What this looks like in practice
Recent engagements across different sectors and different starting points, and the resources that came out of them.
Mapping the landscape before committing to build.
What it produced
A prioritised view of where AI fits the practice, what the realistic constraints are, and a scoping foundation for the top opportunities.
A consulting firm wanted to understand where AI fit their practice before committing to anything. There was genuine interest at the leadership level, but no shared view of the opportunities — and real uncertainty about what was realistic given their team and the nature of their client work.
I embedded as a fractional operator — working across the business over several weeks to build a grounded picture. That meant talking to the people doing the work, mapping the tools and workflows in use, and identifying where AI could genuinely contribute versus where it would create more overhead than it removed.
The output was an AI Automation Review — a prioritised map of opportunities across the practice — alongside a scoping kit for the top initiatives, with effort estimates, risk flags, and a clear view of what a good outcome would look like.
Building a shared framework — and a tool to make it stick.
What it produced
A shared framework for AI use across the team, a prioritisation model for where to focus, and a workflow coaching agent built to support junior staff.
The team knew they should be using AI more effectively — there was individual usage scattered across the firm, but no shared understanding of what good looked like. Junior staff in particular were cautious: unsure what was expected, uncertain about the guardrails.
I ran five structured coaching sessions built around the actual work — the process-heavy tasks consuming the most capacity. The sessions produced a shared framework for how to use AI well in this context, and a prioritisation model for where to focus effort.
I built a workflow coaching agent — designed to support junior staff through the firm's own processes, reducing the volume of routine questions going to seniors. It runs on their existing tools.
Making the right answer easier to reach than the wrong one.
What it produced
An operations agent connected to the practice's own policy library — live and in use, giving staff consistent, policy-grounded answers in plain language.
Different staff were handling the same routine operational situations differently — not because of bad intent, but because the policy knowledge was spread across documents that weren't easy to navigate under pressure. The same question would get a different answer depending on who you asked.
I designed and built an operations agent connected directly to the practice's own policy library. Staff ask in plain language; the agent surfaces the relevant policy and gives a consistent answer. No retraining, no new process — just a faster, more reliable way to get to the right information.
What's possible for your industry.
Each cheat sheet covers the highest-value automations for a specific industry — the problems they solve and how the agents work. Swipe through to find yours.
Fifteen minutes to work out if this fits.
A short call to see whether your business is a fit. You get a free AI readiness assessment either way.
- Fixed price, agreed before we start. No hourly creep.
- Half invoiced at the start, half on delivery.
- You decide at the end: proceed, adjust, or pause.