Approve Use Cases And Assign Risk Tiers
Treat AI like any other change-controlled process. Use a lightweight intake that records objective, owner, data sources, and failure modes before anyone builds or buys. For structure, borrow language from the NIST AI Risk Management Framework and its living Playbook.
If you sell into Europe, note the EU AI Act schedule. Most rules take effect on August 2, 2026, with additional obligations in 2027 for embedded high‑risk AI. High‑risk systems must meet requirements like data quality, human oversight, and logging, so put those criteria into your internal approval form now.
Lock Down Data And Tool Access
Shadow tools are the fastest path to trouble. IBM’s 2025 study found a widespread “AI oversight gap,” with 97 percent of affected organizations lacking proper AI access controls and 63 percent without AI governance policies. The 2025 Verizon DBIR also shows third‑party involvement in breaches has roughly doubled year over year, which matters when teams connect plug‑ins to product data.
Publish a short list of approved AI tools, the data they may touch, and who can use them. Block uploads of confidential formulas, pricing, and customer PII to public chatbots. For industrial firms, breach costs run high, with 2024 industrial sector breaches averaging USD 5.56 million, so prevention beats cleanup.
Keep Humans Accountable At Checkpoints
Decide where human review is required and write it down. In technical services, set confidence or risk thresholds that route AI answers on code compliance, compatibility, or safety to a qualified reviewer before release. The EU AI Act formalizes this idea, requiring proportionate human oversight for high‑risk systems.
Extend the same guardrail to claims and marketing. The FTC has acted against AI misuse and deceptive claims, including the Rite Aid facial recognition case, action against IntelliVision’s bias‑free claims, and a 2025 suit over AI‑tied earnings claims (Air AI). If AI helps write customer‑facing copy, require substantiation and legal review.
Log, Test, And Trace Changes
Your AI outputs are only as trustworthy as your evidence trail. Keep versioned prompts, training datasets, model settings, and evaluation results. The NIST AI RMF Playbook highlights transparency and documentation outcomes you can adapt to shop‑floor reality (Playbook). If you operate in the EU, high‑risk systems must support automatic logging over their lifetime and providers must keep those logs for set periods (Article 19), with post‑market monitoring plans.
Make test-before-release a habit. For example, when deploying an assistant that suggests resin‑to‑substrate combinations, hold out recent tickets from Saint‑Gobain‑like applications and compare AI answers to approved technical bulletins. Keep a simple changelog so auditors and buyers can see when, why, and how the system changed.
What Good Looks Like On A Plant‑Ready Page
One page per use case works. It names the business owner, purpose, where the model can and cannot be used, the data it is allowed to read, the review checkpoints, and the rollback plan. Add links to current prompts, last evaluation run, and incident contacts. If your org already follows ISO management systems, ISO/IEC 42001 gives a compatible governance skeleton for AI (ISO/IEC 42001).
A Practical Rollout Rhythm That Teams Will Follow
Start with two to four use cases that touch clear pain, like answering product spec questions, building quote checklists, or triaging quality complaints. Apply the same four guardrails, even if the first implementation is a spreadsheet and a shared folder. Revisit the pages quarterly, and update them as 2026 rules and harmonized standards land in the EU and as NIST refreshes its guidance (NIST AIRC updates).
