New data from MasterClass reveals a striking disconnect: 56 percent of employees worry their skills will become irrelevant as technology evolves, yet 54 percent say their employers have failed to provide adequate AI training or support. The real story behind this data isn’t that workers fear AI — it’s that they’re frustrated their employers aren’t equipping them to succeed with it.
When two-thirds of professionals are teaching themselves AI skills, that’s not resistance to technology. That’s organic demand from people to help make them more effective at their jobs. Many enterprises are being pushed to adopt more AI tools. Rather than being thoughtful about lasting business impact, they’re reverting to half-baked attempts. They drop AI tools at employees’ feet, check the box and show “adoption” to investors and boards.
At Voxel, we’ve deployed our AI product across hundreds of industrial sites, and what we’ve learned is that workers embrace AI quickly when they can see it solving problems they actually face. Adoption isn’t about selling AI. It’s about knowing how to materially change business results by using purpose-built AI.
Why Workers Feel Frustrated With AI Adoption
- 56 percent of employees worry their skills may become irrelevant.
- 54 percent say employers fail to provide AI training.
- Workers are self-teaching AI to stay effective.
- Adoption succeeds when tools solve real, daily problems.
- Rushed rollouts create cynicism and stall progress.
The Technology Isn’t the Problem
When AI makes someone’s job easier or more impactful, adoption isn’t a challenge. The challenge is keeping up with demand. We’ve seen safety managers go from spending hours manually reviewing incident reports to focusing on strategic safety culture initiatives. Warehouse supervisors who once worried about unsafe behaviors during shift changes now have 24/seven visibility that lets them coach proactively rather than react to injuries. These aren’t abstract productivity gains. They’re fundamental shifts in how people spend their time and energy at work.
Despite the potential of AI, I'm seeing a dangerous “build it and ship it” mentality across the tech industry. Companies are rushing AI products to market without considering implementation strategy, long-term impact or adoption sustainability. This approach creates the exact frustration we see in this data: Workers are left to figure out tools that weren’t designed with their actual workflows in mind. The result is a proliferation of AI solutions that solve theoretical problems rather than the practical challenges workers face.
Look at how many companies have rolled out AI chatbots that can’t actually resolve customer issues or predictive analytics tools that generate insights no one knows how to act on. The pressure to show AI adoption has created a market flooded with solutions looking for problems. Workers see through this immediately. They know when a company is implementing technology for boardroom optics versus a tool that offers genuine operational improvement. This creates cynicism that makes future AI adoption even harder.
Bridging the AI Skills Gap
The skills gap isn’t really about technical capabilities. It’s about organizations failing to bridge the gap between AI deployment and practical application. Too many companies are treating AI like a generic software rollout from the last 20 years (i.e. “Here's our new system for managing HR activities, so enter your PTO requests here now instead of the old system”) instead of a workforce transformation. This moment represents an existential change in how work is done, not just modernization of existing tech processes.
The 54 percent of respondents who say employers are failing to train them on AI aren’t asking for computer science degrees; they’re asking for clarity on how these tools fit into their daily workflows and career growth. Without an enterprise-wide AI strategy, workers end up managing multiple disconnected AI tools that create more complexity rather than reducing it. Smart organizations recognize this moment as an opportunity to build both capability and trust simultaneously, but only if they approach it strategically rather than reactively.
The path forward requires a fundamental shift in how organizations approach AI implementation. Instead of asking, “How can we deploy AI?” leaders should ask “What problems are our workers actually trying to solve?” This means involving frontline employees in AI selection and design processes, not just training them on predetermined tools. It means measuring success by business outcomes and worker satisfaction, not just deployment timelines or short-term bursts of adoption that won’t sustain.
Your Employees Are Ready — Help Them Prepare
Start with focused programs with clearly defined pain points to be addressed, rather than broad, enterprise-wide rollouts of generalized AI tools. Then find AI companies that are purpose-built to solve those problems – great tech is still key, but the novelty in transformational AI is increasingly less about the patterns being recognized than its ability to equip people to take meaningful action to solve concrete problems.
For example, if warehouse workers struggle with identifying safety hazards in their daily work, deploy AI tools that both detect unsafe anomalies while also surfacing those risks with critical context and the tools to then take action.
In parallel, ensure there is a clearly established feedback loop from frontline users to leadership, as well as from those same users to the AI providers directly. Designate champions within departments or facilities to be super-users. These champions should be empowered to influence tool selection and implementation strategies.
Companies fall in love with the notion of “self-service” tools, but if you fundamentally change the nature of work, it’s critical there are consistent interactions across all key parties: workers, management and AI vendors. The impact of physical AI only comes when workers adopt rather than having it imposed from above, so don’t lose sight of the actual goals: Changing how work is done, not checking the box on having deployed AI.
What’s encouraging is that this data shows workers aren’t afraid of becoming irrelevant. Workers are actually ready to evolve. The pressure 49 percent of workers feel to incorporate AI isn’t coming from fear of replacement. It’s coming from seeing its potential. Companies that invest in proper AI education and, critically, change management will see stronger employee engagement and retention. People are ready. The question is whether leadership will step up to support them rather than just checking boxes.
