You may have heard the claim:
“Brownfield plants with legacy systems can just add AI to map messy data, read screens, and speed up decision-making — no rebuild required.”
It’s an attractive idea, but it oversimplifies reality and can mislead decision makers.
Here’s why:
1️⃣ Legacy Systems Are a Data Integration Nightmare
Older PLCs, CNCs, and SCADA systems often use proprietary or obsolete protocols.
They lack semantic tagging (ISA-95/UNS), unified historians, or consistent time-stamped logs. AI thrives on clean, contextual, real-time data — but most brownfield plants don’t have it.
Connecting decades-old controllers often means costly gateways, reverse engineering, and months of manual data modeling.
2️⃣ Screen Scraping Is Fragile and Risky
“AI reading screens” sounds clever until:
- HMI layouts change, and the OCR pipeline breaks.
- Latency turns “real time” into seconds or minutes.
- Unsupported Windows XP/7 HMIs become a security liability when you add screen capture agents.
Screen scraping is a stopgap, not a foundation.
3️⃣ “Messy Data Mapping” Isn’t a Quick Fix
- Much of the critical information in older plants still lives in handwritten logs or operator memory. AI can’t infer what was never captured.
- Adding IoT sensors and edge devices is often required — a rebuild by another name.
- Data cleaning and contextualization usually take more time and budget than the AI modeling itself.
4️⃣ People & Process Are Often the Hardest Part
AI adoption fails if operators don’t trust its recommendations or if IT/OT networks can’t support secure connectivity.
Sustaining AI requires continuous retraining, data governance, and a modern support model — features that are rarely in place in older facilities.
5️⃣ ROI Can Disappoint Without Modernization
The cost of retrofitting, unifying, and maintaining data pipelines can approach the cost of partial modernization.
Predictive AI only delivers value if the failure modes are frequent and well-instrumented — often not the case in legacy environments.
✅ A More Realistic Playbook
Instead of expecting AI to “just work,” brownfield success usually requires:
- Building a unified namespace or data broker layer.
- Retrofitting sensors and modern gateways where needed.
- Investing in data governance and contextual modeling before AI.
- Change management and operator adoption planning.
With this groundwork, AI can truly accelerate insights and decision-making. Without it, projects stall or deliver little ROI.
💡 Takeaway:
AI is powerful — but in brownfield plants, it’s not a magic overlay. Success stems from combining AI with incremental modernization, a robust data infrastructure, and practical cultural adoption strategies.