Renewal season forces a decision leaders dread: Which AI tools earned their budget again, and which drained it. Most teams decide with vendor pitch decks and whoever argues loudest. A better approach uses three data inputs — usage, adoption maturity and productivity impact — to make that call an evidence-based decision.
The 3 inputs your AI tool audit needs
An AI tool audit needs more than a spreadsheet of logins. It needs three inputs that show whether a tool is earning its cost or wasting it.
Usage data: who’s actually using the tool
Start with usage broken down by seat, not license count. The average organization now runs seven AI tools, according to ActivTrak’s 2026 State of the Workplace research. That sprawl hides unused seats. A tool at 40% weekly active seats deserves a different renewal conversation than one at 90%.
Adoption maturity: Experimentation vs. workflow fit
Usage alone doesn’t say whether adoption has matured. Some teams still treat a tool as an experiment months after purchase, while others have built it into daily work so deeply that removing it would disrupt output. Ask managers about how their team uses each AI tool; are the tools being tested or in the process of adoption or are they now fully integrated into workflows? Understanding how tools are used helps predict renewal risk better than a usage percentage alone.
Productivity impact: What changed after adoption
The hardest input to gather, and the most valuable, is what changed after adoption. ActivTrak’s behavioral data found that after employees started using AI, time spent across nearly every work category increased, with email up 104% and chat and messaging up 145%, per the State of the Workplace report. That cuts against the assumption that AI simply frees up hours. Compare output or cycle time before and after adoption, not adoption alone.
A framework for keep, scale or cut decisions
Once the three inputs are in hand, match what the data shows against three plain criteria.
When to keep an AI tool
Keep a tool when usage is steady, adoption has matured into a real workflow and the tool ties to a measurable output gain, even a modest one. Keeping a tool doesn’t require flawless ROI, just evidence it’s doing the job it was bought to do.
When to scale an AI tool
Scale a tool when usage and productivity impact are strong but adoption is concentrated in one team. ActivTrak’s data shows only 3% of users show a usage range that correlates with maximized productivity, while the vast majority (57%) spend less than 1%, per the State of the Workplace report. If a tool is proven in one group but underused elsewhere, scaling adoption is usually the faster win.
When to cut an AI tool
Cut a tool when usage stays low quarter over quarter or an already-licensed tool covers the same job. Some SaaS frameworks define underutilized tools or subscriptions as any application that has 30% or less of available users logging in over a 90-day period. Renewal is the natural checkpoint to make that call before your organization pays for another year.
| Keep | Scale | Cut | |
|---|---|---|---|
| Usage | Steady week-over-week; 60%+ weekly active seats | Strong in 1-2 teams; underused organization-wide | Below 30% of licensed seats active over 90 days |
| Adoption Maturity | Embedded in daily workflows; not just experimentation | Proven in one team; others still in experimentation | Still in experimentation months after purchase; no workflow integration |
| Productivity Impact | Measurable output gain, even modest; before/after data shows positive shift | Clear impact in strong-use teams; gains not yet realized elsewhere | No measurable output change; or duplicate coverage by existing licensed tool |
Evaluating AI software ROI
Calculating AI software ROI starts with asking the right question. Most teams default to what’s easiest to measure, like cost per seat and total logins, rather than what actually signals value.
Getting the metrics right means understanding the usage that reflects real workflow adoption and accounting for tools employees are using outside official channels.
Metrics that separate signal from noise
License cost per seat and login frequency are easy to pull but say little about value. Stronger signals include weekly active use relative to total licenses and time-to-productivity for new adopters.
Unauthorized tools complicate the picture further: Nearly half of generative AI users access tools through personal accounts even after their company has banned them, according to Netskope data cited by Vectra AI. An audit limited to procured licenses will always undercount real usage and risk. ActivTrak’s AI impact resources outline how to close the gap.
Building AI tool portfolio management into renewal cycles
AI tool portfolio management makes a one-time audit a routine. Build a standing inventory of every licensed and shadow AI tool, refresh usage data quarterly and flag renewals 90 days out. ActivTrak’s guide to SaaS cost optimization explains a framework leaders can also implement to cut AI tool waste.
AI adoption is no longer the open question for leaders. More than 95% of organizations have adopted AI in some form, and the gap between adoption and understanding its impact (what ActivTrak calls the AI measurement gap) is now the defining leadership challenge. The leaders who win will prove, with data, that every tool in their stack earns its budget.
Stop guessing at renewal time. See real usage, adoption and productivity data for every AI tool in your stack. Explore AI Insights.
FAQ
What is an AI tool audit and how often should companies audit their AI tool portfolio?
It’s a structured review of usage, adoption maturity and productivity impact for every AI tool in a company’s stack, used to decide whether to keep, scale or cut each one. Most teams run it quarterly, with a deeper review before each major renewal.
What usage data actually predicts whether an AI tool is worth its cost?
Weekly and daily active seats, measured against total licenses, predict this better than total logins. High license counts with low weekly engagement usually mark the first candidate to cut.
What criteria justify keeping, scaling or cutting a tool at renewal?
Keep tools with steady usage and a measurable output gain. Scale tools that perform well in one team but remain underused elsewhere. Cut tools with persistently low usage or overlap with an already-licensed tool.
What are the most common mistakes leaders make when calculating AI ROI?
The most common mistake is measuring logins instead of workflow impact. A close second is excluding unauthorized, employee-adopted tools, which undercounts both usage and risk.
How should finance and IT collaborate on AI tool renewal decisions?
Finance should own cost and utilization data; IT should own usage and security data. The keep, scale or cut decision works best as one joint call from a shared dataset, not two competing spreadsheets.
