The question "where can AI help us?" gives you either an endless list or a frozen "I don't know". In half a day your management team weighs your own processes and chooses where to start with confidence.
From public sector to SMEs: businesses building their AI systems with And AI.
A list without an order never gets done. Standing still is the real cost.
The question "where can AI help us?" gives too many options or none at all. Both lead to standing still.
Without a way to compare processes, every proposal feels equally logical. So the choice keeps stalling.
Begin with a high-risk process or messy data, and AI moves the wrong way even faster.
You want to tackle everything, so no single project actually lands. No quick win, no proof, no budget.
This is how you recognise work that fits AI
Work that repeats pays off fastest. One-off exceptions are harder to automate well.
The more manual effort it takes today, the bigger the gain when AI handles part of the work.
A weekly report from your own data: no big data project up front, just start with what you have.
Start where a slip is easy to correct. High-risk decisions can wait for later.
We weigh together on impact and feasibility, in half a day
We put your team's work in view. Not a theoretical model, but your own processes.
What does it deliver if AI steps in here? Time, quality, calm in the team. We make it concrete.
How easy is this to build, really? Data, risk, and complexity decide whether it is a quick win.
At the top sit the processes that pay off quickly and prove the case for the next step.
Not every process is suitable. Naming that honestly is the win: you start where it is safe.
Where a mistake has big consequences, care comes before speed. Those processes wait for later.
If a process runs on unreliable information, AI amplifies the errors. Clean up first, then start.
Work that runs differently every time is hard to capture. The first step there delivers little.
No slide deck, but concrete candidates you can act on tomorrow
Chosen with reasoning, ready to start. Not everything at once, but the place where you get the proof.
For each process a rough estimate of impact and feasibility, so the choice is traceable.
Who owns what and where you are heading. A plan that fits your organisation and scale.
The method to weigh the rest of your list yourself later. A shared picture in the management team.
Half a day, in-company on location or at And AI
Three organisations that started with the right process
AI lead capture on the work that kept recurring and costing time.
AI-driven appraisal: a process running on information that was already there.
An AI assistant on the need that recurred most: being reachable.
A process is a good fit for AI when it recurs often, costs time now, runs on information you already have, and a small mistake is not a disaster. In the workshop you recognise these traits in your own work and turn them into concrete candidates.
Processes with high risk, sensitive decisions, or messy data are less suitable for a first step. That is where AI moves the wrong way faster. We name what to leave for now, so you start where the win is safe.
You weigh each process on impact and feasibility. That surfaces the quick wins: work that pays off quickly and proves the case for the next step. You leave with a shortlist of two or three processes.
No. You start with processes that run on information you already have. A big data project up front is not needed. We deliberately pick work where the data is good enough to start.
Half a day · in-company on location or at And AI
