The Industrial AI Blog
The media specialised in AI applied to industry: maintenance, quality, inspection and asset integrity. In-depth articles and free tools, no account needed.
IA & 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.
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.
All articles
Engineering and projects
Production bottleneck: how do you tell the real constraint from its symptoms?
A line underperforms because of one hidden constraint. How to find it with your MES, ERP and CMMS data, and what AI adds to the search.
Maintenance strategy
Are you doing too much preventive maintenance? What your data answers
Too much preventive work costs as much as a breakdown. How your history reveals useless or mistimed tasks, and the ladder from calendar to condition-based.
Quality and compliance
You costed poor quality: how do you analyse it to actually cut it?
A costed figure for poor quality does not shrink on its own. How to slice it by product, line, supplier and defect to find priorities and act.
Software and data
Is your CMMS data asleep? What it already reveals about your maintenance
A CMMS holds years of rarely used history. Which KPIs to draw from it, how AI helps read it, and where data quality sets the hard limit.
The field toolbox
- Corrosion ratemm/yr
- MTBF · MTTR · uptimereliability
- Thickness logtrend
- Inspection intervaldue date
- Report templateCMMS
- Unit convertermm · psi
- Lost hoursROI
No account, everything runs in the browser.
Open the tools
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.
Software and data
The Best AI Software for Industry, Sector by Sector
Food, pharma, medical or manufacturing: which AI software to choose for your sector, your constraints and the state of your data.
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.
Engineering and projects
AI in the engineering office: speed without delegating the decision
AI in the engineering office speeds up search, drafting and compliance review. What it really proposes, and what the engineer keeps in hand.
Engineering and projects
AI in industrial production: where it really helps, where it over-promises
Where AI really helps in production: scheduling, process tuning, OEE, energy. Where it over-promises. The real prerequisite: data quality.
Quality and compliance
AI and industrial quality: what it flags, what the human decides
AI in industrial quality surfaces defects, sorts nonconformities and finds recurring causes. What it flags, what the human decides.
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