Company lore and the 'why' behind technical decisions often disappear when employees leave. An AI agent can analyze the entire codebase and its commit history to answer questions and reconstruct narratives, effectively turning your repo into a searchable archive.

An AI agent with access to work product can serve as an impartial manager. It can analyze performance quantitatively, like a sports coach reviewing game tape, and deliver feedback without the human biases, office politics, or emotional friction that complicates traditional performance reviews.

Formal AI competency frameworks are still emerging. In their place, innovative companies are assessing employee AI skills with concrete, activity-based targets like "build three custom GPTs for your role" or completing specific certifications, directly linking these achievements to performance reviews.

The best test of knowledge is the ability to teach it. By having employees explain a new AI tool or workflow to their peers, they are forced to solidify their own understanding and identify knowledge gaps. This process turns passive learning into active expertise.

Rather than creating assessments that prohibit AI use, hiring managers should embrace it. A candidate's ability to leverage tools like ChatGPT to complete a project is a more accurate predictor of their future impact than their ability to perform tasks without them.

A powerful and safe use of AI for managers is not to generate performance reviews, which can feel impersonal, but to perform 'agentic search.' The AI can pull context from code, Slack, and documents to highlight employee wins and contributions that a manager might otherwise miss, especially as individual output increases.

To make AI adoption tangible, Zapier built rubrics defining "AI fluency" for different roles and seniority levels. By making these skills a measurable part of performance reviews and rewards, you create clear incentives for employees to invest their time in developing them, as behavior follows what gets measured.

Current AI adoption metrics focus on productivity (hours saved) rather than capability. A team can appear highly productive due to AI-generated outputs, while its members are actually becoming less capable of operating without the tool, creating a hidden vulnerability.

AI tools can automate tasks that were previously blockers for certain employees. People with great ideas who struggled with the mechanical skills of coding or data analysis can now execute on those ideas, potentially transforming them from low to high performers and leveling the playing field.