EY: Futures Reimagined, Megatrends 2026 and Beyond
Source: EY, Futures Reimagined: EY Megatrends 2026 and beyond, 2026
Key takeaways
EY describes the current operating environment as NAVI: nonlinear, accelerated, volatile and interconnected. Leaders can no longer rely on one forecast of the future or assume that established operating models will remain fit for purpose.
Eight megatrends define the report: superfluid enterprise, the human machine hybrid, the productivity reset, talent rewritten, migration infrastructure, global resource rush, the currency of trust and capitalism rebalanced. The human element runs through all eight. Technology expands what organisations can achieve, but people determine whether that potential is realised.
The superfluid enterprise removes operational friction. Agentic AI, smart contracts and digital twins enable data, talent and capital to move across former silos while traditional hierarchies begin giving way to flatter, networked structures. Human contribution consequently moves away from task execution towards judgement, creativity, strategic thinking, context engineering and orchestration.
Productivity itself needs to be redefined. In an AI enabled economy, hours worked and units produced become less useful measures of value. Quality, accuracy, relevance, decision speed, adaptability and the amount of human correction required become increasingly important.
Talent becomes a portfolio of human and machine capabilities rather than a pipeline of jobs. EY introduces the concept of Talent Debt, the unrealised potential that accumulates when capabilities fail to evolve at the pace required by technological and market change. Learning therefore needs to move into the flow of work, with human and AI systems continuously adapting together.
Trust becomes economic infrastructure. EY argues that trust is moving from a soft cultural concept towards a measurable source of competitive advantage as AI, misinformation and institutional fragility increase uncertainty.
AI transformation requires organisational redesign rather than technology deployment alone. Leadership, governance, operating models, workforce systems, performance measures and risk management all need to change around new capabilities.
Transformation itself must become continuous. Traditional two to three year programmes are increasingly mismatched to the pace of change. The leadership advantage therefore shifts from certainty towards adaptability, scenario thinking and the ability to make no regret moves across several possible futures.
Executive summary
EY’s Futures Reimagined: Megatrends 2026 and beyond examines how organisations should prepare for a world in which technological, demographic, geopolitical and sustainability forces are interacting at unprecedented speed. EY calls this the NAVI world, defined by change that is increasingly nonlinear, accelerated, volatile and interconnected. Technology can reach tipping points unexpectedly, geopolitical decisions can reshape markets within days, and climate, demographics, supply chains and political developments increasingly interact to produce second and third order consequences.
EY’s response is not to predict one future. Instead, the report asks leaders to consider multiple possible futures and identify actions that remain valuable across different scenarios. Eight megatrends form the centre of that analysis. The first three examine the emerging human and machine economy: Superfluid enterprise explores organisations where operational friction is increasingly removed, The human machine hybrid looks at the expansion of human capability through AI and other technologies, and The productivity reset examines what happens when traditional measures of productivity become less useful.
The next four examine resources that become increasingly important or scarce. Talent rewritten reframes talent as a continuously evolving portfolio of human and machine capability. Migration infrastructure examines talent mobility as economic infrastructure. Global resource rush looks at growing competition for critical minerals, Arctic resources and orbital capacity. The currency of trust argues that trust itself is becoming an increasingly scarce and valuable economic resource. The final megatrend, Capitalism rebalanced, examines how incentives and capital allocation may need to evolve as resilience, human capability, sustainability and long term value become more important.
A common theme connects all eight. Technology is not treated as an isolated driver. EY repeatedly brings the argument back to people, judgement, organisational design and leadership. The report envisages organisations moving from task execution towards orchestration, from hierarchy towards networks, from periodic learning towards continuous co-learning, from productivity measured in hours towards value measured in outcomes, and from fixed transformation programmes towards constant adaptation.
The central leadership challenge is therefore not predicting exactly what happens next. It is creating an enterprise capable of functioning across several plausible futures.
Key stats and quotable claims
In 2021, only 1% of global executives said they were surprised by political risk events or their impacts most or all of the time. By 2025, that had risen to 35%.
Employee disengagement costs the global economy an estimated US$8.8 trillion annually, around 9% of global GDP.
Internal friction costs companies approximately US$15,000 per employee annually.
EY cites estimates that data silos can cost organisations 20% to 30% of revenue through operational inefficiencies.
AI powered automation and smart contract governance are associated in the report with two to three times ROI, 35% to 50% operational cost savings and 50% to 70% reductions in cycle time.
78% of global organisations already use AI in at least one business function.
The global human augmentation market is projected to reach US$1.39 trillion by 2034.
One cited field experiment involving 2,310 participants found human AI teams achieved 73% higher productivity than human only teams.
EY cites research suggesting the combination of humans, AI and narrative can generate a 265% increase in creativity compared with humans alone.
AI is projected to have a cumulative global economic impact of US$19.9 trillion through 2030, with a projected 3.5% increase in global GDP from AI by 2030.
88% of workers now use AI at work, compared with 22% in 2023.
Almost 40% of core skills are expected to change within five years.
Employees receiving substantial AI training can save 14 hours per week, compared with three hours among those receiving very limited training.
EY estimates Talent Debt in the US alone at more than US$1 trillion in unrealised potential.
13% of the global workforce lacks confidence in the resilience of their skills while also lacking sufficient opportunities to develop them.
64% of employees report rising workloads.
Companies can lose approximately 30% of their value after a major loss of trust, according to research referenced by EY.
EY highlights a 26 percentage point gap between trust in the technology industry and trust in AI itself.
64% of consumers view AI security breaches as a major concern, compared with only 32% of C suite leaders.
Companies with comprehensive training programmes are cited as generating 218% more income per employee than organisations without them.
Overall summary
EY’s starting point is that the operating environment itself has changed. Traditional strategy assumed enough continuity for leaders to forecast a likely future, choose a plan and execute towards it. In a nonlinear and interconnected environment, a geopolitical decision, technological breakthrough, climate event or demographic shift can instead trigger consequences across several systems simultaneously. The organisation therefore needs to be designed for multiple futures.
The superfluid enterprise represents one possible direction. Agentic AI, smart contracts and digital twins reduce coordination friction and allow increasingly autonomous execution. Data, capital and talent flow more freely, while the role of management moves from supervising individual activities towards establishing objectives, values and guardrails. As machines perform more analysis and execution, human value does not necessarily disappear. EY argues that judgement, creativity, strategic thinking, empathy, context and the ability to orchestrate systems can become more valuable.
Productivity must consequently change as well. If machines can produce enormous volumes of work at extremely low marginal cost, quantity becomes a weaker measure of success. The more useful question is whether humans and machines produce accurate, relevant and valuable outcomes with less correction, faster decisions and greater adaptability.
That creates an entirely different talent challenge. Organisations can no longer think about talent simply as people moving through a linear pipeline of hiring, training and promotion. Capability increasingly exists across employees, contractors, partners, AI models and agents, which means the organisation increasingly manages a capability portfolio. Capabilities can appreciate through learning or depreciate through technological change, and EY calls the gap created when learning fails to keep pace Talent Debt.
Trust becomes equally structural. More autonomous technology requires people to trust data, systems, leadership and decision making. If that trust breaks, adoption slows and organisational value can disappear rapidly. Across the report, the same leadership implication keeps appearing: AI transformation is ultimately an organisational redesign challenge. Technology creates new possibilities, but value depends on how organisations redesign work, build capability, establish governance, preserve trust and adapt their operating model.
Deep dive
Welcome to the NAVI world
From tariffs to ChatGPT, COVID-19 to the Great Resignation, extreme weather events to supply chain shocks and unforeseen knock-on impacts — if the last few years have felt like a roller-coaster ride, in which a succession of surprises has shaken your business, you’re not alone. Longitudinal data from EY-Parthenon Geostrategy in Practice shows that in 2021 just 1% of global executives were surprised by political risk events or their impacts “most or all of the time”; in 2025, that number jumped to 35%.
It’s not just geopolitics. Multiple disruptive forces are shaping the global operating environment, including climate change, technological innovation, demographic shifts, and the rising influence of non-state actors. These are examples of a new operating environment. EY calls this the NAVI world after the four characteristics that distinguish it from the pre-pandemic era. This is a world in which change is increasingly nonlinear, accelerated, volatile and interconnected.
It’s nonlinear in the sense that change comes in bursts and sudden tipping points. Technology S-curves can precipitate tipping points, in which technologies suddenly transition from gradual improvement to huge advances, catching companies by surprise. Today, these S-curves are steeper, bringing more frequent tipping points. The pace of change has also accelerated. AI is advancing at an unprecedented rate, from ChatGPT breaking records for the speed of user adoption at its launch to the rapid clip at which new AI capabilities are being rolled out.
The environment is volatile as unexpected shocks and swings rock markets, politics and the natural world. Complex relationships between the forces of disruption also mean change is increasingly interconnected. Shocks can trigger cascades of downstream impacts, often culminating in unexpected outcomes. Trade policy shifts, for example, can ripple through supply chains, redirect capital flows, reshape energy investment and alter migration patterns, often simultaneously and in ways that compound one another. The NAVI world presents more than an organisational challenge. It redefines what it means to work, lead and create value.
Eight interconnected megatrends
Navigating the NAVI world requires a way to separate the signal from the noise while planning for multiple potential futures. The primary forces — technology, demographics, sustainability and geopolitics — are root causes of change that are evergreen and continuously evolving. Filtering the day-to-day headlines and broader global trends through these four primary forces enables leaders to understand the NAVI world in a more structured way.
Intersections between primary forces create megatrends: global, cross-sector scenarios that shape how organisations operate, compete and create value. In the NAVI world, the future could evolve in multiple ways. Leaders can no longer rely on a single set of predictions. They need to assess the business implications of several alternative futures, which is why scenario analysis forms the basis of EY’s approach.
EY explores eight megatrends, anchored by two throughlines: superfluid enterprise, which reimagines how organisations operate, and capitalism rebalanced, which explores whether the system itself must adapt. The six megatrends in between trace the implications of human capability, productivity, talent, migration, trust and the competition for critical resources.
Superfluid enterprise explores how eliminating friction reinvents the company, while the human machine hybrid considers what happens when technology expands human capability. The productivity reset redefines value when traditional metrics no longer apply, and talent rewritten examines the co-evolution of human and AI capability. Migration infrastructure looks at building the systems that turn demographic pressure into advantage, global resource rush discusses innovation and competition at Earth’s edges, and the currency of trust investigates why credibility becomes increasingly scarce. Capitalism rebalanced then explores a reallocation of capital and resources towards diversification, resilience and long-term value creation.
Future back planning in an uncertain environment
The eight megatrends are intended to spark not only more expansive thinking, but also future-back planning and the creation of more resilient strategies. Future-back planning, which starts with a vision for the future and works backward to identify the investments needed today, has long been the gold standard for responding to disruptive innovation.
In a NAVI world, future-back planning requires two upgrades. First, the future visioning must account for the increased complexity of the new operating environment by scanning more widely and incorporating the impact of interconnections between disparate forces. Second, it is no longer sufficient to develop one vision of the future; several are needed to effectively navigate heightened levels of uncertainty.
This raises a practical question: how do you prepare for multiple different futures at once? By distinguishing what is certain from what is uncertain and making no-regret moves. The EY Futures Reimagined framework enables leaders to identify no-regret moves that can be made today and which would enhance resilience and growth prospects across multiple future scenarios. These can include rethinking an organisation’s strategy, its business models and operating models, how it manages talent and risk, and what its transformation agenda needs to become.
1. Superfluid enterprise
What happens when operational friction disappears?
For generations, successful organisations have excelled at managing friction: the barriers that slow decisions, hinder coordination and raise operational costs. But what happens when that friction vanishes? The convergence of agentic AI, blockchain-enabled smart contracts and digital twins is making it possible to find out. The result is what EY calls the superfluid enterprise.
It is an organisation where operational frictions have been all but eliminated, and traditional hierarchies give way to flat, networked structures where humans and AI agents collaborate to an unprecedented extent. Data, talent and capital flow smoothly across former silos, while autonomous systems operate around the clock and adapt instantly to market changes.
For workers, this transformation redefines what it means to contribute: less task execution, more judgment, creativity and orchestration. For leaders, the shift is equally profound, from directing workflows to orchestrating ecosystems and from approving decisions to setting the values and guardrails within which autonomous systems operate. Roles are destined to change, and the key to success for organisations will be their ability to redesign work in ways that amplify human potential rather than diminish it.
The economics of removing friction
The economic case for this transformation is both urgent and compelling. Employee disengagement costs the global economy US$8.8t each year, which represents about 9% of global gross domestic product. Internal friction costs companies an average of US$15,000 per employee annually, while data silos alone cause organisations to lose 20% to 30% of their revenue through operational inefficiencies.
Companies that successfully reduce this friction through AI-powered automation and smart contract governance report remarkable returns: two to three times ROI on AI investments, 35% to 50% operational cost savings and cycle time reductions of 50% to 70%. However, capturing these gains requires rethinking how value itself is measured.
Three building blocks of the superfluid enterprise
The superfluid enterprise rests on three core technologies working together. The first is agentic AI, which coordinates complex, multi-step processes across organisations. In the superfluid enterprise, these agents will understand context, make decisions within set parameters and adapt to changing conditions without human intervention.
The second is smart contracts. Smart contracts replace traditional contracts that require human interpretation and enforcement. They execute automatically when predefined conditions are met, enabling business processes to run at algorithmic speed with consistency and transparency.
The third is digital twins. Digital twins — dynamic virtual replicas of physical objects, processes or systems — offer transparency and control for autonomous operations. When AI agents run large parts of a business process, digital twins can provide real-time, auditable representations that preserve visibility and oversight. Together, these technologies begin to shift organisations from systems where humans coordinate every stage of work towards systems where autonomous technologies increasingly coordinate themselves.
The human quotient
The most effective transformations focus on forming partnerships that use these technological building blocks to enhance human abilities rather than replacing them. EY cites research showing that the combination of humans, AI and narrative — the distinctly human ability to create meaning and context — leads to a 265% boost in creativity compared to humans alone, while AI alone achieves just 120% of human-only performance.
Human roles in superfluid organisations therefore centre on three capabilities: context engineering, or framing problems so AI systems can function effectively; strategic thinking, including asking whether the organisation is solving the right problems; and story-driven innovation, transforming AI-generated insights into compelling narratives that motivate teams and drive change.
Organisations successfully navigating this transition understand that the goal is about more than efficiency through replacement; it is about amplification through collaboration. The companies that see the highest returns on AI investment are those that redesign work to leverage uniquely human capabilities while letting AI handle coordination, analysis and execution tasks that machines do more effectively.
Three horizons of transformation
EY describes three phases on the path towards superfluidity. Horizon 1 is foundation building, where organisations develop AI-native capabilities while maintaining familiar business structures. The focus is on deploying the building blocks, such as agentic AI pilots, smart contract experiments and digital twin implementations. Already, 78% of global organisations use AI in at least one business function, but most remain in early experimentation and EY argues that the transformation ahead will dwarf today’s adoption.
Horizon 2 is autonomous coordination. This phase marks the shift from “humans in the loop” to “humans on the loop.” Agentic AI coordinates across functions, smart contracts automate complex agreements and digital twins provide real-time visibility. Hybrid governance models blend human strategic oversight with AI-powered operational decisions. EY envisages AI agents managing 80% of what is classified today as routine decisions, while humans focus on exceptions, creativity and ethical oversight.
Horizon 3 is full superfluidity. This signifies enterprise-wide autonomous operations with the three building blocks fully integrated. Humans focus on strategic guidance, creative innovation and ethical oversight. This redefinition of human roles, from task execution to judgement and creativity, becomes central to the future operating model.
Actions for leaders
The move to superfluid operations is a journey, and EY argues that organisations are still in Horizon 1. Leaders should first lay the technology foundation by investing in technology that eliminates data silos, connects existing systems and creates stable platforms for AI tools. They should then establish governance that evolves with adoption. Three-quarters of technology leaders cite governance as their main concern when deploying autonomous systems, yet only 18% have established comprehensive AI governance councils.
The fundamental shift is from “approving every decision” to “ensuring decisions align with our values and objectives.” Organisations also need to build key workforce capabilities and initiate cultural change, systematically investing in context engineering, strategic thinking and story-driven innovation while measuring success through business impact rather than traditional productivity metrics.
Finally, organisations need to redesign structures for networked operations. Traditional hierarchical structures will give way to networks of AI agents coordinating operational tasks alongside flatter human talent models where capabilities flow across organisational boundaries. Leaders must learn to orchestrate both.
2. The human machine hybrid
Expanding human capability rather than simply automating work
The human-machine hybrid explores how new technologies will enable humans to expand their capabilities. For workers, this means evolving from task performers to capability orchestrators, combining human judgement with machine precision. For leaders, it demands new approaches to team composition, performance measurement and ethical oversight.
The organisations that thrive will be those that redesign work to amplify human potential, not simply augment human output. The global market for human augmentation technologies is projected to reach US$1.39t by 2034, and current adoption represents only the earliest phase of this transformation. The question is no longer whether human capabilities will be augmented, but whether organisations will shape this transformation in ways that amplify human potential or be reshaped by competitors who do.
AI enhanced cognition
The most immediate form of human augmentation comes from AI systems that enhance rather than replace cognitive abilities. Medical professionals using AI-enhanced diagnostics are seeing improvements in sensitivity, diagnostic speed and accuracy, while legal professionals using AI contract analysis have achieved very high accuracy while drastically reducing review time.
A 2025 MIT and Johns Hopkins field experiment with 2,310 participants found that human-AI teams experienced 73% higher productivity and created higher-quality content, with a notable exception: human-only teams still outperformed on image creation. The lesson is that hybrid approaches can outperform either humans or AI working alone. As human AI partnerships mature, EY expects the performance differential to widen further, and the creativity multiplier is why the human element is not optional. It is what unlocks the full value of autonomous systems.
Hybrid intelligence creates a leadership challenge
The technologies reshaping human capability do more than change what organisations can do; they change how they must be led. As augmentation moves from experimentation to deployment, leaders face challenges that go beyond adoption. These include how to make strategic decisions alongside AI systems that may outperform human analysis, how to manage teams with widely varying capabilities, and how to navigate the societal and regulatory implications of enhancement.
The leadership playbook is being rewritten in real time. Teams will increasingly include humans, AI agents, robotic systems and potentially enhanced individuals working across varying cognitive and physical abilities. Leaders must coordinate these hybrid teams while ensuring all components contribute to shared goals.
Human expertise becomes more valuable in bounded AI environments
As AI takes over bounded strategy tasks, including pricing optimisation, logistics coordination and pattern recognition across large datasets, the premium on distinctly human expertise rises rather than falls. Organisations will need to deepen, not just maintain, their bench of senior domain experts because these are the people who can do what AI cannot: manage human-machine collaboration, interpret AI-generated insights within broader strategic and cultural contexts, and exercise the nuanced judgement that separates tactical optimisation from transformative strategy.
At the same time, traditional entry-level pipelines for developing that expertise are being disrupted by automation. That makes it even more critical for companies to invest in new pathways such as mentorship with augmented workers, simulation-based learning and cross-functional rotations that build the next generation of hybrid-ready leaders.
Accountability in hybrid systems
Integrating enhanced humans with traditional teams presents novel management challenges. Leaders need to learn how to coordinate human-machine collaboration, create accountability systems that include both human and artificial team members, foster an understanding of how AI agents process information and make decisions, and design physical and digital environments that support smooth human-machine integration.
Beyond logistics, leaders must address psychological dimensions, including how employees adapt to working alongside enhanced colleagues, manage identity shifts as roles evolve and maintain team cohesion across capability differences. When failures occur in hybrid human AI systems, determining liability becomes complex, which means organisations need frameworks that clearly define responsibilities for both human and machine components while ensuring human oversight of critical decisions.
Transform talent frameworks
EY notes a significant investment gap: while 47% of organisations prioritise AI investments for growth, only 15% invest adequately in workforce preparation for human AI collaboration. This gap limits ROI.
The recommendation is to reimagine career pathways, create compensation frameworks that acknowledge augmentation productivity while preserving equity and develop alternative routes for building expertise as AI absorbs traditional entry-level roles. The broader principle is that organisations should measure productivity gains, innovation outcomes and competitive advantages while also tracking employee wellbeing and adoption barriers. The goal is learning, not just efficiency.
Preparing for the hybrid future
Human-machine hybrid capabilities will significantly boost performance beyond what humans or machines achieve alone. Organisations will thrive by strengthening distinctly human capabilities — creativity, ethical judgement, strategic insight and empathetic leadership — rather than simply automating existing processes.
These advances also raise questions about fairness, workforce transformation and social stability. Leaders who develop hybrid capabilities while addressing ethical implications will shape not only their organisation’s future, but also the trajectory of human augmentation itself.
3. The productivity reset
From hours worked to value created
The story of productivity has always been a tale of what we measure, how we think and how we manage. Rising productivity drives economic growth, raises living standards and boosts corporate returns. We are now on the cusp of a fundamental reshaping of this narrative: what drives productivity, how it is measured and what the term even means.
AI is the headline force, but it converges with regulatory shifts, geopolitics and supply chain rewiring, energy constraints, demographic change and the climate transition. Together they make productivity a dynamic system rather than a static factory setting. For workers, this transformation redefines what contribution means: less task execution, more judgement, creativity and orchestration of intelligent systems. For leaders, it demands new approaches to measurement, governance and the design of work itself.
Quantity becomes less meaningful when machines can generate almost unlimited output
In the coming years, productivity may no longer be captured by the traditional ratio of output to input. What has been a relentless race for quantity may flip as machines generate seemingly infinite content, shifting the focus towards quality and creativity.
Productivity increasingly becomes the capacity to convert information, insight and innovation into sustained economic value. The challenge is how to quantify gains that occur not on production lines but in digital ecosystems, decision speed, system adaptability and the creativity unlocked through human-machine collaboration. Technology reshapes what organisations can achieve, but the human element determines whether that potential is realised.
A different productivity formula
The shift from labour hours to valuing outcomes redefines productivity for the AI era. Traditionally, success was measured by output per hour — briefs written or statements audited — assuming time drove results. Today, with AI assistants and agents, time is increasingly abundant while human insight and accuracy become more important constraints.
EY proposes thinking about productivity through the quality and impact of outcomes relative to the supervision required:
Productivity = Accuracy × Relevance × Impact / Human cognitive input
The less correction needed for valuable results, the higher the productivity. This is important because it places human review and correction directly into the economics of AI enabled work.
The next productivity wave is agentic
The public release of generative AI in late 2022 marked a transformative moment. Its ability to generate human-like content, including articles, images and code, captured attention and investment, while companies that had viewed AI as a niche tool began seeing it as a strategic driver. Automation extended beyond repetitive tasks into creative work, knowledge work and decision support.
IDC forecasts AI will have a cumulative global economic impact of US$19.9t through 2030, driving a 3.5% increase in global GDP. Yet these projections represent early estimates and current adoption is only the beginning of the transformation ahead.
The next wave of productivity will come from agentic AI: systems that autonomously manage workflows, make semi-intelligent decisions and coordinate tasks across functions. These higher-order agents represent the first true step towards the superfluid enterprise, where friction disappears and autonomous systems operate continuously.
Leadership moves from conductor to orchestrator
The gap between what AI vendors promise and what CEOs will stake their reputation on remains wide. Closing it requires leaders to manage by exception and orchestration, not process adherence. Instead of ensuring everyone follows fixed procedures, leaders should focus on stepping in when results fall outside expectations, coordinating people, technology and processes to achieve outcomes.
This is a leadership shift from conductor of tasks to orchestrator of systems and also requires operating models built around outcomes rather than activity. Most organisations now accept that AI’s success depends less on algorithm sophistication and more on data quality, a foundation many are still struggling to build.
At the same time, the boundary between automation and AI is blurring. Companies need not only process automation but also decision intelligence: systems that augment human judgement with real-time analysis. This revolution depends on governance and trust. As intelligent agents make decisions and interact with one another, human oversight becomes essential because without guardrails, productivity-boosting technology could amplify vulnerabilities.
Five actions for leaders
EY argues that leaders should first rebuild around clean, connected data, because data quality rather than algorithm sophistication often determines AI success. Second, organisations should shift to outcome-based operating models, designing performance systems around results rather than activity and tracking value through speed, quality, resilience and new revenue streams rather than hours and inputs.
Third, leaders should embed decision intelligence into workflows, making AI a core capability enhancing decision-making speed, accuracy and consistency rather than a bolt-on experiment. Fourth, companies should digitise physical operations through AI and digital twins where relevant. Finally, leaders should invest in agentic systems with strong governance, ensuring oversight, accountability and ethical safeguards remain in place as intelligent agents increasingly make decisions, monitor systems and interact with one another.
The future of productivity
In the AI era, productivity hinges less on hours worked and more on how well organisations turn data, models and judgement into outcomes. Success requires treating AI as a foundational shift: rebuilding around clean data, embedding intelligence into workflows and measuring value by speed, quality and resilience.
Yet the most important shift may be the simplest to state and the hardest to achieve: ensuring that productivity gains translate into better work, as opposed to just more output. The factory worker whose judgement guides autonomous systems, the auditor whose capabilities shape AI-driven analysis and the clinician whose insight directs machine diagnostics will define the new productivity. The organisations that thrive will be those that measure, protect and invest in this human value.
4. Talent rewritten
From talent pipelines to capability portfolios
For decades, organisations competed for human talent as if it were scarce and fixed. This battle is outdated. Human capability is becoming intertwined with AI systems that learn, perform and evolve, while talent is no longer confined within organisational walls but distributed across ecosystems of employees, contractors, partners, managed services and intelligent machines.
What we see today is only the opening chapter of this shift. Current adoption patterns, while accelerating rapidly, represent the earliest phase of a co-evolutionary process whose full implications will take years to unfold. Darwinian co-evolution offers a useful analogy: just as two species shape each other’s development, humans and AI systems now refine one another through every interaction, prompt and dataset. This process will be nonlinear and unpredictable, but it has significant potential to elevate human value.
Talent Debt
The structural challenges are urgent. Capabilities now expire faster than traditional learning systems can renew them. Learning systems built for 18-month cycles cannot keep pace with requirements that evolve in 18 weeks.
This creates what EY calls Talent Debt — the unrealized potential that accumulates when human and machine capabilities fail to evolve together. Addressing Talent Debt requires rethinking traditional approaches to learning and building a co-learning enterprise. The next wave of successful organisations will be those that view adaptive capability, not sheer headcount, as the real benchmark of competitive strength.
The rise of shared intelligence
A century ago, the boundary between labour and capital was easy to draw: labour clocked in through the factory gates while capital sat in the machines on the factory floor. Today, a single workflow might combine an employee in Hong Kong, a contractor in Buenos Aires, a model running in the cloud and an AI agent trained overnight.
This expanded view shifts focus from ownership to orchestration. Value lies in how capability is coordinated, not where it sits. 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. Most organisations are still learning how to manage human AI capability portfolios, and the practices emerging today will look rudimentary within just a few years.
Managing the balance sheet of human and AI potential
Structural shifts in the labour market have made traditional talent approaches obsolete. Aging populations across the industrialised world and China are shrinking labour supply. By 2050, more than two billion people will be over 60. Already, a quarter of workers face a skills mismatch, nearly 40% of core skills are expected to change within five years and 64% of employees report rising workloads.
This creates a profound dual challenge. Organisations must be able to respond immediately to fast-shifting skills demands by redeploying talent, retraining teams and updating AI systems at pace, while simultaneously building the deeper capabilities, mindsets and infrastructures required for longer-term competitiveness. Success depends on delivering short-term skills agility and long-term capability resilience at the same time.
The traditional talent pipeline is no longer enough
The traditional talent pipeline of hire, train, retain and promote was designed for a world where skills and capabilities evolved slowly. That world has gone. The rapid adoption of AI illustrates the scale and speed of the shift. EY research found that 88% of workers now use AI at work compared with just 22% in 2023.
Employees increasingly see AI not as a tool but as a collaborator embedded in daily workflows. Yet even at 88% adoption, most usage remains shallow, with users aiming for task-level assistance rather than deep co-creation. The trajectory from tool use to true human AI partnership is only beginning.
Organisations can no longer rely on linear pipelines. Instead, they must manage portfolios of capability assets combining human expertise, AI models and collaborative tools. Each asset has its own performance profile, depreciation curve and reinvestment horizon, making capability management continuous: organisations need to assess the return on investment from each capability and decide where to invest next.
The leadership pipeline risk
This portfolio approach also exposes a fundamental risk: how to grow the human capabilities with the longest-term value, particularly leadership, in organisations where career paths are being reshaped by automation and AI. Traditional entry-level roles have served as proving grounds for future leaders, giving early-career employees chances to build judgement, stakeholder awareness and the ability to orchestrate complex work.
Without redesign, fewer people will have the opportunity to develop the capabilities required to lead in an AI-enabled world. EY cites research showing companies with high AI exposure reducing junior headcount, while the concentration of losses among entry-level positions disrupts traditional skill development pathways.
Leadership pipelines will weaken unless organisations deliberately redesign the early-career experience. Forward-looking organisations can integrate AI literacy with leadership fundamentals, avoid KPIs that reward speed and automation alone, create “slow lanes” for critical thinking through structured reviews and reflection, and make early-career employees co-responsible for improving AI systems.
Talent liquidity versus Talent Debt
For today’s organisations, 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.
EY research found that 83% of employees using AI daily are confident their current skills will remain relevant in three years’ time, compared with 67% of those who use AI occasionally. Employees with substantial AI training can save 14 hours per week with AI use, compared with only three hours for those receiving very limited training. The next major inflection point in workforce transformation will occur when learning becomes fully embedded in daily workflows.
The scale of Talent Debt
Talent and learning outcomes remain suboptimal for many individuals, organisations and economies. EY defines the gap between the capabilities an organisation has and those it needs as Talent Debt — the opportunity cost of not learning fast enough. Like financial debt, it compounds if learning and reinvestment lag technological or market shifts.
Using EY Work Reimagined data, the report estimates that 13% of the global workforce lack confidence in their skills resilience and also lack opportunities to address this through development. In the US alone, this translates to a Talent Debt of more than US$1t in potential value, representing a silent drag on productivity and innovation.
The challenge intensifies as the half-life of skills shrinks. Without continuous investment in human and machine learning, capabilities depreciate, eroding competitive advantage over time.
Building the co-learning organisation
Organisations that master the partnership of human and AI talent will learn faster than the pace of change. EY identifies four co-evolutionary principles. First, reciprocal influence drives progress, meaning decisions about human learning and AI development directly affect one another and talent and technology strategies must therefore be designed together.
Second, interaction frequency determines adaptation. Learning needs to be embedded into the flow of work, creating continuous feedback loops between people and systems rather than periodic training. Third, evolution is uneven, meaning organisations need to continuously measure where learning is taking hold and target investment towards areas of emerging value. Fourth, context matters because the design of human AI learning depends on the organisation’s structure and operating model. Humans may be instructing agents, collaborating with them or being assisted by AI-driven orchestration, and each model requires different capabilities.
Mindset, skill set and toolset
Capability emerges not only from what people know but also from how they work with intelligent systems. Three elements must reinforce one another. Mindset comes first: leaders need to cultivate an environment where humans and AI learn from each other in real time, supported by curiosity, experimentation, psychological safety to challenge AI outputs and a sense of ownership for both personal development and intelligent system performance.
The second element is skill set. As AI takes on more analytical and operational tasks, distinctly human strengths become differentiators, including judgement, ethical reasoning, creativity, storytelling, collaboration and empathy. These capabilities must be actively protected because when AI substitutes for critical thinking, human capability can erode. Even in superfluid enterprises, organisations need designed friction: intentional slowing points that prompt analysis and reinforce deeper thinking.
The third element is toolset, covering the systems, platforms and physical environments that make continuous learning possible. These include adaptive learning platforms, real-time feedback loops, integrated governance frameworks and fluid reskilling pathways. The workplace therefore becomes an ecosystem supporting exploration, experimentation and human AI collaboration.
The new talent imperative
In the age of shared intelligence, the war for talent is over. The new imperative is learning. Talent will increasingly be a partnership between human and AI, with capabilities that co-evolve through mutual adaptation.
The risk lies in accumulating Talent Debt. Organisations that master co-learning will define the future, while those that do not will find their capabilities and competitiveness steadily eroding.
5. Migration infrastructure
Treat migration as economic infrastructure
The previous megatrends explore how organisations build adaptive capability through superfluid structures, human-machine collaboration, productivity measurement and talent development. Migration is the scaffolding — the systems, policies and physical capacity — that determines how that capability flows across borders.
The future belongs to countries and companies that treat migration as economic infrastructure rather than a temporary response to labour shortages. Demographic headwinds, human displacement from climate change, geopolitical volatility and AI-driven disruption are converging, and the ability to attract, integrate and retain global talent will increasingly define which organisations grow and which fall behind.
Migration infrastructure therefore offers a path to success not as a temporary fix to plug today’s skills gaps, but as permanent economic infrastructure that creates competitive advantage regardless of how automation unfolds. Its foundations — visa processing, credential recognition, housing and integration systems — will determine whether countries can convert demographic pressure into economic advantage.
Demographics create structural demand
The supply of talent exists and is being amplified by displacement. Sub-Saharan Africa’s working-age population will surge from 883 million to 1.6 billion by 2050, while in South Asia between 18 and 20 million people enter working age annually. At the same time, labour shortages are becoming systemic across healthcare, manufacturing, construction and technology.
Yet the systems connecting supply and demand are fracturing under strain. The OECD old-age dependency ratio is expected to reach 52% by 2060, with several countries moving significantly higher. In the EU in 2024, 100% of population growth came from net migration as deaths outpaced births. Migration is therefore shifting from economically beneficial to economically essential and becoming the only source of workforce growth for many advanced economies.
Automation does not eliminate the need for migration
Automation will transform labour markets, but it will not eliminate the need for people and rarely eliminates entire occupations. It reshapes them. Healthcare, elder care, construction and skilled trades will require physical presence and human judgement long after AI matures.
Migration infrastructure therefore creates optionality in uncertain futures. Countries with mature migration systems can adjust talent inflows as automation’s impact becomes clearer, while those without face permanent shortages now or costly crisis-mode building later.
Migration systems built today create advantages that compound over decades. Credential recognition frameworks, once established, continue to unlock underused talent, while talent pipelines deepen and widen. For businesses, proprietary global talent channels become long-term competitive moats. For governments, predictable and well-designed pathways make labour markets more resilient to demographic, technological and geopolitical shocks. The winners of the future will be those who built capacity to absorb talent at scale rather than those who wait for technological change to solve shortages that have already arrived.
6. Global resource rush
Competition moves towards Earth’s edges
A frontier separates ambition from opportunity. At Earth’s edges — altitude, distance and depth — three domains are opening to new resource extraction and geopolitical competition. The first is the deep, encompassing deep earth and deep sea reserves of critical minerals facing significant supply gaps from existing sources. The second is the Arctic, home to strategic minerals, energy reserves and increasingly valuable shipping routes. The third is Earth orbit, a limited natural resource offering platforms for observation, communications, research and defence.
Technology, geopolitics and climate change are causing these frontiers to open simultaneously. Digital transformation and the clean energy transition drive demand for critical minerals at volumes existing sources cannot provide, while geopolitical competition intensifies as governments treat resource supply as a national security concern.
For workers, these frontiers create new categories of employment in areas such as space operations, Arctic logistics and autonomous mining systems. For leaders, they demand capabilities in domains where few organisations have operated and where governance frameworks remain incomplete.
Critical minerals and the deep
Digital transformation and the clean energy transition both depend on minerals and metals facing significant supply gaps. Data centres, AI infrastructure, wind turbines, batteries and EVs all require materials that existing sources cannot fully provide, with significant gaps expected by 2040 without new supply.
One potential source is going deeper underground. Deep earth mining extends the supply of metals already being produced from existing mines, including copper, gold, nickel, zinc and platinum group metals. But depth brings a different physical regime. Higher stress conditions can trigger seismic events, heat increases ventilation and cooling costs, and declining ore grades increase energy and water use per unit of metal.
These risks increasingly require the removal of people from hazardous environments and accelerate adoption of automation, remote-controlled equipment and digital mine modelling. Attention has also turned towards deep ocean resources. The Pacific Ocean’s Clarion-Clipperton Zone contains an estimated 21.1 billion tonnes of polymetallic nodules containing enormous quantities of critical metals. The technological potential is significant, but so are the environmental and governance questions.
Earth orbit becomes strategic infrastructure
The direct space economy is projected to grow from US$613b in 2024 to US$1t by 2032, and already two-thirds of the space economy’s value comes from the private sector. But the broader value of activity dependent on orbital assets — GPS, communications, data transmission, power grids and financial transactions — is nearly incalculable.
Private sector innovation has pushed down the cost of putting a satellite in orbit dramatically. Active satellites surged from approximately 3,300 in 2020 to 13,000 by October 2025, with further large constellations expected. Yet expansion creates congestion. Open access to limited orbits creates a tragedy of the commons, while debris, radio frequencies, light pollution, military activity and commercial competition all make orbital governance increasingly important. With more than 100 countries planning to enter space, current approaches to orbital resource management may prove unsustainable.
7. The currency of trust
Trust becomes a scarce economic asset
Trust has always mattered. What is changing is its scarcity, and therefore its value. In a world of accelerating technological disruption, persistent misinformation and institutional fragility, trust is emerging as the defining intangible asset of the next economic era.
Over the past two decades, technology has increased the value of a series of intangible resources. First came the Data Economy, where companies built advantage through their ability to produce, understand and monetise enormous volumes of information. Then came the Attention Economy, where social media businesses created value by capturing engagement and influencing behaviour.
EY argues that we are now approaching the Trust Economy, in which trust becomes a new intangible resource driving competitive advantage and growth. Trust has been diminishing in recent years, ironically partly as a by-product of the Attention Economy that preceded it.
The trust deficit destroys value
Trust is eroding across three dimensions simultaneously: trust in technology, social trust and trust in institutions. Together, they create a compounding deficit that constrains strategy, slows adoption and destroys value.
Analysis referenced by EY found that companies can lose 30% of their value when they lose trust. EY research also finds that trust is the top factor consumers consider when deciding whether to use a product or service, ranking higher than brand reputation, personal recommendations or price.
This demands new approaches to governance, technology deployment and organisational culture. The organisations that thrive will be those that treat trust not as a compliance obligation but as a strategic asset to be invested in, grown and protected. Current trust levels represent only the early signals of a structural shift, and the infrastructure organisations build or fail to build around trust over the next decade will determine competitive position for years to come.
The AI trust gap
Technology was once the world’s most trusted sector, but that position has weakened, driven by data privacy scandals and growing awareness of social media’s role in fuelling misinformation and polarisation. AI is widening the gap. There is a 26-point difference between trust in the technology industry and trust in AI specifically.
The concerns are broad: job displacement, privacy erosion, misinformation and the opacity of algorithmic decision-making. EY research also reveals a significant gap between those building AI and those affected by it. 64% of consumers rate AI security breaches as a major concern compared with only 32% of C suite leaders, and similar gaps exist across privacy, explainability, unreliable outputs and the social consequences of AI.
Trust requires infrastructure
Responding to the trust deficit requires action across four dimensions that together form what EY calls a trust stack, from foundational governance to leadership behaviours. At the top sit people and psychology. Leaders need to set the tone through visible trust-building behaviour and understand how people interpret uncertainty and fairness.
The second level concerns profit mechanisms. Business models need to be evaluated for the incentives they create and whether those incentives strengthen or erode trust. The third concerns protocols and platforms, where technologies such as blockchain and explainable AI can provide infrastructure for verification, transparency and auditability. The fourth concerns policies and processes, with strong governance, risk management, compliance and clearly articulated principles forming the foundation.
Trust is built slowly but can be decimated in a flash. Strong governance therefore extends well beyond regulatory compliance to articulating the principles an organisation stands for and creating policies that translate those principles into practice.
Trust as infrastructure
In the Trust Economy, trust is not a soft concept — it is infrastructure. Organisations that invest in it systematically, measure it rigorously and protect it deliberately will navigate disruption with resilience and unlock growth that competitors cannot access.
The implications extend beyond any single organisation. As technologies reshape human capability and productivity metrics evolve, trust becomes the connective tissue that holds the system together. Without it, the superfluid enterprise stalls, human-machine collaboration falters and the talent ecosystems organisations depend on erode. The organisations that lead in the Trust Economy will not simply be those with the most effective algorithms or the most data. They will be those that build the credibility to be believed, the transparency to be verified and the integrity to be trusted.
8. Capitalism rebalanced
A system optimised for yesterday’s environment
Capitalism is thriving. It is doing exactly what it is incentivised to do: efficiently allocating capital towards activities deemed to offer the highest short-term financial returns, rewarding scale and market dominance, and externalising or ignoring costs that are not priced by markets.
But there is an increasingly urgent problem with the current capitalist system. The existing incentive structure was optimised for a stable world, and in today’s NAVI environment that optimisation becomes a vulnerability. What once delivered efficiency now exposes weaknesses in resilience and long-term value creation.
Take supply chains. Over the past decade, successive shocks have exposed just how fragile the global economy is. Companies have responded by diversifying supply chains and trading some efficiency for resilience, yet other parts of the economy have continued to concentrate. The top 0.001% own three times more wealth than the bottom 50% of humanity, while the largest AI oriented technology companies account for a significant share of public market value and AI is accelerating winner-take-most dynamics.
For workers, this means navigating a world where skills, compensation and job security are being rewritten. For leaders, it demands the ability to position their organisations for long-term value creation amid shifting expectations and incentives.
Rebalancing does not necessarily mean abandoning profit
EY explores one possible path: not abandoning profit maximisation but expanding the definition of value. Governments can focus on long-term investments in citizens, infrastructure and economies, while investors can allocate capital more widely with an emphasis on diversification, resilience and long-term gains. Companies can expand their mandate to maximise value for a broader set of stakeholders.
The result would be a rebalanced system in which allocation of capital and resources is optimised for long-term value creation.
Six pivots
EY identifies six potential pivots. Government budgeting can move from a spending mindset towards an investment mindset, while investors can balance short-term profits with long-term returns. Companies can expand from shareholder value towards broader stakeholder value, and markets can price environmental externalities more effectively.
Economies can also invest in infrastructure that strengthens human resources, including migration and education infrastructure, while companies invest more deliberately in talent and innovation as intellectual resources. The last of these connects directly to workforce transformation because the traditional talent pipeline of hire, train, retain and promote was designed for a world where skills evolved slowly and work was largely predictable. More continuous and agile investments in human resources are now required.
EY cites evidence that companies with comprehensive training programmes see income per employee 218% higher than those without them.
Value creation in a rebalanced system
Taken together, these pivots could broaden opportunity and support value creation across financial, human and natural capital. EY identifies three opportunities. The first is entrepreneurship as a growth driver, where diversifying capital allocation can increase financing opportunities for smaller businesses and entrepreneurs.
The second is trust becoming a stronger resource. Companies can rebuild trust through community investment, unconventional partnerships, collaboration and stronger shared culture. The third is expanded productivity gains, with more diversified capital allocation allowing a wider set of organisations to access capital, experiment and scale productivity-enhancing ideas.
These gains can then be reinvested into talent and technology, accelerating an economy in which people focus increasingly on judgement, creativity and problem-solving while machines scale execution.
How leaders can shape the future
Rethink the enterprise itself
The NAVI world is a paradigm shift that requires leaders to reimagine new and different futures shaped by megatrends. Each of the eight megatrends requires new approaches to familiar constructs, from talent to productivity to trust. By the same token, many traditional ways in which organisations and the leaders at their helm operate are no longer fit for purpose in this new environment.
Success depends on reimagining the enterprise, which requires rethinking both how companies operate and how leaders lead. It is no longer enough to disrupt your business model and transform your enterprise. In the NAVI world, you have to disrupt how you disrupt, transform how you transform and innovate how you innovate.
EY identifies four organisational pillars leaders need to rethink.
1. Rethink business and operating models
“Innovating how you innovate” means not just developing new business models but also rethinking the process of business model innovation itself. This needs to become a continuous exercise that uses a portfolio approach to explore multiple business models simultaneously, providing resilience amid increased volatility and helping organisations prepare for multiple versions of the future.
Exploring multiple futures allows organisations to chart a course through uncertainty by identifying no-regret moves. What is true across multiple scenarios and versions of the future? What core competencies and investments will you need regardless of which scenario comes true?
Operating models also need to be rethought to build organisations that are more adaptive and ready for volatile and uncertain conditions. This entails rethinking talent models, reimagining organisations around human machine partnerships and ultimately reducing operational friction.
2. Rethink risk management
The new challenge is not just managing risks but rethinking risk management itself. Traditional risk management approaches use slow and intermittent processes that are misaligned with an accelerated and volatile environment. Organisations are often siloed and manage risks as independent rather than interconnected forces.
Aligning risk management with the NAVI world requires comprehensive transformation. It requires a more holistic and strategic view of risk, methods suited to analysing nonlinear scenarios and a much tighter integration between strategy and risk. Risk management stops being primarily a compliance exercise and becomes part of strategy.
3. Rethink enterprise transformation
An environment of accelerating change and growing volatility is transforming transformation into something more fluid, flexible and adaptive to changing market conditions. Instead of a linear process with a neat beginning, middle and end, transformation is becoming more nonlinear, iterative and continuous.
The days of two-to-three-year planning cycles are over. The new reality is one in which planning horizons have shrunk to months, not years. Instead of transformation initiatives that set a fixed map to be implemented over several years, companies will increasingly use approaches that are more iterative and adaptive.
This involves not drawing a roadmap as much as identifying a direction of travel, establishing a North Star to guide the overall transformation journey, with shorter transformation projects that can be iterated and adjusted as conditions change.
4. Rethink leadership
The NAVI environment requires new approaches to leadership. The actions of leaders model desirable behaviours and set the tone at the top, while leadership teams play an outsized role in building organisational agility and developing new strategies and business models. Critically, many of the changes now needed may require senior leaders to step out of their comfort zones and adopt approaches that feel antithetical to their instincts.
Most leaders are hard-wired and socialised to communicate decisiveness and certainty. But in the NAVI world, certainty isn’t strength — it’s limitation. Certainty creates blind spots, spawns failures of imagination and builds inflexibility. The new confidence does not come from certainty; it comes from agility. Embracing uncertainty and making it a strength means becoming comfortable with appearing vulnerable and having the confidence to say: “I don’t know.”
Leadership teams often operate based on agreement or consensus, but in profound uncertainty, success may instead hinge on living with the ambiguity of disagreement for extended periods. Systems thinking becomes increasingly important because it maps interdependencies, feedback loops and cascading effects across ecosystems. Neuroscience and behavioural science can also help organisations design environments that channel human behaviour towards better decisions and build the adaptive reflexes the NAVI world demands.
Build the confidence to shape the future
The eight megatrends provide a starting point for exploring multiple future scenarios. Organisations can customise them to their circumstances, expand on them and use them to guide business model innovation. Leaders should explore interconnections between individual megatrends to surface impacts that might otherwise have been missed and look for common threads and implications to identify their organisation’s no-regret moves.
Frequent strategy refreshes can provide new directions for shorter transformation sprints. Moving ahead is easy; moving ahead with confidence in an uncertain world is challenging. The leaders who make the shift will be those who move with conviction, informed by multiple scenarios, grounded in no-regret moves and ready to adapt as the NAVI world evolves.
The goal is not to anticipate every risk or disruption because that is impossible. Instead, the goal is to build the resilience and agility to adapt to whatever comes your way. You can’t be ready for every disruption, but you can be ready for any disruption.
Methodology
EY describes Futures Reimagined as the product of a 15 month, multi stage exercise in crowdsourcing, ideation, research and analysis. The eight megatrends were developed through workshops and consultations involving futurists, executives, entrepreneurs, academics and EY subject matter experts.
The research also incorporated more than 75 interviews with EY and external experts and data from multiple EY studies, including its AI Sentiment Index, Work Reimagined Survey, CIO Sentiment Survey, CEO research and responsible AI studies. Sector analysts across 18 sectors contributed perspectives on how the different megatrends could evolve across industries.
The result is not intended as one prediction of the future. The report instead provides scenarios and frameworks leaders can use to think through several possible futures and identify actions that make sense across more than one of them.
Conclusion
The strongest message across EY’s eight megatrends is that technological capability alone does not determine the future of the enterprise. Agentic AI can remove friction, human machine systems can expand capability, new forms of intelligence can dramatically increase productivity and talent can become more fluid and global. But every one of those opportunities depends on decisions about organisational design, human capability, governance, trust and leadership.
The superfluid enterprise changes how work flows, the human machine hybrid changes what people can do, the productivity reset changes how value is measured and talent rewritten changes how organisations think about capability and learning. Migration infrastructure changes how talent moves, the global resource rush changes what resources organisations compete for, the currency of trust changes what gives organisations permission to operate, and capitalism rebalanced changes the incentives through which capital and resources may ultimately be allocated.
Together, these shifts point towards an organisation that looks materially different from the hierarchy built for the previous industrial era. Work becomes more networked, AI performs more execution and coordination, humans concentrate more heavily on judgement, context, creativity and orchestration, learning becomes continuous, governance moves closer to the operation of intelligent systems, trust becomes measurable infrastructure and transformation stops being episodic.
The common requirement is adaptability. EY’s NAVI framework therefore does not ask leaders to predict exactly what happens next. It asks them to build organisations capable of operating successfully when what happens next cannot be predicted with confidence.
Key takeaways
The NAVI world is defined by change that is nonlinear, accelerated, volatile and interconnected. Traditional long range prediction becomes less reliable, making scenario planning, no regret moves and organisational adaptability more important.
Agentic AI and other technologies can remove large amounts of organisational friction, but the resulting enterprise requires a different relationship between humans and machines. Human contribution increasingly shifts towards judgement, creativity, context, strategic thinking and orchestration. Productivity therefore needs to move beyond hours and activity towards the value and quality of outcomes, with the level of human correction and supervision required becoming part of understanding the economics of AI enabled work.
Talent also needs a new model. Organisations increasingly manage a portfolio of human and machine capabilities rather than a linear talent pipeline. Capabilities depreciate when learning fails to keep pace, creating the Talent Debt EY estimates at more than US$1 trillion in the US alone. Continuous co-learning becomes essential because human development and AI development can no longer be managed independently.
Trust becomes strategic infrastructure as autonomous systems make more decisions and AI increasingly mediates interactions. Organisations need governance, transparency, explainability and visible accountability if adoption is to scale. Finally, enterprise transformation itself must become continuous because fixed multi year programmes are poorly suited to an environment where technology, markets and risk evolve faster than traditional planning cycles.
The central leadership advantage is therefore not certainty about the future. It is the ability to redesign the enterprise repeatedly as the future changes.
