AI Proficiency: the metric that tells you whether employees are getting real value from AI

If you are tracking adoption of your internal AI tools, you already know something important. You know how many people are using them, how often, and in which parts of the organization. That data puts you ahead of most teams deploying AI at enterprise scale.

But adoption answers one question: are people using the tools? It does not answer the question that determines whether the investment was worth making: when employees interact with AI agents, how productive are those interactions?

According to McKinsey's Superagency in the Workplace report, there is a 6x engagement gap between AI power users and typical employees in the same organizations. EY's 2025 Work Reimagined Survey found that 88% of employees use AI daily, but only 5% use it in advanced ways. Deloitte's 2026 State of AI in the Enterprise, based on a survey of 3,235 senior leaders, identified the AI skills gap as the single biggest barrier to integrating AI into workflows, with 84% of organizations reporting they have not redesigned jobs or workflows around AI capabilities.

High adoption and low proficiency is the most common state enterprise AI deployments find themselves in. Usage counts are high. The productivity gains are not materializing. The gap between the two is where most enterprise AI ROI is currently being lost.

AI Proficiency is the metric designed to close that gap.

What AI Proficiency measures

AI Proficiency measures how effectively employees interact with AI agents — capturing the quality of their prompting, their ability to get useful answers on the first attempt, and how well they are using AI to accomplish real work rather than simple queries.

It is based on implicit feedback: behavioral signals within AI conversations that reveal the quality of an interaction without requiring surveys or self-reporting. These signals are already present in every conversation your employees have with AI agents. AI Proficiency reads them, anonymizes them, and makes them measurable at the team and department level.

The signals that determine a proficiency score include:

Rephrase rate. When an employee needs multiple attempts to get a useful answer, it indicates either a prompting quality issue — the employee does not know how to frame requests effectively — or an agent quality issue where the AI is not understanding clear requests. High rephrase rates within a specific team point directly to where proficiency development is needed.

First-turn resolution. The proportion of interactions where the AI addresses the employee's actual need in the first response, without requiring clarification or follow-up. High first-turn resolution reflects both a well-configured agent and employees who know how to ask precisely for what they need.

Task complexity over time. Whether employees are using the AI agent for increasingly complex and high-value tasks, or keeping it for simple queries while continuing to handle demanding work manually. Proficiency growth shows up as employees migrating toward more sophisticated use — not just more frequent use.

Session depth. The depth and quality of engagement within a conversation. Employees who are genuinely proficient with an AI agent tend to conduct richer, more productive sessions. Employees who are struggling tend to have shallow sessions that end without resolution.

All of this is fully anonymized and aggregated. No individual employee is identified. No conversation is attributed to a specific person. Teams see proficiency at the level where it is useful for decisions — by department, by role, by tool, by time period — not at the level of individual behavior.

What becomes visible when you measure proficiency

Proficiency data opens up questions that adoption metrics alone cannot answer.

A team with strong adoption and strong proficiency is getting real value from its AI tools. That is a signal to invest further, expand use cases, and understand what is working so it can be replicated elsewhere.

A team with strong adoption and lower proficiency is a different kind of signal. It could mean the agent needs better configuration for that team's specific workflows. It could mean there is a training opportunity that would meaningfully improve productivity. It could mean the AI performs differently across different task types, and the team has not yet found where it adds the most value. Each of these is a specific, actionable insight. None of them are visible in adoption data alone.

Proficiency data also shows change over time, which is where its real value lies. Whether enablement programs are working. Whether new AI tools are easier or harder for employees to use effectively. Which teams are developing faster. Where proficiency is plateauing. These trends tell enterprise AI leaders where to focus attention and investment in a way that usage statistics cannot.

Why this matters now

The first phase of enterprise AI deployment was about access. Get the tools deployed. Get employees using them. Measure that adoption is happening.

Most large enterprises have completed that phase. The tools are deployed. Adoption is happening. The next question — and the harder one — is whether any of it is making a measurable difference to how work gets done.

Deloitte's 2026 research identified insufficient worker skills as the biggest barrier to integrating AI into existing workflows. 53% of organizations named educating the workforce to raise AI fluency as their top talent strategy response. But most of those organizations have no way to measure whether that education is working — no baseline to measure against, no way to track change over time, no visibility into which teams are developing and which are not.

AI Proficiency makes that measurable. It connects the enablement investment to the behavioral outcome: not just how many employees attended an AI training session, but whether the way they interact with AI agents actually changed afterward, and whether their sessions are producing better outcomes as a result.

This is the metric for the next phase of AI deployment — where the question is not whether employees have access to AI, but whether that access is translating into the productivity and business value that justified the investment.

Nebuly

Nebuly is the ROI platform for enterprise AI. It connects to the AI agents your business runs on, the assistants your customers interact with, and the tools your employees use every day, including Claude, ChatGPT, and Copilot, and translates that activity into business value. How much time is being saved across teams. What revenue your AI is influencing. What adoption and AI proficiency look like in practice, across departments and geographies. All aggregated at the organizational level, never tied to individuals.

If you need clarity on what your AI investment is actually delivering, book a demo.