Why experience and emotion will outlast expertise in the age of AI
For much of the last hundred years, expertise was the golden ticket. If you needed answers, you sought out the people who had them: the doctor, the lawyer, the consultant. Knowledge was rare, and that rarity made it valuable - and often, expensive.
Now knowledge is everywhere – searchable, summarised, and served through a prompt. AI has democratised expertise at machine speed. What once required years of study can now be queried in seconds. We used to call early AI “expert systems” for a reason: their promise was to codify the judgment of specialists.
If you’re looking to buy expertise, it feels like magic. If you’re selling it, it feels like the ground is shifting beneath your feet.
Accountants, lawyers, analysts, designers - whole careers have been built on the ability to charge for what you know. But in a world where almost everything is knowable, what keeps you valuable?
That’s the puzzle at the heart of this conversation.
The New Triad of Work
Let’s look at a new way to think about value in the age of intelligent work - a model built on three layers: Expertise, Experience, and Emotion.
AI is chipping away at the first, turning up the volume on the second, and shining a light on the third.
- Expertise – what you know; the codified, repeatable, technical component of work.
- Experience – how you apply it; the judgment, context, and pattern recognition born of lived practice.
- Emotion – why people trust you to do it; the relational, ethical, and empathetic dimension that sustains loyalty.
Together, they make up the New Triad of Work. When AI flattens the knowledge curve, the real difference moves from what you know to how you decide - and why people trust you.
Why Now: The End of Knowledge as Advantage
AI is the greatest leveller of knowledge since the printing press.When Gutenberg’s machine arrived, monks lost their monopoly on copying Bibles. When the internet appeared, encyclopaedias lost their monopoly on facts. Now, AI is stripping professionals of their monopoly on knowing.
The price of expertise has dropped to almost nothing. A junior analyst with ChatGPT can now create a report that used to be the domain of a senior consultant. A graduate lawyer can draft a contract in seconds that once took a partner hours.
For buyers of expertise, this is deflationary magic. For sellers, it’s margin collapse.
But there’s a deeper shift happening. As AI absorbs expertise, the shelf life of professional knowledge becomes shorter. Technical advantage is no longer a moat; it’s a moment. In this world, judgment becomes the new differentiator.
That’s why experience - applied knowledge in context - matters more than ever.
The Framework: The Three Layers of Value
1. Expertise – Knowledge as Commodity
Expertise once meant authority. You were an expert because you knew things others didn’t. That edge is gone. AI can pass bar exams, summarise medical journals, and write code at scale.
Knowledge is now everywhere, and expertise has become a commodity.
But expertise still matters - just in a different way. It’s now the baseline, not the thing that sets you apart. Clients assume you’re technically competent; they don’t pay extra for it. The value has moved.
2. Experience – Judgement as Differentiator
Experience is what happens when knowledge meets consequence. It’s the collection of patterns, intuition, and the scars you earn from decisions made under pressure.
Experience is measured by the quality of your decisions, not just technical skill. It’s why a client pays more for the lawyer who has stood in court a hundred times. The experienced lawyer knows the letter of the law matters less than the person holding the whistle.
Judgement grows with exposure: the more situations you’ve lived through, the richer your instincts become. AI can simulate data, but not consequence. It can predict what should happen, but it can’t feel what actually did.
As expertise gets automated, experience becomes more valuable. It’s the only kind of knowing you can’t download.
3. Emotion – Trust as the Ultimate Currency
Emotion is the least-discussed yet most decisive layer of work. It’s about trust, ethics, empathy, and relationships.
Clients still buy with emotion and explain with logic. They may want speed and price, but they stick around for confidence and care.
David Maister’s Trusted Advisor model captured this decades ago: trust isn’t built solely on brilliance but on reliability, intimacy, and self-orientation. In the AI era, this emotional contract becomes even more important.
When everyone looks equally smart, clients pick the one who makes them feel safest. In a world of AI, this is the opportunity to “turn up the human”.
Application: The Lawyer Test
Picture hiring a lawyer. You look for all three layers of the triad - but not in equal measure.
Expertise gets you in the door. You assume they know the law.Experience earns your confidence and often drives price. You trust they’ve handled cases like yours before.Emotion builds loyalty. You believe they’ll fight for you, not just represent you.
AI can handle the first layer well - drafting, searching, summarising - but it can’t copy the second and third. It doesn’t know what it’s like to face an unpredictable judge or a hostile jury. It doesn’t earn trust when the stakes are personal.
A good lawyer applies knowledge. A great lawyer finds a strategy to win.An exceptional one makes you feel seen and understood along the way.
That’s the shift: AI makes good cheaper, but it makes great even rarer.
For leaders, the same logic holds. The organisations that thrive will be the ones that:
- Automate the teachable – use AI to compress routine expertise.
- Accelerate experience formation – expose people to diverse, high-stakes learning faster.
- Design for emotion – make trust, transparency, and ethics measurable, not mystical.
When you rebalance work like this, you stop fearing automation and start shaping it.
Playbook: The New Professional Equation
- Stop selling knowledge. Start selling judgment.
Move beyond 'we know' to 'we’ve seen, learned, and adapted.' Show expertise as lived experience. - Design for Exposure, Not Tenure.
Speed up learning by moving people through a mix of tough, varied problems. - Measure Decision Quality.
Swap activity metrics for outcome metrics. Experience isn’t about effort; it’s about discernment. - Make Emotion a Design Principle.
Trust isn’t soft; it’s structural. Track response time, follow-through, and client sentiment as rigorously as revenue. - Balance Machine Efficiency with Human Depth.
Use AI to remove friction, not connection. - Rebrand Expertise as Assurance.
Knowledge shows you’re competent; emotion earns confidence. Sell the second.
Provocation: The Human Dividend
AI has handed out knowledge to everyone and sped up mediocrity. That’s the uncomfortable truth.
When everyone has the same information, expertise stops being an advantage and starts being infrastructure. What still grows is experience and emotion - the two things machines can’t fake, only mimic.
This is the new professional divide.
- Those who trade in answers will compete with algorithms.
- Those who trade in judgment and trust will earn the premium.
Every leader now faces a choice: keep investing in what the machine already knows, or start building what only humans can feel.
Because the future of work won’t belong to the people who know the most.It will belong to the people who can decide best and be trusted most.
So before you ask how to make your organisation smarter, ask a harder question:How much humanity are you willing to automate away?
If you haven’t been keeping up with current events, the next wave of digital disruption is upon us in the form of artificial intelligence (AI). For experienced leaders who’ve already weathered the disruptive impacts of Web 2.0, e-commerce and mobile technology, this may feel like yet another industry trend we need to come to terms with. But the truth is, artificial intelligence is a seismic shift that will completely transform how we both work and live.
From healthcare to transportation, AI will empower us to make more informed decisions at an unprecedented scale. If you’d asked us two years ago which sectors wouldn’t be severely impacted by AI, we would’ve mentioned STEM, the creative arts and empathy-based healthcare. Yet, in the past six months, we’ve seen all three experience the powerful effects of AI. On the surface, AI promises greater productivity, streamlined processes and limitless innovation. But this promise comes with caveats, as concerns emerge about potential job displacement, compelling organisations to adapt their structures and strategies. This article delves into the layers of AI’s impact in the workplace, exploring its influence on knowledge workers, organisational design and the pivotal role of the C-suite.
The AI-Driven Shift: A Complicated Transition
While AI’s transformative potential is undeniable, it brings with it many complexities that greatly impact the workforce. On the one hand, AI presents an enticing prospect of automation, alleviating the burden of mundane tasks and enabling employees to concentrate on high-value, strategic work. On the other hand, these advancements also give rise to real concerns about job displacement and the widening skills gap as the demand for AI expertise skyrockets. And the question of whether the number of new jobs AI creates will surpass those lost remains to be seen.
Beyond the individual level, organisations are grappling with the AI revolution, often finding themselves ill-prepared to navigate this transition. A lack of readiness, caused by either underestimating the reach of AI or struggling to keep pace with its rapid evolution, means a strategic re-evaluation among those in leadership positions is needed. We saw this denial during the previous wave of digital disruption, resulting in unprepared incumbent players displaced by new, more agile organisations that embraced or embodied the emerging technology.
Adding to the intricacies of this transition, governance, regulation and ethics present further challenges. As AI continues to permeate business operations, concerns about privacy, data security and ethical use of AI have taken centre stage, and regulators are scrambling to keep up. Many governments around the world are still struggling to grasp the very concept of TikTok, let alone comprehend the complexities of the impact of AI on the workplace and society. On the flipside, though, corporations find themselves in a unique position to self-govern, developing robust ethical frameworks that will guide their AI implementations now and into the future. C-suite executives, then, have an extraordinary opportunity to drive AI strategy and ensure it aligns with emerging regulations and ethical standards.
Navigating the Impact of AI: The Burning Question
Amidst these formidable challenges, a pivotal question arises: “How can C-suite executives effectively assess the impact of AI on the workforce?” Addressing this complex puzzle calls for more than a straightforward approach; it needs a comprehensive strategy that brings together the technological finesse of AI with the intricate dynamics of the human workforce.
Scenario Planning: Anticipating the Unknown
In our experience collaborating with clients, we’ve come across a diverse range of perspectives about the impact of AI on the workforce. While some perceive AI as a threat to their businesses, others recognise it as an incredible opportunity. We firmly believe that predicting the future is an impossible task, and it’s also unnecessary. The strategic tool of scenario planning is a much better way to navigate the uncertain terrain of AI transformation. By envisioning various outcomes and preparing suitable responses, organisations can address potential vulnerabilities and seize opportunities, whether by embracing automation or adopting AI-driven business models.
Typically, we develop three to five scenarios for each client, outlining the conditions needed for each to occur. Then we comprehensively assess the positive and negative impacts of each scenario on the business. In formulating responses, we often discover a common subset of actions applicable across all scenarios. The beauty of this approach is that it doesn’t require the ability to predict the future. In fact, these ‘no regret’ actions foster consensus within the executive team, as they establish strategic flexibility regardless of the scenario that ultimately unfolds.
Redesigning the Organisation for AI
In recent years, we’ve witnessed the convergence of strategy and organisation design to the point where they’re now almost indistinguishable. This has also corresponded with an excessive emphasis on talent. While talent is undoubtedly crucial, we firmly believe in the adage that “even a flawed system can outperform the most skilled individual”. As a result, we consider organisational design to be essential for all leaders striving to align their organisations with their strategic objectives. By investing in organisational design, they can create work environments that not only promote the growth and success of their talent but also reduce obstacles along the way, decreasing frustration.
When considering the impact of AI on an organisation, it’s crucial to acknowledge the necessary adjustments to its design. As AI will influence various aspects of organisation design, including ways of working, incentives, talent management and structure, integrating it into daily operations and strategies requires a comprehensive reassessment of capabilities, team dynamics, collaboration and decision-making processes.
To drive performance, leaders must identify any necessary roles and skills and determine the most suitable organisational model for managing operations integrated with AI. It’s also important to consider critical questions: How will decision-making authority be distributed among teams? What roles are required for data collection, analytics and operational execution? How can potential conflicts over resources, data ownership, skills and capabilities be avoided or resolved?
Unveiling these answers will shed light on the distinct challenges and opportunities that AI presents for each organisation. In reality, reshaping an organisation for AI is not a one-time endeavour; it requires an ongoing process of adaptation and evolution. Our approach has always encouraged leaders to treat organisation design like product design – managing ‘release cycles’ of iterative change rather than opting for a drastic overhaul. As AI technologies advance and mature, organisations must continuously adjust and refine their structures, processes and cultures. It’s undeniably a formidable task, but one that can yield substantial rewards for enhanced efficiency, innovation and competitiveness.
The Emergence of the AI Vanguard: Chief AI Officer and Chief Data Officer
As organisations grapple with the complexities of integrating AI, there’s a growing demand for specialised roles to address these challenges. The rise of Chief Data Officers (CDO) and Chief AI Officers (CAIO) within the C-suite reflects this need. The CAIO acts as a strategic compass, ensuring AI aligns with organisational objectives and overseeing implementation across functions. In contrast, the CDO manages data as a strategic asset, ensuring quality, compliance and accessibility for AI initiatives. Together, they lead the charge in navigating the transformation, and driving AI development and deployment.
The specific roles of these executives, however, vary based on company size, maturity level and strategic objectives. Both positions require a deep understanding of data science and AI technologies. Their emergence is driven by the vital role data plays in this transformative process. Data, often called the ‘new oil’, undeniably fuels AI. To be truly prepared, it’s essential that an organisation handles data intelligently. This involves understanding data origin, implementing effective cleaning and storage procedures, and employing efficient analysis methods. Developing robust data management and analytics capabilities is absolutely crucial when preparing for the advent of AI. In an era where many organisations can access sophisticated AI models, proprietary data sets have become a critical competitive differentiator and they are vitally important in this landscape.
In many respects, the positions of CDO and CAIO represent a convergence of traditional Clevel roles. They combine knowledge usually held by executives from IT, operations, analytics and finance. By creating these specialised positions within their executive teams, organisations can reap the rewards of AI while managing potential risks. This also allows companies to drive innovation and gain a competitive edge in the rapidly changing technological landscape.
The advent of the Chief AI Officer (CAIO) and Chief Data Officer (CDO) roles significantly impacts the role of the traditional Chief Information Officer (CIO). While the CIO has historically managed data and technology, the introduction of CAIO and CDO roles brings a more nuanced division of responsibilities. We expect the CIO will continue to oversee the technological infrastructure of the organisation, ensuring its strategic alignment with business goals. Meanwhile, the CAIO will focus on embedding AI technologies into business operations and the CDO will primarily manage data governance and strategy. Together, the CIO, CDO and CAIO form a triumvirate working in unison towards a common goal: leveraging data and AI to drive innovation, efficiency, and strategic advantage. This collaboration requires clearly defined roles and open communication channels to ensure seamless operational and strategic integration.
Charting the Future: Embracing AI Transformation in the C-Suite
The potential of AI in the business world is significant, but its realisation ultimately depends on the C-suite. As the decision-making core of any organisation, the C-suite has the power to drive AI adoption and integration. As a C-suite executive, you’re responsible for developing and executing AI strategy, aligning AI applications with business objectives, and cultivating an AI-ready workforce. These crucial components require the executive team’s vision, commitment and leadership. It’s your role to champion the cause, overcome resistance and propel the organisation into the AI age.
But for AI transformation to truly succeed, it requires more than just technological upgrades and talent acquisition. A fundamental redesign of an organisation is needed – one that remodels its structure and processes, morphing it into an environment that supports AI. Shaping the organisation’s design is a powerful tool for the C-suite. By ensuring that the design aligns with strategic goals, leaders can encourage a culture of innovation and agility – principles essential for navigating the dynamic AI landscape of the future. So, while the road to AI maturity may have its challenges, the rewards it promises undeniably make the journey worthwhile. The future is here… and it’s time for the C-suite to take action!
Humanly Agile is business design practice that helps senior executives bridge the gap between strategy design and execution. We create organisation designs that enhance agility, and human-centred change programs that accelerate transformation.
Jamie Pride is the Managing Partner of Humanly Agile and a recognised authority on organisation design. He has worked with many Fortune 500 companies to help them develop strategies, structures and cultures which are ready for the AI revolution. With over 25 years’ experience in strategy design, culture change and leadership development, Jamie helps organisations succeed in the era of disruptive technology.
Peter Ryan is an Executive Advisor with Humanly Agile and has over 30 years of Big 4 experience in technology advisory, implementation and managed services delivery in Australia, Asia, USA and UK. His work has focused on defining technology strategy, designing pragmatic operating models and implementing complex advanced technology enabled solutions across multiple industries.
