Safe AI-use policy starter
A plain-language policy your team can adopt this week.
What data can go into AI tools, how to check what comes out, and who to ask when unsure.
AI agents for mid-sized plants: compliance and EHS, operations records, quality and traceability, and energy savings. The routine paperwork runs itself, and your people approve what matters.
8 in 10 professionals surveyed say AI has improved their own productivity, yet only about 6% of companies attribute 5% or more of their earnings to it. Closing that gap is the work. McKinsey, 2026
$2.7 trillion
forecast worldwide spending on AI in 2026, up 49.5% on last year. Your competitors are part of that number.
Gartner, September 202640%
of large companies are now scaling AI agents, up from 27% a year earlier. Among smaller companies it is 22%, and not moving.
McKinsey, State of AI 202684%
of manufacturers report measurable value from AI in their operations, yet only about one in five use cases is scaled across sites.
Deloitte, AI in Manufacturing 20261.7×
revenue growth at AI-leading companies compared with laggards. 60% of companies still see minimal gains.
BCG, September 2025Each one starts from a rule you answer to or money you can measure. On-premises, Azure or AWS, within the platforms your IT team already approves.
The procedures, permits and logs regulators ask for, written and kept current by AI. Your authorized people approve every one.
Field tickets and contractor invoices checked against the contract and the work actually done, so you pay only for what happened.
Every certificate checked against your spec and every lot traceable, so bad material stops at the dock and audits hold no surprises.
Leaks, failed steam traps, demand peaks and billing errors found in the bills and meter data you already have.
From machine shops and food plants to chemical plants and water utilities: the rules each industry answers to and the costs it fights every month, and the agents that take that work on, with your team approving every decision that matters.
Small before big. Measured, not promised.
We sit with operators and engineers, pull a sample of historian data and documents, and return a ranked list of opportunities with a value estimate and an honest read on data readiness. Fixed fee.
One asset, one process or one workflow. We agree the baseline and the success measure before writing any code.
The model runs in shadow mode beside your team. We measure it against the baseline, not against a slide.
If the numbers hold, we extend it to the next units and hand over what you need to own it. If they do not, we say so.
Ten questions on leadership, data, skills, safe use and workflows.
See where your team stands, which area is holding you back, and a sensible first step.
Take the checkQuotes, work orders, invoices, reports: what could an AI agent take off them?
Enter the volume and the time each one takes. See the hours and cost freed up each year.
Run the numbersA plain-language policy your team can adopt this week.
What data can go into AI tools, how to check what comes out, and who to ask when unsure.

Tell us about it. A 30-minute call is enough to say whether AI is the right tool, and if it is not, we will tell you that too.
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