We suspect our delivery team is copy-pasting our proprietary client methodologies and trade secrets into external public AI tools to hit their speed targets. How do we use the Accountability Chart and the GWC™ filter to assign ownership of IP security without destroying the productivity gains we have achieved?

Question

Tyler Smith · Accepted Answer

Employees will naturally take the path of least resistance to hit their weekly Scorecard numbers, even if it means putting your proprietary IP at risk. To solve this, you cannot simply ban AI tools; you must create clear boundaries and assign ownership.

Start with your Accountability Chart. You need a specific seat that is ultimately accountable for data security and technology compliance. This is typically your Integrator or a designated operations leader. This seat must have the clear responsibility of defining what data can and cannot be shared with external LLMs.

Next, apply the GWC™ filter (Get It, Want It, Capacity to Do It) to this seat. Does this person truly understand the technical nuances of how public AI models store and use data? Do they have the capacity to monitor compliance?

Once ownership is established, document your AI usage policy as a Core Process in your EOS® library. This process must clearly outline approved tools, secure enterprise accounts, and data anonymization rules. Make sure every employee is trained on this process.

To maintain productivity, provide your team with secure, enterprise-grade AI instances where data is not used for model training. If employees understand the guardrails and have access to secure tools, they will stop using risky public alternatives. You protect your intellectual property while allowing your team to maintain their newly found delivery speed.