AI adoption: how managers make the difference

Article written by eBloom

AI use is booming in companies, but real transformation is still lagging behind. In a previous article, eBloom already highlighted that 89% of business leaders worldwide are seeing no measurable impact of AI on their company’s productivity, while only 12% of employees in organisations that have adopted AI say it has genuinely transformed the way they work.

Yet one figure completely changes how we should read these results: according to Gallup, the decisive factor is neither budget nor technology. The role of the manager can make the difference between organisations where AI adoption takes hold and those where it does not.

AI is booming, but transformation is still lagging behind

A quick reminder before we go further. 95% of corporate AI pilots have generated no measurable return on investment (MIT Project NANDA, 2025). A survey of nearly 6,000 executives confirms the same pattern when it comes to productivity (NBER, cited by Gallup, 2026).

Individual adoption is widespread, but organisational transformation remains rare. So why is there such a gap?

The manager’s role in AI adoption: the number-one factor after technical integration

Gallup identified the two main factors associated with frequent AI use within an organisation.

The first, unsurprisingly, is technical integration into existing tools. The second, far less expected, is active support from employees’ direct managers.

It ranks ahead of company policy, access to tools or internal communication, even though these are where most investments tend to be concentrated (Gallup, 2026).

The impact is significant. When a manager actively supports AI use, employees are 8.7 times more likely to say that AI has transformed the way they work and 7.4 times more likely to say that it saves them time (Gallup, 2026).

The problem is that this kind of support remains relatively rare. Fewer than one in three US employees say they receive it today. In Germany, a separate Gallup study puts the figure at just 21% (Gallup Germany, cited by Gallup, 2026). This is therefore not an isolated US issue.

The manager paradox: expected to drive transformation while under growing pressure themselves

This is where things become more complicated.

Managers are being asked to lead AI adoption within their teams at precisely the moment when their own engagement is declining. Manager engagement has fallen by nine percentage points since 2022, from 31% to 22% (Gallup, 2026).

Caught between senior leadership expecting results and teams that need reassurance and support, middle managers are dealing with an expanding workload, often without the resources to match.

And yet managers themselves are not resisting AI. In France, 55% of executives now use it at least once a week, an increase of 15 percentage points in just one year (Apec, 2026).

Managers are adopting AI for their own work, often faster than the rest of the organisation. The real gap lies elsewhere: knowing how to use AI yourself and knowing how to bring an entire team on board are two very different skills.

Without structured support, a manager may be perfectly comfortable using ChatGPT for their own work while lacking the time, space or resources needed to help their team do the same.

What does “actively supporting” AI adoption actually mean?

This question deserves more than a paragraph.

Actively supporting AI adoption does not mean simply sending employees a tutorial or activating a ChatGPT licence for the whole team.

It is about a mindset, routines and a practical way of supporting change on a day-to-day basis.

That is exactly what Pierre Baron, Chief People Officer at Yago and a member of the software.brussels cluster, had to build from scratch within his organisation — including what worked, what did not and what he would do differently today.

At Yago, the starting point was relatively favourable: the vast majority of employees wanted to learn more about AI. The challenge was therefore not to convince people, but to help each team identify where AI could genuinely bring value to their work.

For managers who had not yet identified relevant use cases, Pierre Baron followed a simple sequence: listen, experiment, then train.

He starts by identifying what takes up time in the team’s day-to-day work, shows them a similar use case already tested by another team leader, and only introduces training once the value has been demonstrated.

If he had to start again, he would avoid taking an overly top-down approach. Initially, management thought it had identified the right use cases, with mixed results.

A better approach is to work alongside teams from the outset and identify with them where AI can genuinely help.

Managers are best placed to do this, provided they have the time and resources to listen to their teams rather than simply relay a plan decided higher up.

The challenge is no longer the technology. It is management.

Your employees are already using AI. Your managers are too.

What is missing is not another tool. It is a clear understanding of what is actually happening within each team, so that managers can play this role without drowning in dashboards or carrying the transformation alone.

🎥 Want to go further? Pierre Baron, Chief People Officer at Yago and a member of the software.brussels cluster, explains how he equipped his managers to deal with AI: what worked, what he would do differently and the advice he would give organisations that are just getting started. Watch the full conversation with Margot Wuillaume, CEO of eBloom.