Manager support is tied to AI use at work in two new surveys. Gallup finds that 78% of employees who strongly agree their manager supports AI use it frequently, against 44% of those who do not strongly agree. Jabra's WorkLife 2026 finds that employees with an AI-capable manager are 2.4x more likely to use AI daily. Both numbers come from asking employees what they think of their manager. Neither observes what the manager did, or what the team's work looked like afterward.
Two things are true at once. The pattern is consistent across two separate surveys and large enough to act on. It is also built from perception, so it cannot tell you which of your managers are helping, which are blocking, or what either is doing to the work.
What do Gallup and Jabra say about managers and AI use?
In both surveys, manager support means an employee's rating of whether their manager actively supports, or is capable of guiding, their use of AI. It is a perception of the manager, not a record of anything the manager did.
| Gallup | Jabra WorkLife 2026 | |
|---|---|---|
| Published | Page dated July 27 2026, updated September 30 2026 | Press release, October 13 2026 |
| Population | Employees in AI-adopting organizations; the page does not state a country, sample size, or fielding window for these figures | More than 6,000 knowledge workers in France, Germany, India, Poland, the UK, and the US; fielded May 2026 |
| Manager measure | Employee strongly agrees their manager actively supports their team's AI use | Employee describes their manager as capable of supporting them with AI |
| Headline result | 78% use AI frequently with strong manager support, 44% when they do not strongly agree | 65% use AI daily with an AI-capable manager, 27% without (2.4x) |
| How common | 36% strongly agree, in AI-integrating organizations | Half of workers do not describe their manager as capable |
| Main limit | Perception data; the comparison group mixes neutral and negative answers | Vendor-run survey; the full report is gated and the exact question wording is not in the release |
What Gallup found
Among employees in organizations that are adopting AI, 78% who strongly agree their manager actively supports their team's AI use say they use it frequently, against 44% of those who do not strongly agree, a 34 point gap. In Gallup's Q2 2026 US tracker, frequent use means a few times a week or more; the manager page does not restate the definition. The employees with supportive managers were also 9.3 times as likely to strongly agree that AI has transformed how work gets done in their organization.
The same Gallup page reports that only 36% of employees in AI-integrating organizations strongly agree their manager actively supports their team's use of AI. Gallup sells workplace consulting, and we cite its page as published.
What Jabra found
In a May 2026 survey of more than 6,000 knowledge workers, 65% of employees with an AI-capable manager use AI daily, against 27% of those without, a 38 point gap that Jabra reports as 2.4x. Employees with an AI-capable manager rated their career optimism 8.3 out of 10 on average, against 5.5. Half of workers do not describe their manager as capable of supporting them with AI.
Jabra sells audio and video equipment, and the study gives weight to talking to AI by voice. We cite the release as published and have not read the gated report.
Does manager support raise AI use, or just travel with it?
Neither survey can say. Both are cross-sectional associations, measured once, and the sources suggest the same employees both rated their manager and reported their own AI use, though neither publishes the design in full. An employee who already likes AI may be more likely to rate a manager as supportive. A manager who supports AI may also lead a team whose work suits AI better.
Gallup makes a similar caution in a companion report on how many ways employees use AI, noting that employees who see more value may find more uses. The same logic applies here. The direction of the link is unknown, and neither source says whether the comparison groups are comparable on role, tenure, or tool access.
That does not make the finding useless. A 34 point gap in frequent use is a reason to look at managers. It is not a basis for assigning credit or blame to any one of them.
Why do leaders and individual contributors report different AI workplaces?
How the leadership gap shows up
Both surveys show the leadership layer living in a different AI workplace from the people below it. In Gallup's data, 51% of leaders used AI frequently in Q2 2026, up from 17% in 2023, against 36% of managers and 26% of individual contributors. Leaders were also more likely to call the impact extremely positive, 21% against 13%.
Jabra shows the same split from another angle. 47% of senior leaders said their work changed significantly or fundamentally over the past year, against 18% of non-managers. 64% of non-managers had received no AI training or only a brief introduction, against 26% of senior leaders. Among those who were trained, 39% of non-managers rated the training very or extremely relevant to their role, against 76% of senior leaders.
What this means for an adoption readout
A company-wide average blends the layer that uses AI most with the layers that use it least. It also blends teams whose managers differ. Reading adoption by level and by team is the minimum, and it fits a pattern we covered when Deloitte reported that 84% of companies have not redesigned jobs around AI: people below leadership may be using AI inside jobs that were never redesigned around it. None of these surveys tests that directly.
Ratings add one more problem. As we wrote about the Visier finding that 48% of surveyed US employees admit exaggerating their AI use, a rating about AI is also a statement about what the respondent thinks is expected.
How do you measure manager effects on AI use without asking?
A manager effect is the difference in AI use or work outcomes between teams that is associated with how their manager runs AI adoption, after accounting for what else differs between those teams. Five steps keep it honest:
- Observe use instead of asking about it. Read AI tool activity from the systems where work happens, at team level.
- Compare comparable teams. Pick teams with a similar role mix, tenure, and tool access, where the manager's enablement practice differs. Say comparable, never matched.
- Pair use with an outcome the business already tracks. Usage alone is activity, and the question is whether the work changed.
- Attach a confidence level to every comparison and disclose the limits. Small teams produce noisy results.
- Report at groups of 5 or more people, so findings describe teams and not individuals.
A sixth check is cheap: ask whether training relevance ratings predict later observed use. The 39% against 76% training relevance gap is a perception, and observed behavior after training is the test of it.
Where Levos fits
Levos is a Human Capital Operating System: an intelligence layer above the existing stack that reads behavioral signals from where work happens. AI Impact is one of its six signal families, and the measurement methodology compares adopting teams with non-adopting teams using controlled cohort analysis with confidence scoring, with the limits disclosed. A manager sees the detail for their direct reports, the same access they already have today across their team's tools, and anyone above that sees team aggregates of at least 5 people, apart from a logged escalation path for urgent issues. Team level is also where a manager's influence would show up. We do not claim full attribution.
For a view of how the platform reads team-level signals, see the Levos platform. Learn more about the broader approach to measuring what AI changed in our guide to measuring AI ROI across your workforce.
Frequently asked questions
Does manager support increase employee AI use?
Two 2026 surveys link them, but neither shows direction. Gallup finds 78% of employees who strongly agree their manager supports AI use it frequently, against 44% of those who do not strongly agree. Jabra finds 65% of employees with an AI-capable manager use AI daily, against 27% without. Both rest on employee perception at one point in time, so they show a strong association, not proof that manager support raises use.
How many employees say their manager supports their AI use?
Gallup reports that 36% of employees in AI-integrating organizations strongly agree their manager actively supports their team's use of AI. Jabra reports that half of workers do not describe their manager as capable of supporting them with AI. The questions, scales, and populations differ, so the two figures should not be merged into one number.
Why do leaders and individual contributors report different AI experiences?
Gallup reports frequent AI use by 51% of leaders and 26% of individual contributors in Q2 2026. Jabra reports that 47% of senior leaders and 18% of non-managers say their work changed significantly or fundamentally, and that 64% of non-managers had no AI training or only a brief introduction, against 26% of senior leaders. The surveys do not isolate whether access, training, or job design explains the gap.
How can a company measure a manager's effect on AI adoption?
Compare observed AI use and an outcome the business already tracks across comparable teams that differ in how their manager supports AI, attach a confidence level to each result, and report at groups of 5 or more people. This supports a bounded comparison, not full attribution, and it avoids relying on how employees rate their manager.
Measure the team, not the rating
If your AI readout rests on how employees rate their managers, and the board is asking what changed, that gap is solvable. Request a Demo and we will walk through what a team-level comparison contains. The approach and its limits are on the measurement page.
Levos is accepting design partner applications from US organizations of 150 employees or more with an active AI rollout. Large organizations typically start with one function or division, measured against comparable teams that have not adopted yet. That is a stronger attribution design than a company-wide before-and-after.
