The conversation around AI in the workplace often focuses on productivity. Organizations are investing heavily in AI because they believe it will help employees work faster, accomplish more, and improve efficiency. In many cases, that expectation is proving correct.
What receives less attention is how AI is changing expectations for the people using it.
As AI becomes embedded in everyday workflows, companies are beginning to expect employees to produce more output, complete work faster, and contribute at a higher level earlier in their careers. Work that once required several years of experience to complete can sometimes be accomplished with the assistance of AI tools.
At the same time, many employers are already concerned about the readiness of newer hires. Our research at D2L found that 48% of HR leaders say AI is increasing productivity expectations for entry-level roles, while employers also report growing challenges around communication, interpersonal skills, and problem solving among recent hires.
Those trends are beginning to collide. Companies are raising expectations at the same moment they’re expressing concern about foundational workplace skills. AI may be increasing what employees can produce, but that does not necessarily mean it is accelerating the development of the capabilities that support long-term success.
Productivity and capability are not the same thing
It is easy to understand why expectations are rising.
AI can help employees conduct research, summarize information, generate content, organize ideas, and complete routine tasks more efficiently than ever before. Work that once consumed hours can often be completed in minutes.
When productivity increases, organizations naturally begin to adjust their expectations. If employees can accomplish more, leaders assume they should accomplish more.
The challenge is that productivity and capability are not the same thing.
An employee may be able to create a report more quickly with AI without becoming better at evaluating information. They may be able to generate a presentation faster without becoming a stronger communicator. They may complete tasks with greater efficiency while still struggling to navigate ambiguity, make decisions, or solve unfamiliar problems independently.
AI can accelerate execution. Developing expertise requires something different.
Professional growth has always depended on experience. People learn by encountering difficult situations, receiving feedback, working through uncertainty, collaborating with others, and gradually building judgment. Those capabilities are developed over time. They cannot simply be generated on demand.
The skills employers value most still require practice
Many of the skills organizations say they need are inherently human.
Communication improves through conversations, presentations, and collaboration. Interpersonal skills develop through working with colleagues, resolving disagreements, and building trust. Problem solving improves when employees wrestle with challenges that do not have obvious answers.
These capabilities are difficult to outsource.
Our research found that HR leaders report declines in communication skills, interpersonal skills, and problem-solving abilities among recent entry-level hires compared to cohorts from three to five years ago. AI is not responsible for those trends, nor is it the only factor shaping workforce readiness.
However, organizations should consider whether increasing performance expectations could unintentionally make those challenges harder to address.
An employee who uses AI to draft an email may produce a better message, but that does not necessarily improve their communication skills. Someone who relies on AI to structure a recommendation may deliver stronger output without becoming better at analytical thinking. The work gets completed, but the underlying capability may not develop at the same pace.
The result can be a growing gap between what employees are able to produce and what they are actually prepared to do independently.
Experience remains the foundation of expertise
One of the most common assumptions surrounding AI adoption is that employees can move immediately to higher-value work once routine tasks are automated.
In many cases, that is true. Few organizations want employees spending time on repetitive activities that technology can complete more efficiently.
The more important question is what happens next.
Historically, foundational work served as preparation for more complex responsibilities. Employees learned how decisions were made by participating in the work that supported those decisions. They developed judgment by solving problems, observing experienced colleagues, receiving coaching, and gradually taking on greater responsibility.
AI can make work more efficient, but it does not eliminate the need for those experiences.
The ability to navigate uncertainty, understand context, recognize patterns, and make sound decisions remains critical in every profession. Those capabilities continue to depend on practice and experience, even as technology becomes more powerful.
Organizations should be careful not to assume that reviewing AI-generated work produces the same developmental outcomes as creating that work from scratch.
The leadership pipeline could become the next challenge
The long-term implications extend beyond individual employees.
Again, our research showed that 58% of HR leaders worry that reducing entry-level opportunities because of AI could create a shortage of qualified senior leaders within five years.
That concern reflects a fundamental reality about workforce development. Future managers, specialists, and executives do not appear overnight. They develop through years of accumulated experience, increasing responsibility, and continuous learning.
Organizations have always relied on a steady progression of talent. Employees gain experience, develop judgment, take on larger responsibilities, and eventually move into leadership roles. That progression has never happened automatically. It depends on people having opportunities to practice decision-making, solve increasingly complex problems, and learn from both successes and mistakes.
AI changes some of the activities that historically helped employees build those capabilities. As more routine work becomes automated, organizations will need to think carefully about where future leaders gain the experience that prepares them for larger roles.
Future managers, specialists, and executives are developed over years of experience. If employees have fewer opportunities to build judgment, solve problems, and learn through practice, organizations may find it increasingly difficult to prepare people for those roles.
The workforce implications of AI may ultimately have less to do with how many jobs exist and more to do with how expertise develops. Productivity gains are valuable, but organizations also need a reliable way to cultivate the people who will lead teams, manage complex decisions, and guide the business in the years ahead.
Learning cannot be an afterthought
For decades, much workplace learning happened naturally through work itself. Employees developed expertise because the tasks required to operate a business were often the same tasks that built professional capability.
AI changes that equation.
As technology takes over more foundational work, organizations may need to become much more intentional about how employees develop critical skills. Communication, collaboration, problem solving, leadership, and decision-making cannot be treated as capabilities that emerge automatically from increased productivity.
Mentorship, coaching, experiential learning, rotational assignments, collaborative projects, and structured development opportunities are becoming increasingly important. The goal is not to preserve inefficient work. The goal is to ensure employees continue building the judgment and expertise organizations will need in the future.
AI can help people work faster. It can help them produce more. It can help organizations operate more efficiently.
What it cannot do on its own is build judgment, strengthen relationships, develop leadership skills, or teach employees how to navigate complex human situations.
As organizations continue adopting AI, the most important question may not be how much productivity the technology creates. It may be whether companies are investing in human development at the same pace that they are raising expectations.
Because the future workforce will need both.
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