Manufacturing · Playbook 25

I've worked in this sector

Predictive maintenance

Failures spotted days or weeks ahead, work orders drafted with the right spares, and engineers deciding every job. The AI reads the shop floor; it never controls a machine.

  • Predictive ML
  • Agents with tools
  • Event-driven integration
  • Human in the loop
  • HSE: PUWER 1998
  • IEC 62443
  • NCSC OT guidance
  • UK GDPR

At a glance

The problem
Machines fail between planned services, and the signs were in the data nobody had time to watch
Typical volume
Around 1,200 maintained assets across four plants, about 300 of them critical, with tens of thousands of sensor tags
Who's involved
Maintenance and reliability, controls and OT engineers, operations, stores and purchasing, IT and security, health and safety
Pilot
10–12 weeks on one asset class at one plant, after a 3–4 week discovery
Platforms
AWS, Azure, or a sovereign or on-prem stack, with the OT side always on-prem

An illustrative scenario. The numbers are typical examples, not a client case study. The cloud services and UK rules were checked on 9 November 2026.

The business problem

Most plants maintain on a calendar and fix what breaks in between. A spindle bearing wears, a compressor runs hot, a press hydraulic pump starts to cavitate. The signs show up in vibration, temperature, current draw and cycle times days or weeks before the failure, and they are usually already in the PLCs and the plant historian. Nobody has time to watch them, so the first anyone knows is a stopped line.

An unplanned stop costs far more than the repair. In a typical multi-plant manufacturer, one critical asset down for a shift can mean missed customer deliveries, overtime, scrap from a half-finished batch and an engineer pulled off planned work. Then the part isn't in stores, because the spares list was built for the planned schedule, and the line waits for a courier.

The data is also on the wrong side of a wall, and should stay there. Shop-floor control systems (operational technology, or OT) were built for safety and uptime, not for connection to the internet. Any design that reads from them has to keep the OT network separate, read-only and safe. The AI can advise the maintenance team; it must never be able to touch a machine.

What AI changes

  • Sensor and PLC data from each plant is collected at the edge, read-only, through the historian or an OPC UA server, and passed out of the OT network one way.
  • Models watch each critical asset for patterns that came before past failures, and give a plain-English reason and a confidence for every alert.
  • A maintenance agent turns an alert into a draft work order in the CMMS, with the likely fault, the asset's history, the manufacturer's procedure and the spares it will need.
  • It checks stores and open purchase orders for those spares, and flags a shortage before the job is planned, not on the day.
  • A maintenance engineer reviews every draft, decides whether to act, and plans the job into a planned stop. Nothing is raised, ordered or scheduled without them.
  • The AI has no route back into the OT network. It can't change a setpoint, stop a machine or override an interlock, by design rather than by instruction.

How success is measured

Agree these, and measure the baseline, before anything is built. Example targets are typical, not promises.

Unplanned downtime on critical assets

Hours lost to failures nobody planned for, on the asset classes in scope.

ExampleDown by a third in the first year

Alerts that led to a real finding

Share of alerts where the engineer found a genuine fault on inspection. Too low and people stop looking.

ExampleAbove 60%, reviewed monthly

Failures with no warning

Failures on monitored assets that the model didn't flag. This is the measure that keeps the model honest.

ExampleFalling quarter on quarter

Draft work orders accepted

How often engineers approve the agent's draft with few changes. Near 100% can mean drafts are being approved unread.

ExampleStable, and sampled weekly

Spares ready when the job starts

Planned jobs where every part was in stores on the day.

ExampleAbove 95%

Planned versus reactive work

The share of maintenance hours that were planned in advance.

ExampleRising towards 80% planned

UK regulation and controls

Cross-sector rules are on the UK regulation map.

  • HSEProvision and Use of Work Equipment Regulations 1998 (PUWER), regulations 5 and 6

    Work equipment must be kept in an efficient state, in efficient working order and in good repair, and inspected where its condition could create a risk, with inspection results recorded. Predictive maintenance helps meet that duty but doesn't replace it. Statutory inspections and the risk-based schedule stay in place, every alert and decision is logged against the asset, and the CMMS remains the record HSE would ask to see.

  • HSEOther statutory examinations (for example LOLER 1998 and the Pressure Systems Safety Regulations 2000)

    Thorough examinations by a competent person for lifting equipment and pressure systems carry on exactly as now. The model can add evidence to them; it never signs one off or extends an interval.

  • NCSCSecure connectivity principles for operational technology (January 2026)

    The NCSC and partner agencies set out eight principles, including limiting and centralising connections to OT, using standard secure protocols, hardening the OT boundary, logging all connectivity and having an isolation plan. The design follows them: one brokered, outbound-only route per plant through a demilitarised zone, and a tested way to cut it.

  • IECIEC 62443 (industrial automation and control system security)

    The plant network is split into zones and conduits, and the edge gateway sits in its own zone between OT and IT. The OT and controls engineers own the risk assessment, and the AI platform is treated like any other connected supplier system.

  • Government / NCSCNIS Regulations 2018 and the Cyber Security and Resilience Bill

    Most manufacturers aren't operators of essential services, and the Bill as introduced doesn't add manufacturing. It does bring medium and large managed service providers into regulation, which may include whoever runs your IT or OT support, and lets regulators designate critical suppliers to essential services. Check both in discovery.

  • ICOUK GDPR and the Data Protection Act 2018

    Sensor data is rarely personal, but work orders, sign-offs and shift logs name engineers and operators. Keep that data for maintenance, not for judging individuals' performance, and say so in the DPIA screening and to the workforce.

The solution

Showing AWSAzuresovereign / on-prem services. Switch with the toggle; every diagram and table on this page follows it.

Predictive maintenance: reference architecture
  1. 01Shop floor (OT)

    • Machines, PLCs and sensors
    • Plant historian or OPC UA server
    • Add-on vibration and temperature sensors
  2. 02Edge and intake

    • AWS IoT GreengrassAzure IoT Operations on Arc-enabled KubernetesOPC UA client and MQTT broker (e.g. Eclipse Mosquitto) on Kubernetes at the plantEdge gateway in the plant DMZ
    • AWS IoT Core + Kinesis Data StreamsEvent HubsApache KafkaTelemetry into the cloud
    • Timestream for InfluxDBFabric Real-Time Intelligence (Eventhouse)TimescaleDB or InfluxDBTime-series history
  3. 03Intelligence

    • SageMaker AIAzure Machine LearningMLflow and Kubeflow on KubernetesFailure prediction models
    • Bedrock Knowledge BasesFoundry IQ (Azure AI Search)OpenSearch, or PostgreSQL with pgvectorManuals and maintenance procedures
    • Agent
      Bedrock AgentCoreFoundry Agent ServiceLangGraph on Kubernetes, with open-weight models via vLLMMaintenance planning agent
  4. 04Orchestration

    • Step FunctionsLogic Apps / Durable FunctionsTemporal, or n8nAlert-to-work-order workflow
    • EventBridge + SQSEvent Grid + Service BusApache Kafka or RabbitMQEvents and queues
  5. 05Systems of record

    • CMMS or EAM (work orders, asset history)
    • ERP (stores, spares, purchasing)
    • Production schedule
  6. 06People

    • Person
      Maintenance engineer: reviews and approves
    • Person
      Maintenance planner: schedules the job
    • Person
      Reliability engineer: owns the models
  • Cloud or platform service (AWSAzuresovereign / on-prem)
  • AI agent
  • A person decides
  • Your existing systems
  • Outside your organisation

Components, side by side

Predictive maintenance: components on AWS, Azure and a sovereign or on-prem stack
LayerAWSAzureSovereign / on-prem
Reading the shop floorRead from the historian or an OPC UA server, not from PLCs directly. Read-only credentials, and nothing that can write back.AWS IoT Greengrass at each plant, reading OPC UA or the historianAzure IoT Operations (OPC UA connector, MQTT broker, data flows) on Arc-enabled KubernetesAn OPC UA client and an MQTT broker (e.g. Eclipse Mosquitto) on Kubernetes at the plant
Asset modellingAWS IoT SiteWise is offered in Ireland and Frankfurt, not London. Amazon Lookout for Equipment ended on 7 October 2026 and IoT Events on 20 May 2026, so neither is an option.Your own asset model in the time-series store; AWS IoT SiteWise only if EU residency is acceptableAzure IoT Operations asset definitions, plus Fabric Real-Time IntelligenceAn asset register in PostgreSQL, keyed to CMMS asset IDs
Telemetry and historySend features and summaries up, not every raw sample. High-rate vibration is usually reduced at the edge.AWS IoT Core, Kinesis Data Streams, Timestream for InfluxDBEvent Hubs, Fabric Real-Time Intelligence (Eventhouse, Activator)Apache Kafka, TimescaleDB or InfluxDB
Failure predictionStart with anomaly detection per asset class. Failure labels come from CMMS history, which is often messy, so budget time to clean it.Amazon SageMaker AIAzure Machine LearningMLflow and Kubeflow on Kubernetes
Procedures and manuals (retrieval)The manufacturer's manuals, your own task lists and past job notes, so drafts cite the procedure rather than guess.Bedrock Knowledge BasesFoundry IQ, built on Azure AI SearchOpenSearch, or PostgreSQL with pgvector
Reasoning and draftingBoth clouds offer a choice of models, including Claude. The language model drafts; the prediction comes from the ML model.Amazon BedrockMicrosoft FoundryOpen-weight models served with vLLM on your own GPUs, or IONOS AI Model Hub
Agent runtimeThe original Bedrock Agents closed to new customers on 30 July 2026; new builds use AgentCore.Amazon Bedrock AgentCoreFoundry Agent ServiceAn open-source agent framework (e.g. LangGraph) on Kubernetes
Workflow and eventsAWS Step Functions, EventBridge, SQSLogic Apps or Durable Functions, Event Grid, Service BusTemporal or n8n; Apache Kafka or RabbitMQ
Link to the CMMS and ERPUse the CMMS and ERP's own APIs. Drafts land in a draft status the agent can't change.API Gateway, plus your existing integration platformAPI Management, plus your existing integration platformKong or your existing API gateway
Human reviewNo new screen to learn. The alert, the evidence and the draft sit on the work order.In the CMMS, where engineers already workIn the CMMS, where engineers already workIn the CMMS, where engineers already work
Platform at the plantAzure IoT Operations is supported on several Kubernetes distributions, including K3s, RKE2 and OpenShift.Greengrass on industrial PCs; AWS Outposts only for very large sitesArc-enabled Kubernetes (e.g. AKS Edge Essentials or Azure Local)Kubernetes on-prem (e.g. OpenShift, Rancher)
Identity, keys, OT securitySeparate identities for OT and IT. No shared accounts across the boundary.IAM Identity Center, KMS, device certificates in AWS IoT CoreEntra ID, Key Vault, Azure Device Registry certificatesKeycloak or your existing Active Directory; OpenBao or HashiCorp Vault; an HSM
Monitoring and auditLog every alert, every draft and every engineer's decision, with the model version used.CloudWatch, CloudTrailAzure Monitor, Log AnalyticsPrometheus, Grafana, OpenTelemetry
UK data residencyAzure IoT Operations connects clusters to Azure Arc through a listed set of regions, none of them in the UK today. Cloud resources can still sit in UK South; check what metadata goes where.eu-west-2 (London)UK South / UK WestYour own UK data centre, or a UK or EU provider

On a phone, the table shows the platform you pick. Sovereign / on-prem means open, portable components you can run in your own data centre or with a UK or European provider; IONOS is shown as one example, and others exist. What sovereign cloud means →

Hybrid and on-prem

Many organisations keep core systems and sensitive data on their own servers. This is how the same design works across both.

Stays on-prem

Everything that runs the machines: PLCs, safety systems, SCADA and the historian stay on the OT network at each plant, untouched. So does an edge gateway in the plant DMZ that collects data and can keep running alone.

Uses cloud AI

Model training, the time-series history across plants, the procedures index and the planning agent run in AWS or Azure, or on your own servers with open-weight models. Only sensor features and asset IDs leave the plant, not control logic or recipes.

How they connect

Each plant gateway sends data out through the DMZ over an outbound-only, encrypted connection, over a private link (AWS Direct Connect or Azure ExpressRoute) or the internet with mutual TLS. Nothing in the cloud can open a connection into OT.

If the link drops

The machines don't notice. The edge gateway buffers data locally (Azure IoT Operations supports up to 72 hours offline) and catches up when the link returns. Engineers carry on with planned maintenance and their usual checks, as before.

The agents

What each agent may do through its tools, what it may not, and where a person signs off. The limits are enforced by the platform, not the prompt.

Maintenance planning agent

May do

  • Read the alert, the model's reasons and the asset's recent sensor trend
  • Look up the asset's work order history and the manufacturer's procedure
  • Check stores stock, reservations and open purchase orders for the spares needed
  • Find the next planned stop in the production schedule
  • Draft a work order in the CMMS, in draft status, with its evidence

May not do

  • Send anything to a PLC, SCADA or any system on the OT network
  • Release, approve or close a work order
  • Place a purchase order or move stock
  • Change the production schedule
  • Extend or cancel a statutory inspection or planned service

Where a person signs offA maintenance engineer approves every work order and decides whether the machine keeps running. Purchasing approves any spares order. The agent's permissions are enforced by the platform's identity and policy controls and by the network design, not by instructions in a prompt.

Middleware and integration

Data flows one way. The edge gateway reads from the historian or an OPC UA server using read-only accounts, and publishes over MQTT to the cloud or the central platform. There is no return path into the OT network, which is enforced by firewall rules in the DMZ, and for the most critical lines a data diode is worth considering.

The CMMS and ERP are reached only through their APIs and your existing integration layer. Every alert becomes an event with an ID, so a retry never creates a second work order, and if the CMMS is down the drafts wait safely in a queue.

The AI can be switched off without touching production. If it's paused, maintenance runs on the existing schedule and inspections, exactly as it did before.

Process and people

  • A reliability engineer owns each model and its alert thresholds, with change control like any other plant system.
  • An alert review in the daily maintenance meeting for the pilot, so engineers' findings feed back into the model.
  • Clear override rules: engineers record why they rejected an alert, and those reasons are reviewed monthly.
  • The OT security owner signs off every new connection, and the isolation plan is tested before go-live.
  • Statutory inspections, PUWER checks and planned services stay on their existing schedules until evidence supports a change, which the maintenance manager approves.
  • Engineers and planners trained before go-live, including what the model can't see: a new noise, a smell, a leak.

Delivery plan

  1. 3–4 weeks

    Discovery

    Survey the OT estate at one plant, rank assets by criticality, check historian data quality and CMMS failure history, and agree the OT/IT boundary.

  2. Gate · must be true to passPilot asset class chosen, data access agreed with controls engineers, OT security review started, baseline downtime measured.

  3. 10–12 weeks

    Pilot

    One asset class at one plant, in shadow mode: alerts and drafts go to the reliability engineer only, and are compared with what actually happens.

  4. Gate · must be true to passAlert precision and lead time within agreed limits, no route into OT found in testing, and the maintenance manager signs off.

  5. 4–6 weeks

    Production

    Drafts go live in the CMMS for the pilot assets, with monitoring, support and a rollback plan.

  6. Gate · must be true to passIsolation plan tested, support model in place, engineers and planners trained.

  7. Ongoing

    Scale

    Add asset classes, then plants. Reuse the edge pattern and the alert-to-work-order flow at each site.

  8. Gate · must be true to passEach new asset class passes the same pilot gate; each plant passes its own OT security review.

Roles and RACI

Who is responsible, accountable, consulted and informed, by activity
ActivityOperations directorMaintenance managerOT / controls engineerSolutions architectI can fill thisTechnical PMI can fill thisEngineersI can fill thisSecurityI can fill thisHealth & safety
Business case and benefitsAccountableResponsibleConsultedConsultedConsultedInformedInformedInformed
Target architecture and OT/IT boundaryInformedConsultedConsultedAccountable and ResponsibleConsultedConsultedConsultedInformed
OT security review (IEC 62443 zones and conduits)InformedConsultedResponsibleConsultedInformedConsultedAccountableInformed
Data access to historian and PLCsInformedConsultedAccountable and ResponsibleConsultedConsultedResponsibleConsultedInformed
Build and integration with CMMS and ERPInformedConsultedConsultedAccountableResponsibleResponsibleConsultedInformed
Model testing against failure historyInformedAccountableConsultedConsultedConsultedResponsibleInformedInformed
PUWER and safety reviewInformedResponsibleConsultedConsultedInformedInformedInformedAccountable
Go-live decisionAccountableResponsibleConsultedConsultedResponsibleInformedConsultedConsulted
Run, monitor and improveInformedAccountableConsultedConsultedConsultedResponsibleConsultedInformed

Business case and benefits

Operations director
A · Accountable
Maintenance manager
R · Responsible
OT / controls engineer
C · Consulted
Solutions architect
C · Consulted
Technical PM
C · Consulted
Engineers
I · Informed
Security
I · Informed
Health & safety
I · Informed

Target architecture and OT/IT boundary

Operations director
I · Informed
Maintenance manager
C · Consulted
OT / controls engineer
C · Consulted
Solutions architect
A/R · Accountable and Responsible
Technical PM
C · Consulted
Engineers
C · Consulted
Security
C · Consulted
Health & safety
I · Informed

OT security review (IEC 62443 zones and conduits)

Operations director
I · Informed
Maintenance manager
C · Consulted
OT / controls engineer
R · Responsible
Solutions architect
C · Consulted
Technical PM
I · Informed
Engineers
C · Consulted
Security
A · Accountable
Health & safety
I · Informed

Data access to historian and PLCs

Operations director
I · Informed
Maintenance manager
C · Consulted
OT / controls engineer
A/R · Accountable and Responsible
Solutions architect
C · Consulted
Technical PM
C · Consulted
Engineers
R · Responsible
Security
C · Consulted
Health & safety
I · Informed

Build and integration with CMMS and ERP

Operations director
I · Informed
Maintenance manager
C · Consulted
OT / controls engineer
C · Consulted
Solutions architect
A · Accountable
Technical PM
R · Responsible
Engineers
R · Responsible
Security
C · Consulted
Health & safety
I · Informed

Model testing against failure history

Operations director
I · Informed
Maintenance manager
A · Accountable
OT / controls engineer
C · Consulted
Solutions architect
C · Consulted
Technical PM
C · Consulted
Engineers
R · Responsible
Security
I · Informed
Health & safety
I · Informed

PUWER and safety review

Operations director
I · Informed
Maintenance manager
R · Responsible
OT / controls engineer
C · Consulted
Solutions architect
C · Consulted
Technical PM
I · Informed
Engineers
I · Informed
Security
I · Informed
Health & safety
A · Accountable

Go-live decision

Operations director
A · Accountable
Maintenance manager
R · Responsible
OT / controls engineer
C · Consulted
Solutions architect
C · Consulted
Technical PM
R · Responsible
Engineers
I · Informed
Security
C · Consulted
Health & safety
C · Consulted

Run, monitor and improve

Operations director
I · Informed
Maintenance manager
A · Accountable
OT / controls engineer
C · Consulted
Solutions architect
C · Consulted
Technical PM
C · Consulted
Engineers
R · Responsible
Security
C · Consulted
Health & safety
I · Informed

R ResponsibleA AccountableC ConsultedI Informed· not involved

Where I'd fit

Option A

Lead the architecture

If there's no architect on the programme, I'd own the design end to end: the OT/IT boundary, the edge pattern for each plant, the integration with your CMMS and ERP, and the gates. I'd work closely with your controls engineers, who know the machines better than anyone, and report to your programme board.

Option B

Support your architect

If your partner or your own team already has an architect, I'd take on the edge-to-cloud data flow, the agent and the CMMS and ERP integration under their lead, and give them a second senior opinion on OT security and on whether the models are good enough to trust.

Where this goes wrong

  1. Goes wrong

    The pilot connects straight to PLCs, and the OT team finds out afterwards.

    How the design avoids it

    Read from the historian or OPC UA, through a DMZ, with the controls engineers designing the connection from day one.

  2. Goes wrong

    Too many alerts, and engineers stop reading them within a month.

    How the design avoids it

    Few assets, tuned thresholds, a reason on every alert, and alert precision measured from the start.

  3. Goes wrong

    The CMMS failure history is too poor to learn from.

    How the design avoids it

    Check it in discovery. Start with anomaly detection and engineers' feedback, and fix failure coding as you go.

  4. Goes wrong

    Planned services get stretched because 'the model says it's fine'.

    How the design avoids it

    Intervals only change on evidence, with the maintenance manager's approval, and statutory inspections never move.

  5. Goes wrong

    A cloud dependency nobody planned for stops a plant.

    How the design avoids it

    Design for the link to fail. Machines never depend on the cloud, and the edge keeps collecting on its own.

On the horizon

Changes already announced or under way that affect this design. More in the UK horizon scan.

  1. From 20 January 2027
    The EU Machinery Regulation (2023/1230) applies. It adds cybersecurity requirements for connected machinery and stricter assessment for safety components that use self-learning AI. Great Britain will keep recognising CE-marked machinery that meets it (SI 2026/867).

    Keep AI out of safety functions entirely. Ask suppliers of new machines how they'll deliver security updates and what data interfaces they offer.

  2. Late 2026–2028
    The Cyber Security and Resilience Bill brings medium and large managed service providers into regulation, with faster incident reporting, and lets regulators designate critical suppliers.

    Check whether your IT or OT support provider is in scope, and update contracts and incident reporting to match.

  3. From 2026
    The Industrial Strategy's advanced manufacturing plan extends Made Smarter adoption support for SME manufacturers in England, and the government's June 2026 AI adoption plan for advanced manufacturing proposes a scan, pilot and scale route.

    If any plant or key supplier qualifies as an SME, check what support is available for the pilot.

  4. 20 September 2029
    Azure IoT Central retires. Microsoft points customers to Azure IoT Hub and Device Provisioning Service for connectivity, and Fabric for analytics.

    Don't build anything new on IoT Central. If a plant already uses it, plan the move as part of this work.

  5. 2028
    The NCSC's post-quantum cryptography milestone: a full migration plan by 2028.

    OT equipment lasts 20 years or more. Record the encryption used on every plant connection, and choose gateways whose cryptography can be updated.

Questions to ask your partner or vendor

Good answers are specific. Vague answers to any of these are worth following up.

Copy them into your next supplier meeting

  1. Can anything in the cloud open a connection into our OT network? How do you prove it can't?
  2. Do you read from our historian, an OPC UA server, or the PLCs themselves, and with what permissions?
  3. How will you measure alert precision and missed failures on our assets, not a benchmark?
  4. What happens to the plants if the cloud or the link is down for a day?
  5. Who owns the models and thresholds after you leave, and how are they changed?
  6. What does it cost to run per plant, per year, including the edge hardware?

Planning something like this?

Whether you need someone to lead the design or to support the architect you already have, let's talk it through.

Sam Naseem
Sam NaseemBirmingham, UK · UK & Europe
Available now