EY MEGATRENDS

Why shared intelligence will redefine talent

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26 minute read 16 ene 2026
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Organizations must embrace new capability ecosystems, using learning to drive the co-evolution of human and AI potential.

In brief

  • Talent has been redefined as a partnership between human and machine intelligence to create capabilities that advance through mutual adaptation and shared learning.
  • Talent isn’t scarce, capability is. The organizations that win next will be those that treat capability — not headcount — as the true measure of competitive strength.
  • CHROs, Chief Learning Officers, CFOs and CEOs must treat learning as capital and capability as a living portfolio from within and without the organization.

This article is part of the second set of insights in the new EY Megatrends series New frontiers: The resources of tomorrow. 

For decades, organizations competed for human talent as if it were scarce and fixed. That battle is outdated. Human capability is now intertwined with rapidly advancing artificial intelligence (AI) systems that learn, perform and evolve to create shared intelligence. Talent is no longer confined within organizational walls but distributed across ecosystems of employees, contractors, partners, managed services and intelligent machines.

Darwinian co-evolution offers a powerful analogy for how human and AI capabilities develop. Just as two species shape each other’s development, humans and AI systems now refine one another through every interaction, prompt and dataset. While this process will be nonlinear and unpredictable, it has significant potential to heighten human value, helping to enable people to move into higher-order roles as commissioners, curators, orchestrators and ethical stewards of capability.

Structural challenges mean that organizations are compelled to act. Skills – learned abilities to perform specific tasks – are now expiring faster than traditional learning systems can renew them. Learning systems that update in 18 months cannot keep pace with requirements that evolve in 18 weeks. This increases what we call “Talent Debt” : the unrealized potential that accumulates when human and machine capabilities fail to evolve. In the US alone, we estimate that this could represent more than US$1 trillion in unrealized value.1

Leading co-learning organizations are responding by cultivating three mutually reinforcing elements:

  • Mindset: grounded in curiosity, trust and experimentation
  • Skill set: built on human strengths such as judgment, intuition and imagination
  • Toolset: comprising AI systems, learning platforms and analytics that support continuous co-learning

Effective capability only emerges when all three elements reinforce one another.

The goal is not simply to train people faster. It is to create an environment where both human and machine intelligence evolve together, amplify each other’s strengths and continuously raise the organization’s potential.  The next wave of successful organizations will be those that view adaptive capability — not sheer headcount — as the real benchmark of competitive strength.

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Chapter 1

The rise of shared intelligence: talent becomes co-evolutionary

Talent has become a dynamic and co-evolving blend of human and machine capability, working across ecosystems rather than within fixed organizational walls.

Talent, redefined

A century ago, the boundary between labor and capital was easy to draw: labor clocked in through the factory gates, while capital sat in the machines on the shop floor. Today, that line has all but disappeared. A single workflow might combine an employee in London, a contractor in North America, a model running in the cloud and an AI agent trained overnight. Organizations are confronting a fundamental redefinition of “talent,” where human capability is augmented by machine intelligence and, crucially, sits across distributed networks of contractors, ecosystem partners and managed services providers. This expanded view shifts the focus from ownership to orchestration, where value lies in how capability is coordinated, not where it sits.

The real competitive advantage now lies in how intelligently organizations design and govern their ecosystems of capability and how they connect learning across boundaries. In a world where intelligence is abundant, advantage comes from clarity of purpose: knowing where you add value, what you must own and where you should partner.

New frontiers of AI-human collaboration

The integration of new forms of AI talent has the potential to move human capability to new realms. The organizations that gain the greatest return from this shift won’t be those that simply automate tasks but those that redesign work around human strengths.

Our unique human experiences – our upbringing, relationships, feelings – drive our divergent thinking; these drive new ideas and will remain distinctly human.

Dana Daher

Executive researcher, HFS

Agentic AI represents the current frontier – systems capable of reasoning, taking initiative and collaborating autonomously. But co-evolution implies that AI will not stop there. As human-machine interaction deepens, AI systems are beginning to move beyond logic and optimization toward forms of affective intelligence: the ability to recognize, interpret and even respond to human emotion. This suggests fascinating parallels with Charles Darwin’s studies on the symbiosis between species, a key example of which is the orchid evolving a deep nectar tube and the moth, in turn, developing a proboscis long enough to reach it.

These newer AI systems are designed to understand how people feel and think, which is a critical step in developing AI that can collaborate even more meaningfully across multiple human contexts.

Newer AI systems impacting the workplace:

  • Neurosymbolic AI: combines neural learning with symbolic reasoning, enabling systems that can both recognize patterns from data and apply structured logic – an approach that promises greater reliability and interpretability
  • Autotelic AI: systems demonstrate genuine self-direction within defined human boundaries
  • Collaborative AI: negotiates trade-offs and shares reasoning transparently with human partners
  • Reflective AI: may one day simulate self-awareness – an ability to evaluate its own performance and adjust goals dynamically, as distinct from consciousness
  • Affective AI (also known as emotion AI): learns to respond appropriately and in an empathetic way to emotional and social dynamics, potentially helping to build trust and psychological safety in digital workplaces, if managed appropriately
  • Physical AI: emerging as a recognized field combining robotics, materials science and biological design
  • Embodied and biohybrid intelligence: AI is integrated with physical and biological systems, via adaptive robotics or AI that learns directly from neural signals

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Each development reinforces a co-evolutionary cycle: As AI becomes more attuned to human needs, humans, in turn, refine their own capability to engage with these systems effectively.

 

Crucially, this shift places greater emphasis on the existence of innate human characteristics and the natural divergence of human thinking. As Dana Daher, executive researcher at HFS, puts it, “Our unique human experiences – our upbringing, relationships, feelings – drive our divergent thinking; these drive new ideas and will remain distinctly human.”

 

Just as in nature, co-evolution is impossible to predict with precision. But exploring plausible horizons helps illustrate how human and AI capabilities may reinforce each other at different periods. EY’s Superfluid Enterprise outlines three horizons for the future: foundation building, autonomous co-ordination and full superfluidity.


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Chapter 2

Managing the balance sheet of human and AI potential

Financial principles can be used to assess the value of untapped potential, using learning to maximize the return on talent investments and avoid the costs of “Talent Debt.”

Why talent models must change

Structural shifts in the labor market have made traditional talent approaches obsolete. Demographic and technological forces are reducing the supply of human capability while accelerating the pace at which skills become outdated. By 2050, more than 2 billion people will be over 60, shrinking labor supply.2 At the other end of the labor market, younger generations are increasingly reluctant to work in certain sectors, such as manufacturing.3 Already, a quarter of workers face a skills mismatch and nearly 40% of core skills are expected to change within five years.4 At the same time, 64% of employees report rising workloads.5

Skills requirements now change at extraordinary speed. For example, the “prompt engineer” role rose and declined rapidly as users developed more sophisticated prompting skills and tools became better at prompting themselves.6 This illustrates how quickly the landscape can shift. Learning systems built for 12- to 18-month cycles cannot keep pace with skills that evolve every 12 to 18 weeks.

This creates a profound dual challenge. Organizations must be able to respond immediately to fast-shifting skills demands – redeploying talent, retraining teams and updating AI systems at pace – while simultaneously building the deeper capabilities, mindsets and infrastructures required for longer-term competitiveness. Agility in this context means avoiding latency, or lags, in the operational and strategic deployment and building of skills.

In the example of the prompt engineer, organizations may have avoided investing in redundant roles by focusing on how to build the capabilities they need, using talent from a range of sources, rather than simply adding headcount.

Success depends on delivering short-term skills agility and long-term capability resilience at the same time. Organizations that solve for both will be able to turn disruption into a source of sustained advantage.

The essential shift from pipelines to portfolios

The traditional talent pipeline – hire, train, retain and promote – was designed for a world where skills evolved slowly and work was largely predictable.  That working world has gone.

The rapid adoption of AI illustrates the scale and speed of the shift occurring. Our Work Reimagined 2025 study found that 88% of workers now use AI at work, compared with just 22% in 2023. Increasingly, employees see AI not as a tool but as a collaborator – a colleague embedded in day-to-day workflows. And this momentum shows no sign of slowing. Adoption is expected to accelerate further as more advanced AI applications enter the workplace. “I think we’re at an inflection point now and growth is just going to get faster and more disruptive,” says Simon Brown, EY Global Learning and Development Leader.

In this environment, organizations can no longer rely on linear talent pipelines. Instead, they must manage portfolios of capability assets – combining human expertise, AI models and collaborative tools. Each of these “assets” has its own performance profile, depreciation curve and reinvestment horizon. Managing capability becomes a continuous process of assessing what’s working, what’s lagging and where to invest next. Sustained performance depends on how fast an organization can learn relative to the pace of change.

The shift from pipelines to portfolios mirrors financial diversification. Talent leaders increasingly need to balance:

  • Long-term skill investments with rapid learning cycles
  • Enduring human capabilities with machine intelligence
  • Structured programs with self-directed development

Managing the balance sheet of human and machine potential

Managing talent as a portfolio enables organizations to reallocate capability at speed of change and to build a workforce that is continuously renewed, rather than periodically replaced.

“Ultimately, capability is capital,” says Matthew Kearney, Partner, People Consulting, Ernst & Young LLP.

AI and human talent each have different risk-return profiles and time horizons. High-yield “growth” assets may be AI-driven automation or emerging digital skills, while “steady value” assets lie in enduring human strengths like leadership, creativity and empathy. Both are critical. Strategic balance ensures adaptability as markets, technologies and priorities shift.

This approach reframes workforce management as intelligence capital allocation, requiring Chief Human Resource Officers (CHROs) and Chief Talent Officers (CTOs) to borrow approaches from portfolio managers to sustain enterprise agility and strategic foresight. In this context, learning is a compounding asset that underpins organizational adaptability and long-term value creation.

Ultimately, capability is capital.

Matthew Kearney

Partner, People Consulting, Ernst & Young LLP

However, this portfolio approach also exposes a fundamental risk: how to grow the human capabilities with the longest-term value – particularly leadership – in organizations where career paths are being reshaped by automation and AI. Traditional entry-level roles have long served as the proving ground for future leaders, giving early-career employees the chance to build judgment, stakeholder awareness and the ability to orchestrate complex work. As many of these tasks become automated, the risk is clear: Without redesign, fewer people will have the opportunity to develop the capabilities required to lead in an AI-enabled world.

Recent research illustrates the scale of the challenge. A study by King’s College London found that companies with high exposure to AI have been reducing junior headcount between 2021 and 2025.7 The authors warned:

“The concentration of job losses among entry-level positions disrupts traditional skill development pathways where workers master progressively complex tasks through hands-on experience. Without junior roles serving as training grounds, firms may struggle to develop senior talent internally.”

There is a second risk: even where entry-level roles remain, employees may become overly reliant on AI – allowing the system to do the complex thinking for them. This can quietly erode the development of foundational skills: critical reasoning, synthesis, narrative construction and contextual judgement.

Forward-looking organizations recognize that building future leadership capability is too important to leave to chance. They are acting intentionally, reshaping early-career work to preserve – and even accelerate – the development of human capability by:

  • Integrating AI literacy with leadership fundamentals: pairing technical skills with systems thinking, strategic communication and decision-making exercises
  • Avoiding KPIs that reward speed and automation alone, which encourage juniors to bypass thinking and default to the model
  • Creating “slow lanes” for critical thinking through structured reviews, reflection time and “explain your reasoning” rituals that force deeper cognitive engagement
  • Making early-career employees co-responsible for improving AI systems – through feedback loops, error logging, or prompt library development – so they see themselves as shaping the tools rather than merely consuming them

In short, the disappearance of traditional entry-level work is not inevitable, but leadership pipelines will weaken unless organizations deliberately redesign the early-career experience for an AI-enabled world.

These shifts elevate the role of the CHRO and CTO from administrators of today’s workforce to stewards of tomorrow’s capability. Long-term value creation will depend on leaders who can imagine and engineer, the pathways through which human and machine capabilities grow together.

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Talent liquidity: the new measure of agility

For today’s organizations, agility is defined by the ability to reconfigure, relearn and respond in real time. Talent liquidity describes how fast they can redeploy capability, retrain people and retrain AI systems when priorities shift. In doing so, they shrink operational latency, the day-to-day lag in redeploying and upskilling talent, while also reducing strategic latency – the deeper delay in institutional learning, leadership evolution and infrastructure renewal.

 

Generational differences are accelerating this dynamic. Digital natives are entering leadership roles with a natural affinity for AI tools and continuous learning. As we found in our Work Reimagined 2025 study, 83% of employees using AI daily are confident their current skills will remain relevant in three years’ time, compared to only 67% of those who use AI occasionally. Employees with 81 or more hours of AI training per year save 14 hours per week, compared to just three hours for those with fewer than four hours of training. Tellingly, Gen Z are twice as likely as Baby Boomers to receive this level of training.


These shifts signal where leading organizations are heading next: toward learning built directly into the flow of work. The next major inflection point in workforce transformation will occur when learning becomes fully embedded in daily workflows. At that point, the boundaries between working and learning dissolve and the enterprise becomes a living system of adaptive intelligence.

“Talent Debt”: the value of untapped potential

Despite progress, talent and learning outcomes remain suboptimal for many individuals, organizations and wider economies. Significant unrealized potential remains.

We can quantify the gap between the capabilities an organization or economy has and those it needs to compete effectively and frame this as “Talent Debt.” This captures the unrealized or under realized potential of an organization or economy’s people and systems – the opportunity cost of not learning fast enough. Like financial debt, it grows if learning and reinvestment lag technological or market shifts. Talent is, therefore, a depreciating asset that needs constant attention.

Using data from the EY Work Reimagined Survey, we can identify workers who lack confidence in their skills resilience and who do not have the opportunities to address this through learning and development. Globally, this represents 13% of the workforce – a huge check on future growth prospects and a risk to organizational resilience.

“Talent Debt” globally
of the workforce believe their skills are not sufficient for the next three years.

In the US, data from Work Reimagined shows 11% of surveyed workers lack confidence in their skills and say they are not being provided the opportunity to develop these skills at work. We can quantify the financial value of this “Talent Debt” by extrapolating this percentage across the US economy and applying a current wage value to this segment of the workforce. This gives us a figure of more than US$1 trillion, which represents the potential value of unrealized worker potential and a significant silent drag on productivity and innovation. This challenge is especially stark as skills now have half-lives of just two to five years. Without continuous investment in human and machine learning, capabilities depreciate – eroding competitive advantage and compounding over time, like unpaid interest.


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Chapter 3

The co-learning organization

Co-learning will be at the core of the next-generation organization – ecosystems where humans and machines continuously teach and improve each other.

Organizations that master the partnership of human and AI talent will learn faster than the pace of change – and, in doing so, define the future of talent.

Co-evolution offers a powerful design approach, and four principles provide practical guidance:

1. Reciprocal influence drives progress

A change in one species can alter the trajectory of another. Similarly, decisions about human learning and AI development directly affect each other. Talent and technology strategies must be designed together to ensure that advances in AI elevate rather than erode human potential.

2. Interaction frequency determines adaptation

Species that interact more often evolve faster. For organizations, this means embedding learning into the flow of work – creating continuous, real-time feedback loops between people and intelligent systems, not relying solely on periodic training.

3. Evolution is uneven

Natural ecosystems develop hot spots of rapid change and cold spots of stagnation. Organizations should do the same: continuously measure where learning is taking hold, identify where it is not and target investment toward areas of emerging value.

4. Context is crucial

Evolution depends on the environment. Likewise, the design of human-AI learning depends on the organization’s structure and operating model. Are humans instructing agents, collaborating with them, or being assisted by AI-driven orchestration? Each scenario requires distinct skills and governance.

Together, these principles move organizations away from linear development models toward living learning systems – continuously sensing, adapting and renewing themselves. In a co-learning organization, every project, workflow and platform becomes an opportunity for mutual improvement between humans and intelligent systems.

Building the mindset, skill set and toolset for co-learning

Technology’s shift from passive to active makes it essential to move beyond traditional skills models toward a broader and more adaptive framework of mindset, skill set and toolset. In an AI-enabled enterprise, capability emerges not just from what people know, but from how they work with intelligent systems and how they use the time saved to maximize the value of being human. This is rapidly becoming a strategic imperative: the EY CHRO 2030 study shows that 85% of employers now view a strategic HR function as critical to success and organizations with strong HR leadership consistently outperform their peers.

Mindset – creating the conditions for continuous, mutual learning

A co-learning organization begins with mindset. Leaders must cultivate an environment where humans and AI learn from each other in real time. This requires:

  • Curiosity and experimentation: encouraging people to probe, test and iterate with AI systems
  • Psychological safety: giving teams confidence to challenge AI outputs and improve them
  • A sense of ownership: where individuals feel responsible for both their own development and the performance of the intelligent systems they use

Technology is also reshaping what employees expect from learning. As Jenny Lin, Siemens Global Head of Learning and Growth, explains, “Technology evolution is driving a need for learning to happen in the moment of need … adaptive, at my fingertips and consumable in a format I can understand immediately.”

Together, these perspectives capture the essence of the new mindset: learning must be immediate, integrated and designed for continuous, mutual adaptation between humans and intelligent systems.

Skill set – elevating the human capabilities that complement AI

As AI takes on more analytical and operational tasks, distinctly human strengths become the differentiators. Learning leaders must therefore seek to develop capabilities such as:

  • Judgment, ethical reasoning and systems thinking, to guide and govern increasingly autonomous systems
  • Creativity, storytelling and contextual framing, transforming insights into actions that resonate with people and business objectives
  • Collaboration, communication and empathy, allowing humans and AI to work together effectively across workflows and teams

These skill sets enable humans not only to coexist with AI but also amplify its potential. As cultural anthropologist Jitske Kramer notes, “AI potentially gives us the chance to elevate our humanity. By freeing up time, there is the opportunity to elevate the role of creativity, mindfulness, morality and how we relate to each other.”

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But this outcome is not guaranteed. When AI is used as a substitute for critical thinking, storytelling and deep engagement – a process known as “cognitive offloading” – human capability risks being eroded. Critical thinking is hard and many people will seek to avoid it. Organizations need to be conscious of this reality and create the space and the incentives to ensure this crucial capability is nurtured. As AI speeds execution, organizations will depend on designed friction for better thinking – intentional slowing points that prompt analysis, challenge conclusions and reinforce critical thinking in AI-assisted work.

 

This challenge is compounded when individuals begin to see AI as a rival rather than a partner. People who see themselves in competition with machines limit their contribution. As Kramer cautions: “We should stop trying to be better machines.”

All of this reinforces a critical point: the design and deployment of AI systems must enhance, not diminish, human capability. Trust underpins this. As Kramer explains: “Human-AI collaboration depends on trust. This puts increased greater responsibility on those who design and build AI systems to do so with intent – and with clarity about who benefits.”

Strengthening the human skill set, therefore, is not only about developing individual capability; it is about ensuring that AI is introduced and governed in ways that reinforce human judgment, creativity and confidence, rather than replacing or weakening them.

Toolset – designing the infrastructure for learning in the flow of work

The toolset is what enables scaling. This includes the systems, platforms and physical environments that make continuous learning possible:

  • Adaptive learning platforms: providing personalized, on-demand content
  • Real-time feedback loops: connecting human performance and AI system behavior
  • Integrated governance frameworks: promoting ethical, responsible and transparent co-learning
  • Fluid reskilling pathways: allowing both people and AI systems to be updated and redeployed quickly
  • Physical environments: acting as amplifiers of human and machine potential – with spaces that give people the confidence, agency and psychological safety to experiment, learn and collaborate

The workplace becomes an extension of the toolset – an ecosystem that supports exploration, experimentation and human-AI collaboration. As Harvey Lewis, Partner, Technology Consulting, Ernst & Young LLP, observes: “The key is shifting from a prescribed curriculum to enablers of learning. Rather than train people for specific skills or roles, we should give them the tools to understand the principles of the new environment.”

A new mandate for learning leaders

Together, mindset, skillset and toolset form the foundation of a co-Learning organization. As learning becomes mutual – humans learning from AI and AI learning from humans – the enterprise evolves into a self-improving system.

This also reshapes the role of the CLO. Lin describes the shift clearly: “You can no longer be a reactive service provider to the business. As a Chief Learning Officer, you need to think like a Chief Strategy Officer – understanding how everything connects with what you want to achieve at the highest level. Strategic thinking, systems thinking, business acumen … these are now a must.”

In this model, the CLO becomes a strategic architect of capability, allocating learning capital, shaping organizational intelligence and designing the conditions for continuous human-AI evolution.

In the age of shared intelligence, the war for talent is over. The new imperative is learning.

C-suite actions, considerations and questions

Cross-cutting strategic questions for all leaders

  • Do we have clarity on which capabilities must remain distinctly human, which can be automated and which should be co-developed with partners and AI?
  • How are we measuring the depreciation and reinvestment of talent – and what is our plan to reduce “Talent Debt” before it becomes a strategic risk?
  • Are we designing our talent systems for co-evolution – where humans and machines learn together and remain mutually reinforcing over time?
  • What new leadership skills do we need to have to lead and manage talent systems that are based on human-machine co-evolution?
  • What guardrails do we need to put in place to manage the co-evolution of talent in an ethical and empathetic way?

CEOs – architecting the human-AI enterprise

Considerations

  • Are we moving fast enough to reduce strategic latency in our skills and operating models?
  • Do our choices about automation, outsourcing and capability centers align to our long-term purpose and value creation?
  • What are the cultural and ethical guardrails needed to maintain trust as AI becomes a core colleague?

Actions

  • Build an adaptive capability ecosystem, shifting from linear pipelines to dynamic capability portfolios across employees, partners, AI agents and managed services.
  • Set the enterprise direction for responsible AI adoption; promoting human creativity, purpose and judgment remain central.
  • Champion leadership for orchestration, enabling teams to work across distributed networks of human and algorithmic capabilities.

Strategic questions for leaders

  • What will the role of capability be to our future organization in building competitive advantage?
  • Do we clearly understand which capabilities in our organization must remain human, which can be automated and which should be co-developed across our wider ecosystem?
  • Is our organization designing adaptive capability systems for co-evolution – where humans and machines learn together, ensuring both remain relevant and mutually reinforcing over time? 

CFOs – applying financial discipline to talent

Considerations

  • What is our current level of “Talent Debt” and how quickly is it compounding?
  • How much of our automation savings are reinvested into human capability and co-learning?
  • Do our financial metrics capture the value created through skills development, adaptability and cross-functional collaboration?

Actions

  • Quantify talent depreciation and unrealized potential (“Talent Debt”) using real-time workforce analytics and scenario modeling.
  • Treat learning and capability building as capital investment, evaluating returns through productivity, innovation and resilience.
  • Establish a Talent Balance Sheet, tracking accumulated capability, reinvestment rates and areas of emerging risk.

Strategic questions for leaders

  • How will we best apply financial principles to talent and measure return on capability to the organization?
  • How are we measuring the depreciation and reinvestment of talent and what is our plan to reduce unrealize potential (“Talent Debt”) before it compounds into strategic risk?
  • How can we treat learning as capital investment, with performance returns measured in productivity, innovation and resilience?

CHROs – designing the future workforce architecture

Considerations

  • As career pathways are redefined, how are we building the capabilities required for the next generation of leaders?
  • What are the design, leadership and management principles of a talent system that is fluid, dynamic and is based on the human-machine intersection?
  • How should our physical spaces be redesigned to enable human agency and maximize human augmentation through the intersection with technology?

Actions

  • Stand up a skills intelligence system integrating human, digital and algorithmic capability data for dynamic workforce planning.
  • Redesign roles and teams for co-evolution, enabling humans and AI to learn and refine each other continuously.
  • Embed talent marketplaces that enable rapid redeployment, experimentation and development in live work contexts.
  • Adopt key principles such as systems thinking, in the design of your talent system.

Strategic questions for leaders

  • How do we redesign organization and roles, so humans and AI evolve together, with each focused on their respective strengths?
  • How do we design and resource a capabilities architecture that adapts faster than the environment – across employees, partners, AI agents and managed services?
  • How do we ensure that our AI systems are designed and built with intent, to ensure humans prioritize valuable skills such as critical and creative thinking?

Chief Learning Officers – building the co-learning organization

Considerations

  • Is learning consumption fast enough for skills requirements that can evolve every 18 weeks?
  • Where are the biggest co-learning opportunities between humans and AI in our workflows?
  • How do we track the real-world impact of learning on performance, innovation and resilience?

Actions

  • Create continuous co-learning loops where humans and AI train, improve, validate and expand each other’s capabilities.
  • Prioritize learning for emerging AI-complementary skills, including critical thinking, systems reasoning, human creativity and AI oversight.
  • Redesign learning architecture for minimal latency, enabling micro-learning, just-in-time capability updates and AI-personalized development paths.

Strategic questions for leaders

  • What will it take for us to prioritize and incentivize learning for AI and new capabilities, embedding continuous co-learning loops between humans and AI?
  • What would a skills intelligence system look like that mapped human, digital and algorithmic capability?
  • What concrete actions can we take to build the right mindset, skill set and toolset capability model for our organization?

Summary

Talent will be a partnership between human and AI to create capabilities that will co-evolve through mutual adaptation and shared learning. Organizations must harness co-learning between humans and AI to sustain resilience, relevance and competitiveness. Risk lies in accumulating “Talent Debt” — the untapped potential when skills, systems and mindsets fail to keep pace with change. To mitigate this risk, CHROs, CLOs, CFOs and CEOs must treat learning as capital, capabilities as a living ecosystem and intelligence as a shared, ever-renewing, co-evolving asset.

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