AI work built to survive real workflows.

I help teams move from AI curiosity to working tools, sharper decisions, and repeatable delivery. The examples here show internal transformation work, rapid prototype sprints, and practical automations tested in real use.

Approach

Build First. Learn Fast.

The work here spans professional delivery, internal transformation, and rapid personal prototyping. Some of it was built inside teams. Some of it was built solo. All of it comes from the same belief: the fastest way to learn what AI is actually good for is to build something usable and put it in context.

I care most about AI when it becomes part of a workflow: removing friction, sharpening judgement, speeding up execution, or opening up a genuinely better way of working.

Professional AI work

From AI Interest to Embedded Practice.

These examples show the kind of work I do best: turning broad AI intent into focused experiments, usable tools, and habits that teams can keep using after the first burst of excitement.

AI Transformation

Lightful
Internal transformation work

Embedding AI Across the Company

Led a company-wide initiative to embed AI into day-to-day work. The work focused on finding high-friction tasks across teams, then designing experiments, training, and hands-on interventions that helped people adopt AI in practical ways. A key part of the job was enabling teams to build their own tools and workflows instead of waiting on a central bottleneck.

Internal AI transformation project screenshot

What this involved

Finding repeatable operational pain points across teams
Running experiments, training sessions, and working interventions
Helping non-specialists build useful AI-supported workflows

What mattered

Grounding AI adoption in real work rather than abstract policy
Balancing speed with enough structure to make adoption stick
Creating momentum that could continue without constant oversight

Result: measurable time savings across multiple teams and sustained adoption of AI in day-to-day work.

AI Sprint

Lightful
6-week prototyping sprint

Six Prototypes in Six Weeks

A focused sprint to test AI product ideas quickly in real-world conditions. Six working AI prototypes were built in six weeks, including tools I built directly and others built with the team. The emphasis was on structured prompts, multi-step workflows, and getting to real signal fast rather than polishing too early.

What we did

Built six functioning prototypes in six weeks
Used each prototype to test a distinct product idea or workflow
Applied AI in ways that were concrete enough to evaluate properly

Why it worked

Compressed learning into days rather than months
Made vague AI opportunities tangible to the wider team
Created a repeatable sprint model for future work

Result: clear signal on what was worth building, and proof that useful AI products can be created in days, not months.

Automation AI

Personal workflow
Slack + calendar automation

Daily Briefings, Delivered Automatically

A lightweight workflow built to reduce context-switching and improve daily planning. It pulls calendar data automatically, uses AI to generate a structured daily briefing, and posts the output straight into Slack. It is a small example, but it captures the kind of practical internal automation many teams could adopt quickly.

How it works

Automatically reads the day’s calendar context
Generates a clear briefing rather than raw data
Delivers the result inside an existing communication channel

What it demonstrates

Useful AI automation can be small and sharply defined
Value often comes from structure and timing, not novelty
Simple patterns are easier to spread across teams

Result: reduced manual planning and a repeatable pattern for simple internal automation.

Side projects & rapid prototyping

Small Products That Pressure-Test the Tools.

Alongside structured client and team work, I build small products to explore ideas and solve real problems. It keeps me hands-on and keeps the tooling, patterns, and workflows honest.

Even Stevens

Grassroots football coaching app that tracks players, substitutions, and playing time. Designed for real-time use on the touchline and built solo using AI-assisted tools.

Oxford Cycle Parking Map

Simple utility for finding cycle parking in Oxford. Map-based, fast, and mobile-friendly, built to solve a real local problem without unnecessary complexity.

Between Sessions

Conference planning tool that turns a crowded programme into a usable daily plan, maps sessions across Oxford with walking times, and suggests useful stops between events.

How I Work

How I Pressure-Test AI Ideas.

Start with a Real Problem

Begin with something people genuinely experience and would notice being solved.

Build Something Usable Quickly

A working prototype creates more learning than a long speculative plan.

Test in Real Conditions

Put it in front of the people who would actually use it, inside the constraints they really have.

Iterate on What Works

Follow the evidence, keep what proves useful, and strip away what does not.

Ways to Work Together

Bring me in to build, test, and ship.

I work either as a senior product leader inside the team or through focused advisory support around AI, product strategy, and delivery.

Full-time

Senior Product Leadership

CPO, VP Product, or Head of Product roles
Leading product, design, data, and engineering work together
Owning strategy, execution, and outcomes rather than just roadmap process
Particularly strong in AI-enabled products and mission-driven organisations

Fractional / advisory

AI & Product Advisory

Rapid AI opportunity assessment grounded in real workflows
Prototype sprints to test ideas quickly with genuine users
Support for embedding AI into team practice, not just tooling demos
Hands-on guidance where delivery matters as much as strategy

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