This is how we run a Pilot Implementation at ARTECH.
TL;DR
- A pilot means one micro-app for one process, running on real data, in 4-6 weeks.
- We work on-site at your company, with your team — the knowledge stays in-house, not with the vendor.
- At the end you have a working tool, a measured result, and a decision: scale it or not.
- AI here is a development tool that speeds up the build — not a magic off-the-shelf product.
Why a pilot, not a "proof of concept"
A classic PoC has one flaw: it proves that "it can be done" but leaves nothing you can actually use. After the presentation you're back where you started — richer in knowledge, poorer in time.
A pilot is different by design. The goal isn't to prove the technology — that's rarely in doubt today. The goal is a working tool that solves one real problem on your data, plus a firm answer to whether it's worth developing further.
How a pilot unfolds — week by week
The scope varies by process, but the rhythm is usually similar.
Week 1 — choosing the process and getting the data in order
Together with the department team, we pick one process with the highest return potential and a clear starting point. The first few days are usually spent putting in order the data the app will use — that's a normal part of the work, not a side task.
Weeks 2-3 — the first working version
Version 0.1 is built on real data. Rough, but functional — we test it with the people who do this process every day, and refine it in short iterations.
Weeks 4-5 — iteration and fine-tuning
The app matures: it handles edge cases, integrates with what's needed, and starts genuinely saving time. The team learns to use it.
Week 6 — measurement and decision
We measure the effect against the starting point (response time, number of cases handled, hours freed up for experts) and make a decision: scale it, stop, or carry the approach over to the next process.
Practical tip: Measure pilot success not by "does AI work," but by whether the process is faster or cheaper after implementation — and by how much. A number without a baseline means nothing.
What you get at the end
- A working micro-app for one process, on your data — not a demo.
- A measured result against the state before the pilot.
- Knowledge inside the company — we work with your team, so they understand how it works and how to develop it further.
- A data-driven decision, not a vendor's promise.
Note: A pilot won't replace executive sponsorship. Without someone who owns the topic and gives the team time, even the best tool won't make it into daily work.
The pilot's short timeline is possible because we treat AI as a development tool that speeds up building the app — not as a finished product that "does it all by itself." That's how a solution tailored to your process comes together in a few weeks, instead of another license for a system you have to bend to fit your needs.
What we don't know yet
Before acting on this article, it is worth checking whether the answers to these questions are known on your side:
- whether the process is ready to be supported — a pilot ≠ a process fix; if the work is badly organised, you organise the work first;
- which single number is the measure of success, and what it stands at before the start;
- who on the company's side keeps the topic alive and gives the team time;
- whether the data the application needs can be read in the first week;
- what happens after week six if the decision is not to continue.
Frequently asked questions
How long does a pilot AI implementation take?
Four to six weeks for a single process is the reference point: a week to choose the process and tidy the data, two or three for the first working version, the rest for iteration, and the last for measurement and a decision. A longer run usually means the scope was too broad, not that the work was more thorough.
How does a pilot differ from a proof of concept?
A PoC proves it can be done but leaves nothing anyone can use — after the presentation you are back where you started. A pilot ends with a working tool on real data and a firm answer on whether it is worth developing.
How do we measure whether the pilot succeeded?
Not by asking whether the AI works, but whether the process is faster or cheaper afterwards — and by how much. That requires a number measured before the start: response time, cases handled, or hours returned to experts. A number without a baseline means nothing.
What is needed from the company's side?
Access to the team whose process is changing, and someone who keeps the topic alive and gives that team time. Without that support the tool will not enter daily work, however well it functions technically.
What's next?
Do you have a process that's causing the most pain right now? Book a consultation — we'll tell you whether it's a good fit for a pilot, or start with the Applied AI Test to see which process to begin with.
