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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsAI adoption counts show whether employees are using AI; they do not show whether it improves work. A more useful assessment pairs usage data with three questions: what happens to the time AI frees, how work relationships and coordination change, and whether employees trust leadership’s stated purpose for AI. These are practical lines of inquiry, not a standardized or validated measurement system.
Why AI usage metrics are not enough
Counts such as the number of people prompting an AI tool or how often they use it measure activity. On their own, they cannot establish whether the work is better, faster in a valuable way, or beneficial to the organization. As Kate Niederhoffer put it in her Built In article, “Put simply, just because people are using AI doesn’t mean it’s accomplishing anything.”
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This is a version of the measurement problem often described through Goodhart’s Law: when a proxy becomes a target, people can optimize the proxy without producing the outcome it was meant to represent. If adoption becomes the goal, a rising prompt count can look like progress even when quality, collaboration, or employee experience has not improved.
In an article published August 27, 2026, Niederhoffer proposes looking beyond usage to the disposition of saved time, effects on human relationships, and employee trust in leadership’s purpose for AI. Built In reports the figures below as findings associated with BetterUp research and a Gartner survey; the underlying studies were not independently reviewed here, so they should be treated as reported claims rather than proof of causation or universal benchmarks. Read the Built In article.
1. What happens to the time AI frees?
Efficiency only becomes an organizational outcome when the capacity it frees is put to useful work. Ask employees and managers where that time goes: into development, planning, new projects, improved service, or simply a larger volume of existing tasks?
Built In reports that managers save about six hours per week through AI, and that managers who redirected saved time toward employee development, their own growth, new projects, and strategic planning saw a 65 percent increase in team AI performance. Both figures are BetterUp-related research as reported by Built In in 2026. The article does not provide independently checked methods for these results, so they do not establish that reallocating time alone caused the reported increase.
Useful questions to ask
- What work did the team do with the time AI saved?
- Did the time go toward higher-value work, or did it disappear into more tasks and meetings?
- Did work quality, delivery, or employee development change alongside usage?
- What guidance do managers give employees about how to use time AI frees?
Built In reports that a Gartner survey earlier in 2026 covered 12,000 employees and managers across 40 countries, and that 7 percent of organizations provide guidance on using AI-saved time. These are Gartner figures as reported by Built In; the original survey details were not reviewed, so no additional methodology or interpretation can be established from them.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall2. Is AI supporting relationships or replacing conversations?
AI can help people prepare for conversations, organize information, or handle routine coordination. It can also become a substitute for direct check-ins. To understand which is happening, pair usage data with indicators of coordination and employee experience, and ask whether important conversations are still taking place between people.
Built In reports that BetterUp-related research associated using AI to avoid direct check-ins with a 12 percent decrease in team coordination, a 26 percent increase in burnout, and a 29 percent increase in intent to leave. These are associations reported by the article, not established causal effects. They are not universal benchmarks for evaluating an individual team.
Signals worth examining
- Whether teams still coordinate directly when a task depends on shared context or judgment.
- Whether employees feel supported by managers and colleagues, rather than left to navigate work alone.
- Whether coordination, burnout, or intent-to-leave indicators shift over time as AI use changes.
- Whether AI is helping people prepare for human interaction or displacing interaction that the work requires.
Changes in these indicators need context: role, task type, starting conditions, time period, and work quality can all affect comparisons. A single score cannot show that AI caused a change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.3. Do employees trust leadership’s purpose for AI?
Employees may interpret AI as a tool intended to augment their capabilities—or as a signal that leadership plans to replace them. Ask directly what employees believe the organization is trying to achieve, whether they trust leadership’s explanation, and whether they see AI helping them do their work.
Built In reports that employees who trust leadership had 46 percent higher odds of viewing AI as augmentative, based on BetterUp-related research reported in 2026. “Higher odds” does not mean a 46 percent increase in the probability that an employee will view AI positively, and the reported relationship does not prove that trust alone produced the perception.
Best Value
Questions for employees
- Do you believe AI is intended to support your capabilities, reduce work, or replace roles?
- Has leadership explained why AI is being introduced and how decisions about its use will be made?
- Can you raise concerns about AI’s effects on your work without penalty?
- Where does AI help you, and where do you need more human judgment or support?
Pair activity measures with outcome questions
There is no standardized set of measures or validated benchmark for the three dimensions Niederhoffer discusses. Teams can nevertheless use them as prompts for a more complete review, provided they interpret results alongside work quality and differences in role, task, baseline, and time period.
| What to examine | Activity view | Outcome question |
|---|---|---|
| Use of AI | Who uses AI and how often? | Is the work more useful or higher quality? |
| Freed capacity | How much AI is used? | How is time saved being allocated? |
| Relationships and coordination | Which tasks involve AI? | Are people coordinating effectively and maintaining needed direct conversations? |
| Trust and perceived purpose | How broadly has AI been adopted? | Do employees believe AI augments their capabilities, and do they trust leadership’s explanation? |
Keep the measures together rather than treating any one of them as a verdict. Adoption data can explain exposure to AI; work quality, time allocation, coordination, and employee perceptions help clarify what that exposure may mean.
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