Is a PoC the Right Path for Manufacturing AI?
PoCs get a bad rap in factories—they often feel like science projects that never leave the lab. They don’t have to. When a PoC is designed to prove scale, not just possibility, it becomes the fastest path to real plant results. This piece distills a candid question from AI Frontiers 2025 into a practical, no-fluff playbook drawn from the panel’s advice (watch the on-demand video here).
So, is a PoC the right path? Yes—when you run it as a pilot-to-production step. That means proving repeatable impact under real operating conditions (across shifts, assets, and sites), not a one-off tech demo. The guidance below focuses on how to structure that kind of PoC so it earns the right to scale.
Why PoCs Disappoint
Traditional PoCs often answer “can the model work?” on a narrow, idealized slice of data. That misses what plants actually need: evidence the result will hold up across assets, shifts, and sites—without heroics or one-off scripts. When a PoC doesn’t reflect real workflows, its gains look “limited,” and momentum dies.
Reframe: Prove a Minimum Scalable Unit (MSU)
Aim to validate the smallest unit that can be cloned—process + data + workflow + change practice. An MSU-style pilot:
- Runs under production constraints (noise, variability, shift changes).
- Instruments the workflow, not just the model (alerts → actions → outcomes).
- Produces reusable artifacts you’ll carry to the next site (connectors, SOP addenda, acceptance tests).
Design It Like Operations, Not a Hackathon
Use manufacturing discipline to build trust and resilience:
- Design the experiment.
Treat it like DOE: define controllable factors (e.g., alert thresholds), noise factors (material/shift mix), and the response you’ll judge. - Use real controls.
A/B or A/period/B on the same line/cell to separate signal from seasonality. - Operational acceptance tests (OAT).
Script and pass failure scenarios: alarm storms, data dropouts, sensor drift, network segmentation, user lockouts, rollback. If it can’t pass OAT, it’s not ready for Site #2. - Engineer the human experience.
Put guidance where people work (HMI/MES, maintenance app). Capture reasons when operators defer or override; those reasons drive the next release.
What “Good” Looks Like
- Financial gating: move forward only if the modeled payback at a few sites meets your hurdle (e.g., <12 months), verified with Finance.
- Reusable kit: during the pilot, produce connection profiles (historian/MES/CMMS/PLM/EDMS), an alarm taxonomy, role/permission maps and audit events, rollback SOPs, and a site onboarding checklist (hardware, network, cybersecurity, safety).
- Shift-proof performance: results hold nights/weekends, not only with the A-team.
- Known failure modes: you understand behavior under missing inputs, noisy sensors, or product mix changes—and how the system recovers.
Move From “Limited Gain” to Material Impact
- Concentrate lift: target the highest-loss bottleneck cell first, not a plant-wide sprinkle.
- Reduce intervention latency: measure detection → guidance → action. Shortening this time-to-fix often multiplies realized benefit even if detection accuracy stays constant.
- Optimize decisions, not just predictions: track when recommendations changed an action (pulled-forward maintenance, corrected setup, intensified inspection) and whether outcomes improved.
- Price the alerts: quantify the cost of false positives (fatigue) and false negatives (missed failures). Tune thresholds for business optimality, not just model F1.
Three Gates That Earn Scale
- Problem Fit: Clear loss, clear baseline, credible control.
- Plant Fit: Passes OATs, reduces time-to-fix, holds across shifts.
- Network Fit: Artifacts packaged so a second site can onboard in weeks, not quarters, with minimal engineering time.
If you stall at Gate 2, redesign the pilot rather than abandoning PoCs altogether. If you pass Gate 3, plan a wave rollout (e.g., 3–5 sites per quarter) and keep instrumentation consistent so deltas remain visible.
What It Means for Manufacturers
A PoC is the right path for manufacturing AI—when it proves scale, not just possibility. Engineer a Minimum Scalable Unit (MSU) that holds up under real-world variability, captures how work changes, and ships the artifacts that make the next site faster. That’s how pilots move from “interesting” to material, and why leadership funds the next wave.
Source: AI Frontiers 2025 panel transcript, “Building Data Accuracy and AI Trust in Smart Manufacturing.”
