Your AI steering committee needs power, not just opinions

You built an AI steering committee. Six months later, nothing’s shipped.

I see this pattern constantly. Companies create committees filled with smart people who meet monthly, discuss fascinating possibilities, and produce exactly zero decisions. The committee becomes where AI initiatives go to die in polite conversation.

The problem isn’t who’s on the committee. It’s what the committee can actually do.

What steering actually means

There’s research from the Business Development Council showing companies with advisory committees saw 24% revenue improvement. Sounds great until you read the fine print: these advisory committees had actual decision-making power over specific domains.

Most AI steering committees get built as advisory bodies. They discuss. They recommend. They provide input. Then someone else decides. Usually someone who wasn’t in the room and doesn’t understand the context.

McKinsey’s research on AI governance found that many organizations report their CEO is responsible for overseeing AI governance, while others say it’s the board of directors, and some split ownership across an average of two leaders. This fragmentation of authority creates the exact problem you’re trying to solve.

A steering committee without budget control, hiring authority, and project veto power is just a very expensive focus group.

Steering means controlling direction. Not suggesting it. Controlling it.

Info-Tech Research Group’s framework defines an effective AI governance structure as one where the committee has clear mandates, roles, responsibilities, and actual decision-making authority over the AI lifecycle.

For a mid-size company, that breaks down to four specific powers:

Budget allocation. The committee controls the AI budget directly. Not recommends. Controls. If they approve spending on a RAG implementation, finance cuts the check. No secondary approval needed.

Project decisions. The committee can kill projects. Not just suggest killing them. Kill them. They can also greenlight pilots under a specific threshold without asking permission from anyone else.

Vendor and tool selection. When the committee picks a platform or vendor, that’s the decision. Final. Done.

Resource assignment. If the committee says pull three engineers from Feature Team A to work on the AI initiative, those engineers move. Tomorrow.

Without these four powers, you have a book club for AI enthusiasts.

The size trap

Research on optimal committee size is clear: committees of 5-9 members make better decisions than larger groups. Some studies push that down to 3-5 for decision speed.

Why? Two reasons. First, communication complexity explodes with size. A 5-person committee has 10 communication paths. A 9-person committee has 36. Second, research shows that small teams make decisions faster and with lower resource costs.

But mid-size companies panic. They want representation. Engineering wants a seat. Product wants a seat. Operations, finance, security, compliance - everyone wants in. You end up with 12 people who can’t agree on where to order lunch, much less whether to bet the company on AI transformation.

Here’s what actually works for a 50-500 employee company:

Chair: CEO or COO. Non-negotiable. Authority flows from the top. If your CEO or COO won’t chair this, you’re already announcing that AI isn’t actually a priority.

Operations leader. Someone who understands current workflows and can spot where AI creates actual value versus theoretical value. This person’s job is to kill ideas that sound clever but don’t map to real operational problems.

Finance representative with budget authority. Not a finance analyst who has to check with the CFO. Someone who can approve spending up to your committee threshold on the spot.

Technical person who evaluates feasibility. CTO if you have one. Otherwise, your most senior technical lead who understands what’s possible versus what’s vendor fantasy. This person saves you from committing to six-month projects that physics won’t allow.

Subject matter expert (rotating). For each major initiative, bring in the person who knows that domain cold. Replacing customer service? The head of customer service sits in. This seat rotates based on what you’re building.

Five people maximum. No exceptions. If you think you need more, you’re confusing representation with decision-making.

Operating rhythm that doesn’t waste time

Monthly strategy sessions are where ambition goes to become PowerPoint. Gartner’s toolkit on AI governance emphasizes that effective committees balance strategic oversight with operational agility.

The rhythm that works:

Weekly 30-minute decision meetings. Tuesdays at 9 AM. Same time every week. No slides. Someone brings three decisions that need making. Committee makes them. Meeting ends.

Fast-track approval for small pilots. Anything under a defined threshold - say, equivalent to a month of a single engineer’s time - the technical member can approve alone between meetings. They report it the following week. This prevents the committee from becoming a bottleneck.

Quarterly strategy reviews. Four times a year, you zoom out for 90 minutes. Review what shipped, what failed, what you learned. Adjust the roadmap. These are the only meetings where PowerPoint is allowed.

Metrics review every month. Ten minutes of the weekly meeting. Someone shows the numbers. Time-to-deployment for approved projects. Success rate of pilots. Adoption metrics for what shipped. No discussion unless something’s broken.

This rhythm prevents the classic committee failure mode: meetings that feel important but produce nothing. Research on AI governance implementation shows typical timelines of 12-24 months. You can’t afford to spend that time talking.

Authority boundaries and escalation

Clear boundaries prevent chaos. Before your first committee meeting, write down exactly what the committee controls versus what goes to full leadership.

Committee decides without escalation:

• Pilot projects under your budget threshold
• Tool and vendor selection for approved initiatives
• Resource allocation within the AI budget
• Project cancellation for initiatives that aren’t working
• Timeline adjustments for active projects

Committee recommends, leadership decides:

• AI strategy and multi-year roadmap
• Budget allocation above the committee threshold
• Changes to company-wide AI policies
• Decisions that affect more than one major department
• Anything requiring board approval

Automatic escalation triggers:

• Security issues that affect customer data
• Regulatory compliance questions
• Projects that would affect revenue by more than a defined percentage
• Anything that requires changing employment terms

Write these down. Share them with the whole company. When someone wants to escalate something that’s in the committee’s domain, you point to the document and say no.

This prevents the passive-aggressive escalation game where people route around the committee whenever they don’t like a decision.

Success metrics and evolution

IBM’s research on AI governance metrics identifies several critical indicators, but three matter most for mid-size companies:

Decision speed. Track time from “committee receives question” to “decision made.” Target: same meeting for straightforward choices, one week maximum for complex ones. If you’re averaging more than two weeks, your committee is too big or lacks authority.

Implementation rate. What percentage of approved pilots actually ship? Track this monthly. If fewer than 70% ship, either your technical feasibility check is broken or you’re approving things without proper vetting. If more than 95% ship, you’re probably being too conservative.

Project ROI. For completed initiatives, measure actual impact against projected impact. Don’t just track the successes. Track everything. Failed pilots teach you what doesn’t work, which is valuable. But if your hit rate is below 40%, something’s wrong with how you evaluate opportunities.

One meta-metric matters more than the others: is the committee accelerating AI adoption or slowing it down? Ask people outside the committee. If teams are routing around the committee or delaying proposals because they dread the process, you’ve built the wrong thing.

Your first steering committee won’t be your last. As your organization matures with AI, the committee’s role shifts.

Early stage - you’re approving lots of small pilots and learning what works. Committee meets weekly, sometimes more. The focus is speed and learning, not perfection.

Growing stage - you’ve got patterns that work. Committee starts setting standards rather than approving every project. Teams can self-approve anything that fits the established patterns. Committee only reviews novel approaches.

Mature stage - AI is integrated into normal operations. Committee shrinks or disbands. The powers that used to be centralized in the committee distribute to functional leaders who own their domains.

McKinsey’s transformation research shows this evolution takes 18-36 months for most mid-size organizations. Plan for it. Don’t build a permanent bureaucracy.

The goal isn’t to have a steering committee forever. The goal is to accelerate through the phase where you need one.

Most companies build committees that discuss AI. Build one that controls it. Give it real power or don’t bother building it at all.