What keeps employees from reaching the levels of AI skills they want to and need to? David Blake, CEO and Co-founder of Degreed thinks lack of confidence in AI skills is the biggest hurdle…
Artificial intelligence is reshaping work at a pace few predicted even two years ago. Tasks that once required deep specialization can now be performed with AI assistance in minutes. Analysis, content creation, software development, and decision support are increasingly augmented by intelligent systems embedded directly into daily workflows. On the surface, human capability has expanded dramatically.
Yet inside many organisations, progress feels slower than expected. AI tools are deployed, training programs are rolled out, and skills frameworks are updated. Adoption, however, remains uneven. The reason is not a lack of technology or talent. It is something far less discussed, and far more consequential: confidence.
Lack of confidence in AI skills keeps us back
As we begin 2026, the most critical skill gap facing organisations will not be technical ability. It will be belief. AI will dramatically increase what people can do, but belief in that expanded capability will not automatically follow. Companies that recognize and close this confidence gap will move faster, adapt more effectively, and outperform those that continue to focus solely on skills training.
The friction many leaders are seeing in AI transformation is not accidental. For decades, learning and development has been built around a simple equation: teach people new skills, and performance will improve. That logic worked when change was linear and roles evolved gradually. It breaks down when technology redefines expectations faster than people can recalibrate their sense of competence.
Lack of confidence in AI skills: A reversal of the traditional skills gap
In the AI era, employees often have access to powerful tools but hesitate to use them fully. They question whether their outputs are good enough. They worry about over-reliance on AI or making the wrong call. Managers, meanwhile, struggle to redefine what โgood performanceโ looks like when work is co-created with machines. This hesitation shows up as slower decision-making, underutilized tools, and a quiet retreat to familiar ways of working.
What we are witnessing is a reversal of the traditional skills gap. In many cases, people already have the capability, but they do not yet trust themselves to operate at that higher level. Confidence has not caught up to possibility.
This matters because confidence is not a soft, abstract concept. It is a core driver of performance. Confident employees experiment more, apply new tools faster, and adapt their judgment to new contexts. Confident leaders empower teams, delegate differently, and model new behaviors. In AI-enabled organisations, confidence directly influences speed, quality, and return on investment.
Without confidence, even the most sophisticated AI systems remain underused. Technology advances, but behavior lags behind.
A new era for learning
Closing this gap requires a fundamental shift in how organizations think about learning. Traditional training focuses on transferring knowledge: how a tool works, what a system can do. Confidence, however, is built through experience. It comes from applying new capabilities in real situations, receiving feedback, and seeing evidence that โI can do this.โ
This is why coaching, practice, and micro learning are becoming central to effective AI transformation. Coaching helps individuals interpret changing expectations and apply AI to their actual work, not hypothetical use cases. Practice embedded directly into the flow of work allows people to build confidence incrementally, without the risk or friction of formal training environments. Micro learning, when done well, is no longer about delivering smaller pieces of content, but creating frequent moments of reinforcement that normalize new behaviors and reduce fear of experimentation.
The organisations pulling ahead are shifting their focus from skill acquisition to readiness. Readiness includes skills, but it also includes confidence, resilience, and trust โ in technology, in leadership, and in oneself. This is why the human side of transformation is finally becoming a line item in enterprise budgets. Leaders are realizing that AI adoption stalls not because systems fail, but because people are unconvinced or unsupported in the transition.
Train your employees or get left behind
The implications are significant. By the end of this year, competitive advantage will not belong to the companies with the largest content libraries, the most detailed skills taxonomies, or the most advanced AI tools. It will belong to those that help their people believe they can operate at a higher level and give them the structure, support, and space to prove it to themselves.
About David Blake
David Blake believes that learning is too important to stay the way it is and has spent his entire career innovating in higher education and lifelong learning. Prior to Degreed, he helped launch aย competency-based, accredited university and was a founding team member at Zinch (acquired by Chegg NASDAQ: CHGG). David was selected as a Top EdTech Entrepreneur by the Stanford School EdTech Lab, sponsored by Teach For America and New Schools Venture Fund. David then co-founded both Learn In and BookClub. In 2022 he returned as CEO of Degreed when the company acquired Learn In.
About Degreed
Degreed is the leading AI-powered learning platform for enterprise workforce transformation, helping organizations build the skills they need to stay ahead. With deep skill-building experiences, seamless integration with your HR technology suite, and intelligent automation, Degreed personalizes development at scaleโso your workforce is always ready for whatโs next. To learn more about Degreed, visit www.degreed.com.
