The Evolving Role of Expertise in AI

Last month, a Fortune 100 CIO said their company spent millions on an AI decision system that their team actively sabotages daily. Why? Because it optimizes for data they can measure, not outcomes they actually need. This isn't isolated. After years advising tech leaders, I'm seeing a dangerous pattern: organizations over-indexing on AI for decisions that demand human judgment. Research confirms it. University of Washington studies found a "human oversight paradox" where AI-generated explanations significantly increased people's tendency to follow algorithmic recommendations, especially when AI recommended rejecting solutions. The problem isn't the technology. It's how we're using it. WHERE AI ACTUALLY SHINES: - Data processing at scale - Pattern recognition across vast datasets - Consistency in routine operations - Speed in known scenarios - But here's what your AI vendor won't tell you: WHERE HUMAN JUDGMENT STILL WINS: 1. Contextual Understanding AI lacks the lived experience of your organization's politics, culture, and history. It can't feel the tension in a room or read between the lines. When a healthcare client's AI recommended cutting a struggling legacy system, it missed critical context: the CTO who built it sat on the board. The algorithms couldn't measure the relationship capital at stake. 2. Values-Based Decision Making AI optimizes for what we tell it to measure. But the most consequential leadership decisions involve competing values that resist quantification. 3. Adaptive Leadership in Uncertainty When market conditions shifted overnight during a recent crisis, every AI prediction system faltered. The companies that navigated successfully? Those whose leaders relied on judgment, relationships, and first principles thinking. 4. Innovation Through Constraint AI excels at finding optimal paths within known parameters. Humans excel at changing the parameters entirely. THE BALANCED APPROACH THAT WORKS: Unpopular opinion: Your AI is making you a worse leader. The future isn't AI vs. human judgment. It's developing what researchers call "AI interaction expertise" - knowing when to use algorithms and when to override them. The leaders mastering this balance: -Let AI handle routine decisions while preserving human bandwidth for strategic ones -Build systems where humans can audit and override AI recommendations -Create metrics that value both optimization AND exploration -Train teams to question AI recommendations with the same rigor they'd question a human By 2026, the companies still thriving will be those that mastered when NOT to listen to their AI. Tech leadership in the AI era isn't about surrendering judgment to algorithms. It's about knowing exactly when human judgment matters most. What's one decision in your organization where human judgment saved the day despite what the data suggested? Share your story below.

To use AI well, we need human expertise and judgment. But we’re cutting off the very pipeline that provides it. AI can convincingly generate responses that look brilliant, especially to the untrained eye, but this can also include fabrications and misinterpretation of nuance. This is why we need deep human expertise to can spot the difference and effectively wield these powerful tools. >>> This is the problem we’re racing toward: As we automate more of the foundational work that once built expertise, and plug junior talent into short-term AI training roles with no long-term arc, we’re not just accelerating AI. We’re hollowing out the very judgment we’ll rely on to keep it aligned. This is the real crisis: not that AI makes mistakes, but that we’re dismantling our ability to recognize them. That’s not just a workforce issue. It’s a strategic failure. We are solving for short-term efficiency and undermining the long-term capacity we’ll need to govern these systems wisely. >>> Here’s what’s happening: This generation enters a turbulent job market. They have education, but little experience. Businesses see an opening: smart, affordable talent to annotate and train models. But these roles rarely lead to career-building paths. Meanwhile, seasoned experts will retire—and we don’t have replacements in the making. The result? A fragile AI future. Fewer people who can challenge model outputs, who understand both context and consequences. >>> What we need to be exploring now: How do we bootstrap the next generation of expertise? And that takes all of us: 1. Industry: How can we ensure we don’t treat AI training roles as disposable? How can we create onramps? Fund apprenticeships? Link these jobs to richer skill development> 2. Early career professionals: Explore how to use your unique vantage point. You see how AI is evolving, you are working on it every day: use that to find what it will . Become the person who can do what AI can’t. 3. Everyone else: Let’s really use this moment to amplify the conversation. There is no playbook here, we’ve never had to grow human expertise in the shadow of a system this fast and powerful. If we fail to build human capability alongside machine capability, we don’t just lose jobs, we will lose judgment, and that cost will come due just as AI’s power peaks. Let’s not wait for that reckoning, let’s take a long view of what we will need. >>> Please share your thoughts, and let’s get this conversation going: > How do we grow real expertise in a world where “learn by doing” work is disappearing? > What new kind of  role or program could “bootstrap” the next generation of experts? > If you're early in your career: What do you wish leaders understood about what it’s like to navigate this moment? ____ 👋 Hi, I'm Alison McCauley. Follow me for more on using AI to advance human performance. https://lnkd.in/gYYUA_E6?

"As artificial intelligence (AI) tools become increasingly capable, not just in execution but in analysis, synthesis and even creative generation, we’re approaching a strange inflection point: skills are being devalued just as we’ve learned to champion them. This isn’t to say that skills are obsolete. But we are rapidly entering a post-skills era, where the tasks that once defined expertise are outsourced to algorithms, and the remaining human value lies in something much harder to define: judgment, context and critique. We look at efficiency only it has a hidden cost. What we’re at risk of losing isn’t just skill. It’s understanding. The kind that comes from wrestling with complexity, making mistakes and building fluency from the ground up. When AI handles the middle steps, we’re left with the output, but not always the experience to evaluate its quality. That instinct isn’t something you download. It’s something you cultivate. And that cultivation takes time, exposure and often, struggle. That's why it is important to cultivate human-centered AI -where we bring our human judgement, context, and critical things to co-intelligence. https://lnkd.in/g88mz7RW

The rise of AI is reshaping the demand for graduates in Professional Services, with fewer opportunities emerging in traditional Law, Consulting, and Finance graduate programs each year. These once-reliable training grounds for early professional development are eroding, leaving many graduates feeling disenfranchised and uncertain about their career paths. At the heart of this transformation is the way AI is reshaping tasks within knowledge-based professions, altering their economic value and influencing future pay trends. Tasks that once required human expertise—typically performed by entry-level employees—are increasingly automated, reducing their market value. While continuous learning remains essential, AI's ability to scale its "learning" diminishes the competitive edge of human skill-building. This creates a cycle of commoditisation in Professional Services: as AI advances, more tasks become automated, reducing the uniqueness and value of many skills. For individuals who have invested years in education and training for these professions, this trend may seem unsettling. However, it also presents opportunities for those who are willing to adapt. The future belongs to those who cultivate capabilities that AI cannot easily replicate: original thought, creative expression, complex problem-solving, and strong interpersonal skills. Importantly, there is a growing demand for professionals with hands-on expertise and a deep understanding of specific industries. Graduates who focus on acquiring practical experience, learning how industries operate, and mastering the nuances of implementation will be better positioned to succeed in this evolving landscape. So, what should graduates do? Pursue roles and environments that offer real world exposure—internships, rotational programs, startups, or NGOs—where practical expertise can be developed. Embrace multidisciplinary learning to understand not just technical knowledge but also its application in various contexts. Most importantly, focus on enhancing human-centric skills such as empathy, adaptability, leadership, and creative thinking. In this way, a later career transition as a trusted advisor becomes even more valuable. While AI reshapes the world of Professional Services, the most resilient careers will be those that blend industry-specific expertise with the distinctly human qualities that no algorithm can replicate. The future of work isn't just about adapting to AI—it's about defining what only you can uniquely offer.

For the last two years, the conversation about AI's impact has been dominated by the visible, the tactical, and the immediate. We are focused on new tools, evolving job descriptions, and the race to upskill our teams to use them. This is the "Surface Wave." It is real, it is important, and it is consuming all of our attention. But the real story, the one that will determine the winners and losers of the next decade, is happening beneath the surface. The "Undercurrent" is the deeper, strategic, and often invisible re-architecting of the organization itself. It's the shift in power structures, the creation of new data ecosystems, and the fundamental change in how decisions are made. This integration of human and machine intelligence is creating a new organizational physics, and most leaders are still using an old map. Think about it: A company builds a strategic intelligence unit designed to be "AI-native". The "Surface Wave" is giving the human analysts a suite of powerful AI tools for market research and data synthesis. But the "Undercurrent" emerges when the AI is integrated not as a tool, but as a de facto member of the team. Suddenly, the org chart is no longer a simple 2D hierarchy. You have a hybrid entity where the AI directly feeds insights to every team member, bypassing the traditional top-down flow of information from a human manager. The AI might even be given a "voice" in strategic meetings, presenting conclusions that directly contradict the team leader's intuition. The challenge is no longer about adopting a tool. It becomes a profound question of organizational design and leadership. What is the role of a human leader when the AI can provide more comprehensive data-driven direction? How do you manage a "team" that is a fluid network of human and machine cognition? And how do you measure performance when the most valuable output is a collaborative insight that is impossible to attribute to any single human or algorithm? This is the real transformation, and it requires leaders to move from being managers of people to being conductors of a complex, hybrid intelligence. I strongly feel that leaders who cannot distinguish between the two waves will be pulled under. But will organizations invest in the foresight this requires? #FutureOfWork #AIStrategy #TwoWaveTransformation

The more we study human/AI collaboration the more we realize how difficult it is to speak in absolutes. We are easily sucked into the idea that #AIautomation will solve all of our problems, until it doesn't. Thx to my good friend Bas van de Haterd (He/His/Him) for sharing this excellent study "Falling Asleep at the Wheel: Human/AI Collaboration in a Field Experiment on HR Recruiters," by Fabrizio Dell'Acqua of Harvard Business School. The study explores the dynamics of human effort and AI quality in recruitment processes and reveals yet another paradox of AI: Higher-performing AI can sometimes lead to worse overall outcomes by reducing human engagement and effort. When it comes to hiring, this finding is pretty significant. Especially when one layers in the presence of bias that (hopefully) can be mitigated by the efforts of recruiters to be objective (We can dream can't we!). Here is a quick summary of the article's findings and implications. Key Findings: 💪 Human Effort vs. AI Quality: As AI quality increases, humans tend to rely more on the AI, leading to less effort and engagement. This can decrease the overall performance in decision-making tasks. 🙀 Lower Quality AI Enhances Human Effort: Recruiters provided with lower-performing AI exerted more effort and time, leading to better performance in evaluating job applications compared to those using higher-performing AI. 🎩 Experience Matters: More experienced recruiters were better at compensating for lower AI quality, improving their performance by remaining actively engaged and using their expertise to supplement the AI’s recommendations. Implications for Talent Acquisition Leaders: ⚖ Balanced AI Integration: While it may be tempting to implement the most advanced AI systems, it’s crucial to ensure that these systems do not lead to complacency among human recruiters. Talent acquisition leaders should focus on integrating AI tools that enhance rather than replace human judgment. 💍 Training and Engagement: Investing in training programs that encourage recruiters to critically assess AI recommendations can help maintain high levels of human engagement and performance. 🛠 Custom AI Solutions: Consider developing AI systems tailored to the specific needs and skills of your recruitment team. Custom solutions that require human input and oversight can prevent "falling asleep at the wheel" and ensure optimal performance.

Are you experiencing "EXPERTISE VERTIGO" (yes, I made that up). Are we losing real expertise in an AI-oversaturated world? In a conversation with a friend yesterday, I admitted something I rarely say out loud: despite years in advertising and feeling like a true expert, today I sometimes feel completely overwhelmed by how quickly everything is changing around me. New tools, new ways of working, new stuff to know, a never ending flow of advice, input and of course, AI to master. And I study everyday, hours at a time, use new AI tools, and I even work with an AI start-up to see how a faster, more nimble and youthful world tackles challenges today. Remember when becoming an "expert" meant mastering a relatively stable body of knowledge? Now expertise feels like trying to stand on the tippy top of a tall building with no guardrails to prevent a fall. I'm not alone. Lots of friends and professionals I’ve spoken with across many industries are experiencing what I call “EXPERTISE VERTIGO” – that unsettling feeling that no matter how much you know, you're perpetually behind the curve as AI capabilities expand at breathtaking speed. But here's what I've realized: this isn't about "faking it till you make it." It's about embracing a fundamentally new relationship with knowledge. True expertise today isn't about knowing everything (impossible) but about knowing how to learn continuously, collaborate effectively, and combine human wisdom with new capabilities and tools including those that are AI based. The impostor syndrome is real—I feel it too. But perhaps we need to redefine what "expertise" means in 2025. It's less about having all the answers and more about asking better questions, navigating uncertainty with confidence, and bringing uniquely human perspectives to problems. Here are 7 questions I ask myself all the time to make sure I am staying an expert in an AI-accelerated world: 1. What don’t I know? – Identify gaps in your understanding. 2. What questions should I be asking? – Challenge assumptions and dig deeper. 3. Who can I learn from? – Seek out experts, mentors, and diverse perspectives. 4. How does this connect to what I already know? – Build a knowledge framework. 5. What is the bigger picture? – Understand context and long-term impact. 6. How can I test or apply this knowledge? – Move from theory to practice. 7. What will still matter in 5 years? – Separate trends from lasting principles. So, I still feel overwhelmed. Truth. And yes I may suffer from Expertise Vertigo but I am giving it my best shot and allowing myself to breathe and let go when I have to. The real of imposter syndrome is real but I ain't giving up. SoulPurpose Advisory #artificialintelligence #agencylife #agencynewbusiness

Organizational restructuring driven by AI implementation is happening faster than most professionals are prepared to handle, creating both displacement risks and advancement opportunities. The key differentiator isn't technical AI expertise - it's strategic positioning around uniquely human capabilities that complement rather than compete with artificial intelligence. Roles emphasizing relationship management, complex judgment, and trust-building remain inherently human-centered and difficult to automate. Training and change management capabilities become increasingly valuable as organizations need professionals who can help teams adapt to new AI-enhanced workflows. Cross-functional communication skills that bridge technical and business domains create essential value as AI implementation requires coordination across diverse organizational functions. Strategic thinking and creative problem-solving represent human cognitive advantages that enhance rather than replace AI analytical capabilities. The professionals thriving during AI transformation aren't those avoiding the technology, but those learning to leverage it as a productivity multiplier while focusing their human capabilities on higher-value activities. Future career security lies in becoming irreplaceable through uniquely human skills rather than trying to outperform machines at tasks they're designed to optimize. How are you preparing for AI integration within your industry and role? Sign up to my newsletter for more corporate insights and truths here: https://vist.ly/3yhre #deepalivyas #eliterecruiter #recruiter #recruitment #jobsearch #corporate #artificialintelligence #futureofwork #careerstrategist

It’s not every day you get to sit down with someone who has shaped the course of computing history. I had the privilege of speaking with John Hennessy - Shriram Family Director of the Knight-Hennessy Scholars Program, Chairman of Alphabet Inc., and President Emeritus of Stanford University, and recipient of the 2017 Turing Award (alongside David Patterson) for their pioneering work on RISC architecture, which today powers 99% of all new computer chips. We spoke about the future of AI, the evolving role of humans, and what separates good institutions from great ones. John has seen it all: the rise of the personal computer, the birth of the internet, the mobile revolution—and now, the AI wave. But to him, this moment is different: “This is probably the biggest technology wave yet—because it's about intelligence itself.” Here are a few insights that stayed with me: - Humans + AI = Better Together John believes we’re moving into a future where humans and AI working in tandem will outperform either alone. This is the heart of collaborative intelligence. - The Limits of Moore’s Law, and the Promise Beyond While hardware innovation is hitting limits on power and energy, software and system architecture are unlocking new possibilities, especially with AI accelerators. - Lifelong Learning Is the Real Edge For students, entrepreneurs, and leaders alike, the most critical skill is learning how to learn. In a world that’s shifting fast, adaptability is everything. - Great Institutions Reinvent Themselves Excellence isn’t static. Whether it’s a university or a startup, staying relevant means staying curious—and knowing when to change. This conversation offered one of the clearest and most thought-provoking perspectives I’ve heard in the Humans in AI series—grounded in decades of experience, yet focused squarely on the future. Watch the full video below. What’s one belief about work, learning, or leadership that you think AI will force us to rethink? #HumansInAI #CollaborativeIntelligence #AI #Leadership

𝗗𝗼 𝗬𝗼𝘂 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗳𝗿𝗼𝗺 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗧𝗼𝗼𝗹? Data Analytics driving by AI Tools are transforming industries. But are we using them effectively? I often sense a disconnect when speaking to business owners. They create dashboards and reports with ease. Yet, there’s a crucial element often overlooked. A Data Expert is essential for using AI analytics correctly. AI Tools are valuable, but they have limitations: • They speed up data cleansing. • They bridge gaps between datasets. • They generate predictive visuals quickly. However, we must ask ourselves: • When did we last validate our assumptions? • Are the models still relevant to current conditions? 𝗟𝗲𝘁’𝘀 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗮 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲: Zillow's iBuying program aimed to revolutionize real estate. They relied heavily on AI for home valuations. Initially, it seemed promising. But Zillow's algorithms misjudged market dynamics. As a result, they overpaid for homes significantly. This led to over $1 billion in losses. Zillow's failure highlights a critical lesson. Even with vast data, AI can misfire without human insight. A data expert could have identified these flaws early on. AI tools require constant monitoring and adjustment. Don't let algorithms run unchecked in your business. 𝗖𝗼𝗺𝗯𝗶𝗻𝗲 𝗔𝗜 𝗽𝗼𝘄𝗲𝗿 𝘄𝗶𝘁𝗵 𝗵𝘂𝗺𝗮𝗻 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗳𝗼𝗿 𝗯𝗲𝘁𝘁𝗲𝗿 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀. Regularly reassess your models and assumptions. This is the key to informed decision-making. Are you leveraging both AI and human insight effectively? Your experiences could help others avoid pitfalls! Got questions? Let's discuss in the comments below! #PostItStatistics #DataScience #ai Follow Dr. Kruti Lehenbauer or Analytics TX, LLC