An AI Context Audit is not a workshop about whether AI matters. It is a structured review of the business knowledge, rules, workflows and opportunities that determine where AI can actually work inside your organization.
Moxie’s AI Context Audit is a two-week, fixed-fee engagement designed to give leadership teams a concrete answer to four questions: What do we already have? What is missing? Where can AI create meaningful value? And, what should we do first?
The result is a practical roadmap grounded in the way your business actually operates, not just a generic list of AI use cases.
What the AI Context Audit looks at
The Audit focuses on the inputs and conditions AI needs to produce useful, reliable work. That means looking beyond the model itself and examining the context around the work.
1. Your current AI readiness
We map where the organization stands today: what is already in place, where teams are experimenting, and where the foundation is not yet strong enough to support a useful build. The goal is to establish a clear starting point before new initiatives are prioritized.
2. The business context
We inventory the knowledge that makes your company distinct and that an AI system would need to understand to perform a specific task well. Depending on the use case, that can include:
- Brand guidelines and how you communicate with customers
- Product and service information
- Approved claims, restrictions and off-limits language
- Workflow rules, approvals and how work actually gets done
- Institutional knowledge that lives in old decks, wikis or even people’s heads
This is the context that turns a broadly capable AI model into something that can work inside your business.
3. What is missing, outdated or contradictory
The Audit identifies the gaps that can derail an AI initiative before it begins. That may be missing documentation, conflicting guidance, outdated information or critical knowledge that has never been captured before outside of individual team members.
Instead of discovering those issues after a pilot produces generic, off-brand or incorrect output, they become part of the plan from the start.
4. The opportunities worth pursuing first
Not every possible AI use case belongs at the top of the list. We evaluate opportunities based on value, risk and complexity so the roadmap distinguishes between what is ready now, what needs foundational work first and what should come later.
The objective is not to tackle everything at once. It is to identify the safer, higher-value opportunities that can create measurable progress first.
5. The guardrails a use case has to operate within
For regulated or sensitive work, requirements cannot be an afterthought. The Audit accounts for the rules a use case needs to respect — including, where applicable, HIPAA requirements, legal guidelines and client-specific data governance — so those constraints are considered before implementation begins.
What happens during the two weeks
The engagement is deliberately tightly scoped. Rather than open-ended discovery, the Audit moves through four practical stages:
- Map the starting point. Establish the organization’s current AI readiness and the business areas worth examining.
- Inventory the context. Identify the knowledge, rules and source material AI would need for those areas.
- Flag the gaps. Surface what is missing, contradictory, outdated or inaccessible before it becomes a build problem.
- Prioritize the roadmap. Sequence opportunities according to value, risk and complexity and identify the most practical place to start.
At the end of the engagement, the team should be able to move from “we should be doing something with AI” to a defined first move and a clearer view of what it will take to get there.
What you receive at the end of the Audit
The deliverable is not a slide deck full of generic AI buzzwords. It is a set of practical outputs your specific team can use to make decisions and move forward. You’ll receive:
- AI readiness assessment. A clear picture of where the organization stands today and how prepared it is to move on priority use cases.
- Context inventory. A view of the business knowledge already available, where it lives and what an AI solution would need to use.
- Gap list. The missing, outdated or contradictory information that should be addressed before it undermines a build.
- Prioritized roadmap. A sequenced view of the opportunities worth pursuing first, with more complex or higher-risk use cases staged behind them.
- A clear first move. A recommendation for the first opportunity worth acting on, why it belongs first and what has to be in place to build it.
What happens after the Audit
The Audit is designed to stand on its own as a decision-making roadmap. It also creates a cleaner starting point for implementation because the foundational context, gaps, priorities and constraints have already been identified.
Moxie’s AI Services Group doesn’t stop at consulting, it has the engineering depth to help move from that roadmap into delivery. Our approach was developed first inside Moxie’s own operations and has since been applied to custom AI-assisted workflows, including work in regulated healthcare environments.
The point is not to start with a model and search for somewhere to use it. It is to start with the work, understand the context that makes that work successful, and identify the first AI opportunity that is both valuable and realistic to build.
Start with the Audit
If your organization knows it needs a more concrete AI plan, the AI Context Audit gives you a defined place to start: two weeks, one fixed fee, a clear view of what you have and what is missing and a prioritized roadmap for what to do next.
Learn more about Moxie Labs and the AI Context Audit at MoxieLabs.co, or email info@moxielabs.co to book an audit.
