The Most Effective Strategies for Integrating Agentic AI Into Legacy Manufacturing Environments Without a Full Overhaul
Answering the audience’s questions
Manufacturing is not a greenfield playground for shiny new technology. It’s a world of legacy machines, siloed data, and decades-old dashboards that keep production running. Into this reality comes agentic AI—a powerful but new tool promising faster decisions, smarter insights, and measurable improvements.
But for manufacturers, the big question isn’t what agentic AI can do in theory. It’s how to make it work inside plants that weren’t designed for it. At AI Frontiers 2025, an audience member voiced this concern directly:
“What are the most effective strategies for integrating agentic AI into these existing environments to deliver measurable improvements without a full infrastructure overhaul?”
The panel’s answer was pragmatic: don’t start over. Work with what you already have, layer in context, and focus on practical steps that deliver results.
Work with the data already in your systems
Most plants are sitting on years of operational data, but much of it is locked away in dashboards, HMIs, and siloed databases. The first step isn’t a technology overhaul. It’s to free that data and make it usable.
That means putting it into standard formats, cleaning it enough to be reliable, and applying basic governance so it can be trusted. Without structured inputs, agentic AI cannot deliver dependable results.
Give data meaning, not just pipes
Simply connecting machines isn’t enough. To get real value, manufacturers need to give their data context. Tools like ontologies, knowledge graphs, or standardized semantic models create a common language for different systems to “speak.”
Once data is contextualized, traditional analytics can start to reveal patterns, and agentic AI can go further—suggesting next steps, drafting reports, or supporting decision-making.
Respect the brownfield reality
Every manufacturer knows that no two machines are the same. Equipment from different decades and vendors has to coexist on the same line. Ripping and replacing it all isn’t practical.
A more effective approach is to use interface classes or modular abstractions that let very different machines contribute consistent datasets—for example, energy consumption or performance metrics. This makes it possible for AI agents to deliver insights without requiring every system to be rebuilt.
Target the jobs no one wants to do
Agentic AI is especially powerful when it takes on the tedious tasks that engineers and operators dislike. Test logs, maintenance manuals, and HMI screenshots are full of information but too messy to process manually.
Here, agents can parse and summarize the material, turning it into clean, actionable insights. This doesn’t replace human expertise—it supports it. Engineers still review the output, but instead of spending hours gathering information, they spend minutes validating it.
Start small, then grow into a platform
The safest and most effective adoption strategy is to start with quick wins. That might mean a contextualized dashboard, an AI-generated draft of a downtime report, or an agent that helps classify common events. These early use cases prove the value of the technology without major disruption.
Once those wins are established, manufacturers can take the next step: scaling through a platform model. This means creating shared services across plants, ensuring lifecycle management of agents, and building the operational discipline to keep AI reliable at scale.
A roadmap manufacturers can act on today
Integrating agentic AI into legacy environments isn’t about tearing everything down and starting over. It’s about making existing systems more useful.
By freeing trapped data, adding semantic context, bridging legacy machines with modular frameworks, applying AI to repetitive workflows, and scaling from small wins to enterprise-wide platforms, manufacturers can see measurable improvements now—not years from now.
Agentic AI may be new, but it doesn’t require a new factory. It requires a smarter approach to the one you already have.
Special thanks to Vatsal Shah (Litmus), Rajkumar Mylvaganan (ZF Group), Jonathan Wise (CESMII), and Andrew Scheuermann (Arch Systems) for sharing their insights in response to this question during AI Frontiers 2025.
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