AI Governance & Risk Management

Weekend Reflections #5 | The Speed of Doubt: When AI Outruns Trust

[Views are my own] Last week I ran a competitive analysis that would normally have taken me half a day. With AI, the first version took ten minutes. That was not the strange part. The strange part was that it was good. I did not discard it. I did not

Human-AI Collaboration

Weekend Reflections #4 | The Molting

For centuries, automation stripped roles of their execution layer. The roles that survived were the ones with judgment underneath.

Human-AI Collaboration

Weekend Reflections #3 | The Fluency Tax

[Views are my own] This week, I noticed something in my own habits. As an Italian living in Berlin and working mostly in English, I now default to English with AI even when it is not the language that feels most natural to me. That felt worth paying attention to.

Human-AI Collaboration

Weekend Reflections #2 | The Mirror Test

I tested whether I could catch myself anthropomorphizing AI. Forty-five minutes later, I had shared more than intended. The trap is not ignorance; it is fluency.

AI Governance & Risk Management

The Semantic Supply Chain: Capability Contract & Inputs

Part 1 of 4: Capability Contract & Inputs [Views are my own. Not legal or compliance advice.] When you last prompted a GenAI application, did it feel like persuading a person or programming a machine? I still catch myself doing it. I start talking to it like a colleague. The

AI Governance & Risk Management

The Invisible Skeleton: Why Governance Is the Price of Freedom

[Views are my own] I have a confession to make. I write a lot about Governance, Systems, and Process. And almost every time I do, the reaction is the same: people assume I am the enemy of fun. At every time someone suggests introducing a "process" or following

AI Governance & Risk Management

Instruction Governance: The Missing Layer of Enterprise AI (Part 4 of 4)

[Views are my own] Part 4 – Implementation and The Business Case In this series, we’ve covered the "Triad" (Part 1), the "Golden Set" (Part 2), and the "Federated Framework" (Part 3). Now, we answer the hardest question: How do we implement this without

AI Governance & Risk Management

Instruction Governance: The Missing Layer of Enterprise AI (Part 3 of 4)

[Views are my own] Part 3 – The Three Pillars of AI Governance Most enterprises are now past the “one AI pilot” phase. You have dozens of teams, dozens of models, and far more prompts than anyone can count. Each team has its own agent, its own prompt file, its own

AI Governance & Risk Management

Instruction Governance: The Missing Layer of Enterprise AI (Part 2 of 4)

[Views are my own] Part 2 – Why We Must Evaluate Our Instructions Before We Evaluate AI In Part 1 of this series, we diagnosed the core problem with AI evals: we often evaluate the model's answers before validating our own instructions. We also introduced Step 1: The Governance

AI Governance & Risk Management

Instruction Governance: The Missing Layer of Enterprise AI (Part 1 of 4)

[Views are my own] PART 1 - Stop Blaming the Model – Start Evaluating Your Instructions The Debate: Engineering Rigor vs. Enterprise Reality This article was born from a recent discussion with peers, fellow VPs and Product leaders, on how to best approach AI evaluation. The prevailing view was that we should

AI Governance & Risk Management

The Judgment Economy (Part 3/4): Credibility vs. Plausibility

This is the central tension for every enterprise. Generic AI is built for plausibility (it sounds correct). Enterprise AI must be built for credibility (it is correct, auditable, and grounded in your business data). This requires a new, essential human function: Level 3: The Trust Broker.

Data-Driven Decision Making

The Judgment Economy (Part 1/4): Signal vs. Noise

We are drowning in information. Generative AI accelerates this, creating a flood of "Polished Emptiness" – plausible-sounding content with no substance. As AI commoditizes generation, the last true scarcity is trust. Value is shifting from creation to curation and judgment.

AI Governance & Risk Management

The Parrot and the Library: Why AI Won't Kill Search (It Will Crown It)

AI will not kill search; it crowns whoever controls a fresh index. Synthesis engines give fast answers but depend on live, trusted crawling for freshness, coverage, and provenance. The future is hybrid: shelves and summaries, with winners owning both discovery and explanation. Clicks fund freshness.

AI Governance & Risk Management

Control, Delegate, or Disappear: Thriving in the Age of AI Agents

Agents aren't UX upgrades. They're decision-makers. Mistaking automation for intelligence is a strategic failure. The shift is from operating tools to governing outcomes. Delegate objectives, embed escalation and governance, or your product becomes invisible in an agent-led economy. Govern it. Now

AI Governance & Risk Management

AI and the Speed Trap: Why Reskilling Alone Won't Save You

AI won't erase jobs; speed will. The stability-to-obsolete window is collapsing. Stop chasing perishable skills like prompt engineering. Invest in durable capabilities: problem framing, systems thinking, learning velocity, and influence to thrive through constant role shifts.

AI Governance & Risk Management

AI Doesn't Hallucinate. It Makes Mistakes.

Calling AI errors “hallucinations” humanizes machines and inflates expectations. Language is the UI for trust; misuse becomes a shipped bug with churn, support cost, and legal risk. Treat wording like code: define terms, show process, and label errors precisely.

AI Governance & Risk Management

When AI Turns Your Product System Into a Self-Fulfilling Prophecy

AI now shapes product strategy, not just predicts it. Left unquestioned, it becomes a prophecy engine: reinforcing biases, narrowing options, and derailing learning. Treat AI as input, test counterfactuals, review second-order effects, and keep humans in the reasoning loop. Question it. Validate it.

AI Governance & Risk Management

When Words Lie: The Invisible Risk in Your Product

Words shape trust. Product language isn’t neutral: it trains mental models. “Friend” now means barely acquainted; “I’m thinking...” makes users over-trust AI. Audit copy, cut unearned metaphors, and test for over-trust. Tech runs on tokens; products run on words. Clarity is a trust contract.

Data-Driven Decision Making

Stop Doing Discovery Karaoke

Match discovery to risk, not habit. Most teams default to interviews, tests, and betas, which miss blind spots. Treat Compliance & Ethics as a first-class risk. Build a simple map: risk → stage → methods. Use AI to speed clustering, summarization, and signal detection. Focus on uncertainty reduction