AI skills are becoming the new office rent: a recurring cost of staying relevant at work. For HR leaders, that changes the question from whether employees should use AI to whether the organization can define competent use clearly enough to reward it, train it, and govern it. The old divide centered on remote versus in-person work. The new divide runs between employees who can use AI in HR to improve real workflows and employees who still treat it as an optional add-on.
Widening AI Skill Gap
That divide will widen because the World Economic Forum projects that AI skills and other core job skills will change substantially by 2030. Yet many employers are pushing for speed before they have built the operating model. McKinsey found that nearly all companies invest in AI, while only 1% of leaders describe their organizations as mature in AI deployment. That gap should make HR cautious. A mandate to “use AI” can easily become a vague demand to work faster without giving people clear rules, safe tools, or credible measures of quality.
That is why HR strategy has to move beyond tool access. Real AI fluency means knowing when to automate, when to verify, when to involve a human reviewer, when to document AI assistance, and when sensitive data should stay out of a model. Anthropic’s task-level research found that AI use leaned toward augmentation more than automation, with 57% of usage suggesting human capability support and 43% suggesting direct task execution. That matters for HR because most employees need redesigned work rather than a lecture about prompts.
The productivity upside still deserves attention. In a field study of 5,172 customer support agents, researchers found that a generative AI assistant produced a 15% average productivity gain, with larger benefits for less experienced and lower-skilled workers. For HR, that finding should sound less like a layoff trigger and more like a learning design opportunity. AI can compress the ramp-up period for newer employees when the work has clear patterns, immediate feedback, and visible quality standards.
But the gains arrive unevenly. AI leaves bad process, unclear accountability, weak management, and low trust exposed. Harvard Business Review described the rise of workplace AI “workslop,” meaning polished AI-generated output that forces colleagues to decode, correct, or redo the work. In the underlying survey of 1,150 U.S. full-time employees, 40% reported receiving such work in the prior month. That should worry HR more than employee resistance. A company can have high AI usage and still lose productivity if people use the tool to pass half-thinking downstream.
AI Employee Training
The practical answer starts with employee training that treats AI as a work skill rather than a software feature. Training should include task selection, prompt framing, source checking, data protection, bias review, documentation norms, and escalation rules. Employees should learn what good AI-assisted work looks like in their function. A recruiter, HR business partner, payroll analyst, learning designer, and frontline supervisor all need different examples, because their risks and quality standards differ.
HR also needs to update performance management. If managers reward speed alone, employees will optimize for volume. If managers reward evidence, judgment, collaboration, and verification, employees will use AI more responsibly. The National Institute of Standards and Technology’s AI governance framework gives employers a useful reminder that trustworthy AI requires ongoing risk management rather than one-time policy language. HR should translate that principle into everyday expectations: disclose AI use when it affects decisions, check outputs before passing them on, protect confidential data, and keep humans accountable for consequential judgments.
The better employers will build talent development around workflows rather than generic enthusiasm. Accenture’s LearnVantage push signaled the scale of enterprise AI upskilling, while KPMG’s AI innovation rewards show a different lever: paying attention to employee experiments that create client value or operational efficiency. HR should borrow the lesson, even without the same budget. Recognize useful experiments, scale what works, and stop celebrating flashy demos that never change a measurable outcome.
This also means mapping tasks before cutting jobs. A job title hides many different activities. Some tasks deserve automation, some deserve augmentation, and some deserve insulation because they involve trust, judgment, conflict, confidentiality, or employee rights. HR can lead that mapping with managers and employees together. The goal should be workforce transformation that raises capability while reducing avoidable risk, rather than a top-down squeeze dressed up as innovation.
The highest-risk employee today may have touched AI before; the real risk comes from believing the old definition of competence will survive unchanged. Yet leaders should be careful too. Companies will lose ground if they turn AI into a fear test. They will gain ground by making AI adoption concrete, fair, measurable, and tied to better work. That is also the future of career mobility: proving that human judgment becomes more valuable as machine output gets cheaper.
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Dr. Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps tech-forward leaders stop overpaying for AI while boosting engagement and innovation. He serves as the CEO of the AI consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his forthcoming book with Georgetown University Press is The Psychology of AI Adoption at Work: From Resistance to Results (2026). His most recent best-seller is ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI (Intentional Insights, 2023). His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.