Insights
Essays on why enterprise AI stalls, what installing rather than training means, and how leaders reason with AI. Written by Dan Gildoni.
What is an AI knowledge base, and why can it find the fact but miss the decision?
How an AI knowledge base works, what to look for, and the test that shows whether the right fact changes the answer.
Read the guide →Retrieval succeeded. Decision use failed.
A 2026 study found that the model could retrieve the right risk disclosure while its judgement stopped moving. This page separates the finding from its limits, then hands leaders the test.
Read the evidence →We Rolled Out Copilot and Nothing Changed. What Do We Do Now?
The licences went out, the work stayed the same. Why enterprise Copilot rollouts stall, in the vendor's own numbers, and the two moves that change the result.
Read essay →We Have Too Many AI Tools and No One Uses Them Properly. Where Do We Start?
Start from the bottleneck, not the inventory. Pick the one problem that costs a team most, solve it properly, and let the result decide the next tool.
Read essay →How Do I Know Whether AI Adoption Is Actually Working?
Licence counts and training completions measure spend, not adoption. The honest signals: voluntary return, less generic output, and decisions that changed.
Read essay →How Do I Get AI Adoption to Work After Previous Transformations Failed?
Change fatigue is manufactured by prior transformations, and the scepticism is rational. Do not announce a programme. Start with one leader, one owned problem.
Read essay →How a Leader Should Use AI Before a Consequential Decision
Before a consequential decision, use AI to pressure-test the thinking, not to produce the answer: ground it in your own data, then make it argue against you.
Read essay →Why AI Training Doesn't Stick in Large Organisations
Everyone has the tool and the training, and behaviour barely moves. The training teaches the tool instead of the reasoning habit, runs as a one-off event, and walks into the Zero-Barrier Trap. What installs a habit that holds.
Read essay →How CEOs Use AI for Decision Making
How senior leaders actually use AI in decisions, and the move that separates good use from bad: AI as a structured challenger that sharpens judgement, not a vending machine that erodes it.
Read essay →Name the Problem Before You Build the Answer
AI keeps handing back generic strategy because the real problem has not been named. Turn the Flip on your own goal, and the machine stops dispensing answers and starts thinking with you.
Read essay →What Academic Integrity Means Now
The old definition has quietly broken. Integrity is not catching cheats with a detector; it is the guarantee that the learning is real. The stronger, defensible version.
Read essay →Does AI Weaken Thinking? What the Evidence Shows
The calm, evidenced answer to "is AI making us stupid?". Used to avoid thinking, it does; used to provoke thinking, it does the opposite. The fix is built in.
Read essay →It Feels Like Cheating. It Isn't.
Why so many capable people feel like frauds for using AI at work, and the one line that tells you whether they should. The work-world companion to "Is Using AI Cheating?".
Read essay →Is Using AI Cheating? The Wrong Question
Is using ChatGPT to write an essay cheating? It is the wrong question. The real one is whether you did the thinking, and the only person you can cheat is you.
Read essay →Havruta: The Oldest Form of Rigour, Rebuilt for AI
Universities are fighting AI the wrong way. The fix is not detection, it is making the machine ask first. A practitioner's guide to using AI in higher education without killing thinking, grounded in the evidence.
Read essay →The Havruta Principle
Havruta is the oldest discipline of paired reasoning we have: two minds, one question, an argument that ends in a sharper version of both sides. The story behind the name, and why the oldest study method is the right way to think with AI.
Read essay →Why 95% of Enterprise AI Investments Deliver Zero Return
The cause of enterprise AI failure is not data, skills, or tools. It is how leaders think with AI, and the Havruta Methodology that closes the gap.
Read essay →You're Not as AI-Ready as You Think. That's the Good News.
Most executives believe they are AI-ready. Very few have rebuilt a single decision process. That gap is the real state of enterprise AI, and the most fixable gap there is.
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