AI implementation: how a pilot project becomes part of daily operations

A working pilot is the easiest part of an AI implementation. The real challenges appear on the way from demo to daily operations: interfaces, data quality, responsibilities and the question of whether the solution is still used three months later. This article describes the phases in between – and the typical points where projects stall.

The four phases of an implementation

• Analysis: record processes, data and systems, and clarify where AI measurably helps – and where it does not. The outcome is a scoped project with a goal and a metric.
• Pilot: implement a tightly limited version and test it with real but controlled data. The goal is a sound decision, not a perfect result.
• Integration: connect the validated solution to CRM, ERP, document management or existing tools, set access rights and run it with production data.
• Operations: monitor, maintain and develop further. From here on, you see whether the implemented effort actually pays off.

Integration: the part that gets underestimated

An AI solution that exists next to the existing system landscape will not be used. Three questions decide: where does the data the solution needs come from – and how current is it? Where do the results flow so they arrive in the daily work instead of in yet another tool? And who may see and change what? Practical points follow: interfaces to existing software, a consistent data baseline, and access rights that reflect the actual distribution of responsibility in the company.

Bringing people along: the difference between rollout and usage

Technically working implementations often fail because nobody uses them. The reasons are rarely technical: someone who has done a task by hand for years needs to understand what the solution takes over, what stays theirs and who reacts when something goes wrong. What works is involving the affected employees early, running training on their own process practice rather than the tool surface, and naming a clear contact person for questions. Acceptance comes from the solution visibly taking work off people's plates – not from a mandate.

Operations: what is needed after go-live

• Monitor quality: spot-check results, document error sources, define thresholds that trigger rework.
• Keep an eye on costs: compare running usage costs against the measured benefit.
• Absorb changes: models, plans and interfaces from providers change. Anyone relying on one provider needs a plan for outages and switching.
• Build in feedback: a fixed loop from users into further development keeps the solution aligned with the process – not with the original concept.

Why implementations fail – and what helps

• The pilot stays on demo data: a pilot on cleaned sample data proves little. First real data – controlled – belongs in the test phase.
• No owner after go-live: without named responsibility, solutions fade within months.
• Success is never measured: without the baseline, the benefit cannot be shown later.
• Responsibilities end with the vendor: operations and further development must be anchored in the company or clearly agreed – before the pilot starts.
• Too many parallel projects: better one solution in daily operations than four halfway there.

A checklist to start

• One project, one goal, one metric.
• An honest record of the data situation before the first build step.
• Named owners on both the company and the vendor side.
• A clear plan for integration, access rights and training.
• Operations and further development in the budget – not negotiated after go-live.