AI and maintenance
What AI can read in a report, what it does not decide, and how to build the business case.
Articles on ai and maintenance
AI and maintenance
AI in the Dairy Industry: What It Changes Across Real Processes, and What It Does Not
How AI is transforming the dairy industry: pasteurisation, spray drying, separators, cleaning in place and traceability, with the honest limit of each use.
AI and maintenance
AI in the Food Industry: 15 Concrete Use Cases, from the Line to the Audit
Fifteen concrete use cases for AI in the food industry: line vision, predictive maintenance, process, traceability and IFS/BRC audits, with honest limits.
AI and maintenance
AI in the Beverage Industry: Applications and Concrete Examples
How AI applies in the beverage industry: bottling-line vision, CO2 and carbonation control, aseptic and hot-fill, predictive maintenance and traceability.
AI and maintenance
AI agents in industry: what they do, what to fence
Copilot or agent: what agentic AI already does on the factory floor, what to automate, and the guardrails to set before you start.
The field toolbox
- Corrosion ratemm/yr
- MTBF · MTTR · uptimereliability
- Thickness logtrend
- Inspection intervaldue date
- Report templateCMMS
- Unit convertermm · psi
- Lost hoursROI
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AI and maintenance
RAG in industry: answering from YOUR documents, not the web
RAG makes AI answer from your documents, not the web. Its principle, where it breaks (retrieval) and the governance that holds it together.
AI and maintenance
Generative AI in industry: the use cases that actually work
Which generative-AI use cases really deliver in industry in 2026, which are over-hyped, and how to pick the first one to run.
AI and maintenance
How to choose a profitable AI project among several ideas
How to select and prioritise the most profitable AI project in a plant: the sorting criteria, a scoring grid, and the traps that turn a pilot into a money pit.
AI and maintenance
AI data leaks: the risk comes from your people and your contractors
Data leaks through AI do not come from the AI itself but from the people and third parties who use it: shadow AI, contractors and what the AI Act now requires.
AI and maintenance
The real risks of AI in industry, and why local deployment reduces them
The real risks of AI in industry: data leaks, invisible errors, vendor lock-in, security. Most come down to one thing, keeping control of your data.
AI and maintenance
Why responsible industrial AI is local, bespoke and off the cloud
Responsible AI in industry is not a label but an architecture: protecting sensitive data calls for bespoke, on-premise tools that stay off the cloud.
AI and maintenance
Predictive AI in maintenance: extending your equipment's life
What predictive AI really changes in maintenance: anticipate degradation to act at the right moment and extend the useful life of your equipment.
AI and maintenance
Local AI off the cloud: deploying an LLM or LMM on site
Why deploy AI on-premise rather than in the cloud: what an LLM or LMM does on an industrial site, the tasks it automates and the real constraints.
AI and maintenance
Making the case for an AI maintenance project to your board
How to cost an AI maintenance project so it survives the boardroom: what you measure, what you own as an assumption, and what you must never promise.
AI and maintenance
Can AI Replace the Inspector? The Line Between Preparing and Deciding
What AI can genuinely do with inspection and maintenance data, what it must never decide on its own, and why the sign-off stays human.
AI and maintenance
Delivering a first AI project in maintenance: a 90-day roadmap
How to run a first AI project in industrial maintenance without spreading yourself thin: the scope to pick, the first 90 days, and the traps that sink it.
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