AI initiatives rapidly and responsibly.

As the AI landscape continues to evolve at breakneck speed, the organizations that will thrive are those that recognize governance not as a necessary evil, but as a competitive advantage that enables sustainable, scalable AI transformation.

The choice is clear: implement governance proactively as an enabler, or face it reactively as an obstacle. The difference isn't just measured in speed to market—it's measured in billions of dollars of potential value and risk.

‍