Start Here: What Is Binding Versus Voluntary
Most manufacturers will not find a single AI law that covers every workflow. The federal government sets direction and enforces existing laws, while states regulate privacy and some high‑risk uses. The NIST AI Risk Management Framework is voluntary, yet it is the closest thing to a common playbook for building controls.
Federal policy shifted in 2025 when the 2023 AI executive order was rescinded. Agencies still reference NIST guidance and sector laws, but there is no single federal AI statute for commercial manufacturing uses. Treat NIST as your baseline and layer in binding laws by data type and use case.
Data You Use Triggers State Privacy Laws
Sales and technical services teams often process customer contacts, site photos, support chats, and telemetry. These datasets can include personal information, which means state privacy statutes apply to how you collect, share, and train models. Use a current map like the IAPP’s state privacy laws tracker to confirm notice, consent, opt‑out, and data rights where you operate.
If you fine‑tune or retrieve from knowledge bases that include personal data, document purpose limits and retention. Keep service provider agreements tight on training rights, sub‑processors, and cross‑border transfers. For product analytics that are genuinely de‑identified, record the technical steps that make re‑identification unlikely.
Sales and Marketing Claims About AI Are Enforced
The FTC is using long‑standing truth‑in‑advertising rules for AI promises. In 2025 the Commission finalized an order against a company that marketed AI abilities without adequate substantiation. Your sales collateral, demos, and website must avoid guarantees, avoid unverifiable accuracy claims, and match real‑world performance.
For technical services, the same principle applies. Do not say a model “ensures” code compliance or “eliminates” field failures. Frame outputs as assistance, show conditions and limits, and log comparative tests that back any quantified statements.
High‑Risk AI: Watch Colorado’s Law
Colorado’s AI Act targets high‑risk systems that can drive consequential decisions such as hiring or access to services. The law’s effective date was delayed to June 30, 2026, and further amendments are possible this year. If your tools score applicants, route warranty claims, or auto‑approve credit for distributors, expect documentation, impact assessments, notices, and anti‑discrimination controls.
You can read the original bill summary for scope and definitions on the legislature’s site, but note that dates and obligations are evolving as of 2026. The summary page remains useful for core concepts like developer and deployer duties (Colorado General Assembly bill page).
Employment Screening Tools Sit Under Separate Rules
If HR uses automated employment decision tools for hourly plant roles or field techs, separate local or state requirements may apply. New York City’s law requires bias audits and candidate notices before use. Treat HR automation projects as their own compliance stream with legal review and public‑facing disclosures.
Practical Controls For Technical Services and Sales Knowledge
Start by mapping the decisions your AI actually touches. Advisory chat, CPQ suggestions, and spec lookups carry lower regulatory risk than automated approvals, yet they still need safeguards. Keep a human in the loop on any customer‑facing recommendation that could create safety, warranty, or code compliance exposure.
Adopt NIST’s functions in a lightweight way. Identify and measure model risks, govern prompt and output logs, and manage change control when you retrain or swap providers. Keep a short model card for each workflow that lists data sources, known gaps, prohibited uses, and contact for escalation.
Tighten your data story. Add an AI paragraph to your privacy notice that covers training, fine‑tuning, and sharing. In contracts with model vendors, reserve the right to disable training on your data, require breach notice, and require deletion on exit. For field photos and site documents, strip personal data where feasible before ingestion.
Strengthen customer‑facing accuracy. Calibrate confidence thresholds for answers that cite code sections or certifications. When confidence is low, route to a human and display a clear handoff note. Require evidence links for any claim about performance, tolerances, or approvals, and record which document version was used.
Document what you do not automate. List decisions that will remain manual, like substitutions that change UL listings or fire ratings. This list reassures auditors and sales leaders that you know the boundary between assistance and authority.
What To Do Next
Designate one accountable owner for AI in commercial operations who can convene Legal, Quality, and IT. Start with a simple register of AI use cases and the data each one touches. Pilot a NIST‑aligned control set on a single workflow, then replicate.
For 2026 planning, monitor Colorado’s rulemaking, your operating states’ privacy updates, and new FTC actions on AI marketing. Keep links to primary sources in your internal wiki and schedule quarterly reviews. You will not need a big‑bang program to stay compliant, just steady housekeeping that scales with adoption.
