Here’s a question I’ve started asking leadership teams: how many AI tools are your people using right now?
Most can’t answer it. That isn’t criticism, because it’s hard to know. Employees are trying ChatGPT and Copilot on their own. Business units are buying AI-enabled apps. And the vendors you already depend on are quietly adding AI features to platforms you signed contracts for years ago.
AI is already inside most organizations, whether or not anyone approved it, and governance is usually a step or two behind.
Governance shouldn’t be the department of “no”
When people hear “AI governance,” they often picture a committee whose job is to slow things down. That’s the wrong goal. The point is to give your teams clear guardrails so they can move faster without guessing what’s allowed.
In practice, those guardrails should answer a few basic questions:
Which AI uses are approved, and which are off-limits?
What data can go into these tools, and what must never leave the building?
Where does a person need to review the output before it’s used?
How do we vet the AI that vendors are building into their products?
How do we test and monitor these tools once they’re running?
Get those answered and you’ve covered most of the risk. You haven’t built a bureaucracy along the way.
You don’t have to start from a blank page.
Established frameworks and standards do most of the heavy lifting here.
Leading AI risk management frameworks follow a simple logic: decide who’s accountable, understand where AI is being used and what could go wrong, measure those risks, and then do something about them.
International standards for AI management systems add structure on top of that. You may never pursue certification, and many organizations don’t need to. But they’re a useful blueprint for the policies, roles, and processes a mature program needs
I use both as reference points and fit them to each organization. A 200-person company doesn’t need the same program as a global bank.
How I help
Through TechExec Consulting Services, I work with executives and boards to assess where their AI governance stands today and build a realistic plan to strengthen it. That typically includes:
- A readiness assessment and an inventory of where AI is being used
- Classifying use cases by risk
- Alignment with NIST AI RMF and ISO/IEC 42001
- Acceptable-use policies, guardrails, and clear ownership
- Third-party AI risk review
- Reporting that gives the board real visibility
- A prioritized roadmap, so you know what to tackle first
I’m not interested in handing over another policy binder that sits on a shelf. My aim is for leadership to be able to answer five questions with confidence:
- What AI are we using?
- What risks does it create?
- What guardrails are in place?
- Who’s accountable?
- Could we prove it if a regulator, auditor, or customer asked?
You won’t eliminate every AI risk, and nobody can promise you that. What you can do is understand your risks, put sensible guardrails in place, and make sure someone owns each one.
If your organization is exploring or adopting AI, I can help you identify the right opportunities, develop an implementation strategy, and put the appropriate governance and guardrails in place. If you’d like an independent look at where you stand today and a practical path forward, send me a message. I’m happy to talk it through.
