The method stays the same; the depth changes. Sometimes only the first phase is needed, sometimes the whole arc. What never changes is that before building anything, we check it is the right thing to build.
A workshop with your people to show what is already happening in your industry: innovation scenarios, benchmarks, emerging use cases. From there I surface the real needs, identify where products, services and processes can be improved, and we prioritise them together.
Interviews with users and stakeholders, process mapping, requirements gathering and writing. This is where we separate the places automation and AI genuinely create value from the places they do not — and saying so is part of the job.
Solution and adoption design, functional architecture, prototype in Figma. Something you can put in front of people and argue about, not a slide describing an intention.
A working prototype or MVP, built in Figma or directly with generative AI tooling — Claude Code, Lovable and similar — which gets you something navigable in days rather than weeks. Then it is validated with real users, before committing development budget. If the solution does not hold up, this is the cheapest possible moment to find out.
Backlog, sprints, coordination of the technical teams, prioritisation by value. Through to production release and measuring what actually changed.
No stock photography. These are real sessions: envisioning workshops, process mapping, use-case work with client teams.
You can start anywhere. Most of the time we start with the workshop, because it costs little and it is the only honest way to find out whether there is something there.
Scenarios, opportunities, priorities.
Half a day or a full day with your people. I bring innovation scenarios from your industry, benchmarks and use cases already running elsewhere. You leave with a concrete list of opportunities, prioritised together, and clarity on which ones deserve a closer look.
Output: opportunity map, agreed priorities, a defined next step.
From needs to prioritised use cases.
Interviews with users and stakeholders, as-is process mapping, requirements gathering. I check where automation and AI genuinely stand up to the numbers, and where a simpler intervention solves the problem better. If a use case does not hold, I say so.
Output: prioritised use cases, written requirements, a roadmap with estimates and technical risks stated openly.
Something you can try, before you spend.
Solution design, functional architecture, Figma prototype or working MVP — built with generative AI tooling where it helps, which sharply shortens the gap between an idea and something you can actually try. We put it in front of real users and gather evidence. The point is not to impress: it is to decide, with data, whether to invest in development.
Output: prototype or MVP, validation findings, a reasoned go / no-go recommendation.
From approved solution to production.
Full product ownership: backlog, sprints, coordination of the technical teams, stakeholder management, prioritisation by value. This is what I have been doing for twelve years on enterprise clients, and the reason projects reach the finish line.
Output: solution released to production, backlog maintained, adoption metrics tracked.