"AI, sure — but where do we start?" It’s the question we hear most often. The temptation is to rush toward the trendy tool. The right approach is the opposite: start from your business, and prioritise by ROI. Here is the method, in four steps.
Step 1 — Map, don’t dream
Before talking technology, list your real processes: sales, support, admin, production. For each, spot the tasks that are repetitive, time-consuming and low-value. Those are your candidates.
Don’t look for the most spectacular use case. Look for the one that comes back every day and that no one enjoys doing.
Step 2 — Estimate value, not prowess
For each candidate, ask two questions:
- How much time does it take, every week, across the team?
- What does a delay or an error cost on this task?
A slow quote loses deals. A mis-keyed invoice costs in corrections. Put an order of magnitude against each line. You get a first ranking, based on money and time — not on enthusiasm.
Step 3 — Start small, prove fast
The classic mistake is launching a big cross-company "AI" project. It bogs down. Do the opposite: choose a short, measurable, reversible scope. One agent, one process, one quantified objective.
The point of a small scope isn’t caution. It’s speed: you get proof in weeks, you bring the team on board, and you fund what follows with the gains.
Want to prioritise your use cases in 45 minutes? Book your free diagnostic — you leave with three costed leads.
Step 4 — Measure, then scale
Define the success metric before you build: response time, automation rate, hours freed. Measure it during, compare after. If the ROI is there, extend the scope. If not, adjust — without having committed an oversized budget.
It’s that discipline that separates a project that lasts from a forgotten experiment.
The roadmap in one sentence
Start from your processes, rank them by ROI, begin with the most profitable on a short scope, measure, then scale. AI isn’t a leap into the unknown: it’s a series of sober decisions, taken in the right order.