Whether you’re feeling the pressure from competitors already upgrading their technology, or you simply want to modernize your operations for cost-effectiveness and impact, AI in manufacturing has a lot to offer even smaller outfits. For most of us, however, the real question is, “Where do I even start? And how do I know it won’t be an expensive mistake?”
You don’t need a million-dollar budget or an eager team of data scientists to put AI to work for you. What you need is a smart, simple first project to prove value and build confidence for bigger AI implementations. If the IMEC recent white paper, AI on the Shop Floor, caught your attention, and you’re ready to put AI to the test, here’s everything to know about choosing that key first project, and how to make the most of it.
Start Where It Hurts the Most
What are the parts of your existing manufacturing setup keeping you up at night?
Take a look at your operations. Are you:
- Facing a packaging line that goes down every few weeks, reducing your profits?
- Finding quality issues that somehow slip through inspection, driving customer complaints?
- Missing seasonal trends and patterns your competitors always seem to spot?
Your specific pain points will vary by sector, size, and your specialties. But all of them make fertile ground to find the best first AI project. You already know the cost of the problem. When you can show clear savings from day one, you’ve got a winner.
Don’t worry about size or scale. In fact, a smaller-scale project is an ideal starting point: it has the potential to deliver big results, but a minimal impact if you need to make adjustments or find holes in your planned implementation.
Finding the Right Solution: Three Project Types that Work
Now you know where to focus your AI pilot project, let’s look at potential solutions to your problems. Here are some uses of AI in manufacturing that consistently deliver fast payback for each of the three pain points we mentioned above:
Predictive maintenance
Predictive maintenance is your insurance policy against machine downtime. Putting time and effort into routines that keep machinery running smoothly helps you avoid future issues.
Predictive maintenance also doesn’t need major overhauls. Simple vibration sensors and temperature monitors are enough to give weeks of warning, and many can be successfully retrofitted to existing equipment at minimal cost.
Instead of waiting for that ominous bump or grind that puts your production line out of action for weeks, the data gathered from these sensors warns of tiny changes in advance, allowing you to proactively schedule maintenance for times that suit you.
AI-powered quality control
Quality control issues causing problems? Never miss a defect again.
Camera vision systems for quality inspection are remarkably plug-and-play, integrating with almost all existing production lines without major modifications. These cameras can:
- Catch surface defects
- Identify dimensional problems
- Find assembly issues undetectable to human eyes
- Generate structured, clean data for future AI projects
Smart manufacturing data
You’re already collecting data. Think of production counts and material usage, or downtime logs. Machine learning applications (that’s AI) can turn that data into real insights. If bottlenecks are your pain point, or you want to optimize efficiency and resource use, it’s time to put AI to work.
Digital KPI dashboards help improve operational efficiency, using your data to spot trends and patterns even skilled analysts may miss. Once you see the patterns, you can make targeted improvements that add up to significant savings.
How to Pick Your Winner
You have your pain point to fix, and an AI solution for it. Should this be your first project? See how it measures up on this checklist:
- Is your data ready? AI needs good data to work. If you’ve got sensors, production logs, or quality records going back several months, you’re in good shape. If not, start collecting now—it’s worth the wait.
- Can you measure the impact? Vague promises like “better insights” won’t impress your accountants. Ensure you can track relevant metrics (downtime hours, defects per 1000 units, optimized inventory flow).
- Does the solution integrate? The best first AI project works with your existing processes and equipment without major investment. Retrofit sensors, add-on camera systems, and cloud-based analytics platforms can often plug into older machinery without major modifications.
- Is it quick? For the pilot AI project, you want quick and simple. If it needs a months-long implementation timeline, it’s too complex for a first project.
If you answered yes to each question, you have your first project for AI in manufacturing. Congrats!
Digital transformation doesn’t happen overnight—nor does it need to. Start small to prove value, then roll savings into the next upgrade. This approach spreads costs across budget cycles and reduces risk.
Skip the Common First AI Implementation Traps
IMEC has seen manufacturers make these very common mistakes. Don’t be one of them:
- Starting too big: Your first project should prove the concept, not change everything. Pick one line, one process, one piece of equipment.
- Forgetting about your people: Your operators and techs know what you do better than any AI consultant. They’ll be using these systems every day. Include them in planning.
- Too much tech: Even the best AI tool is only as good as the support it offers. Don’t get wrapped up in buzzwords like operational efficiency. Focus on real problems and solutions.
- Being a loner: You don’t have to be an AI expert to make AI in manufacturing work for you. Don’t be shy to partner with specialists to skip the trial-and-error and get winning solutions.
Starting with AI in Manufacturing: Your Next Move
The manufacturers succeeding with AI don’t have the fanciest technology or biggest budgets. They’re the ones who started with a clear focus, then measured the results, learned any needed lessons, and built on their successes.
Your shop floor is ready for AI that helps drive real efficiency gains and solves real problems. Now you know how to get started with your first AI pilot project, the right way. IMEC specializes in offering manufacturers like you support and guidance as you navigate these decisions.
We’re here to help you turn your biggest operational challenge into your first AI success story. Feel free to reach out to us for further support today.
*This article was developed through the combined expertise of contributors from IMEC and Goodman Lantern.
