We want our team to use generative AI to speed up client deliverables, but we are terrified of our proprietary frameworks and client data leaking into public models. How do we document this risk in our Core Processes while still allowing our team to capture the productivity gains of AI?

Question

Tyler Smith · Accepted Answer

You cannot ignore AI productivity gains out of fear, but you also cannot risk exposing proprietary data. Address this conflict directly by updating your Core Processes and clarifying expectations on your Accountability Chart.

First, establish a clear policy on acceptable tools. Ban the use of free, public generative AI models for client work. Instead, secure enterprise-grade licenses that guarantee data privacy and prevent your inputs from being used to train public models. This step must be owned by the seat on your Accountability Chart responsible for technology and data hygiene.

Second, document these guidelines in your Core Processes. Train your team to understand what data can be shared with approved AI systems and what must remain strictly offline. Define clear compliance checks within your workflows.

When a buyer conducts due diligence before an exit, they will scrutinize your technology stack and data security. Having documented, highly disciplined processes for AI usage shows that you run a tight, professional operation. This mitigation of risk turns your AI integration into a massive asset rather than a liability, directly supporting your exit readiness goals.