FREE GUIDE · INFUSEDML
Is your manufacturing business
ready for AI?
Ten questions to ask before starting an AI project. Answer them honestly and you will know whether you have a good first project — and what to do about it if you don’t.
- 01
What problem are we trying to solve?
Start with a specific operational problem — downtime on one line, missed defects, slow quoting — not with “we should use AI”.
- 02
What does that problem currently cost?
Lost production, scrap, labour, warranty claims or late deliveries. If it isn’t expensive, AI probably isn’t worth it yet.
- 03
What data do we have?
Sensor and PLC logs, images, test results, maintenance records, documents. Most sites have more than they realise.
- 04
How reliable is the data?
Gaps, mislabelled records and sensors that drift all matter. A quick look early on saves a lot of effort later.
- 05
How often does the problem occur?
A model learns from examples. Something that happens weekly is easier to learn than something that happened twice in ten years.
- 06
What decision would AI change?
A prediction is only useful if someone acts differently because of it: a repair planned, a part rejected, a setting changed.
- 07
Who would use the output?
Operators, engineers, planners or managers — and how would it reach them? The best model is useless if nobody sees it.
- 08
How would we measure success?
Agree the measure before you start — fewer failures, less scrap, faster quotes — and what you do today to compare against.
- 09
Can we test the idea without changing the production system?
Most ideas can be proved on historical data first, so there is no risk to the line while you find out.
- 10
What happens if the pilot succeeds?
Think ahead about who owns it, how it is deployed and supported, and what it will cost to run.
NEXT STEP
Want help answering
these questions?
Book a free 30-minute AI Opportunity Conversation. We can go through them together for one problem in your business — no technical sales pitch.
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