As artificial intelligence becomes more embedded in enterprise operations, its impact is extending beyond automation and efficiency. It is reshaping how expertise is developed, applied, and evaluated across the organization.
A recent TNCR Executive Research snapshot examines this shift through three questions: how AI is affecting the balance between breadth and depth, the strategic value of expertise, and the distribution of advantage across the workforce.
Taken together, these questions provide a useful framework for understanding how AI is influencing capability, judgment, and competitive advantage.
Elevating, Not Diminishing
If there was once concern that AI would commoditize expertise, the survey results suggest otherwise. A combined 64% of respondents believe AI will increase the value of deep expertise, with 28% saying significantly and 36% saying somewhat. Only 19% believe AI will reduce its value.
This finding is central to how expertise should be understood in this context.
AI can generate content, code, summaries, recommendations, and analysis at scale. What it cannot independently determine is whether those outputs are accurate, contextually appropriate, or suitable for decision-making. That responsibility remains with leaders and practitioners who bring domain expertise and business context.
As AI handles more of the production layer, human expertise moves up the stack. It becomes less about generating answers and more about validating, interpreting, governing, and applying them well.
Experienced leaders and practitioners remain essential because they are better equipped to identify flawed assumptions, detect subtle inaccuracies, recognize missing context, and assess the implications of acting on incomplete or weak outputs.
In this environment, judgment becomes a primary differentiator.
Blurring, Not Erasing the Line
Specialists bring deep expertise in a specific domain, while generalists bring broader perspective across functions, disciplines, or business problems. Traditionally, organizations have treated those strengths as distinct and, at times, competing advantages.
Feedback from the TNCR CIO Community suggests that distinction may be starting to blur. While some respondents believe generalists will become better specialists and others believe specialists will become stronger generalists, the largest share indicates that both shifts are happening at the same time.
Historically, depth and breadth often required a tradeoff. Specialized knowledge was built through focused experience, while cross-functional range was typically developed through broader exposure across roles or disciplines. AI changes that equation by making knowledge, analysis, and context more accessible at the point of need.
For generalists, that can mean greater ability to contribute in specialized areas. For specialists, it can mean stronger visibility into adjacent functions and broader business decisions.
The implication is not that specialists and generalists become interchangeable. It is that AI may be reducing the practical distance between breadth and depth, allowing each to extend further into the territory of the other.
For CIOs, the issue is becoming less about which profile matters more and more about how to build teams where both forms of capability can expand and reinforce one another.
Equalizing Capability, or Concentrating Advantage
The most evenly divided response in the research may be the most revealing. When asked whether AI will narrow or widen the gap between professionals, responses were evenly split with only a small minority expecting no meaningful change. This divide reflects two competing views of AI’s impact: whether it levels capability across the workforce or increases the advantage of those who use it best.
AI lowers barriers by giving more people access to advanced capabilities, helping less experienced workers perform at a higher level, and reducing the distance between those with years of experience and those still developing it. At the same time, AI tends to reward those who know how to use it well. Individuals who ask better questions, integrate AI into their workflows, evaluate its outputs carefully, and act on insights with confidence are likely to move faster than those who do not.
As a result, AI may function as both an equalizer and an amplifier.
Whether it narrows or widens the gap depends less on the technology itself and more on how organizations adopt it. Broad access, practical training, and strong leadership can help close the gap. Uneven rollout, unclear expectations, and weak enablement can make it worse.
For technology leaders, this creates a significant organizational challenge. The most consequential divide may not be between traditional high and low performers, but between those who are effectively AI-enabled and those who are not.
Leadership Implications
Taken together, these findings point to a broader redefinition of expertise. In the AI era, expertise is no longer defined solely by what an individual knows, but by how effectively knowledge, judgment, and intelligent systems are combined to produce sound outcomes.
Capability is becoming more fluid. Roles will continue to evolve, and leaders will need to think less in rigid job descriptions and more in adaptable skill sets.
Judgment is becoming the key differentiator. As AI scales execution, human value shifts toward interpretation, decision-making, accountability, and discernment.
Enablement will shape equity. The extent to which AI closes gaps or widens them will depend on how broadly and effectively organizations equip people to use it.
Over time, advantage will come not from AI alone, but from how effectively leaders understand its implications for expertise and act on them.
About TNCR Executive Research
TNCR Executive Research brings forward the perspectives of CIOs and senior technology leaders on the issues shaping enterprise leadership. Drawn from the 50K member TNCR CIO Community, these insights are intended to help leaders benchmark priorities, understand peer sentiment, and better interpret the shifts influencing technology and business strategy.
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