AI transformation / worked example
Can AI reduce the work behind a review pack?
Use an AI expert to identify a recurring management workflow, define human review and compare the evidence needed before expanding an AI pilot.
Synthetic inputs · authored analysis · not live AI · not a customer result.
Start with the source register.
What is available, what it says, and what it cannot establish.
| Source | Observation or proposal | Limit |
|---|---|---|
| A1 · Historical work log | Five comparable review packs: mean preparation 95 minutes + mean review, including rework, 55 minutes = 150 minutes total active effort. All five were accepted. | Synthetic baseline, small sample. No AI-assisted pilot output has been measured. |
| A2 · Workflow and access map | Authorized source material may support a draft; a named manager reviews it. Final priorities and publication require human approval. | Source permissions and acceptance criteria must be confirmed for any real pilot. |
| A3 · Proposed pilot brief | Five comparable review packs over two weeks, using the same acceptance criteria as the baseline. | A proposal only. The quality and effort targets are not achieved results. |
01 / Signal
Start with repeated work and an owner.
A1 shows five review packs with a mean of 150 minutes of total active effort each. Preparation takes 95 minutes; review and rework take 55. This creates a question about workflow value, not a claim that an AI tool will save time.
AI helps: Map recurring work and identify where gathering and synthesis may help.
Manager checkpoint: Choose a real workflow with an accountable owner and comparable accepted outputs.
02 / Structure
Make the review and permission boundaries explicit.
Separate gathering, synthesis, management judgment and external action. A2 permits a draft from authorized material; final priorities and publication stay with the manager. Define material errors and the acceptance criteria before testing.
AI helps: Draft the workflow map, source register and a list of approval checkpoints.
Manager checkpoint: Confirm source access, the reviewer and what must never happen without approval.
What if drafting is faster but checking takes longer?
Include checking and all rework in the total effort comparison. A faster draft can still create more work or lower quality. Revise or stop if the accepted output takes more effort, crosses an approval boundary or introduces material errors.
03 / Options
Compare usefulness with the cost of getting it wrong.
Compare a review-pack draft, inquiry classification and an autonomous workflow. A human-reviewed pack is a bounded starting point for this example because A1 provides a baseline and A2 names a reviewer. It is not the best pilot for every organization.
AI helps: Compare repeatability, evaluation effort and the consequences of mistakes.
Manager checkpoint: Choose a pilot the team can evaluate safely within its actual controls and capacity.
| Option | Reason to consider | Trade-off |
|---|---|---|
| Human-reviewed review pack | A recurring draft with a comparable baseline and a named reviewer. | Review and rework may erase preparation savings. |
| Inquiry classification | A repeatable task with potentially clear quality criteria. | Needs labeled examples and a check on misclassification costs. |
| Autonomous workflow | Could reduce manual handoffs after suitable controls exist. | Higher control burden and consequences of unauthorized action. |
04 / Pilot
Test five comparable packs before widening the scope.
A3 proposes a two-week pilot with five comparable packs, an operations owner and a named manager reviewer. Use authorized samples, record preparation plus all review and rework, and approve the scope and spending before starting. Do not publish unchecked drafts.
AI helps: Prepare a pilot charter, review checklist and value scorecard.
Manager checkpoint: Approve the samples, acceptance criteria, budget and review date; review each output before use.
05 / Measure
Count accepted work and total effort, not tool usage.
The proposed target is a mean total active effort of at most 120 minutes per pack, a 20% reduction from the synthetic 150-minute baseline, while all five packs meet the same acceptance criteria with no material error. No result has been observed. Include review and rework; record waiting separately.
AI helps: Compare like-for-like results and show whether effort, quality and controls support expansion.
Manager checkpoint: Scale, revise or stop after inspecting actual results. These are workflow measures, not application response-time or activation targets.
- Synthetic baseline
- 95 min preparation + 55 min review and rework = 150 min mean active effort per pack; five of five accepted.
- Proposed effort target
- At most 120 min mean total active effort per comparable pack — 20% below baseline.
- Proposed quality guardrail
- Five of five accepted against unchanged criteria, with no material error; human approval before publication.
- Observed pilot outcome
- Not yet observed. No saving or quality result is claimed.
What this example cannot establish.
This is a synthetic five-pack baseline and a proposed test, not a productivity claim. Match workload complexity and acceptance criteria, record total effort and review the limits of a small sample before considering a wider rollout.
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Help me choose a useful AI pilot for our recurring management work. Map preparation, review and rework, identify source permissions and human approvals, compare pilot options, and propose a small test measured by accepted outputs and total effort. Keep final decisions and publication with the manager.Create a free account