Your teams are excited about AI. Almost nothing has shipped.
Most organizations now have enthusiasm, a list of ideas, and a pilot or two that never made it into daily use. The gap is rarely strategy. It is finding someone senior enough to pick the right problems and technical enough to build the answer.
How it works
Discover
One to two weeks mapping how the work actually runs, and where AI pays for itself.
Build
The top use cases built into production tools you can rely on, in weeks rather than months.
Embed
Ongoing training, governance and measurement, so the tools are still used a year later.
Why AI programs stall
Enthusiasm without artifacts.
Workshops generate energy and a backlog of ideas. Weeks later nothing is running in production.
Ideas that don't survive contact with reality.
Promising use cases collapse once they meet real data, real workflows and real compliance constraints.
No capacity to build.
Internal teams are already committed. External partners front-load months of discovery and documentation before anything works, so momentum dies before value arrives.
Pilots that never scale.
A demo that impresses in a meeting falls over under actual operational load, and trust is hard to win back.
How it works
Discover
1 to 2 weeks
I sit with the teams doing the work: finance, operations, sales, marketing, whoever is in scope. We map the actual workflows, not the org chart, and identify where AI produces measurable return. You get a prioritized shortlist with an honest view of effort, payback and risk, including what to leave alone.
Build
working software in weeks, not months
I build the top use cases into working systems. Not prototypes, production tools with the guardrails, approvals and audit trails that let a business actually rely on them. Simple automations land in weeks. Substantial systems take longer and should. What stays constant is that you see working software early and keep seeing it, rather than waiting months for a big reveal.
Embed
ongoing
Adoption is where AI initiatives quietly die. I train the teams, document the workflows, and put in the governance and version control the situation actually warrants: enough to hold under scrutiny, never so much that people route around it. Then we measure real usage against the outcomes we agreed and take the next set of use cases.
What makes this different
One person, strategy through delivery. No handoff between the consultant who scoped it and the team that builds it, which is where most of the value leaks out.
Executive judgment, not just execution. 25 years leading product, including two acquisitions and platforms for Apple, Red Bull, Disney, BBC and ESPN. I know which problems are worth solving.
Built for regulated environments. GDPR, CCPA, HIPAA and ISO 27001 are constraints I design around routinely, not obstacles I discover late.
Right-sized process, never more. I've run programme governance across 49 countries and built a production platform as a team of one. Those need very different amounts of structure. I put in enough workflow, automation and governance to hold under pressure, and not one step more. Most AI rollouts fail from process that nobody follows, or from none at all.
Built to compound, not depreciate. The biggest unspoken worry in AI adoption is that whatever you build now is obsolete in a year. I architect systems that improve with use: usage signals and feedback surface their own improvement opportunities, and each new model generation makes them better rather than dating them. I did exactly this on my own platform.
Proof you can look at. PaxRent is a production platform with double-entry trust accounting, 50-state compliance and an agentic workflow layer, in live commercial use. I designed and built it.
“Twenty-five years of knowing what good architecture looks like is precisely why I can move this fast. The tools compress the typing, not the judgment.”
Engagement formats
Discovery Sprint.
One to two weeks. Workflow mapping, prioritized use case shortlist, effort and payback assessment. Ends with a recommendation you can act on with or without me.
Build Engagement.
Scoped delivery of the agreed use cases into production, with training and handover.
Fractional Partner.
Ongoing. A standing capability for organizations working through AI adoption over quarters rather than weeks.
Workshops and team sessions can be delivered as part of any format.
Start with the discovery sprint.
One to two weeks, a clear picture of where AI actually pays back in your business, and a recommendation you can act on. No obligation to continue.