Writing

Before I let a team build an AI feature, I make them build the test set that proves it can work. A small, honest eval answers the only question that matters in week one — is this possible at all?

Prompt injection isn't a bug engineering patches after the fact. It's a product decision about what your AI is allowed to do with untrusted text. Here's how I scope it.

Not every AI mistake costs the same. Treating all errors as equal is why good models ship into bad features. Here's how to price a wrong answer and design around it.

Speed is not an engineering detail you tune after launch. How long an AI feature makes a user wait shapes whether they trust it, use it, and come back. Here's how to budget latency like the product decision it is.

Automating a broken process doesn't fix it. It makes the breakage faster, cheaper to run, and much harder to see. That's automation debt — and it compounds.

A working AI demo proves the model can do the task once. A product has to do it for everyone, every time, on their worst input. This is the gap between the two — and how to close it.

Eight separate trust properties don't add up to a trustworthy product on their own. This is how the layers stack — calibration, legibility, confidence, correction, failure design, privacy, measurement, scale — into one coherent system.

Trust you earned at ten users doesn't survive automatically at ten thousand actions a day. Scale changes the failure modes — here's what has to be engineered to keep trust intact.

Trust feels unmeasurable, so most teams track satisfaction and call it done. The real signal lives in what users do — accept, override, disengage — not what they say.

Reasoning, confidence, correction, and failure design all assume the user feels safe enough to engage honestly. Privacy is the layer underneath that makes it possible — a product primitive, not a compliance afterthought.

Every AI feature will be wrong sometimes. Trust doesn't come from pretending otherwise — it comes from designing the failure so the wrong answer costs the user almost nothing.

Legibility lets users see the AI's reasoning and confidence tells them when to look harder — but neither matters if they can't act on what they see. This is how override, undo, and correction turn doubt into agency.

An AI that sounds equally sure of everything teaches users to trust it evenly — which is exactly wrong. Here's how to surface uncertainty so people lean in when the system is solid and check when it isn't.

Calibrated trust needs something to calibrate against. Legibility — showing enough of how the AI reached an answer that a user can judge it — is how you give them that. Here's how to build it without dressing up a guess as an explanation.

Trust in an AI product isn't a feeling you maximize. It's calibration — the match between how much a user relies on the system and how reliable it actually is. Here's the distinction and why it decides everything downstream.

Most teams add human review to every AI action or none. Both fail. Where the human sits is one of the highest-leverage product decisions you'll make.

Putting a logo on a PDF is easy. Building reporting that's automatic, branded, accurate across dozens of clients, and honest about missing data is not.

Most marketing tools treat an agency as a customer with many accounts. Agency operations need one system running many clients. The difference determines whether you can grow without hiring.

Smaller companies have a real structural advantage in AI adoption. Here's the sequence that works when you can't fund a six-month transformation programme.

The cost of a manual process isn't the hours it consumes. It's the delay it introduces, the decisions it defers, and the work nobody attempts because the process makes it impossible.

The argument isn't that product leaders should write production code. It's that losing the ability to estimate cost yourself makes you dependent on other people's estimates.

A day-by-day process for getting from an AI product idea to a working prototype in a week — and why the goal is a decision, not a demo.

The twelve questions that come up in every enterprise AI security review, and the answers that move a deal forward.

A working checklist for AI products handling sensitive data — the questions to answer before you write the first line, not after legal review.

If removing the AI from your product would leave nothing behind, you don't have an AI strategy. You have a demo with a business model attached.

Most teams pick their AI approach based on what sounds impressive. Here's the framework that actually decides it — and why fine-tuning is usually the wrong first answer.

A six-step framework for taking an AI feature from idea to production without damaging user trust — the process I use as CPO.

Most AI features fail not because the model is wrong, but because users can't tell when it's wrong. Five failure patterns and the fixes.

Human-AI UX is the practice of designing AI interactions so the system's reasoning, limits, and confidence are legible to the person using them. Here's the framework I use.