The Simple Test: What Job Needs Doing?
If the task is broad, text heavy, and cross functional, general AI often suffices. If the task touches plant safety, tolerances, or regulatory evidence, lean to industry specific AI that is constrained by your rules and process windows. Adoption across U.S. businesses remains modest, hovering near 10% by mid 2025, so expectations should be grounded in staged rollouts (Census BTOS, 2025) [https://www.census.gov/about/history/stories/monthly/2025/julu-2025.html].
A quick rule of thumb: if someone must sign for it, calibrate it, or lock it out, you probably need an industry tuned system with explicit guardrails. That sounds simple but it is definately where most pilots go sideways.
What General Purpose AI Does Well
General AI is strong at summarizing maintenance logs, drafting SOPs, cleaning up supplier emails, and answering policy questions with citations to your internal manuals. These are low risk accelerators that free engineers and technical services from repetitive writing and search.
You still need controls. Apply a lightweight risk lens from the NIST AI Risk Management Framework, which is being revised and supported through the NIST AI Resource Center with practical playbooks and TEVV materials (NIST AIRC, 2026) [https://airc.nist.gov/]. For organizations that prefer international standards language, ISO provides AI risk guidance that maps cleanly to existing quality systems (ISO 23894, 2023) [https://www.iso.org/standard/77304.html].
Where Industry Specific AI Wins
Plant decisions depend on physics, formulations, and constraints. Industry AI can embed resin cure windows, kiln ramp rates, bagging line safe speeds, adhesive compatibility, or warranty rules so recommendations stay inside spec. That matters for SKU selection, like kind substitution, and guided configuration in CPQ for complex assemblies.
Patterns in sensors also differ by process. A quarry conveyor or roll former has different failure modes than a batch reactor. Domain models can include seasonality, shift patterns, and upstream material variation, which reduces false alarms and over maintenance.
Data Reality Check For Plants
Most facilities sit on messy historians, spreadsheets, and tribal know how. Start with one line, one product family, or one failure mode. Use a thin adapter that reads from your historian and MES, and write back only the decision or alert needed in the workflow. NIST and MEP emphasize staged adoption for small and mid sized manufacturers, with training and templates to de risk change (NIST MEP, 2024 update 2025) [https://www.nist.gov/news-events/news/2024/11/cesmii-and-nist-mep-partner-boost-us-manufacturing-smart-technologies].
Build Versus Buy In 2026
Buy when the job is common across plants and vendors can prove referenceable outcomes in similar processes. Build or co develop when your chemistry, curing, or mixing steps are unique. Public programs can offset learning costs. DOE continues to fund smart manufacturing access for small and mid sized facilities, including technical assistance through the Manufacturing USA network and MEP partners (DOE SMLP, 2025) [https://www.energy.gov/mesc/articles/us-department-energy-announces-nearly-13-million-incentivize-smart-manufacturing]. NIST also announced new centers to accelerate AI for U.S. manufacturing productivity, a signal that published evaluations and methods will improve through 2026 (NIST, 2025) [https://www.nist.gov/news-events/news/2025/12/nist-launches-centers-ai-manufacturing-and-critical-infrastructure].
Governance And Safety By Default
Treat any AI that influences people or machines as safety relevant. Keep lockout tagout, machine guarding, and point of operation guarding in force regardless of autonomy level, and document human review for every change to setpoints or recipes (OSHA Machine Guarding, current) [https://www.osha.gov/machine-guarding]. For robotics and mobile equipment, draw from NIOSH guidance on safe human robot interaction and emerging risks as AI driven systems enter shared workspaces (NIOSH Robotics, 2024) [https://www.cdc.gov/niosh/centers/robotics.html]. NIOSH reiterates in 2026 that training and good practice remain vital when introducing advanced tech to manufacturing (NIOSH Manufacturing, 2026) [https://www.cdc.gov/niosh/manufacturing/about/index.html].
Good Pilot Candidates For Construction Materials
- Quality inspection that flags surface defects on panels, sheets, or profiles with a clear review step before rework.
- Energy optimization in curing or drying ovens where goals are framed as ranges, not single targets, and operators approve changes.
- Technical services copilot that answers common spec and compatibility questions using your approved datasheets and test reports only.
Each pilot should have an operator facing view that shows why the system suggests an action, a way to disagree, and a log for audit. This aligns to NIST RMF outcomes on transparency and accountability while keeping focus on throughput and scrap rates (NIST AIRC, 2026) [https://airc.nist.gov/].
What To Measure As You Scale
Pick two or three metrics that a line leader already watches. Common choices include first pass yield, unplanned downtime minutes, and changeover duration. Track error rates for AI suggestions, percent of suggestions accepted, and time to detect drift. Use BLS productivity data only as external context, not as proof of your own ROI. Manufacturing labor productivity rose in 2025, but local improvements must be proven with your baseline and control plans (BLS, 2026 release for Q3 2025) [https://www.bls.gov/productivity/].
Bottom Line For Busy Leaders
General AI speeds paperwork and knowledge tasks. Industry specific AI respects process physics, safety rules, and compliance evidence. Most plants will run both, with general AI serving teams and industry AI driving decisions that touch product, people, or power. Move in small slices, wire in human review, and borrow freely from government backed playbooks so improvements stick without disrupting production.
