AI adoption is often treated as a technology rollout problem. Organisations select a platform, buy licences, issue policies and expect employees to find ways to use it.
But Jabra’s WorkLife 2026 research suggests that approach risks leaving much of the workforce behind. The divide is not primarily between those who are interested in AI and those who are not. It is between employees who are supported to change how they work and those who are expected to fit AI around existing processes.
Thirty percent of top managers are heavy AI users, compared with just 8 percent of individual contributors. Daniella Bell, Future of Work Thought Leadership Manager at Jabra, believes the difference is largely structural.
“The more senior you are, the more freedom or confidence you have to redesign how you work,” Bell says.
“If you are an individual contributor, you are usually answerable to somebody else much more directly. It is harder to change your habits unless you feel enabled to rethink your workflows and your role within the system around you.”
That is a concern because it highlights a new divide where workers are not experiencing AI transformation equally. If individual contributors are given the least time, training and permission to experiment, organisations may miss where much of the potential productivity gain lies.
The Problem with Layering AI on Top
The research found that individual contributors are almost two and a half times as likely as senior leaders to say their way of working has barely changed. Bell does not believe this reflects a lack of curiosity or willingness among employees.
“I do not think individual contributors are less curious or less willing,” she says. “I think it is really about the environment and the permission that they are given.”
That distinction matters. Adding AI to an established process without changing the process itself can create another task rather than removing friction. Employees are asked to learn new tools while continuing to meet the same expectations, follow the same workflows and work within the same time constraints.
By contrast, organisations seeing more positive outcomes are giving teams space to ask a more fundamental question: what should this role or function look like now that AI is available?
“In some organisations, it has been helpful to literally allocate time per week or per month to experiment with creating a new workflow or an agent that takes on part of the most time-intensive workload,” Bell says.
That makes AI transformation less of a top-down instruction and more of a practical, bottom-up process. Employees closest to a task can identify the repetitive work, friction points and opportunities that senior leaders may not see from a distance.
Managers Set the Pace
The clearest finding in the research concerns the influence of managers. Daily AI use is 2.4 times higher among employees who view their manager as AI-competent. Yet only 27 percent of individual contributors consider their direct manager very or extremely competent in AI.
For Bell, the implication is direct: if an organisation can only make one major investment in AI enablement, it should start with its managers.
“Managers are being asked to lead a change that they have not necessarily been prepared for,” she says. “They need training in how to lead a team through AI. They need to feel confident and competent in their own ability, and they also need the time and the mandate to redesign how their team works.”
