Services · AI Opportunity Sprint
Find out where AI will pay off before you build anything
In one to two weeks you get a clear answer to a simple question: which of your workflows are worth automating with AI, which are not, and what a first release would take. You keep the findings whether or not you build with us.
The problem
Most AI projects start with the tool and end with a pilot nobody uses
It is easy to find a demo that looks impressive. It is harder to know whether it will save time or money on your real work, with your real data, inside your real rules.
A short, fixed piece of work tells you before you commit a larger budget. Sometimes the answer is that AI is not worth it for a process, and that is a useful answer too.
Who this is for
- Leaders who have been asked to do something with AI and need a grounded place to start
- Teams with an AI prototype who need to know what it would take to run in production
- Companies comparing several possible AI use cases and needing to pick one
- Organizations in regulated or sensitive fields that need to understand the risks first
Common use cases
- Document, email and data-entry work that takes too many staff hours
- Customer or partner support that needs faster, more consistent answers
- Reports and analysis that are put together by hand
- AI features inside an existing product: assistants, search, prediction, image or document understanding
- A prototype that works in a demo and needs a route to production
Scope
What you can expect
A baseline of the workflows: who does what, how long it takes, how often, and what it costs today
The top three AI opportunities, ranked by expected return and effort
A list of processes where AI is not worth the cost, with the reasons
A risk and control check: data, privacy, human approval and how AI output is reviewed
A roadmap for the first release: scope, order of work, team and rough budget
A clear next step: a fixed-scope pilot, or a recommendation not to build
Our approach
We use AI to work faster. People are responsible for the result.
- 01
Map the workflow
We sit with the people who do the work and write down how it happens today, so there is a baseline to measure against.
- 02
Test the ideas
Where the data allows, we try the most promising ideas on a sample of your real material to see what works and what fails.
- 03
Check the risks
We review data, privacy, approvals and how mistakes are caught, using the same rules we apply in Quality Engineering & AI Governance.
- 04
Decide
We walk through the ranked options and the roadmap with your team and agree what to do next, including doing nothing.
Evidence
Related work
These projects show the kind of work a sprint leads into. They are not sprint results.
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Engagement models
Start small, then grow with confidence
We do not force every project into the same package. Scope and commercial terms are agreed with you after we understand the problem.
Fixed-fee sprint
One to two weeks, with the fee and scope agreed before we start. One workflow takes about a week, and up to three take about two. We need a few hours of your team's time for interviews and a final walkthrough.
Paid pilot
A small project with a fixed end date that tests the approach on one real workflow or module, with clear criteria for success.
Milestone-based delivery
Set deliverables with agreed acceptance criteria, for work that can be scoped with confidence.
FAQ
Common questions
What do we need to provide?
Access to the people who run the workflow, a sample of the documents or data involved, and a few hours of their time across the sprint.
What if the answer is not to build?
Then we say so. You keep the baseline, the risk review and the roadmap, and you owe us nothing further.
Do you need access to our production data?
Usually not. We start with samples, with sensitive details removed where needed, and agree any access in writing before we start.
Are the return estimates guaranteed?
No. They are estimates based on your baseline, and we write down the assumptions so you can challenge them.
How is this different from an AI strategy document?
It tests ideas on your real material and ends with a scoped first release and a decision, not a slide deck.
Want to know where AI will actually pay off?
Tell us what you are trying to achieve. We will give you an honest view of the right way forward.