- Teams with supervisors who embrace AI use AI-based tools at much greater rates—regardless of how good the tools are, how effective the training was, or how great the incentives to utilize the new capabilities are.
- Incorporating employees into the design process around how AI will be integrated into daily operations has consistently been linked to stronger adoption. This process is called co-design.
- Translating individual productivity gains achieved by employees using AI into positive organization-wide benefits requires strategic, deliberate decisions about where freed-up time will be directed.
Employee sentiment research consistently finds that most employees are legitimately concerned regarding how their role will change due to AI. In fact, 47% of workers report a need to acquire new skills due to AI’s impact. Many employees are concerned that they will be displaced—with more than half worried about being laid off as soon as this year according to a survey by Resume Now. Challenger, Gray & Christmas report that AI displaced almost 55,000 workers in the U.S. in 2025.
These are not unreasonable concerns. Historically, automation has impacted employment in ways that were detrimental to many workers within affected roles, and although aggregate impacts may ultimately improve, the immediate impact on those affected often includes significant hardship. Employees that have been assured technological innovation would not negatively impact their job—and then found out it did—will reasonably distrust new assurances.
The Elements of a Successful AI Approach
The organizations that address employee concerns effectively are transparent and factual. They avoid “AI is a tool, not a replacement” sayings which are very general and corporate-sounding.
A better approach is to clarify:
- This is specifically how your role will change
- These are the tasks that will be performed by AI
- Here are the new abilities we will be spending money to develop for this role
- This is what our organization’s structure will look like in eighteen months
Ambiguity validates fear. Transparency builds trust.
Managers Must Lead by Example
Research on AI adoption at the team level indicates that the number one best indicator of whether a team will use an AI-based tool is whether their supervisor actively utilizes those tools.
According to Gallup, employees who believe that their manager supports the team’s use of AI are:
- 2.1x as likely to use AI a few times a week or more
- 6.5x as likely to strongly agree that the AI tools provided by their organization are useful for their work
- 8.8x as likely to strongly agree that AI gives them more opportunities to do what they do best every day
Members of teams with supervisors who model AI-integrated work styles use AI-based tools at much greater rates than members of teams with supervisors who view AI as some other person’s initiative—regardless of how good the tools are, how effective the training was, or how great the incentives to utilize the tools are.
Empowering Individual Contributors
Organizations undertaking change management initiatives around AI that allocate the bulk of their budget for training individual contributors, while providing little-to-no resources for enabling their supervisors to support such utilization, are misdirecting their funds. Supervisor funding enables all members of the supervisor’s team. Funding individual contributors produces only individual returns. For organizations with limited change management budgets, there is one clear direction: fund and enable the supervisors comprehensively prior to allocating resources for anyone else.
Co-Design Also Drives AI Engagement with Employees
Co-design—incorporating the individuals who will utilize AI tools into the process of designing how those tools will be used, and how they will be integrated into workflows—has consistently been linked to:
- Greater AI adoption rates
- Better performance outcomes
- Reduced costly post-deployment corrections
Co-design is also generally viewed as being more resource-intensive than the top-down deployment methods, making it a frequently omitted method due to project scheduling pressures. Organizations that prioritize co-design as core infrastructure for their AI program (not an additional resource consuming overhead) achieve substantially better results.
That said, co-design doesn’t mean every affected employee needs to participate in each design meeting. Instead, practical co-design means having representation for real-world use cases involved in:
- Making decisions regarding where AI outputs are surfaced in existing tools
- How AI recommendation information is displayed
- What context accompanies the recommendations
- What forms the override functionality takes
- How easy it is to activate
A small, diverse set of practitioner representatives produce better design feedback than a larger set of stakeholders asked superficial questions at the completion of a development cycle.
Turning Productivity Gains into Performance Improvements
Organizations experience a productivity paradox when individual employees increase productivity via AI, but the increased productivity does not result in commensurate improvements in overall organizational performance. This disparity is likely one of the most frustrating experiences for executives and senior decision-makers who have invested in AI and are awaiting tangible financial returns on that investment.
There are several structural reasons for this disparity. One primary reason is that time saved by employees using AI tools is typically filled with alternative work activities instead of resulting in quantifiable production output increases or reductions in personnel levels, unless deliberate organizational decisions are made as to what purpose the freed up time will serve.
Organizations that successfully translate individual productivity gains achieved by employees using AI into positive benefits to the organization have deliberately made strategic decisions regarding where freed-up time will be directed. Examples include:
- Higher-value activities
- Quality enhancements
- Volume increases
- Strategically-informed workforce planning
AI ROI Isn’t Magic, It’s Intention
Organizations that demonstrate measurable success with AI are those that monitor how employees utilize their newly-free time and make strategic decisions based upon what they learn.
Ultimately, AI itself does not reshape organizations. In truth, it’s executives and senior decision makers with a vision for what should happen after the tool does its job that can spark positive transformation and maximize ROI.
A: Many organizations face a trust gap, where previous attempts at automation have caused cynicism among teams. Employees may also feel uncertainty about their duties changing or their role being replaced. In these scenarios, organizations should clarify and define a specific, tangible impact for the new AI capabilities. Be clear about how the investment will help employees accomplish more and have a better work experience—but be careful not to be overly optimistic.
A: AI adoption is a social and cultural challenge as well as a technical one. And in that context, manager behavior is vastly important. If supervisors don’t make use of AI, their team likely won’t either. But if leadership sets a strong example, individual contributors are likely to follow along. When managers act as advocates for AI—as opposed to only talking about it—they can be a great force multiplier for organizational adoption.
A: The time saved through AI-enhanced operations needs to be reclaimed with intention. Otherwise, teams can easily end up applying it to low-impact work. The strongest ROI comes when employees can spend more cycles on valuable, strategic initiatives instead of tedious manual tasks. To make this happen, it’s critical for leaders to create and communicate a vision around how employees should use the efficiency gains to drive tangible business outcomes.
