Find Your
First
AI Win
The first AI programme has to survive more than a demo. Operations needs an owner, finance needs a credible value case and IT needs to know what data and systems it will touch. We choose a use case all three can support and measure.
Pick one repeated job
Test a small version
Compare the result
You get evidence before starting a wider programme.
Costed
BUILD AND RUNNING COSTS AGREED
1
USE CASE, PICKED PROPERLY
Baseline first
VALUE MEASURED BEFORE BUILD
A first AI project needs a clear business case
An AI project needs a result the business can check: less time spent on a recurring job, fewer errors, or a service you can now afford to provide. We estimate that value alongside the build and running costs.
Before choosing a project, we ask the team to show us the work it would replace. That gives us a baseline for time, errors and cost.
The first one matters because it teaches the business what a useful AI project looks like.
TYPICAL FIRST PROJECT
- Chatbot on the website, nobody asked for
- "AI for everything" platform, six-figure licence
- Picked by IT, not by the team doing the work
- Data isn't ready, so the demo is faked
- No P&L owner, no number it has to move
- Stops after the pilot
A FIRST WIN
- A task done daily, by a real team
- Bespoke build, sized to the saving
- Picked with the people who do the work
- Uses data you already have, cleanly
- One number it has to move, named up front
- A contained first phase with a credible payback case
How we choose the first project
We score each candidate on financial value, delivery size, available data, failure risk and who will own it. Weak ideas leave the list before they consume a quarter.
Visible on the P&L
Hours saved, deals closed, costs avoided. If finance can't point at the line it's moved, the project shouldn't be the first one.
Small enough to test quickly
If the first version takes months, you've picked something too big. Split it down until it can be tested quickly, or pick a different job.
Data you already have
First wins use data that exists, in a system you already use. If it needs a new data warehouse first, it's a second-year project.
Safe to get wrong
A human checks the output, or the cost of an error is small. The team needs room to spot what's working without a board-level incident every Friday.
An owner who wants it
Someone responsible for the process, with time to test the software and authority to change how the team uses it.
What the leadership team gets
A focused review produces a scored shortlist and an evidence plan for the strongest candidate. The business approves the first implementation against that value, risk and readiness case.
We compare custom work with tools you can buy and changes you can make to the process itself. AI only belongs in the proposal where it helps.
BOOK A DISCOVERY CALLDiscovery
We work with the affected teams, observe the process and review the systems they use. Candidate use cases are recorded in the same format so leadership can compare them.
Score and shortlist
We compare each candidate's expected value with its cost, risks and practical dependencies. You get the full list with our reasoning, a top three, and a recommendation for which one to do first. If the right answer's no, we'll say so.
Launch the first one
The first phase has a defined user group, workflow, systems, baseline and stop criteria. It runs on representative business data and is measured against the agreed result.
Hand over a real roadmap
After the first project is live, you have a baseline and a team that knows how to test the result. We use the same scoring criteria for the next year's plan and leave them with you.
The first useful workflow is usually very specific
For AM2PM, candidate assessments had to return graded reports to the recruitment CRM. For Crystal, one deal had to pass the same compliance checks with the evidence retained. Both started with a defined job and owner.
When this is worth discussing
We work best when there is a real operating problem, enough volume to measure and people from the affected teams who can make decisions.
Usually a good fit
- An established UK business, usually with annual revenue above £10m
- A repeated process with a known cost, delay, error rate or capacity problem
- A senior sponsor and a day-to-day owner who understand the work
- Access to the relevant staff, systems, sample records and security requirements
We may point you elsewhere
- A standard product already covers the process well
- The requirement is a one-off small build with no wider operating case
- There is no owner or access to the people and data needed to test the result
- The plan relies on AI making high-impact decisions with nobody responsible for review
Questions before committing
We've already done a pilot. Is this for us?
Especially. A previous chatbot, Copilot rollout or no-code agent is useful evidence. The first win is the first one that moves a number, which is rarely the first one anyone tried. The previous attempt shows what your business will and won't use.
How is this different from a big consultancy engagement?
This is narrower. One working system, on your data, with a number it has to move. We do the discovery and the build ourselves, so the advice has to survive contact with the actual workflow.
What if AI is the wrong answer here?
Then that's what we say. A lot of the time the answer's a small internal tool, a tidied-up workflow, or a piece of automation that's barely AI at all. We'll recommend the least expensive suitable approach.
Our data's a mess. Should we sort that out first?
The first project runs on the data you have, with the cleaning that job needs. A company-wide data cleanup would delay the result and create a much larger programme.
What size of business is this for?
Established UK businesses, usually above £10m revenue, with repeated workflows, named department owners and enough volume to measure the result. Very early businesses are usually better served by an off-the-shelf tool.
Who needs to be in the room?
We need a leader who can approve the priority, the department owner and people who perform or receive the work. Finance, IT, privacy or security join where their decisions affect the use case.
How much does it cost?
The review and first implementation are scoped separately. The proposal states the delivery cost, expected running cost, dependencies and the saving or revenue assumption used in the investment case.
What happens after the first win?
You keep the shortlist, scoring and baseline, so the next decision does not depend on hiring us again. Your team can run the next use case or ask Vu to scope it as a separate phase.
Talk to us about the first AI project
Tell us what has already been tried, where the cost sits and which measure leadership wants to change. We'll test whether there is a suitable first programme and what evidence would justify it.