What two weeks inside an operation produces.
The report below is a sanitised sample: the client is anonymised as a mid-sized automotive components manufacturer, and every number has been altered or invented to protect confidentiality. The structure, depth and standard are exactly what a diagnostic delivers.
ILLUSTRATIVE FIGURES — all names, values and findings on this page are sample content, not client data.
Three of twenty-three opportunities are worth building this quarter.
Fourteen core processes were mapped across two weeks: interviews with the leadership team and nineteen staff, walk-throughs of the quotation, change, quality and planning workflows, and a review of ten years of ERP and document data. Twenty-three AI opportunities were scored on impact, AI potential and feasibility. Three clear the bar for a 90-day plan; the rest are sequenced or retired with the reason recorded.
Where the hours actually go.
Each bar shows a process area's share of knowledge-worker hours, measured against the area with the heaviest load. The pattern is common in engineering-led manufacturers: the costly work is document-shaped — quotes, change assessments, quality packs — produced from precedent, by hand.
11-day median cycle; 40% of engineering hours spent re-deriving prior quotes
change impact assessed manually across 3 systems
submission packs assembled by hand from templates
8D reports written from scratch each time
weekly replanning driven by spreadsheet exports
The full deliverable maps all fourteen processes with cycle times, system touchpoints and effort estimates, validated in a workshop with the people who do the work.
Every opportunity scored. Most of them retired.
Impact, AI potential and feasibility are each scored 1–5 against written criteria; the product ranks the portfolio. A high score is not an instruction to build — it is an instruction to look closely. Low scores are kept in the report with the reason, so the same ideas do not return unexamined next year.
| Opportunity | Impact | AI potential | Feasibility | Score |
|---|---|---|---|---|
| Quote drafting from historical bids | 5 | 5 | 4 | 100 |
| Change-impact summary across systems | 4 | 4 | 4 | 64 |
| PPAP pack assembly assistant | 4 | 4 | 3 | 48 |
| 8D first-draft generation from claim data | 3 | 4 | 4 | 48 |
| Demand signal consolidation | 4 | 3 | 3 | 36 |
| Supplier document intake triage | 2 | 4 | 4 | 32 |
| Predictive maintenance on line 3 | 3 | 3 | 2 | 18 |
Sample rows — the full map covers all 23 opportunities, each with the data sources it depends on, the workflow it lands in, and a first cost estimate.
Level 1.8 of 5 — and the gap is adoption, not foundations.
Placement on the 0–5 scale uses observable criteria: what is documented, what is measured, who owns it. This organisation scores high on process discipline and data hygiene, and low on AI experience — a favourable position, because foundations are slow to build and adoption is not.
The deliverable includes the full rubric and the evidence behind each dimension score, plus what reaching level 3 would require — and whether it is worth reaching at all.
Six findings explain the gap and point to where to focus.
Five processes account for most knowledge-worker hours; four of them produce documents from precedent.
Bid capacity, not demand, caps new business. The median 11-day quote cycle loses time-sensitive requests.
Ten years of quotes, changes and claims sit in the ERP and DMS — structured enough to build on without a data programme first.
Of 23 scored, three combine high impact with data and systems that are ready today.
Strong process discipline and system hygiene; no AI experience. Level 1.8 of 5 — the gap is adoption, not foundations.
A 2024 chatbot pilot had no owner, no baseline and no workflow integration — the standard failure pattern, not evidence AI cannot work here.
One build, measured, before the second is approved.
The plan funds one opportunity properly rather than several thinly. Each step has an owner, a measure and a date the leadership team reads the numbers. The readout deck that accompanies this report is fit for a board pack as delivered.
Agree the measure (quote cycle time, win rate), name the accountable owner, set the retirement threshold in writing.
Draft-quote generation from the bid archive, embedded in the estimators’ existing tool — not offered beside it.
Read the numbers with the leadership team. Proceed to change-impact summaries only if the first build clears its baseline.
The report and the readout deck, in the format the client receives.
A diagnostic closes with two documents: the written report and the deck the findings are presented from. Both samples below were produced by the same document pipeline every engagement uses — the deck navigates with arrow keys, and each prints to a clean PDF from the browser.
A report like this about your operation, two weeks after we start — with every working document and score sheet behind it.
All figures on this page are illustrative samples, not client results. Strategic AI Consulting publishes no client data without written consent.