7 Mistakes You’re Making with AI Governance (And How to Fix Them)
- David Ruttenberg
- 3 days ago
- 5 min read
Here's the uncomfortable truth about AI governance: most organizations are getting it wrong. They're getting it wrong in ways that cost money. They're getting it wrong in ways that create legal exposure. And they're getting it wrong in ways that undermine the very innovation they're trying to achieve.
The numbers don't lie. Despite massive investments in artificial intelligence, only 5% of companies successfully scale their AI projects (Fountaine et al., 2019). That's a staggering failure rate: and much of it traces back to governance mistakes that are entirely preventable.
The good news? Once you know what these mistakes look like, fixing them becomes surprisingly straightforward. Let's break down the seven most common AI governance errors I see organizations making in 2026: and exactly how to course-correct.
Mistake #1: Nobody Owns the AI
When things go wrong with an AI system: and eventually, something will: who's responsible? If you can't answer that question in five seconds or less, you have a governance problem.
Too many organizations deploy AI tools without clearly defining who owns decisions, risks, and performance outcomes (Mäntymäki et al., 2022). When problems surface, teams point fingers, pass the buck, and watch issues fester.
The Fix: Establish clear ownership from day one. This means naming a specific person or team accountable for each AI system's decisions, performance, and risk management. Document it. Communicate it. Make it impossible to ignore.
Mistake #2: You Haven't Defined Your Risk Appetite
Here's a question that trips up even sophisticated organizations: How much AI risk are you willing to accept?
Without a defined risk appetite, teams operate in a fog. A financial services firm might launch a loan approval algorithm without setting acceptable limits on error rates or demographic bias: then find themselves scrambling when rejection patterns shift unexpectedly (Stahl et al., 2023).
The Fix: Get specific about acceptable levels of error, bias, and automation. Categorize your AI use cases by risk level. Most importantly, connect your AI risk appetite to your broader enterprise risk strategy. AI governance doesn't exist in a vacuum.
Mistake #3: Your Documentation Is a Disaster
Poor documentation is the silent killer of AI compliance. When systems get built quickly: and they always do: records about data sources, model purposes, and known limitations tend to fall by the wayside.
Then the auditors show up. Or the regulators come knocking. And suddenly nobody can explain how your AI actually makes decisions.
The Fix: Document everything that matters: the AI's purpose, its data sources, how information flows through the system, and any known limitations. Keep explanations simple enough that a non-technical stakeholder can understand them. And update your documentation whenever the model or its context changes.
Mistake #4: You're Ignoring Third-Party Risk
There's a persistent myth in AI governance circles that buying an AI solution from a vendor transfers compliance responsibility to that vendor. It doesn't. It never has. It never will.
When a third-party AI system causes harm or violates regulations, your organization still holds the liability (European Commission, 2021). The vendor might face consequences too, but that won't shield you from the fallout.
The Fix: Conduct rigorous vendor risk assessments before adopting any third-party AI. Maintain ongoing oversight of external solutions. Make sure your vendors' governance practices meet your standards: because their failures become your failures.
Mistake #5: You've Forgotten About Data Quality
AI systems are only as good as the data they're trained on. Garbage in, garbage out: it's the oldest rule in computing, and it applies doubly to machine learning.
Yet many organizations fail to regularly review their training data for quality, completeness, and bias. They assume that once a model is trained, the data work is done. It isn't. Data degrades. Populations shift. Bias creeps in through a thousand tiny cracks.
In my own research on sensory sensitivity and neurodivergent populations, I've seen firsthand how training data that excludes certain groups produces systems that fail those groups spectacularly (Ruttenberg, 2025). AI governance must include robust data governance.
The Fix: Implement continuous data quality monitoring. Review your training data regularly for accuracy, completeness, and demographic representation. Don't assume problems get solved at launch: assume they evolve.
Mistake #6: You Treat AI as "Set It and Forget It"
AI systems drift. They change. They degrade. The fraud detection model that worked brilliantly at launch might fail completely when fraud patterns evolve six months later.
Treating AI as a one-time deployment is governance malpractice. The world changes. The world changes fast. And your AI systems need to change with it.
The Fix: Track performance metrics religiously. Review outcomes for unexpected behavior. Schedule periodic model audits and be prepared to retrain or retire systems that no longer perform. AI governance is a continuous process, not a checkbox.
Mistake #7: Your Teams Don't Talk to Each Other
This might be the most damaging mistake of all. When business teams deploy AI without consulting compliance experts: when legal learns about a new system after it's already live: regulatory violations become almost inevitable.
I've watched marketing teams roll out AI personalization tools that violated data protection laws, simply because nobody thought to loop in legal review. The intentions were good. The outcomes were not.
The Fix: Involve legal, risk, and compliance teams early in every AI project. Create cross-functional governance committees with real authority. Define shared accountability that spans business and compliance functions. Make collaboration the default, not the exception.
The Payoff Is Real
Organizations that get AI governance right see measurable results. Companies with strong governance frameworks report 27% higher efficiency gains and 34% higher operating profits from their AI investments (Fountaine et al., 2019).
That's not a rounding error. That's a competitive advantage.
AI governance isn't about slowing innovation down. It's about building the foundation that makes sustainable innovation possible. It's about protecting your organization, protecting your customers, and protecting the promise of AI itself.
Ready to assess your organization's AI governance maturity? Visit davidruttenberg.com to learn more about building human-centered AI systems that work: and that you can trust.
About the Author
Dr David Ruttenberg PhD, FRSA, FIoHE, AFHEA, HSRF is a neuroscientist, autism advocate, Fulbright Specialist Awardee, and Senior Research Fellow dedicated to advancing ethical artificial intelligence, neurodiversity accommodation, and transparent science communication. With a background spanning music production to cutting-edge wearable technology, Dr Ruttenberg combines science and compassion to empower individuals and communities to thrive. Inspired daily by their brilliant autistic daughter and family, Dr Ruttenberg strives to break barriers and foster a more inclusive, understanding world.
References
European Commission. (2021). Proposal for a regulation laying down harmonised rules on artificial intelligence. EUR-Lex.
Fountaine, T., McCarthy, B., & Saleh, T. (2019). Building the AI-powered organization. Harvard Business Review, 97(4), 62-73.
Mäntymäki, M., Minkkinen, M., Birkstedt, T., & Viljanen, M. (2022). Defining organizational AI governance. AI and Ethics, 2(4), 603-609.
Ruttenberg, D. (2025). Towards technologically enhanced mitigation of autistic adults’ sensory sensitivity experiences and attentional, and mental wellbeing disturbances [Doctoral thesis, University College London].
Stahl, B. C., Antoniou, J., Ryan, M., Macnish, K., & Jiya, T. (2023). Organisational responses to the ethical issues of artificial intelligence. AI & Society, 38(1), 249-263.
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