I work with CEOs and CTOs to figure out where AI is genuinely worth the effort, what needs governance around it, and how to get the first real workflow into production. No agency layer. No rotating team. Just me, close to the decisions, until it works.
What to build first. What to leave alone. Which vendor or architecture call saves six months instead of burning them.
The person in the room is the same person doing the follow-through. Not a pitch team that vanishes after kickoff.
The goal is not an AI strategy deck. The goal is one useful system, live, that holds up when real people use it.
Patience for vague AI strategy is running out. Companies are starting to hire people who can pick the right bets, handle the governance, and get a single workflow into production without burning months on dead ends.
McKinsey reports 88% of organisations now use AI in at least one business function. But most are still experimenting. The jump from pilot to production is where nearly everyone stalls.
McKinsey State of AI 2025Deloitte finds scaling and value creation remain the central challenge. The demand has shifted toward leaders who pick the right workflows and actually get them running.
Deloitte State of Generative AI in the Enterprise Q4PwC finds that mature AI programs are embedding responsible AI into testing, observability, and monitoring. It is becoming operational, not aspirational.
PwC Responsible AI Survey 2025The European Commission says AI Act transparency obligations for certain AI systems kick in August 2, 2026. That deadline is closer than most teams think.
European Commission AI Act guidanceIt almost always starts small. Get clear on the first workflow worth doing, stay accountable to real delivery, and only widen scope once the hard part is under control.
A focused engagement to figure out where AI is genuinely worth using, what to leave alone, and what has to be true before anything touches production.
I embed with the leadership team and own the first phase of AI decision-making: priorities, architecture, governance, vendor calls, and the weekly rhythm that keeps things moving.
Hands-on work to move the first workflow that actually matters into production, with the controls and documentation your internal team needs to keep it running without me.
NPLabs is here so you can see the actual shape of the work: regulated workflow complexity, AI inside operations, and the kind of production pressure where things either hold up or they do not.
These are not trend pieces. They show how I think about real regulated environments: where governance creates drag, where validation work gets underestimated, and why most AI effort gets lost in the layer around the model, not the model itself.
Budgets are up. Models are better. But adoption keeps stalling on the same thing: nobody sorted the data governance layer underneath.
The prototype is cheap. The change control, documentation, and review cycles that follow are where months disappear.
A plain-English page for people trying to work out whether they need someone like me, a full-time hire, or an agency.
Why governance is becoming part of delivery, not just a policy document sitting in a folder.
Straight answers on fit, cost model, timelines, and how a founder-led engagement works.
The first call is simple: what is stuck, what has to be true before anything ships safely, and whether this model fits your stage.