AI for UK
Manufacturers
Established manufacturers often run modern machinery alongside an ageing ERP or MES, local spreadsheets and knowledge held by a few experienced people. We start with one production or office process that has a measurable cost.
One plant
ONE MEASURABLE PROCESS TO START
Existing systems
ERP, MES AND SHOP-FLOOR DATA
Named owner
OPERATIONS, FINANCE AND IT INVOLVED
Where manual work slows an established plant
A useful starting point is the part of estimating, planning, quality or maintenance where the plant already records enough information to measure a result.
The difficult part is connecting plant data, operating rules and the people responsible for the result. A works manager, planner and estimator will usually know where the delay sits long before a general AI assessment does.
We connect a small number of useful workflows to the systems you already operate.
TODAY'S MANUFACTURING SYSTEMS
- Quoting from a spreadsheet, by feel
- Planning whiteboard, redone Monday
- Maintenance run-to-failure
- QC by eye, on a sample
- Tribal knowledge in two people's heads
WITH THE FIRST AI LOOPS
- RFQ to quote in minutes, priced consistently
- Schedule reflows when an order, machine or shift changes
- Sensors flag the bearing before it fails
- Vision QC on every part, not every tenth
- SOPs and tribal knowhow in one place the AI can read
Manufacturing workflows worth automating
These five workflows appear repeatedly in UK manufacturing case studies. The right starting point depends on your data, operating cost and who owns the result.
Predictive maintenance
Cheap sensors on the spindles, motors and compressors that cost you when they go. The model flags the change before the part fails. Beverston Engineering's Made Smarter case study is the one most people get pointed at first.
Vision-based QC
A camera and a model that catches the defects your QC team is now sampling for. Every part, every shift. Cuts scrap, cuts the warranty claims you only find out about three months later.
Scheduling and forecasting
Demand forecast from order history, stock and lead times. Schedule that reflows when reality changes, not when the planner gets in on Monday. The version of APS your ERP doesn't have.
RFQ and drawing extraction
Customer sends a PDF, a STEP file and a spec sheet at half four on a Friday. The loop reads them, costs the job against your routings, drafts the quote. Estimator approves or edits. Hours becomes minutes.
Shop-floor copilot
SOPs, drawings, change notices and 20 years of tribal knowledge in one place your team can ask in English. Setter walks up, asks how Line 3 ran the last batch. Gets the answer, not a folder.
How we start
We're a UK AI engineering team. Our work spans engineering, construction, recruitment, financial services and workshop operations. We start with the existing process and the people accountable for it.
We assess the plant and office process, choose a measurable first use case and test it alongside current operations before proposing wider scope.
BOOK A SHOP-FLOOR AUDITShop-floor audit
A day or two on site. We walk the lines, sit with the planner, the estimator and the works manager. We map your ERP, MES, OEE board, spreadsheets and where the numbers don't agree. You get a written list of loops, scoped and priced.
Plant brain
SOPs, drawings, routings, supplier specs, tribal knowhow and the contents of half a dozen shared drives, captured into one place the AI can read. Modelled around ISA-95 so it talks to your ERP and MES instead of replacing them.
First loop, live
Sensors or data feeds, model, tools the AI can call, a human-in-the-loop gate for the bits that need a sign-off. You see it run on your numbers, your parts, your customers. Your team sees the payback before phase two is even quoted.
Govern, then add the next loop
ISO/IEC 42001 for the AI management bits, your ISO 9001 quality system left intact, ICO and UK GDPR handled for any personal data. Then we move to the next loop on the list. Maintenance, then scheduling, then RFQs. Small steps, not big bangs.
Examples from UK manufacturers
Published Made Smarter and UKRI cases help establish what has worked elsewhere. Your investment case still needs evidence from your own plant, systems and operating measures.
Beverston Engineering.
A North West precision machining company used Made Smarter-backed data capture and predictive maintenance on its machining cells. The published case covers the resulting engineering hires and capital investment plan.
Goodflex Rubber Co.
The manufacturer used a Made Smarter West Midlands grant and its own capital to introduce EFACS E/8 ERP with scheduling and MRP. The published case explains the systems change and intended operating gains.
Digital Spare Parts Supply Chain.
NBT Group with Senseye and Northumbria University, £227k Made Smarter Innovation Challenge. AI on inventory and predictive maintenance for the spare-parts supply chain. Published by UKRI.
Sources: Made Smarter Adoption, Made Smarter Innovation Challenge (UKRI), Make UK "UK Manufacturing: The Facts 2024", Make UK / Autodesk "Future Factories Powered by AI" (Sept 2024 survey, 151 companies), Make UK / Sage "Making it Smarter" (2024), ONS International Comparisons of Productivity, BEIS Made Smarter Review (Maier, 2017), BCG "Shaking Up the Factory Floor with Digital and AI" (2024). Adoption figures are survey numbers, not a census.
How we keep the first project usable
BCG's 2024 survey of ~1,800 manufacturing executives found 68% had started implementing AI. Only 16% hit the targets they'd set. The other 52% didn't get value out the other end.
A plant can have a capable model and still miss its target because source data, integration, ownership or operator adoption was not settled. Those dependencies belong in the scope and investment case.
The first phase is designed to settle them against one measurable workflow.
WHAT THE FIRST PHASE MUST SETTLE
- A bounded workflow. We choose a process with usable data, a named owner and a result the business can measure.
- A defined review point. The estimator, planner or quality engineer keeps authority over the decisions that matter. Any later reduction in review must be supported by evidence and policy.
- Representative plant data. The workflow is tested against real cases and exceptions before controlled use on live work.
- An agreed integration boundary. The ERP, MES and local records remain authoritative where appropriate, with interfaces and ownership documented.
- Commercial terms are explicit. Source, models, data, IP, hosting, support and handover are agreed before the build.
We already build around workshop and engineering work
Olton Bedrooms uses custom project, workshop-time and stock software. Our wider engineering work connects business-development and operational data to the systems staff already use.
When this is worth discussing
We work best when there is a real operating problem, enough volume to measure and people from the affected teams who can make decisions.
Usually a good fit
- An established UK business, usually with annual revenue above £10m
- A repeated process with a known cost, delay, error rate or capacity problem
- A senior sponsor and a day-to-day owner who understand the work
- Access to the relevant staff, systems, sample records and security requirements
We may point you elsewhere
- A standard product already covers the process well
- The requirement is a one-off small build with no wider operating case
- There is no owner or access to the people and data needed to test the result
- The plan relies on AI making high-impact decisions with nobody responsible for review
Questions the buying team will ask
We're not a data-rich plant. We've got an ageing MES and Excel. Are we too far behind?
Not necessarily. The first use case should rely on a controlled slice of data and improve it as part of the work. We assess the source quality and manual records before deciding whether the use case is viable.
Is this Made Smarter? Can we get the grant?
Made Smarter runs adoption support and matched-grant schemes through regional partners. If your company qualifies, that support may help fund a diagnostic or technology project. We can scope our work alongside the programme, but eligibility and funding decisions sit with the relevant regional team.
What about the EU AI Act, ISO 9001, the ICO?
Selling into the EU does not make every AI use case high-risk, but the Act may apply depending on the system and your role. Article 50 transparency duties apply from 2 August 2026. The Commission's current timeline puts Annex III high-risk areas on 2 December 2027 and product-embedded high-risk systems on 2 August 2028. We map the workflow alongside your ISO 9001 quality system and apply current UK data-protection requirements where personal data is involved.
How do you handle health and safety on the shop floor?
AI doesn't replace HSE controls. Safety-critical interlocks stay in PLCs and hardware, the way the HSE expects. The AI sits above that, recommending and routing work, not overriding a safety system. Anything that touches a person physically gets a documented risk assessment before it goes live.
Does this rip out our ERP / MES?
We normally connect to the systems already running the plant rather than combining an AI project with an ERP replacement. That can include Sage, SAP Business One, Microsoft Dynamics, Epicor, Infor, a custom MES and local spreadsheets. We agree the data boundary and integration plan with your IT team before building.
Our works manager isn't convinced. He's seen a lot of "transformation" projects.
Good. He's usually right. We won't run the project without him on board. The audit involves a couple of hours with him, the planner and the estimator. If after that any of them think the loop won't pay back, we say so and don't take the build. We'd rather not start than launch something the shop floor ignores.
How much does it cost?
Audit is fixed-fee. First loop is priced before we start, scoped against the audit. Ongoing loops fixed per phase. We tell you the number on the call. If a Made Smarter grant applies, we'll help you scope the application against it.
Talk to us about the manufacturing bottleneck
Tell us which measure matters most, such as quote turnaround, scrap, downtime or planning accuracy. We will map the source data, operational owner and evidence needed for a useful first programme.