Every CFO is being asked the same question. Without a defensible framework, the answer is a guess. Levos measures AI workforce productivity with controlled cohort analysis and confidence scoring, surfaced for the CFO, CHRO, manager, and employee at once.
The Information reported in April 2026 that Uber exhausted its full-year 2026 AI budget by April. CTO Praveen Neppalli Naga said the company was "back to the drawing board" after agentic coding adoption and AI-related costs moved faster than the budget model.
The point is not that AI failed. It is that usage outran measurement. High adoption, more generated code, and faster tool growth still leave the CFO with the same question: what changed in output, quality, cost, and capacity?
of Uber developers classified as agentic coding users
increase in AI-related costs since 2024
of live backend code updates written by AI systems
Enterprise Copilot, ChatGPT Enterprise, Gemini, and Glean contracts are running across thousands of mid-market and enterprise companies. Most of them have no measurement framework. CFOs are answering board questions with vendor-supplied dashboards. Microsoft tells you about Copilot. Google tells you about Gemini. Each vendor grades their own homework. CFOs notice. Boards notice.
Levos is the vendor-neutral measurement layer. We pull behavioral signals from Microsoft 365, Google Workspace, GitHub, Salesforce, and the AI tools themselves. We measure productivity deltas with controlled cohort analysis. We surface confidence scores, not certainty claims.
These are the questions Levos lets the CFO answer with sources, methodology, and confidence scoring.
Which teams are getting measurable productivity lift from AI tools. Every finding includes a confidence score and source citation.
Which AI tools justify renewal at current spend. Cost per active user, outcome correlation, and power-user breakdown by team and role.
Where AI adoption is high but productivity is flat. Surfaced as enablement gaps for the CHRO, not just adoption metrics for the CIO.
How AI is reshaping skills and capacity. Skills derived from demonstrated work in connected tools, not self-reported assessments.
What the methodology says you cannot measure yet, disclosed openly. Reports that claim certainty are reports that hide their assumptions.
Controlled cohort analysis with confidence scoring. Every score cites its source. Every limit is disclosed.
Connect M365, GitHub, Salesforce, and your AI tools. OAuth authorization, no agents installed on employee devices. Data flows through customer-authorized connections only.
Levos pulls behavioral signals into the six signal families. The AI Impact family captures adoption depth and frequency for every AI tool, output correlation, and power-user breakdown by team and role.
We compare adopting teams to non-adopting teams, controlling for tenure, role, and tool stack. We disclose the limits of this approach openly. We do not claim full attribution. We claim controlled cohort comparison with confidence scoring, which is the honest framing.
Report generates with confidence scores, source citations, and methodology references. Export as PDF, push to M365, share to Slack, or upload to Salesforce. The board-ready artifact, on demand.
Capital investment works. Impact concentrated in Engineering, not Sales.
Controlled cohort analysis vs non-adopting teams. Controlling for tenure, role, and tool stack.
Copilot, ChatGPT, Claude, Gemini, Grammarly, GitHub Copilot. Levos pulls behavioral signals from all of them and applies the same controlled cohort methodology. One framework, every tool.
See the full connector list on Platform →
AI Impact measurement only works if employees trust the platform doing the measuring.
Individual data flows only to you and your direct manager, the same access they already have today. Skip-level executives see team aggregates of at least five people only. Every employee can see what Levos sees about them.
Levos data may not be used as the sole basis for termination, demotion, or compensation reduction. Enforced by contract.
Any employee may opt out of personal-level signal collection. A legal entitlement in the customer contract, not a setting.
Microsoft cannot credibly grade Microsoft. Their dashboards are calibrated to make the M365 footprint look productive. Levos is vendor-neutral. We pull from Microsoft, Google, GitHub, Salesforce, and AI tools across vendors, applying the same methodology to all of them. The CFO needs a measurement layer that is not optimizing for the spend it grades.
Controlled cohort analysis with cohort matching. We compare adopting teams to non-adopting teams, controlling for role, tenure, and tool stack. We disclose the limits of this approach openly. We do not claim full attribution. We claim controlled cohort comparison with confidence scoring, which is the honest framing.
Levos only shows team or department views when at least five people are in the group. This prevents anyone from working backward to figure out which specific person is behind a number.
The first AI Impact Report is generated within 30 days of full connector deployment, with confidence scores reflecting data depth. Confidence increases over the first 90 days as data accumulates. We tell you the confidence level at every step.
Then the report says so, with sources. The methodology is honest about negative findings. CFOs use this to redirect spend. CHROs use it to identify enablement gaps. CEOs use it to defend or change strategy. The platform does not exist to make AI look good. It exists to tell you what is true.
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Request a Demo and we will set up a guided demo. See exactly how Levos measures AI workforce productivity and surfaces the report the CFO, CHRO, and CEO all need.
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