Redacted sample · AI Profit Roadmap
Northwood Components Ltd
Specialist contract manufacturer based in the West Midlands. £18m turnover, 84 staff, dual UK and EU customer base across aerospace tier-two and precision automotive. Currently mid-ERP-selection, running Sage 200, a paper-and-spreadsheet shop floor, and a HubSpot CRM that no one in operations opens.
- Prepared for
- Operations Director, Northwood Components Ltd
- Prepared by
- Nuno Neto and Luke Austin, The Margin Labs
- Date
- 27 May 2026
This is a fictional, anonymised composite based on the kind of business we typically work with. Numbers, names and details have been edited. The structure, format and depth are the same as the document you receive after your discovery call.
Executive summary
The on-record problem you brought to the call was quoting velocity (RFQs taking five to nine working days, with win rate slipping when you push past the front of the queue) and planning trust (your weekly production plan no longer survives Monday lunchtime). Both of those are real. Neither of them is, primarily, an AI problem.
The reframe is this: your operating model has outgrown the tooling underneath it. Quoting, planning and quality are running on tribal knowledge, three spreadsheets and a daily standup. Bolt a language model onto that and you get faster wrong answers. Build the spine first (one source of truth for the work, one place buyers acknowledge a job, one daily read on WIP and on-time performance), then put AI where it pays. That order matters more than the AI itself.
We recommend three quick wins your team can run inside the next two weeks with no consultant involvement. We then recommend six ranked opportunities sequenced over twelve months, in the order that pays back fastest, with a clear stop after the third while you complete ERP selection. We also list three things AI is not the answer for yet at your size and data maturity, with a date to revisit each.
The combined annualised value across the six ranked opportunities lands at approximately £262,000 against a phased investment of approximately £113,000. That is a realistic 12-month picture for a business your size, not a vendor pitch. The first quick win pays for itself inside a fortnight.
Three quick wins
Week-one actionable. No consultant required.
The first three things any team can run inside the next two weeks. Pulled out of the roadmap so they pay you back before the bigger work begins.
Daily WIP digest from MES extract + Sage 200
Replace the 07:30 production manager's manual whiteboard snapshot with a one-page automated PDF, emailed at 07:15 to the ops triangle. Pulls last-shift job movements from your existing shop-floor printouts (scanned overnight) and matches them against the open works orders in Sage 200. Two days of joinery in a low-code tool, one day of layout. No new licences.
- Hours saved
- 6/week
- To implement
- 1 week
Quote-on-receipt acknowledgement template
A standard reply, sent within four working hours of every RFQ landing in sales@, that confirms receipt, restates the buyer's spec back to them in your words, asks the two clarifying questions you always end up asking on day three, and gives a date for the priced quote. Stops the buyer chasing, surfaces missing info early, and signals seriousness. Owned by sales support, not estimating.
- Hours saved
- 4/week
- To implement
- 1 week
Engineering drawing tag library for buyers
Build a shared tag library (material, tolerance class, finish, certification, takt band) that buyers apply when uploading drawings to the quote folder. Two afternoons with your senior estimator to define the taxonomy, then a one-page guide for the team. Cuts the back-and-forth on missing context, and it is the foundation every later automation will need.
- Hours saved
- 5/week
- To implement
- 2 weeks
Costed roadmap
Six opportunities, ranked by payback.
Sequenced in the order that pays back fastest. Each opportunity carries an estimated cost, a payback window and an annualised value. Risk is a directional read, not a score.
Estimated annualised value
Six opportunities, ranked by payback. Values in GBP.
Total annualised: £262,000
Unified quoting workspace
Low riskOne workspace where every live RFQ sits, with the drawing, the buyer thread, the costed BOM, the routing draft and the approval state all on one screen. Replaces the four-folder, three-inbox model. Cuts quoting cycle from five-to-nine days to two-to-three, and gives sales a real-time view of the quote book.
- Payback
- 4 months
- Est. cost
- £18,000
- Annual value
- £64,000
Production planning and WIP visibility
Medium riskLightweight MES layer (Tulip-style) on top of your existing Sage 200, sequenced jobs visible on the shop floor, finite-capacity load by cell, real-time WIP. Restores trust in the weekly plan and shortens reaction time when a job slips. Sequenced second so the quoting spine is already feeding it clean job data.
- Payback
- 5 months
- Est. cost
- £24,000
- Annual value
- £58,000
Quality and first-article inspection digitisation
Low riskMove FAI records and in-process inspection from paper to a tablet workflow tied to the works order. Pulls drawing tolerances in automatically, flags out-of-spec on entry, archives a signed PDF against the job. Cuts FAI cycle by roughly half and makes customer-audit prep a one-click pull rather than a two-day scramble.
- Payback
- 7 months
- Est. cost
- £22,000
- Annual value
- £42,000
AI-assisted RFQ triage
Medium riskA small model that reads each new RFQ, extracts spec fields against your tag library, scores fit against your sweet spot (material, volume, tolerance, lead time), and routes to the right estimator with a draft cost band. Sits behind the quoting workspace, not in front of the customer. Worth doing only after opportunities 1 to 3 are in flight, because it depends on the tag library and the unified workspace existing.
- Payback
- 6 months
- Est. cost
- £15,000
- Annual value
- £36,000
Supplier scorecards and early-warning
Medium riskAuto-generated monthly scorecards (on-time, on-quality, on-spec, RMA rate) for your top forty suppliers, plus an early-warning rule that flags drift before it shows up as a missed delivery to your customer. Pulls from goods-in records and the quality module. Becomes feasible once opportunity 3 is in.
- Payback
- 9 months
- Est. cost
- £18,000
- Annual value
- £34,000
Connected dashboard: P and L, WIP and on-time
Low riskOne board-level dashboard that ties contribution by job-family to WIP value and on-time delivery, refreshed nightly. Built in Metabase on top of the spine assembled by opportunities 1, 2 and 3. Gives the leadership team one number to look at on Monday morning rather than three reports that disagree.
- Payback
- 8 months
- Est. cost
- £16,000
- Annual value
- £28,000
No-go list
Where AI is not the answer yet.
Three things we recommend you do not buy or build right now, with the reason and a date to revisit. The honest no-go list is part of why this document is useful.
Customer-facing chatbot
Your buyers are senior engineers placing low-volume, high-spec work. They want a named person who knows their parts, not a chat widget. A chatbot here would lower perceived quality and would not deflect work your team should not already be doing. The right answer is the quote-on-receipt template plus the unified quoting workspace.
AI-generated CAD for safety-critical assemblies
Generative-CAD tooling is improving fast for low-criticality consumer parts. It is nowhere near the accuracy or auditability required for the tier-two aerospace and precision automotive work that is your margin engine. The liability picture is also unresolved. Worth tracking, not worth piloting.
Demand-forecasting AI on the last twelve months of data
You do not have enough clean disaggregated history to train a forecasting model that beats your estimator's gut. You also have a structural change (post-tariff EU mix shift) inside that history, which would teach a model the wrong pattern. Revisit once the data spine from opportunities 1 to 3 has produced eighteen months of clean per-customer demand.
Architecture fit note
The spine, in five layers.
The recommended spine is deliberately small and deliberately yours. Sage 200 stays as the financial system of record. A lightweight MES (Tulip or Katana class) sits over the shop floor to capture job state in real time. HubSpot remains the CRM, but is finally wired to the quoting workspace so sales and ops see the same job. An integration layer (n8n or make.com) moves records between them. A single reporting surface (Metabase or Power BI on top of a small warehouse) gives leadership one read of the business. No vendor lock-in at any layer, no rip-and-replace. This pattern is intentionally compatible with whichever ERP you land on at the end of your current selection process: the MES, the integration layer, and the reporting surface all survive an ERP change with minor reconfiguration rather than a rebuild.
- CRMHubSpot
- FinanceSage 200
- Shop floor (MES)Tulip / Katana class
- Integration busn8n / make.com
- Reporting surfaceMetabase / Power BI
AI Governance Starter
A one-page acceptable-use policy, four sections.
Before any AI tool ships to anyone outside the leadership group, you want a one-page acceptable-use policy that the whole company has read and signed. Not because it eliminates risk, but because it makes the trade-offs explicit, gives your team a clear answer when a customer or auditor asks how you use AI, and protects the people on the shop floor from carrying a decision that should have been made at the top. Four policies below cover the floor of what you need.
Acceptable use
Which tools are approved (named), which are prohibited (named), and what categories of work each approved tool may be used on. Personal use on personal devices is out of scope; work use on any device is in scope. One page, plain English, signed annually as part of the staff handbook update.
Data handling and confidentiality
Customer drawings, pricing, and personal data may not be pasted into any tool that retains prompts for training. Default to UK or EU data residency. Customer-data segregation rules from your existing GDPR policy apply, with one addition: an AI tool is treated as a sub-processor and must be on the approved list before any customer data touches it.
Model selection and audit log
The approved-tools list is reviewed quarterly by the operations director. Each approved tool has a named internal owner, a stated purpose, and a log of significant decisions (chose, dropped, changed config). Six months from now this log is the answer to the inevitable customer-audit question of how you govern AI use, and you will be glad you started it now.
Human-in-the-loop for safety-critical decisions
Any decision that touches a part on a flight-critical or vehicle-critical assembly is reviewed and signed by a named human before it leaves the building. AI output is allowed as input to that review and must be labelled as such. This is the rule that lets you defend a yes-to-AI position to your aerospace and automotive customers without losing them.
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