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AI Transformation Diagnostic

Findings and 90-day plan for an anonymised mid-sized automotive components manufacturer

ILLUSTRATIVE SAMPLE | AUGUST 2026
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1. Executive summary

Fourteen core processes were mapped in 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 pattern matches the wider market. In 2025, 42% of companies abandoned most of their AI initiatives (S&P Global), and only 39% of organisations report any enterprise-level EBIT impact from AI (McKinsey, State of AI). Yet 74% of enterprises that measure AI return on investment report positive returns (Wharton, 2025 AI Adoption Report). Value exists — it concentrates in organisations that redesign workflows and measure results. This diagnostic therefore ranks opportunities by measurable impact and feasibility, sets a baseline and a retirement threshold for each recommended build, and sequences the rest with the reason recorded, so the same ideas do not return unexamined next year.

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14
Core processes mapped, enquiry to claim closure
23
Opportunities scored on impact and feasibility
3
Clear the feasibility bar for the first 90 days
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2. Key findings

Six findings explain the gap; the four below carry the plan. Each is evidenced in the appendix with the process maps and score sheets behind it.

2.1
Document-shaped work

Five processes account for most knowledge-worker hours; four of them produce documents — quotes, change assessments, quality packs — from precedent, by hand.

2.2
Quotation caps growth

Bid capacity, not demand, limits new business. The 11-day median quote cycle loses time-sensitive requests to faster competitors.

2.3
The data already exists

Ten years of quotes, changes and claims sit in the ERP and DMS — structured enough to build on without a separate data programme first.

2.4
Maturity uneven, not low

Strong process discipline and system hygiene, no AI experience: level 1.8 of 5. The gap is adoption, not foundations.

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The AI opportunity map
OPPORTUNITY
IMPACT
FEASIBILITY
SCORE
Quote drafting from the bid archive
5
4
100
Change-impact summary across systems
4
4
64
PPAP pack assembly assistant
4
3
48
8D first-draft generation from claims
3
4
48
Nineteen others — sequenced or retired
≤36
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3. The 90-day plan

01
Baseline and owner for the quote-drafting build

Agree the measure — quote cycle time and win rate — name the accountable owner, and set the retirement threshold in writing.

Weeks 1–2
02
First working build in the quotation workflow

Draft-quote generation from the bid archive, embedded in the estimators' existing tool rather than offered beside it.

Weeks 3–8
03
Measure against baseline; decide the second build

Read the numbers with the leadership team. Proceed to change-impact summaries only if the first build clears its baseline.

Weeks 9–12
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A 2024 chatbot pilot here failed for a known reason — no owner, no baseline, no workflow integration. That is the standard failure pattern, not evidence AI cannot work in this operation.
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NEXT STEP
A report at this depth about your operation, two weeks after we start — with every working document and score sheet behind it.
Book a free consultation to scope the diagnostic.
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