AI is everywhere right now.
Most manufacturers already see the value of AI. But the challenge now is figuring out what to do with it in a practical way.
There’s no shortage of talk about automation and transformation, but not much that connects to what’s actually happening on the shop floor, in engineering, or across daily operations.
The good news is, getting started with AI doesn’t require a big strategy or a major investment. In most cases, the best place to start is smaller and more familiar than you might expect.
One of the more useful ways to think about modern AI is not as a feature or a tool, but as a resource.
More specifically, it helps to think of it as another employee.
That might sound like a stretch, but it’s a practical way to frame what’s changed. AI today can learn processes, follow rules, and complete tasks in a way that looks a lot like how a person would approach the same work.
The difference is speed and scale. Instead of taking weeks or months to get up to speed, AI can process large amounts of information almost instantly. And instead of just surfacing insights, it can take action.
That last part matters. For a long time, AI in manufacturing was mostly about reporting — dashboards, forecasts, and recommendations. Useful, but still dependent on someone to take the next step.
Now, AI can start handling some of that execution.
For most manufacturers, the ERP system is already the foundation. It’s where orders are managed, inventory is tracked, jobs are scheduled, and costs are controlled.
With newer AI capabilities built into ERP systems, that data becomes even more valuable. AI solutions like Genius Cortex can help identify patterns, highlight risks, and improve forecasting.
ERP systems are designed to work with structured data. That’s what they do well.
But in real-world manufacturing environments, not everything fits into that structure. Important information still lives in emails, spreadsheets, PDFs, and conversations between teams. That’s just how work happens.
So even with a strong ERP in place, there are still gaps.
That’s where AI can add value. If you only rely on ERP data, you’re only working with part of the picture.
This is where AI starts to fill those gaps.
Instead of working only inside the ERP, AI can now operate across systems. It can read emails, interpret documents, pull information from different tools, and connect workflows that were previously manual.
Think about something as simple as an incoming request.
A customer sends an email asking for a quote. There might be product details, delivery expectations, and a few conditions buried in the message. Someone has to read it, interpret it, enter the right information into the ERP, and move the process forward.
It’s not complicated work, but it is repetitive. And it takes time.
This is exactly the kind of task AI is well suited for. It can read the request, extract the relevant details, and trigger the next steps automatically — whether that’s creating a quote, updating a record, or notifying the right person.
The ERP doesn’t go away in this process. It still acts as the system of record. But AI helps make sure the right information gets there, and that the process keeps moving.
There’s still a lot of hesitation around AI, and most of it comes back to the same concern: what happens to the people doing the work?
In practice, AI is not replacing roles in manufacturing. It’s changing how those roles spend their time.
The work that AI takes on tends to be repetitive and process-driven. Data entry, basic request handling, moving information from one system to another. Necessary work, but not where most people add the most value.
What remains are the parts of the job that require judgment and experience. Managing exceptions, working with customers, making trade-offs, and making decisions that impact the business.
That shift is already happening in a lot of organizations. People are spending less time “clicking” through systems and more time focusing on the work that actually moves things forward.
One of the biggest mistakes companies make with AI is trying to start too big.
They look at the entire business and ask how AI can transform everything at once. That usually leads to long timelines, high costs, and unclear results.
A better approach is to start with one problem.
Look for a task that is repetitive, time-consuming, and well understood. Something your team does every day that doesn’t require much judgment, but still takes up a meaningful amount of time.
Quoting is a good example in many manufacturing environments. So is processing orders, updating delivery dates, or handling routine customer requests.
These processes are often consistent enough that they can be automated, and the impact is easy to measure.
Another useful trigger is hiring. If you’re about to bring someone on to handle a specific type of work, it’s worth asking whether part of that workload could be handled differently.
That doesn’t mean replacing the role. But it can change what that role looks like and reduce the amount of manual work involved.
This allows teams to respond quickly when plans change—which they often do in ETO.
When companies take this approach, the results are usually straightforward.
You don’t see massive, headline-grabbing transformations. You see time being freed up. Work moving faster. Fewer bottlenecks.
A process that used to take hours each week gets reduced to minutes. A task that required constant follow-up starts happening automatically. Teams have more time to focus on customers, planning, and problem-solving.
That’s where AI starts to feel useful. Not as a big initiative, but as a series of small improvements that add up over time.
In many manufacturing businesses, the process is straightforward but repetitive. A request comes in, someone builds the quote in the ERP, and sends it back. Over time, that work adds up.
In one case, a small team was spending around 15 hours per week on quoting alone. Not because it was complex, but because it had to be done over and over again.
Instead of overhauling the entire workflow, they focused on that one task. They introduced a simple AI agent that could read incoming requests, identify what was needed, and generate the quote directly in the ERP.
The result was practical. Time spent on quoting dropped from about 15 hours to 5 hours per week, freeing up roughly 10 hours for other work.
That time was then used for things that had been pushed aside — following up with customers, addressing issues earlier, and taking a more proactive approach.
When you step back, AI in manufacturing isn’t one thing. It’s a combination of layers working together.
Your ERP system continues to manage your core operations. AI within the ERP helps you understand what’s happening in that data. And AI outside the ERP helps connect systems, structure information, and keep processes moving.
Your team stays at the center of it all, making decisions and handling the situations that require context.
None of this requires starting from scratch. It builds on the systems and processes you already have in place.
You don’t need a full AI roadmap to get started.
In most cases, the best place to begin is much smaller. One process, one task, one problem that your team deals with every day. Something that takes time, follows a pattern, and doesn’t require a lot of judgment.
That’s where AI tends to have the most immediate impact.
It’s also what makes the next step easier. Once you’ve seen it work in one area, it becomes much clearer where else it could apply. Not because of a long-term strategy, but because you can start to see the opportunities in your own operations.
That’s usually how this builds. Not as a single initiative, but as a series of small improvements that add up over time.
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