Why do so many AI implementations stall?

Most stall between purchase and practice. A firm buys licences, runs a launch session, and waits for the productivity to arrive. A few enthusiasts use the tool well. Everyone else tries it on a real piece of work, finds it needs too much setting up, and goes back to the old way under deadline pressure. Six months later, the licences renew and the week looks the same.

The pattern is predictable because nothing about the actual work changed. Implementation, done properly, is the act of changing it. Why AI rollouts fail covers the causes in more depth.

What gets built during implementation?

A working version of one of your processes, running in the tools your team already has. In practice that usually includes:

  • the connections to where the inputs live, such as a shared drive, a practice or case management system, or an inbox;
  • your templates, prepared so they can be filled reliably rather than copied and edited;
  • the drafting rules, written in your house style and terminology;
  • a review step that routes each output to a named person and holds it until they approve;
  • a record of what was produced, from which sources, and who signed it off.

What happens in each week?

Week 1: map it

We sit with the people who do the job and record every input, template, handoff and check. By the end of the week we know where the hours go and what the rebuilt version must do. Access to systems is agreed at this point.

Weeks 2 and 3: build it

The process is built on your own documents, templates and language. It is tested on live work from your current workload, not on a demonstration file, so problems appear while we are still there to fix them.

Week 4: go live

The team is trained on the process that was built, and it becomes how the job is done. The old route is retired rather than left running beside the new one.

The following 30 days

Support continues while the process beds in, covering the edge cases that only turn up in real use.

What does the firm need to provide?

Less than most firms expect. A person who owns the process and can make decisions about it. A few hours from the people who do the job during the first week. Examples of the documents the process produces, good and bad. Access to the systems the process reads from. And someone to review output during testing. You do not need clean data, a written procedure or an IT project running alongside.

How is implementation risk handled?

The main risks are overrun, a build that does not fit the real work, and a process the team quietly abandons. Each is handled by the structure rather than by promises. The fee is fixed and agreed in writing before work starts, so overrun is our cost, not yours; if the process is not live in 30 days, you do not pay. Testing on live work catches the mismatch early. And training plus 30 days of support deal with abandonment, which is covered further on the adoption support page.

Data protection is treated as part of implementation, not an afterthought. How client information is processed, and on what basis under UK GDPR and the Data Protection Act 2018, is agreed before anything is connected. Data protection and AI on client work sets out the questions to settle.

What does "live" mean at the end?

Live means the team is doing the job the new way as a matter of routine, on real client work, with review and sign-off in place. It does not mean a pilot, a proof of concept or a demonstration that works on sample files. That definition is what the 30 day commitment is measured against. If you want to know which process to implement first, the audit estimates it from your own figures.