Why generic AI training stops working on Monday
Most business teams have now had some form of AI training. Someone demonstrated a chat tool, showed how to write a better prompt, and suggested a few uses. Two weeks later, usage has drifted back to rewording emails. The training was not wrong. It simply did not touch the work.
The work in a finance, support or operations team is specific: a month-end commentary built from three reports, a reply drafted from a knowledge base article that may be out of date, a supplier exception that needs the contract clause and the purchase order side by side. Each has inputs, a format, a reviewer and a consequence when it is wrong. AI only becomes part of daily work when it is fitted to those details, and when the person who signs the output off trusts how it was produced.
That is what this engagement does, one team at a time.
Department by department, not company-wide
I work with one team per pilot because the workflow, data boundary and reviewer differ sharply between functions. The department pages describe each in detail; in short:
- Operations: exception handling, supplier and ticket triage, procedure lookup. The risk is acting on a confident but wrong summary.
- Finance: variance commentary, reconciliation support, policy questions. Every figure must link back to its source, and a qualified person approves.
- Revenue operations: account research, CRM hygiene, pipeline review notes. CRM data quality decides what is possible.
- Marketing: briefs, repurposing and research, with brand, claims and rights review.
- Customer support: drafted replies and knowledge upkeep, where stale articles and escalation rules matter most.
- People operations: administrative support such as policy questions and document preparation, never automated employment decisions.
- Client-service teams: proposals, research and deliverable drafting under client confidentiality.
- Product teams: research synthesis that preserves the link from insight to evidence.
If your team fits one of these, start on its page. This page is for the sponsor deciding how to run enablement across functions, or for a team that does not fit neatly.
What the work involves
Mapping. A working session with the team lead and two or three practitioners. We list the recurring tasks, what goes in, what comes out, who reviews it and how often it is reworked. Most teams find six to ten candidates.
Selection. I recommend two workflows that have stable inputs, an existing reviewer and enough volume to measure. Glamorous ideas with no reviewer are parked, not dropped.
Boundary and review design. For each workflow, we write which information may go into which approved tool, what output format is expected, and what the reviewer checks. If the reviewer will not sign off, there is an escalation path back to the old method.
Coaching on live work. Short sessions using the team’s own material, then side-by-side help while people use the workflow on real items. The team writes its own prompts and checklists, so the library reflects how they work rather than how I would.
The signature deliverable, illustrated
You receive role-specific workflow adoption and coaching: the redesigned workflows, the team’s library, the review and escalation plan, and measured results. Illustrative example of a workflow record:
| Field | Entry |
|---|---|
| Workflow | Weekly supplier exception summary for the operations lead |
| Inputs | Exception log export; relevant contract clauses from the shared drive |
| Approved tool | The organisation’s licensed AI assistant; no customer personal data |
| Reviewer | Operations lead, before the summary goes to procurement |
| Measures | Minutes per summary; reviewer corrections per summary; items missed |
| Escalation | Reviewer rejects twice in a week: revert to manual and log why |
Illustrative example. It shows the format, not a client’s results.
How acceptance is judged
The functional lead and I agree the measures before coaching starts: time per item, reviewer corrections, rework and repeat use by the people in scope. Acceptance means the team is using the workflow on live work, the reviewer is signing off output at the agreed standard, and the results are reported against the baseline, including any workflow that did not improve.
Ownership and handover
The team lead owns the workflows, the library and the measure throughout. At the end I hand over a short playbook, the escalation plan and a record of what was tried and discarded. To spread the approach to more teams without repeating the full pilot, the AI champions programme trains internal people to do it. For a platform-specific rollout, see AI tool rollout; for a software team, see AI engineering enablement for software teams.