Your workflow and starting point
A map of the workflow, the bottleneck you chose to address, and baseline numbers with their sources.
AI Strategy Sprint
Bottleneck first.
AI second.
We work with your team to find where a workflow gets stuck, build a prototype agent, and test whether it helps before you take it further.
Find the stuck step.
Work comes in↓Waiting on the same step↓Work moves on
Test a better way.
Agent prepares→Person reviews
A person at the decision point.
Before you automate anything, find the step that matters.
If you have a workflow you want to improve but are unsure where AI fits, the sprint gives you a practical starting point.
We map how a workflow actually runs, including the handoffs and workarounds. Together, we choose one bottleneck and agree what improvement would look like. Then we test an agent against that problem.
Your team helps us understand the work and judge whether the prototype is useful. We focus on the process and what could improve.
From a stuck step
to a clear decision.
We work through the problem with your team, agree how to test the idea, and build a prototype around that scope.
01Kickoff
Choose the workflow, bring in the people who know it, and agree the data rules. A simple task log helps us see where the work goes.
02Understand
Walk through the work with your team using blank screens or test data. Map the steps, the workarounds, and what better would look like. Record the baseline and where the numbers came from.
03Define the bottleneck
Look at time, volume, and risk to find the step worth addressing. We recommend a focus. Your decision maker chooses the problem to take forward.
04Ideate
Explore what an agent could do, what a person should approve, and where the limits belong. Decide how it should hand work back when it is unsure.
05Decide
Choose a solution and sketch how it will work. Agree the test cases and success thresholds before we build, so the goalposts stay put.
06Prototype
Build a working agent in a separate test environment, using realistic, made-up cases. Put the limits in place and check it against the agreed thresholds.
07Test
Your team tries the prototype. They approve, edit, or reject its outputs while we observe what works, what gets in the way, and what needs to change.
08Readout and next step
Review the test results and team feedback together. Decide whether to take it into a pilot, change the approach, or stop.
Test the idea with clear limits.
The sprint is a test environment, separate from your live operation. We learn what the agent can do before giving it real work.
Inside the test environment
Made-up dataPrototype agentHuman approval
No live sends. No bookings. No purchases.
No changes to your systems.
Unsure? Pause and ask a person.
What you’ll have to work from.
A working prototype to test, and the evidence to judge whether it is worth taking further.
A prototype your team has tested
Results from testing the prototype against the measures you agreed, alongside feedback from the people using it.
A recommendation for what comes next
A recommendation based on the findings: go forward, change course, or stop. If a pilot makes sense, a draft plan for what it needs to prove.
Bring the people
who know the work.
A decision maker helps choose the focus. The people doing the work show us where it gets stuck and try the prototype. A technical contact helps us understand the systems around it.
You do not need to arrive with an AI solution. Start with the work you want to improve.
Questions
What is an AI Strategy Sprint?
A focused way to explore one workflow, identify its bottleneck, and build a prototype agent to test with your team. It gives you evidence for deciding what to do next.
Do we need to know where AI fits?
No. Bring a workflow that feels harder than it should. We start by understanding the work and choosing the problem, before choosing the technology.
Will you connect to our live systems?
No. The sprint uses a separate test environment and made-up data. It does not use real customer or employee data, and the prototype cannot send, book, buy, or change anything in your live systems.
Who checks the agent’s work?
A named person approves every output during the sprint. We define what the agent can do, where it must stop, and when it should ask for help.
What happens after the sprint?
We review the findings with you. If the evidence supports it, a pilot is the next step toward real users and data, with the checks agreed first. Changing direction or stopping is a valid outcome too.
Let’s find your next step with AI.
Tell us what your team is trying to do. We’ll work out whether a sprint is the right starting point.