Governance Enables Speed

The fastest AI programs I've seen all have one thing in common: serious governance. That sounds counterintuitive. Most people hear "governance" and picture a chokepoint — layers of approval, months of review, innovation dying in a committee. In practice, the opposite is true. Good governance is what lets leadership say yes faster.

When I built the AI function at a publicly traded investment bank, governance wasn't a phase two add-on. It was foundational. And it's a significant part of why we were able to move as quickly as we did — reaching 98% voluntary adoption across 900 professionals — in an industry where most firms were still stuck in pilot programs and risk assessments.

Here's the core insight: in a regulated environment, the absence of governance doesn't create freedom. It creates paralysis. When there are no clear rules about what's allowed, every individual decision becomes a risk decision. People default to the safest option, which is usually doing nothing. Senior leaders won't champion a tool they might be held accountable for misusing. Compliance teams block what they can't evaluate. The whole system stalls — not because anyone said no, but because nobody could say yes with confidence.

Smart guardrails fix this. When people know exactly what they can do, what requires review, and what's off-limits, they operate within the safe zone with full speed. They stop second-guessing. They stop emailing compliance with hypothetical questions. They just work. The governance framework becomes an accelerant because it removes uncertainty from every individual interaction.

The critical design decision in our governance approach was building it with legal and compliance, not around them. This distinction matters enormously. Many AI programs treat legal and compliance as obstacles to manage — get the tool approved, then keep those teams at arm's length so they don't slow things down. That approach fails for two reasons. First, it builds an adversarial relationship that will surface at the worst possible moment. Second, it means your governance framework doesn't actually reflect the real constraints of your industry, so it eventually breaks.

I brought compliance and legal into the design process from the beginning. Not to rubber-stamp what I'd already decided, but to genuinely co-design the framework. What data classifications apply to AI inputs? What output verification standards do we need? What documentation requirements exist for AI-assisted work product in a regulated context? These aren't questions you want an innovation team answering alone. They're questions that require the people who understand the regulatory landscape.

The result was a framework that compliance owned as much as I did. When a new use case came up, I didn't need to run it through a multi-week approval process. The framework already addressed it, or compliance could evaluate it quickly because they understood the system. Their co-ownership meant they were invested in the program's success, not positioned as its gatekeepers.

One specific governance element that paid outsized dividends: mandatory source-citation and verification standards. Every AI-generated output used in professional work product needed to be verified against source materials, and the sources needed to be cited. This sounds like overhead. It was actually liberating. Once professionals knew they were expected to verify — and had a clear process for doing so — they were comfortable using AI for much more ambitious tasks. The verification standard gave them confidence that they could catch errors before those errors reached a client or a regulator.

This also built trust with leadership. When senior management asked "how do we know AI outputs are reliable," I didn't have to make abstract arguments about model capability. I could point to a concrete verification process with documentation. That trust unlocked budget, executive sponsorship, and broader deployment. Trust compounds the same way fluency does — each positive interaction builds on the last.

The governance framework also made it possible to move fast when the model landscape shifted. When new capabilities emerged, when providers updated their terms, when regulatory guidance evolved — we had a structure for evaluating changes quickly rather than starting from scratch each time. The framework was designed to be a living system, not a static document. We could update a policy in days because the decision-making structure was already in place.

I've watched firms without governance take six months to approve a single use case. I've watched firms with good governance approve a new use case in a week — not because they were less careful, but because they'd already built the evaluation criteria, the decision rights, and the stakeholder alignment. The careful work was done upfront. Each subsequent decision could be fast because the framework was solid.

If you're building an enterprise AI program in any regulated industry — financial services, healthcare, legal — and you're treating governance as a brake to be applied later, you're making the same mistake that stalls most programs. Build the governance first. Build it with the people who understand your regulatory obligations. Make it clear, specific, and owned by the right stakeholders. Then watch how much faster everything else moves.