Too much noise
Every week brings a new model, a new warning and a new promise. Hype on one side, fear on the other, and very little a board can act on.
James A Lang
Where is AI failing right now?
Leaders are responsible for AI decisions they did not build. AIBlindspot shows them the evidence: real AI failures, sorted into the nine areas a board has to manage, with the question to ask about each.
The problem
The news says utopia or dystopia. Your board needs evidence.
Too little evidence
Most AI risk is described, not measured. Without real cases, the hard questions never get asked until something has already gone wrong.
Accountable anyway
The board signs off the investment and answers for the outcome, whoever built the system and however it was sold.
Every meeting, the same question.
Leaders ask me how to stay up to date with AI threats and innovation when the conversation is split between promise and panic. AIBlindspot is my answer. It gathers real AI incidents every day, classifies each one against 55 known blindspots in nine risk areas, and ranks the areas in a live Risk Index, so a board can see what is going wrong, where, and how fast.
It draws on 22 years in the British Army, leading AI and cyber programmes in the Ministry of Defence, and Oxford’s Artificial Intelligence Audit Programme.
The plan
The nine areas boards must manage.
One clear risk and one question for each. Open any area to see the real incidents behind it.
- 1 Business and strategy AI that costs money without serving a clear business goal, or whose value nobody measures.Ask: What will this AI give us in money, time or learning, and how will we know?
- 2 Operations AI that goes wrong in day-to-day running without anyone noticing or knowing what to do.Ask: If an AI system starts getting things wrong, how soon would we know?
- 3 People People who are not ready, trained or confident enough to work well alongside AI.Ask: Do our people have the skills and the trust to use AI well?
- 4 Governance and compliance AI with no clear owner, no proper oversight, or rules and laws that get missed.Ask: Who is accountable when one of our AI systems makes a mistake?
- 5 Technology Systems around the AI that are not built to run it safely, reliably and for the long term.Ask: How do we test and approve a change to an AI model before it goes live?
- 6 Data Data and models behind the AI that are poor, biased, untraceable or not protected.Ask: Is our data good enough, and fair enough, for the decisions we ask AI to make?
- 7 Security and privacy AI that can be attacked, tricked, copied or made to give away what it knows.Ask: How have we tested our AI against people trying to trick or break it?
- 8 The world outside Pressure from markets, partners, regulators and the planet that pushes AI decisions off course.Ask: Are we choosing AI for sound reasons, or because our competitors are?
- 9 Agentic AI Emerging AI that acts on its own, writes its own answers or works with other AI, with too little oversight.Ask: Which of our AI systems can act on their own, and who set their limits?
What is at stake
Clarity. Control. Confidence.
Without the evidence
Decisions follow the loudest headline. Blindspots stay hidden until a customer, a regulator or a journalist finds them. AI money goes on promises that nobody tests.
With AIBlindspot
The board sees where AI is failing in its sector, asks the right question in each area, and protects its investment in AI before the problem arrives.
