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I saw many AI projects fail in manufacturing. One cost six months and gave zero results. AI works. But most teams make expensive errors.
Avoid these seven traps.
- Bad Data Poor data kills projects.
- Run a 30 day data audit first.
- Find gaps and errors.
- Collect data on failures, not only perfect runs.
- Wrong Goals Optimizing the wrong metric is a trap.
- Track quality and waste, not only speed.
- Decide what matters most upfront.
- Think about equipment life.
- Black Box AI Operators ignore AI they do not trust.
- Use simple models you explain.
- Create dashboards for your team.
- Log why the AI made a choice.
- Legacy Tech Old systems do not talk to new AI.
- Map your hardware and protocols early.
- Plan for middleware.
- Integration takes half the work.
- Ignoring People Engineers fight systems they did not help build.
- Involve operators from day one.
- AI helps people. It does not replace them.
- Set clear rules for overrides.
- Too Big Large projects take too long.
- Start with one small use case.
- Build in modules.
- Deliver value every 90 days.
- Set and Forget AI performance drops over time.
- Build pipelines to retrain models.
- Monitor for data drift.
- Budget for ongoing maintenance.
Start small. Focus on data. Work with your team.
Source: https://dev.to/edith_heroux_aca4c9046ef5/ai-driven-manufacturing-workflows-7-mistakes-that-will-derail-your-project-1b85 Optional learning community: https://t.me/GyaanSetuAi