As a leader, if you hear ‘AI governance’ in the boardroom, you’ll tend to fall into one of two camps; you’ll either switch off immediately or start nervously perspiring. And I get it, it’s easy to dismiss governance and put it on the backburner.
But governance should not be dismissed as “fluff” or worse, forgotten altogether. Done well, it is the oil in the engine of any well-run business helping leaders understand risk, experiment with confidence, and protect brand integrity while scaling AI commercially.
For U.S. growth companies, governance is becoming the conversation they need to have before their AI adoption gets ahead of them. In the U.S., this is already becoming a board-level issue, with the NIST AI Risk Management Framework giving organizations a practical route into governance and helping them understand how to make accountability clear.
Regulators, investors and customers are all asking difficult questions around how AI is being used and who is accountable for when it goes wrong. In my experience, the businesses getting governance right aren’t focused on achieving perfection from day one.
Instead, they’re prioritizing a pragmatic, scalable approach by:
• Understanding the risks
• Creating the space for experimentation
• Maintaining close control of data, brand and direction
• Building shared capability and ownership
Know your risk before AI outruns you
Every business needs to understand their own level of risk, and the biggest risks for most revolve around data and accuracy and bias within the models. If you don’t have a handle on these, you are opening yourself up to a boatload of challenges.
To mitigate this, the first step is to establish a level of understanding and visibility. Teams need to know the tools they’re using and the implications of how they operate. This means favoring enterprise-grade tools over free alternatives, where data protection and contractual clarity are stronger.
Another way is to think of AI-powered tools as wrappers around LLM providers. You can’t treat an AI tool as a black box, so good governance requires visibility of what sits underneath. Predictable, consistent outputs come from an understanding that the same model can behave differently depending on how it’s used and by whom.
For investors and growth-stage companies, the risk is overstating what AI is doing. The SEC has already brought “AI washing” actions against firms for allegedly misleading claims about their use of AI, showing that governance now needs to cover marketing, investor communications and product claims too.
Experiment boldly, but inside clear guardrails
Across all industries, AI is subject to constant evolution and change. Not one organization can confidently predict what their operating model will look like in five years. So governance can’t be overly restrictive.
In my own experience, the approach has been ‘controlled experimentation’, with teams encouraged to explore and test capabilities. Most initial outputs are imperfect with duplication, dead ends and partial solutions. Much of this experimentation will likely be discarded, but it’s what we learn in the process that’s valuable.
We’ve taken this approach because there’s currently a gap in the market. Right now, many tools work well for individuals but lack enterprise-level controls and governance features. This will undoubtedly improve, but in the meantime, we still need a layer of internal policy and training to reduce risk without stifling experimentation.
Don’t let AI flatten what makes your brand distinctive
One of the biggest considerations isn’t AI misuse, it’s a dilution of the things that make your business unique. As AI content continues to scale, 94% of businesses are now reporting that ‘preserving brand integrity’ is a primary concern. By default, AI-generated content tends to be lacking personality with ‘average’ answers and a lack of lived-in experience.
Teams can mitigate this by:
• Providing AI tools structured brand guidance and instructions to work within your boundaries
• Embedding tone of voice rules into prompts and workflows and ensuring everyone has access to the right information to deliver consistently
• Using validation tools to assess output quality and ensure standards are met
Keep humans in the loop, or lose the judgment that matters
A lot of what I’ve said is tech focused. But as we keep saying, technology alone isn’t the answer. Despite the doomsdayers saying otherwise, the people in AI adoption remain essential.
No content or outputs should be published without a human review, and this responsibility needs to sit with the individual approving it. AI is a tool, not an author. The work ultimately belongs to the person who signs it off, just as the artist, not the paintbrush, has ownership over a finished portrait.
Build shared AI muscle, not isolated pockets of progress
Over the next 12 to 24 months, success will ultimately come down to how businesses manage tools and capability. I anticipate the biggest challenge being tool sprawl. Often, individuals choose their own tools, which restrict the ability to train, scale and share capabilities efficiently.
Organizations that standardize, while still allowing controlled flexibility, will outperform those that do not. True, this doesn’t lend itself to immediate results, but over time, shared capability, consistent training, and aligned usage will create a stronger foundation.
Today’s pricing is unlikely to hold, and as costs increase, deeply integrated tools could become a commercial risk. The answer is to standardize where it builds shared capability, while keeping enough flexibility to protect resilience, innovation and choice.
Treat governance as your competitive advantage
In the current AI gold rush, it’s tempting to ‘move fast and break things,’ but that only works until it doesn’t. In the U.S., recent action against AI firms such as DoNotPay, Pieces Technologies and state-level moves such as Colorado’s high-risk AI law show how quickly poor validation or high-risk automated decision-making can become a legal, reputational issue.
Good AI governance is about creating the conditions for AI to scale safely, consistently and commercially. It keeps you moving in the right direction and enabling safety without restricting innovation. In the next phase of AI adoption, the winners will be the ones that can prove they are moving fast yet safely and with the governance to scale effectively.
