Practical guide

Build a manufacturing AI acceptable-use policy employees can apply.

Translate general principles into clear decisions about tools, information, tasks, review, escalation, incidents, ownership, and change—then train with realistic examples.

Start with the policy framework

Why a usable policy matters

“Be careful” does not answer an employee’s operating question.

Employees need to know which systems are approved, what information may enter them, which tasks are allowed, what verification is required, who owns the final decision, and how to report a mistake. A policy that only describes AI risks may be technically cautious but operationally unusable.

The goal is not to produce one universal rule for every model and workflow. It is to create a governed decision structure that can distinguish low-consequence drafting with public information from work involving customer data, personnel decisions, controlled technical information, quality acceptance, or equipment control.

Policy owners

Bring the functions that already hold authority.

Illustrative acceptable-use contributors
Role	Contribution	Authority that remains with the organization
Business and process owners	Use cases, users, current workflow, value, consequences, and operating accountability.	Whether and how the process uses the capability.
IT and security	Approved systems, identity, access, integration, technical safeguards, incident handling, and vendor review.	Technology and security decisions.
Privacy, legal, and trade compliance	Personal data, contracts, disclosure, rights, retention, geography, and regulated information.	Interpretation and approval within their disciplines.
Quality and engineering	Controlled sources, competent review, evidence, records, change, release, and technical consequences.	Technical, quality, configuration, and production decisions within actual authority.
HR and workforce leaders	Employee notice, monitoring, training, role expectations, and personnel-use boundaries.	Employment policy and decisions.

Decision matrix

Define tool, information, task, and review together.

Illustrative policy matrix—not a company-specific approval
Situation	Possible rule	Required action
Public or synthetic information in an approved tool for low-consequence drafting.	May be allowed within documented use conditions.	Verify facts and label the output as a draft where appropriate.
Internal information permitted by the tool and company policy.	Allow only for approved tasks and named users.	Follow access, source, retention, review, and record requirements.
Customer, employee, regulated, export-controlled, proprietary, credential, or security-sensitive information.	Prohibit unless the authorized owners explicitly approve the system and use.	Stop and route the question before entry or upload.
Engineering, quality, safety, legal, hiring, compliance, or release decision.	AI may assist only where a workflow-specific policy permits it.	The authorized person reviews adequate evidence and makes the decision.
Tool or workflow materially changes.	Prior approval does not automatically carry forward.	Reassess data, capability, controls, evaluation, training, and ownership.

Starter policy structure

Make the core rules easy to find and copy into company drafting.

Purpose and scope: who, which systems, which work, and which locations or entities the policy covers.
Approved tools and accounts: where the current list lives and who may authorize an exception.
Information classes: what is permitted, prohibited, or requires explicit review before use.
Allowed and prohibited tasks: include practical examples from real roles.
Verification and authority: required sources, reviewers, records, and decisions that AI cannot make.
Transparency and intellectual property: when AI assistance must be disclosed and how ownership questions are routed.
Incidents and mistakes: how to stop, preserve relevant facts, report, contain, correct, and learn without hiding the event.
Training, monitoring, review, and change: owners, cadence, exceptions, and triggers for reassessment.

Train with examples

Policy becomes real when employees can classify a situation.

Use role-specific scenarios: summarizing a public supplier announcement, drafting an internal meeting outline, comparing controlled revisions, uploading a customer drawing, generating a work instruction, evaluating an employee, or accepting a quality record. Ask which tool is approved, what information is involved, what decision could follow, who holds authority, how the output is verified, and where the employee escalates uncertainty.

Review checklist

Revisit policy when the operating environment changes.

Can an employee find the approved-tool list and information rules at the moment of use?
Do examples cover actual manufacturing roles and common gray areas?
Are review, record, incident, and escalation requirements proportional and workable?
Have security, privacy, legal, HR, quality, engineering, and trade-control owners reviewed the portions they govern?
Is there a trigger for new vendors, models, integrations, information classes, users, and consequential workflows?

Build shared practice

Use Company AI Foundations to turn policy into usable team judgment.

Company learning gives a cross-functional team safe examples and shared language. The free Academy remains the independent path for individual learning.

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