For most organizations, the successful application of AI tools will come down to whether the workforce knows how to use them in ways that actually improve day-to-day work. Despite this imperative, training hasn’t kept pace. In the United States, only about 12 percent of workers have received AI-related job training, and two-thirds of HR leaders say their organizations haven’t done enough to prepare employees for an AI-powered future.
Even when training is available, it’s not always translating into real capability. Research shows that many workers still don’t know how to use AI tools effectively, regardless of how much organizations have invested in them. In part, that’s because most training is designed as a one-size-fits-all solution, assuming the same level of confidence and context across very different roles and employees. The result is that some employees move quickly from exposure to application, while others struggle to see how these tools fit into their work at all.
Inclusion leaders are uniquely equipped to address this gap by applying practices they already use in leadership development and workforce training. They understand how to design programs that meet people where they are, account for different starting points, and ensure broader participation. Bringing that lens into AI training ensures adoption succeeds not only with early adopters but across the entire workforce in the long term.
Why Inclusion Matters Early in AI Training
Early signs of uneven AI adoption are already emerging across the workforce. Research shows that women are significantly less likely than men to use generative AI at work, while younger and more highly educated employees are more likely to be early adopters. At the same time, concern about being left behind isn’t evenly distributed: 54 percent of low-income workers say they expect to fall behind in the age of generative AI, compared to 44 percent of middle-income workers. These patterns point to a simple reality: Not everyone is engaging with AI from the same starting point.
Those differences are quickly exposed by the way that training is experienced. Some employees begin to experiment early, find ways to apply what they’ve learned, and build confidence over time. Others receive the same training but struggle to see how it connects to their work or don’t engage deeply at all. Access to relevant examples, time to practice, and manager support all influence whether training turns into real capability, and those resources and opportunities are not evenly distributed.
This isn’t a new problem. Effective training has always required meeting people where they are, accounting for different starting points, and making learning relevant to the work at hand.
However, many AI training efforts are being rolled out too quickly and broadly, without the necessary attention to how it may be received. The result is predictable: Differences in how people learn become differences in who is able to keep up.
Left unaddressed, those gaps impact the organization far beyond the realm of training. They begin to shape who is included in early projects, who builds visibility for using new tools effectively, and who is perceived as being ready for new opportunities as roles evolve. Over time, uneven adoption can turn into uneven access to growth, reinforcing gaps that are much harder to close later.
How to Assess the Inclusivity of Your AI Training
The basic principles that apply to effective learning and development are still true. The difference is that with AI, the pace is faster and the stakes are higher, making it even more important to get the fundamentals right.
The first step is to recognize where gaps already exist. Start by asking yourself these questions as you review your approach:
1. Is training truly accessible to everyone?
Access is the foundation. AI training shouldn’t be limited to select roles, teams, or “high-potential” employees; it needs to be broadly available to anyone who wants or needs to build these skills. Open access is especially important for employees whose roles may change over time, giving them a chance to prepare rather than fall behind.
2. Are there multiple ways for employees to learn and engage?
People don’t learn the same way or at the same pace. Effective programs offer a mix of formats, from live sessions to self-paced modules to hands-on, in-the-flow-of-work learning. Without that flexibility, training tends to favor those who already have the time and capacity to engage.
3. Is manager support consistent across teams?
Manager support plays a major role in whether training turns into real behavior change, but that support isn’t always evenly distributed. In some teams, employees are pulled into projects that give them a chance to apply AI in meaningful ways, while in other teams, that opportunity never materializes. These differences can quickly create uneven adoption across teams, even when access to training is the same.
4. Are you supporting employees who may be most at risk of falling behind?
The impact of AI won’t be felt evenly across roles or groups of employees. Some roles are more likely to be automated or significantly changed, while others are more easily augmented. Those differences mean some employees will need additional support to build new skills and adapt. Inclusive training doesn’t just provide access; it ensures that the people who need the most support are able to engage and build knowledge for whatever comes next.
The Bottom Line
While training is one of the most influential ways organizations shape how this technology shows up across their workforce, it isn’t the only place that inclusion leaders need to focus. AI is changing how work gets done, and that shift touches everything from how roles evolve to how opportunities are created. That makes an inclusion lens essential—not just in training but also across an organization’s broader approach to the use of AI.
To explore this AI frontier further, Seramount is hosting a conversation between AI experts and inclusion leaders. The session outlines five practical strategies to build more inclusive AI programs from the start. Learn more here.
Kayla Haskins is an Associate Director, Product Marketing at Seramount. In this role, she supports DEI Practitioners and Talent Leaders in creating more inclusive workplaces by providing valuable insights and resources through webinars, blog posts, guides, infographics, and more.
With nearly a decade of experience in the technology and non-profit sectors,
Kayla Haskins is an Associate Director, Product Marketing at Seramount. In this role, she supports DEI Practitioners and Talent Leaders in creating more inclusive workplaces by providing valuable insights and resources through webinars, blog posts, guides, infographics, and more.
With nearly a decade of experience in the technology and non-profit sectors, Kayla excels in translating complex ideas into clear, actionable concepts. She is passionate about storytelling and is dedicated to addressing today’s most pressing workplace issues to drive meaningful impact.
Kayla holds a degree in English and Creative Writing from Dickinson College. She lives in Silver Spring, MD with her partner, Nick, and their dog, Zero. In her free time, she enjoys hiking, reading, and spending time with family and friends.
