AI in the Workplace: The Changing Value of Experience

As artificial intelligence transforms how organizations operate, the traditional sources of professional advantage are shifting. Younger, AI-native employees may have an edge in adopting emerging tools, experimenting with new technologies and accelerating execution, but experienced leaders bring institutional knowledge, strategic perspective and the judgment developed through years of navigating complex business decisions.

However, the divide between these groups may be less important than what happens when they work together.

According to the World Economic Forum’s Future of Jobs Report 2025, 39% of workers’ existing skill sets are expected to be transformed or become outdated by 2030. Although AI and big data are among the fastest-growing skills, analytical thinking, resilience, leadership and creative thinking remain essential capabilities.

Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, suggest that neither technological familiarity nor years of experience will be enough on its own. Below, they explore how AI is changing the value of experience, what skills will matter most and how companies can prepare their workforces for what’s ahead.

“The skills that matter most are discernment about what is worth doing and the humility to learn from someone whose knowledge looks nothing like your own.”

– Carol Grojean, Leadership Scientist, Cultural Transformation Strategist and Systems Change Practitioner at Regenerating Connection

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Regenerating Connection Through Cross-Generational Learning

Carol Grojean, Leadership Scientist, Cultural Transformation Strategist and Systems Change Practitioner at Regenerating Connection, believes the future of work depends on creating meaningful connections between generations.

Drawing on her experience at TerraPower, Grojean recalls watching young nuclear engineers work alongside retired engineers who had helped build nuclear plants. Each group brought something different to the relationship.

“The young brought new tools and new ways of seeing,” she says. “The elders brought what you only learn by living with the consequences of your decisions.”

She believes the learning went both ways, with each group gaining a broader perspective through collaboration.

As AI makes information and answers increasingly accessible, Grojean argues that sound judgment becomes more valuable. However, she also identifies a significant challenge: AI is taking away some of the early-career work through which professionals traditionally developed judgment through experience and trial and error.

“The skills that matter most are discernment about what is worth doing and the humility to learn from someone whose knowledge looks nothing like your own,” she says.

Organizations must, therefore, create opportunities for younger and more experienced employees to solve real problems together. Grojean also recommends rewarding leaders for the judgment they develop in others, not simply for their individual accomplishments.

Rediscovering the Value of Critical Thinking

Rohan Roger, CTO at Synoptek, believes AI is democratizing access to information and technical expertise that once took decades to accumulate.

“The competitive advantage will therefore shift toward critical thinking, intellectual curiosity, sound judgment and, perhaps most importantly, the ability to learn, unlearn and adapt,” Roger says.

Interestingly, he believes this shift will bring renewed importance to the liberal arts. History, philosophy, literature and the classics help develop contextual awareness, reasoning and an appreciation of human behavior—capabilities that technology cannot yet fully replicate.

“Knowing how to ask the right questions, challenge assumptions and connect ideas that are seemingly unrelated are going to remain invaluable,” he says.

For companies, this means moving beyond traditional technical training. Roger recommends interdisciplinary, experiential learning that encourages employees to apply knowledge across different situations, challenge established thinking and develop a broader understanding of business problems.

“Younger professionals trust the output because it arrives fast and reads well; experienced leaders distrust it because it did not come from a process they recognize. ”

– Ajay Pundhir, Founder of AI Exponent LLC

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Developing the Ability to Calibrate AI

Ajay Pundhir, Founder of AI Exponent LLC, sees a different challenge emerging between younger and experienced professionals. In his experience, both groups can approach AI with misplaced confidence or skepticism.

“Younger professionals trust the output because it arrives fast and reads well,” Pundhir says. “Experienced leaders distrust it because it did not come from a process they recognize. Both are wrong often enough to be dangerous.”

Pundhir believes the most important emerging capability is calibration: understanding how frequently an AI model produces accurate results within a particular domain and recognizing the patterns behind its mistakes.

“That comes from seeing many outputs checked against reality, which is something a company can engineer,” he says.

He recommends paired reviews in which junior and senior employees independently evaluate the same AI-generated output and compare their conclusions.

“Log who was right,” he says. “Over a year, that log becomes the real talent map, and it will not line up neatly with age or title. Promote from it.”

Combining Curiosity With Experience

Rodney Mason, Chief Marketing Officer at Minty, believes the future belongs to professionals who combine youthful curiosity with the wisdom developed through experience.

Younger employees can use AI to accelerate research, experimentation and execution. Experienced leaders contribute contextual knowledge, pattern recognition and an understanding of which challenges deserve attention.

“The greatest advantage will come from combining these strengths,” Mason says.

He emphasizes that technology alone does not determine the quality of business outcomes. Professionals must also possess imagination, taste and the ability to recognize worthwhile opportunities.

“AI may change how we work, but imagination, taste, judgment and the ability to ask the right questions will define who leads,” he says.

Mason recommends that organizations rethink talent development around continuous learning, cross-generational collaboration and creative problem-solving.

Rather than treating AI proficiency as a standalone technical requirement, companies should create opportunities for employees to use AI collaboratively while developing the human capabilities needed to evaluate ideas and turn them into meaningful results.

Making Institutional Knowledge More Accessible

Daria Rudnik, Team Architect and Executive Leadership Coach at Daria Rudnik Coaching & Consulting, believes one of the greatest opportunities in the AI era lies in improving how knowledge moves between generations.

“Experienced leaders carry things you can’t learn from a textbook or pull from a database: why things work the way they do, what has already been tried and failed, and the unwritten rules of the organization,” Rudnik says.

That context can be particularly important when evaluating AI-generated recommendations. An answer may appear convincing while overlooking previous failures, organizational realities or important business considerations.

Rudnik sees younger professionals bringing speed and curiosity to experimentation while experienced leaders provide the context that makes experimentation productive.

“Companies should build mixed-generation teams where both groups learn from each other and work together to train, challenge and improve the AI systems they rely on,” she says. “The goal is to combine what experienced leaders know with what younger professionals can do, and build the expertise the company will need next.”

Prioritizing Adaptability Over Age

Hastimal Jangid, Co-Founder of RankRabbit AI, rejects the idea that AI will automatically favor either younger professionals or experienced leaders.

“The real advantage will belong to those who combine technological adaptability with business judgment,” he says.

Younger employees may be more familiar with emerging AI tools and more willing to experiment. Experienced professionals, meanwhile, contribute industry knowledge, strategic thinking and decision-making skills. Jangid believes both capabilities are essential.

“Critical thinking, problem-solving, adaptability and AI literacy will become increasingly important,” he says.

Talent development must account for different learning needs across the workforce. Younger professionals need opportunities to develop strategic awareness and business judgment, while experienced leaders need practical opportunities to become comfortable with AI.

Jangid recommends reverse mentoring, cross-generational collaboration and continuous AI training to help bridge these gaps.

The objective is not to favor one generation over another, but to create a workforce capable of using technology effectively while applying human judgment to business outcomes.

Teaching Emerging Talent to Challenge AI

Uttam Kumar, Engineering Manager at American Eagle Outfitters, believes AI is shifting competitive advantage toward contextual wisdom and ethical judgment.

Although AI-native professionals can excel at rapid prototyping and using new tools, Kumar cautions that algorithms cannot replicate human empathy or long-term vision.

“The most critical skills will be critical thinking, strategic discernment and systemic problem-solving,” he says.

He believes organizations must move beyond conventional technical training and introduce scenario-based decision frameworks that help employees understand the broader consequences of their decisions.

“Organizations must teach emerging talent how to challenge algorithmic outputs using real-world business context,” Kumar says.

This approach is particularly important as AI-generated recommendations become increasingly integrated into operational and strategic decisions.

Kumar summarizes the distinction between information and judgment with a familiar industry observation: “Data tells you what is happening, but wisdom tells you what to do next.”

For companies, developing that wisdom means giving employees opportunities to evaluate complex scenarios, consider ethical implications and understand how individual decisions affect the broader organization.

“AI will flatten the advantage of knowing more and widen the advantage of knowing what matters.”

– Rishi Katdare, Senior Technology Executive at Amazon Web Services (AWS)

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Bringing AI Fluency and Business Accountability Together

Rishi Katdare, Senior Technology Executive at Amazon Web Services (AWS), believes AI will reduce the advantage associated with simply knowing more information.

“AI will flatten the advantage of knowing more and widen the advantage of knowing what matters,” Katdare says.

Younger professionals may move faster with emerging tools, but experienced leaders retain advantages in understanding trade-offs, sequencing decisions, context and consequences. Neither advantage is sustainable independently.

Katdare recommends that companies stop treating AI training and leadership development as separate initiatives. Employees should work together on real business decisions, allowing younger professionals to challenge established methods while taking on enough responsibility to develop their own judgment.

“At the same time, senior leaders need hands-on AI fluency, not delegated familiarity,” he says.

Companies should therefore ensure that senior executives actively experiment with AI while creating opportunities for emerging talent to participate in consequential decisions.

Embedding Leadership Development Into AI Workflows

Gabriella Goddard, CEO and Director of AI Catalyst Leader™ at Brainsparker Ltd, believes the central challenge is not determining which generation has the advantage. It is understanding how organizations will develop judgment when AI performs work that traditionally helped employees build experience.

“Younger professionals may be AI-fluent but lack experience challenging its outputs; senior leaders bring judgment but need AI fluency,” she says.

Goddard offers a practical example from her own mentoring work. She recently used AI to create a sample market assessment for a mentee preparing her first investor pitch. After checking the logic and sources, she developed a reusable prompt that helped the mentee understand the findings, challenge assumptions and explore risks independently.

“I refer to this as a Leadership Learning Loop: building learning directly into the task, not just producing an output,” she says.

Goddard recommends that leaders incorporate these learning loops into AI workflows from the beginning. Rather than simply delivering completed AI-generated work, the process should help employees understand how conclusions were reached and where assumptions may need further examination.

This creates opportunities for younger professionals to develop judgment while ensuring experienced leaders continue sharpening their own decision-making capabilities.

Accelerating Experience, Not Just Productivity

Pawan Anand, Portfolio Head of Communications, Media and Entertainment at LTM, believes AI will significantly accelerate professional development—but warns that becoming capable and becoming wise are different processes.

“AI will compress the time it takes to become capable, but not the time it takes to become wise,” Anand says.

Younger employees can gain considerable leverage from AI tools, while experienced leaders retain an advantage in recognizing second-order effects, hidden trade-offs and the consequences of poor decisions.

“The goal is not simply to teach people how to use AI, but to help them accumulate judgment faster than traditional career paths allowed,” he says.

He recommends that companies introduce accelerated-experience programs featuring decision simulations, postmortems, scenario drills and earlier exposure to real business trade-offs.

These activities can help employees understand the consequences of decisions without requiring them to learn every lesson through costly real-world mistakes.

Tips for Developing an AI-Ready Workforce

• Regenerate cross-generational connections. Put younger and experienced professionals side by side on real business challenges. Reward leaders for developing judgment in others, not just for their individual results.
• Prioritize critical thinking and interdisciplinary learning. Move beyond technical training to experiential learning that draws on different disciplines, encourages intellectual curiosity and teaches employees to question assumptions.
• Measure AI judgment and calibration. Have junior and senior employees independently evaluate the same AI-generated outputs, compare their assessments and track their accuracy over time.
• Combine curiosity with experience. Build talent development around continuous learning, cross-generational collaboration and creative problem-solving that brings together technological speed and business perspective.
• Create opportunities for institutional knowledge transfer. Establish mixed-generation teams that share organizational knowledge and work together to train, challenge and improve the AI systems they use.
• Invest in adaptability and reverse mentoring. Encourage younger employees to share their AI expertise while experienced leaders contribute industry knowledge and strategic perspective. Make continuous AI training accessible to everyone.
• Teach employees to challenge AI outputs. Introduce scenario-based decision frameworks that develop critical thinking, ethical judgment and the ability to evaluate AI recommendations against real-world business conditions.
• Integrate AI training with leadership development. Put employees from different generations together on consequential business decisions. Give emerging talent meaningful accountability while ensuring senior leaders develop hands-on AI fluency.
• Build learning into AI workflows. Design AI tasks that help employees understand findings, challenge assumptions and explore risks rather than simply deliver finished outputs.
• Accelerate experience through practical exercises. Use decision simulations, postmortems, scenario drills and earlier exposure to real business trade-offs to help employees develop sound judgment faster.

The Next Generation of Leadership Will Require Both

As AI accelerates how quickly people can acquire knowledge and complete work, companies must be careful not to overlook how professionals develop judgment. Tomorrow’s leaders will need more than technological fluency. They’ll need opportunities to question assumptions, make difficult decisions and learn from experience. That applies to younger employees building their expertise and seasoned leaders adapting to new ways of working.

Organizations shouldn’t favor one generation over another, but instead bring their strengths together. AI may change how work gets done, but knowing when to trust it, when to challenge it and how to turn its capabilities into sound business decisions will remain distinctly human advantages.