For food and drink leaders, AI investment requires a clear connection between operational priorities, delivery costs and measurable results. Assessing the people, processes and systems involved helps establish which applications deserve a pilot. Optimum PPS applies its experience in ERP and operational reviews to help manufacturers make that decision and position AI within their wider digital roadmap.
Why an ERP business case matters
Most UK manufacturers are still experimenting with AI. Make UK’s June 2026 report found that just 2% have it widely embedded across their operations. Where firms do use AI, it sits mainly in HR, finance and admin. Only 11% use it in production and 6% in quality control.
For food and drink manufacturers, a practical AI plan starts with a defined operational priority. It needs suitable information, clear responsibilities and a measurable benefit that justifies the full cost.
Optimum PPS approaches that decision through the people, processes and systems involved. Our food and drink work spans ERP discovery and assessment, operational reviews, system selection, implementation and optimisation. That perspective helps leaders assess where AI fits alongside other improvements and what they need to put in place before investing.
This guide covers where AI pays off for food and drink manufacturers, what to put in place first and how to judge the return.
How is AI used in food and drink manufacturing?
AI applications in food and drink manufacturing can include demand forecasting, equipment maintenance, quality inspection and technical administration. Each addresses a different task and needs its own evidence of value.
The examples below explain how manufacturers can use AI in these areas and the information each application needs.
| Use case | What it helps with | What it depends on |
|---|---|---|
| Demand and promotion forecasting | Planning production and materials around retailer promotions and seasonal peaks | Sales and promotional history, capacity, usable stock and batch expiry |
| Predictive maintenance | Spotting the patterns that come before faults on fillers, ovens and packing lines | Equipment data, maintenance history and well-kept fault records |
| Vision inspection | Checking labels, date codes, seals and packaging on the line | Images of your own products and line conditions, plus an agreed response to rejects |
| Yield and giveaway analysis | Finding where weight, trim or waste drifts from target | Recipe and batch records, checkweigher data and production reports |
| Specifications and questionnaires | Drafting responses to retailer and customer technical questionnaires | Approved, current specifications and a clear review step |
| Audit preparation | Gathering evidence for BRCGS and customer audits | Controlled documents and records with clear owners |
| Shift handovers and fault knowledge | Giving night and weekend teams quick access to approved fixes | Up-to-date fault histories, procedures and manuals |
The technology behind each application depends on the task and the information it uses.
- Predictive models analyse data to estimate demand or identify patterns associated with equipment faults.
- Computer vision examines images for defects.
- Generative AI works with language and documents to support searches, summaries and drafts.
Each application needs to prove it can perform the intended task under everyday operating conditions. For forecasting, that means comparing predictions with actual demand. For inspection, it means testing detection across relevant products, defects, lighting and line speeds.
A document assistant should retrieve accurate information from approved sources and flag when those sources provide an incomplete or conflicting answer.
Key insight
Assess existing ERP functionality, reporting and conventional automation alongside AI. The aim is to choose an approach that meets the requirement at an acceptable cost.
Prepare the process before introducing AI
AI speeds up whatever process it sits on. If that process is poorly defined, AI simply makes the problems arrive faster.
Many AI projects stall because they expose gaps that have sat in the business for years: no agreed owner for a record, several versions of the same specification, or a step that only one person knows how to do.
Treat every AI project as a process improvement project first. Map how the work happens today, including who does it, how often, where it stalls and how much rework it needs. Agree who owns each step and which system holds the approved record. That work often delivers gains on its own, and it gives AI a stable process to build on.
-
1Operator notesThe operator records what happened
-
2AI draftAn approved tool turns the notes into a consistent draft
-
3Operator checkThe operator checks the facts
-
4Manager approvalThe manager approves the record and decides the next steps
Success depends on whether the team produces more complete records with less total effort and can investigate incidents more effectively. Include the work needed to transfer the approved information into the quality system.
What is the best first AI project for a food manufacturer?
A good first project is a repetitive, measurable task with a clear owner, where a mistake is easy to spot and correct. Document-heavy work often fits well. Retailer questionnaires, specification drafts, audit evidence and shift handovers come up often, tie up skilled people and are easy to measure. Specifications and audit evidence still need proper controls, which we cover below.
A manageable pilot has a small group of users, defined information sources and clear limits. Make UK recommends a similar “scan, pilot, scale” approach: test on one line or area, involve operators early and expand only what proves its value.
Five questions to answer before a pilot
- What specific task will improve, and how will we measure it?
- Who owns the process, and which users will test it?
- Which documents or data will the pilot use, and who can access them?
- Who checks the output, and what happens if it is wrong?
- What results would make us stop, change course or expand?
Use a defined group of users and information sources. Include normal working conditions and exceptions, so the assessment reflects real tasks people perform.
How ERP and operational data support AI?
AI is only as good as the information it works from, and in a food or drink business most of that information lives in the ERP. This can include orders, stock, purchasing, production, recipes and costs.
Quality, maintenance, warehouse and shop floor systems often hold additional information. Make UK’s research points the same way, finding that firms with weaker digital foundations are significantly less likely to see a return on AI investment.
Example: forecasting for a retailer promotion
Take a manufacturer preparing for a two-week promotion with a major retailer. Sales history, previous promotional performance and the planned offer can inform that forecast, but a forecast only helps if the team can deliver it.
To turn demand into a workable production plan, the AI needs all the following:
| What the AI needs | Why it matters |
|---|---|
| Sales history | Sets the baseline |
| Promotional data | Separates a temporary uplift from normal demand |
| Capacity and changeovers | Shows what your lines can make, and when |
| Ingredient and packaging availability | Confirms you can source the inputs |
| Batch expiry, quality holds and location | Shows which stock is usable, not just how much is on hand |
| Expected yields and required shelf life | Turns demand into a production plan you can deliver |
Miss any of these and the AI produces a plan that looks sensible but cannot be delivered. A stock figure, for example, tells a planner nothing about whether that stock is on quality hold or too close to expiry to meet the retailer’s shelf-life requirement.
Keep records consistent across systems
Product codes, units, recipes and batch references need consistent meanings or a reliable method of reconciliation. If the warehouse and quality systems refer to the same batch in different ways, the AI needs a reliable way to match them, or it will draw the wrong conclusions.
Decide which system owns each record, who maintains it and how updates reach other systems. A pilot can run on a controlled dataset. Wider rollout needs agreed integration and ongoing data ownership, so include that work in the business case from the start.
How AI helps manufacturing teams access knowledge across shifts
An internal AI assistant can give night and weekend teams faster access to approved procedures, manuals and past fault fixes, provided the sources are current and someone checks the answers.
On multi-shift sites, expertise is rarely spread evenly. Engineering and technical support is usually strongest during the day, so night teams have fewer people to call on.
Example: a recurring fault at 2am
On the shop floor
The flow wrapper on one of the packing lines keeps stopping with the same seal fault, and the senior engineer who knows the machine is off until morning. The night engineer describes the symptom to an internal assistant. It returns the relevant section of the equipment manual, the approved procedure for that fault and two earlier fault records where the same symptom was fixed, each linked to its source document. The engineer checks the source, applies the approved fix and logs the outcome, which strengthens the record for next time.
This approach is called retrieval-augmented generation, or RAG. The assistant grounds its answer in a defined set of your own documents and shows where each part of the answer came from.
The scenario only works if the groundwork is in place. Fault histories need to record the symptom, investigation, approved action and outcome. Each source needs an owner and version control, and access should be limited to the right people.
Test how the assistant responds when information is missing or two documents conflict. Start with one equipment group or recurring fault type and expand once the team has evidence that the assistant finds the right guidance reliably.
How do you manage AI accuracy and food safety risk?
Generative AI can produce plausible but incorrect information, so you need to match the level of control to the consequence of a mistake. A meeting summary needs a quick check. Anything affecting allergens, specifications, labels or batch release needs review and approval by a qualified person through your existing quality and safety procedures.
Testing should cover known cases, exceptions, missing information and conflicting sources. Define what the application should do when it cannot provide a reliable answer.
| Risk | Examples | Control |
|---|---|---|
| Lower | Internal meeting summaries and first drafts for internal use | A light check by the person using it |
| Higher | Allergen declarations, product specifications, label copy, HACCP records, BRCGS audit evidence, equipment operation and batch release | Review and approval by a qualified person, through your existing quality and safety processes |
Permissions need the same scrutiny. An assistant that retrieves guidance carries far less risk than a tool that changes stock records, raises purchase orders or updates the production plan. Give each application only the access it needs, define which actions require human approval and keep a record of authorised changes.
Recipes, prices and supplier terms are commercially sensitive. Before sharing confidential material with any AI service, check how it stores and uses your information, including whether it uses that information to train its models.
A successful pilot is never a reason to remove authorised food safety controls.
How do you support employees to use AI effectively?
Employees need time to learn the application, understand its limits and contribute to testing. Their feedback can reveal extra checking, awkward handovers or workarounds that headline performance measures miss.
Make UK found that half of surveyed companies said staff lacked time to train. Learning therefore needs a place in the delivery plan, with arrangements that fit production schedules and shift patterns. A practical plan should include:
- SponsorResponsible for priorities and resources.
- Process ownerAccountable for day-to-day performance.
- Testing teamOperators, planners, engineers or technical staff involved in testing.
- Checking and reportingClear responsibilities for checking outputs and reporting problems.
- Released capacityAn agreed use for any capacity released.
Trust grows through positive results and a clear response when something goes wrong. Include employee feedback in any decision to expand, alongside cost and performance.
Evaluating AI suppliers and ERP functionality
Many ERP vendors now build AI features into their platforms, and specialist suppliers offer tools that sit alongside them. A built-in option is often simpler to support but ties you more closely to one vendor’s roadmap. A separate tool may do the job better but adds an integration to maintain and another contract to manage.
A pilot can quickly become hard to unwind once data, integrations and workflows depend on one service. Look beyond the demonstration at contractual commitments, integration dependencies, support costs and exit terms. Some dependencies are worth accepting for the benefit they bring. Understand and price them before you commit.
Optimum’s independent, vendor-agnostic approach helps organisations compare existing functionality, specialist tools and other improvement options against operational needs. That assessment also considers the wider ERP and digital roadmap.
How do you measure AI ROI?
An AI pilot gives manufacturers a chance to establish whether an application delivers enough value to justify wider investment. The assessment needs to show how it improves the operation, what it costs to implement and run, and how the business will use any time or capacity released.
Start with a baseline for the task, then compare the pilot’s results with current performance. Include data preparation, integration, licences, training, checking, support and monitoring in the cost assessment.
Keep released capacity separate from cash savings. Faster access to maintenance information may reduce search time, while the effect on downtime also depends on diagnosis, parts and repair. More complete incident records can support investigation, with waste reduction depending on the corrective action that follows.
| Assessment area | Evidence to gather |
|---|---|
| Current performance | Task frequency, turnaround time, errors, rework and staff effort |
| Proposed benefit | The improvement expected and the operational priority it supports |
| Information required | Source systems, data quality, ownership and update frequency |
| Checking and approval | Review effort, expertise required and the consequences of an error |
| Full cost | Licences, integration, training, support, maintenance and ongoing oversight |
| Expansion decision | The results required before adding users, processes, lines or sites |
Before you expand, check the application still performs across different products, shifts and lines. Give someone responsibility for monitoring it and for reviewing changes to the AI service behind it.
Building AI into your digital roadmap
AI works best as one part of a wider plan, weighed alongside ERP improvements, integration, process change and workforce development. A practical sequence looks like this:
-
1Map the processand fix the obvious gaps
-
2Check the datathe application needs, and who owns it
-
3Run one scoped pilotwith named users and clear limits
-
4Measure the resultsagainst a baseline, exceptions included
-
5Decidewhether to stop, change course or expand
Making AI investment work for your food and drink business
AI offers food and drink manufacturers opportunities to improve planning, support production teams and reduce administrative effort. Choosing where to invest starts with understanding the task, the information it needs and the people who will use the output.
A defined pilot gives you evidence to assess the operational benefit alongside the full cost of delivery and ongoing use. That provides a basis for deciding whether to expand, adapt the approach or prioritise other improvements within your ERP and digital roadmap.
How Optimum PPS can help
Since 2017, Optimum PPS has supported 24 food and drink businesses across 67 projects, covering ERP discovery, operational reviews, system selection, implementation and optimisation. Our work spans bakery, confectionery, dairy, seafood, drinks and fresh produce.
Because we are independent, we can tell you plainly whether AI is the right next step, or whether your ERP and data need attention first.
We can help you identify where AI could add value to your operation, assess the processes, data and systems it needs, and define a practical pilot with measurable outcomes. Our independent advice gives you a basis for deciding where to invest and how AI fits within your wider ERP and digital transformation plans.
Get in touch to arrange an AI readiness review
Opens a new email to Stephen Douglas, Business Development Manager
About the author
Beverley Williams, Senior Consultant LinkedIn
Beverley Williams is a Senior Consultant at Optimum PPS. She has 20 years’ experience in ERP and business systems, and works with manufacturers, including food and drink businesses, on process improvement, system selection and ERP implementation.
FAQS: AI in food and drink manufacturing
How is AI used in food and drink manufacturing?
The most common uses are demand and promotion forecasting, predictive maintenance, vision inspection of labels and packaging, yield analysis, and drafting technical documents such as specifications, retailer questionnaires and audit evidence. Make UK’s 2026 research shows most manufacturers still use AI mainly in back-office functions, so production and quality remain the biggest untapped areas.
What is a good first AI project for a food manufacturer?
Choose a repetitive, measurable task with a clear owner, where mistakes are easy to spot. Retrieving approved information and drafting responses to customer questionnaires are good starting points, with checking and access controls in place.
Do we need a new ERP system to use AI?
Not in every case. Many pilots run on selected documents or controlled data exports from your existing systems. Wider use may need better integration or extra information in ERP, so assess the workflow and its data needs before deciding what has to change.
What data do food manufacturers need for AI?
Document assistants need approved, current source material. Planning applications need orders, usable stock, batch expiry, quality holds, yields, capacity and promotional data. In every case, records need consistent definitions and clear ownership across systems.
Can AI be used for food safety and specification work?
Yes, for retrieval and drafting within a controlled workflow. Use authoritative sources, test the application and keep specialist review and approval for anything affecting safety or quality. A successful pilot never justifies removing authorised controls.
How should manufacturers calculate AI ROI?
Compare the operational benefit with the full cost of implementing and running the application, including data preparation, integration, licences, training, checking and support. Keep released capacity separate from cash savings, and use pilot results to test your assumptions before expanding.