The Illusion of AI Expertise: Why I Reject the Title of “Expert”
A recent article by my colleague, Timothy Prosser, MBA, on the nature of expertise made me reflect on my perspectives — particularly in regard to artificial intelligence. Timothy’s article explored the flaws and limitations of expertise, highlighting how cognitive biases, overconfidence, and media influence can distort public perception of who qualifies as an expert. His insights resonated with me, especially as someone frequently introduced as an “AI expert.”
While I am highly knowledgeable about AI — having spent over 18 months studying more than a thousand peer-reviewed articles, publishing extensively, and even writing a book — I find the term “expert” in AI problematic. AI is a vast and evolving domain; no single individual can claim mastery over all its facets. This led me to question: What does being an expert mean, and what responsibilities come with AI knowledge?
This article explores the responsibility of AI professionals, the future of expertise, and why authentic leadership in AI is about continuous learning, specialization, and ethical responsibility — rather than claiming universal expertise.
What Does It Mean to Be an Expert?
The word expert carries weight. It implies mastery, deep understanding, and a level of proficiency that separates someone from the average professional in a field. But what does it mean to be an expert?
In psychology, expertise is often defined by deliberate practice — developing skills through sustained effort, feedback, and refinement over time. Expertise is not just about knowledge but about demonstrable competence, the ability to apply knowledge effectively in real-world situations. Legally, expertise has a more structured definition. In courtrooms, for instance, an expert witness must demonstrate a combination of education, training, experience, and peer recognition to qualify as an authority in a given field. The law does not recognize expertise as a self-proclaimed status — it must be proven through credentials and experience.
Yet, in today’s digital world, expertise is often conflated with visibility. The rise of social media has blurred the lines between experience-based authority and confidence-based marketing. Nowhere is this more apparent than in the field of artificial intelligence.
The Problem with “AI Experts”
I am often introduced as an “AI Expert,” a title that makes me deeply uncomfortable. Not because I lack knowledge — on the contrary, I have spent over 18 months studying more than a thousand peer-reviewed articles on AI, publishing articles on the subject, and even compiling my insights into a book. But AI is not a singular discipline; it is an umbrella term encompassing machine learning, deep learning, robotics, natural language processing, neural networks, and so much more.
To claim expertise in “AI” as a whole would be like calling oneself a “Science Expert.” It is simply too broad. A physicist is not automatically a biologist, just as a data scientist specializing in reinforcement learning is not necessarily qualified to comment on AI ethics or the socio-economic impact of automation.
Yet, social media is full of self-proclaimed AI experts, many of whom base their expertise on “30 hours of ChatGPT.” These individuals confidently advise businesses, speak at events, and sell AI courses despite lacking formal study or industry experience. The Dunning-Kruger Effect, a well-documented cognitive bias, suggests that those with the least competence often overestimate their abilities. AI expertise is increasingly being shaped by those who know just enough to be dangerous.
True Expertise Requires Depth, Not Just Exposure
Genuine expertise in any field is built on depth, not just exposure. In AI, true experts specialize in specific domains — computer vision, algorithmic bias, generative models, AI policy, or another focused area. No single person can claim mastery over the entire field.
If I were to claim expertise in anything, it would not be in artificial intelligence broadly but in the strategic improvement of workflow processes or AI strategy — something I have spent over 30 years refining across industries. I understand how AI can be a tool for business transformation, but I would not claim to be an expert in AI model development.
In contrast, many self-proclaimed AI experts present AI as a force that requires their guidance to understand. This is problematic for businesses making strategic decisions based on AI adoption. False expertise can lead to misinformed AI strategies, wasted investments, and ethical missteps.
The Responsibility of Those with AI Knowledge
If we truly care about the responsible advancement of AI, those of us with knowledge in this space must resist the temptation to inflate our expertise. Artificial intelligence is a vast and ever-expanding domain; no single individual can claim mastery over all its facets. The dangers of overconfidence and misinformation are particularly pronounced in AI, where businesses and policymakers rely on guidance to make strategic decisions. Misrepresenting expertise in AI can lead to flawed implementations, wasted investments, and even ethical or legal missteps that could have far-reaching consequences. Instead of embracing the allure of being perceived as an expert, we must adopt a more responsible and honest approach.
Acknowledge the Limits of Our Knowledge
One of the most important responsibilities of AI professionals, researchers, and practitioners is to be honest about where their expertise begins and ends. AI is not a single discipline but a convergence of fields — including machine learning, computer science, mathematics, ethics, cognitive science, and more. A deep understanding of AI strategy does not equate to expertise in algorithm design, just as a data scientist may not have the necessary perspective to advise on AI’s ethical or regulatory implications. True expertise means being willing to say, “I don’t know,” and directing inquiries to those with the appropriate knowledge. In business leadership, this principle is critical — leaders must ensure they are receiving advice from those with domain-specific expertise rather than individuals making broad claims about AI’s capabilities.
Encourage Specialization Over Catch-All AI “Authority”
Today’s primary issue in the AI landscape is the emergence of generalist “AI experts” who attempt to cover every aspect of the field. While having a broad understanding of AI is valuable, true expertise is cultivated through deep specialization. AI is too expansive for anyone to master all its subfields. Those who contribute meaningfully to AI development — whether in research, business applications, or ethics — do so by focusing on a specific area where they can make an impact. Encouraging specialization ensures that shared knowledge is accurate, relevant, and actionable, rather than a high-level mix of jargon and marketing speak. Businesses looking to integrate AI must seek specialists — whether in natural language processing, computer vision, AI ethics, or AI strategy — rather than defaulting to a single AI consultant claiming universal expertise.
Educate Responsibly to Enable Informed Decision-Making
The responsibility of those with AI knowledge extends beyond technical proficiency; it includes educating stakeholders responsibly. Business leaders, policymakers, and the general public must make critical decisions about AI adoption, regulation, and ethics, often without direct expertise in the field. Those who understand AI must educate responsibly, providing clear, unbiased information that allows others to make informed decisions. This means avoiding overly technical explanations that obscure key takeaways and instead focusing on real-world implications, risks, and opportunities. It also means resisting the urge to sensationalize AI’s capabilities — whether to generate hype or fear. Instead, education should be rooted in objectivity and practical application.
Emphasize Transparency Over Hype
The AI industry is plagued by exaggerated claims, often fueled by media coverage, venture capital interests, and marketing strategies. Many businesses are led to believe that AI will instantly transform their operations, only to find that implementation is far more complex than advertised. Transparency is critical — those with AI knowledge should strive to explain AI’s capabilities and limitations honestly. This includes discussing the challenges of implementation, the importance of data quality, ethical concerns, and the realistic expectations for ROI. AI is a powerful tool, but it is not a magical solution, and businesses that invest based on hype rather than reality will ultimately face disappointment. Transparency builds trust, ensuring that AI is used effectively and ethically rather than as a tool for short-term gains.
This approach is not just an exercise in humility; it is an ethical obligation. AI is too powerful, too transformative, and too nuanced to be guided by half-truths and marketing tactics. As professionals in the field, we must ensure that AI is developed, implemented, and communicated responsibly.
The Future of Expertise in AI and Beyond
In a world of rapidly evolving technology, expertise itself must evolve. The traditional model of expertise, where an individual spends decades mastering a fixed body of knowledge, is becoming increasingly impractical in fields like AI. Instead, the best future experts will not be those who claim absolute knowledge but those who demonstrate intellectual adaptability, interdisciplinary thinking, and ethical responsibility. Expertise will no longer be defined by what someone knows at a given moment but by how they approach knowledge in an ever-changing landscape.
Stay Adaptable — Knowledge is Always Expanding
AI is evolving at an unprecedented pace. Breakthroughs emerge regularly, making it impossible for any individual to maintain static expertise. Business leaders and AI professionals must cultivate intellectual adaptability — the willingness to continuously learn, question assumptions, and update their knowledge based on new developments — those who insist on rigid expertise risk becoming obsolete as AI advances beyond their understanding. True leaders in AI are not those who claim they already know everything but those who recognize that continued learning is an integral part of expertise.
Remain Interdisciplinary — AI is Not Just a Technical Field
AI does not exist in a vacuum — it intersects with ethics, sociology, psychology, law, economics, and business strategy. The future of AI expertise will belong to those who can integrate knowledge from multiple disciplines rather than those who focus solely on technical aspects. Business leaders should actively seek cross-disciplinary perspectives when making AI decisions, ensuring that technological advancements align with human values, organizational goals, and societal impacts. The most impactful AI professionals will be those who understand the broader context — from regulatory challenges to AI adoption’s cultural and economic consequences.
Prioritize Ethics — Ensuring AI is Used Responsibly
With AI’s growing influence, expertise without ethical responsibility is dangerous. The rise of biased algorithms, data privacy concerns, and automation’s impact on jobs all highlight the moral dimension of AI decision-making. Those who work in AI must prioritize ethics as part of their expertise. This means questioning the long-term effects of AI applications, advocating for fairness and transparency, and ensuring that technology serves society rather than exacerbates existing inequalities. AI expertise in the future will be about technical proficiency and the ability to navigate ethical dilemmas with integrity.
Promote Collaboration — The Best Experts Engage with Others
No one person can fully understand AI. The most effective experts will be those who actively collaborate, challenge their ideas, and refine their perspectives through dialogue. This is why I have curated my “Collaboration Circle” of artificial intelligence enthusiasts that offer different insights. In business and research, AI solutions must be co-created by specialists in different fields — AI engineers, ethicists, domain experts, and policymakers. Business leaders must also foster an organizational culture of collaborative learning, where AI insights are shared and refined collectively rather than dictated by a single voice claiming expertise. Authentic AI leadership will be defined by individual brilliance and collective intelligence.
The future does not belong to AI experts but to AI practitioners, researchers, strategists, and ethicists who understand that true expertise is not about knowing everything—it is about knowing enough to ask the right questions.
Final Thoughts
In an era where AI is reshaping industries, expertise must be redefined. Those who genuinely contribute to AI’s advancement will not be those who market themselves as generalist AI experts but those who embrace continuous learning, interdisciplinary thinking, ethical responsibility, and collaboration.
We do not need more self-proclaimed AI gurus — we need thoughtful, responsible professionals who understand the complexity of AI and guide its integration with integrity. We can build a future where AI expertise benefits businesses, society, and humanity if we focus on depth over breadth, humility over certainty, and adaptability over dogma.
I would love to hear your thoughts: How do you define expertise in AI?
Want more insights like these? Explore the world of AI for business leadership in my book, From Data to Decisions: AI Insights for Business Leaders. It’s a curated collection of strategies and lessons from my LinkedIn articles published in 2024, available now on Amazon at https://a.co/d/3r49Cuq
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