AI Policy Development
Most AI policies are written for lawyers and unread by everyone else. We build policies for the people who have to follow them — with training that produces behavior change, not completion certificates.
What we deliver.
Policy Framework
A written AI policy covering data handling, output review requirements, disclosure standards, and prohibited uses — written in plain language with concrete examples, not legal abstraction. Organized by role, not by risk category.
Built for a 200-person organization in three weeks. Covers field operations, customer service, administrative, and management roles with different guidance for each.
Scenario-Based Training
Training organized around situations the specific team will actually encounter — not generic case studies. Half-hour team-specific sessions with realistic scenarios that require judgment, not procedure recall.
Training that produces behavior change requires specificity. What your customer service team faces is different from what your field operations team faces. We build both.
Risk Assessment by Role
A prioritized assessment of which AI risks apply to which roles in your organization, with guidance calibrated to actual risk exposure. Not every role has the same AI risk profile.
The customer service rep drafting AI-assisted communications and the analyst generating financial reports need different guidance. We map the difference.
Governance Structure
Accountability structures for AI decisions — who reviews AI output in which contexts, who owns the policy, when the policy gets reviewed, and what happens when someone doesn't follow it.
Includes a built-in review cadence because every AI policy needs a version 2 within 12 months. Governance that doesn't include its own maintenance becomes shelfware.
What makes it work.
- →Written for the person with the least AI familiarity, not the most
- →Concrete examples over abstract principles
- →Explicit accountability: AI is a tool, humans own the output
- →Role-specific guidance rather than one-size-fits-all
- →Training built around your actual workforce scenarios
- →Built-in review cadence because tools evolve faster than documents
The questions we start with.
Before writing a policy sentence, we ask: what are you actually afraid will happen? Not theoretical scenarios — the specific things that happen when your workforce gains access to AI tools without guidance.
The answers drive the policy. A field technician uploading customer equipment photos to ChatGPT needs different guidance than an analyst using AI to draft a financial report.
The policy we write addresses each scenario directly with concrete examples. "Do not upload customer equipment photos to consumer AI applications" is more actionable than "respect customer data privacy when using AI tools."
Your team is already using AI.
The question isn't whether to have an AI policy — it's whether you have one before the first incident or after. We build policies that take three weeks, not three months, and training that produces the behavior change that prevents the incident.
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