Deloitte - 2026 Global Human Capital Trends: From tensions to tipping points
Executive summary
Deloitte’s 2026 Global Human Capital Trends report argues that organizations have reached a set of tipping points where familiar trade-offs—control or empowerment, stability or agility, automation or augmentation—can no longer be managed through incremental change. Competitive advantage increasingly depends on a distinctly human edge: adaptability, creativity, judgment, trust, and the capacity to learn and reinvent continuously while working with AI.
Across eight trends, the report examines intentional human–machine interaction design, digital trust, decision rights, AI’s cultural effects, dynamic orchestration of capabilities, the redesign of organizational functions, continuous workforce relevance, and the long-term consequences of leadership decisions. Its core prescription is to move beyond technology-first adoption and deliberately redesign work, governance, culture, and learning so humans and machines create value together.
Key stats and quotable claims
7 in 10 business leaders say their primary competitive strategy over the next three years is to be fast and nimble.
Organizations taking a technology-focused approach to AI are 1.6 times more likely not to realize returns exceeding expectations than organizations taking a human-centric approach.
Nearly 60% of workers intentionally use AI at work, but only 14% of leaders say they are adept at shaping human–AI interactions.
Organizations prioritizing work design are twice as likely to exceed AI return-on-investment expectations.
Only 6% say they are leading in the intentional design of human–AI interaction.
Organizations leading on intentional human–AI design are nearly 2.5 times more likely to report better financial results.
Overall summary
The report frames 2026 as a move from tensions to tipping points. AI, economic volatility, geopolitical pressures, changing workforce expectations, and compressed business cycles are converging. Technology is increasingly replicable; people are not. Deloitte therefore identifies the “human advantage” as the capacity to combine human agency, judgment, creativity, and adaptability with machine speed and scale.
The chapters move in the report’s original sequence. They begin with how humans and machines should work together and how organizations can trust information about people and work. They then examine accountability in AI-supported decisions and the cultural debt created when new norms are left unmanaged. The later chapters turn to dynamic orchestration, the future of organizational functions, continuous learning and relevance, and choices whose consequences may echo across workers, organizations, and society.
Deep dive
Introduction — From tensions to tipping points: Choosing the human advantage
From tensions to tipping points: Choosing the human advantage:
The 2026 Global Human Capital Trends survey reveals the intentional choices organizations can make to adapt continuously, move with speed, and lead with a human edge control or empowerment? Stability or agility? Automation or augmentation? Last year, we explored these tensions and the need to navigate the polarities at play. But in 2026, the pace of change is sharpening the edges of these questions. Organizations are no longer just trying to balance competing forces: They are standing at a tipping point. In our 2026 Global Human Capital Trends survey,
7 in 10 business leaders say their primary competitive strategy over the next three years is to be fast and nimble—to quickly adapt to and capitalize on changing business, customer or market needs. Leaders also report that the two most important drivers of success are accelerating how people and resources are orchestrated to perform work and increasing their organization’s and workforce’s ability to adapt to change and speed. The classic S curve of growth has long described how businesses and work evolve: gradual lift, rapid acceleration, and eventual plateau. Today, that curve is compressing. AI and workforce transformation are accelerating the climb and bringing the plateau sooner (figure 1). Organizations are pressed to leap to the next curve more quickly to remain competitive. Long cycles of planning and predictable execution may no longer hold in a world where markets, technologies, and worker and customer expectations shift in real time. Success may now depend more on sensing change, experimenting quickly, and adapting continuously. Today, new data and workforce insights—ranging from organizational digital twins to real-time analytics—make it possible to see where an organization sits on the curve and actively steer how and when to jump to the next one. From tensions to tipping points: Choosing the human advantage
Growth Time Traditional approach Path of high performers Source: Deloitte analysis. Historically, organizations jumped the curve by adding new technology, a strategy that may no longer be enough. Organizations will likely need to make the leap differently. Competitive advantage is now primarily less driven by technology differentiation and more by cultivating the human edge. Technology—especially something as increasingly ubiquitous as AI—is replicable. People aren’t. Humans create competitive differentiation through adaptivity, creativity, and judgement amid uncertainty and change. When it comes to AI, value is unlocked through a reimagination of work that brings the best of humans and machines together in concert. Indeed, recent Deloitte research with 100 C-suite leaders reveals that most organizations (59%) are taking a tech-focused approach when it comes to AI. But those taking a tech-focused approach are 1.6x more likely to not realize returns on AI investments that exceed expectations compared to those that take a human-centric approach.1 This human-centric focus allows organizations to confidently jump the curve rather than stay on the same curve, or worse, fall off the curve entirely (figure 2). Threetippingpointsshapingthefutureofwork What makes this moment different is that the pressures on organizations are no longer sequential, but compounding. Technological advancement is converging with economic volatility, geopolitical tensions, societal expectations, and a rapidly shifting workforce. The boundary between planning and execution is collapsing, even as cost pressures, efficiency mandates, and questions of trust and clarity intensify. Many leaders feel overwhelmed—aware of the challenges but struggling to act decisively. Tensions once manageable over time are now tipping points, where hesitation risks missed opportunities and lasting consequences for organizations, their people, and society. In moments of discontinuity, leaders face a choice: remain tethered to the old curve or leap boldly to the next. Winning organizations see tipping points as an opening rather than a crisis but changing that mindset isn’t easy. Letting go of familiar models, rewiring assumptions, and bringing people along require courage, discomfort, and persistence. By constantly embracing reinvention, they can turn disruption into momentum—unlocking new value, human potential, and growth on the next S-curve. The next curve isn’t on the horizon—it’s unfolding now. From tensions to tipping points: Choosing the human advantage
Time Human performance Human-centric focus Machine-tech-centric focus Lack of intentionality In 2026, three tipping points stand out as especially important— moments where leaders will need to decide whether to cling to the old curve or leap to the next. Each tipping point represents a shift that organizations can no longer defer. They are not distant possibilities but present realities, demanding choices that will define how organizations create value, build trust, and unleash human potential in an AI-powered world. Given the speed and complexity of change, these tipping points can either sweep leaders along or become moments to act with precision and intention. From human + machine to human x machine The boundaries between humans and machines are blurring. Organizations will likely need to redesign work to harness human– machine synergy, moving beyond having humans and machines work side by side. This includes a rethinking of culture, decision rights, and trust in data itself. The questions are fundamental: How does culture evolve when people and intelligent agents work side by side? Who has the authority to decide when algorithms act and when humans intervene? And how can organizations protect themselves against misinformation and untrustworthy outputs in a world where AI is both a collaborator and a risk? From costefficiency to value creation Relentless cost pressures, changing consumer and worker behaviors, and geopolitical shifts have pushed many organizations toward efficiency at all costs. But as that model tips, the focus should shift toward value. This means evolving functions to be fit-for-purpose, investing in innovation, and prioritizing growth through adaptability rather than simply reducing expense. At the same time, demographic shifts and disappearing workforces are making human capacity itself a scarce resource, elevating the need to invest where humans create unique and irreplaceable value. Organizations that succeed will likely not be those that automate the fastest, but those that channel efficiency into reinvestment, fueling new forms of value creation and worker performance.
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The organizations that thrive will likely be the ones to treat discontinuity as momentum, moving quickly to redesign work, roles, and value rather than reverting to old strategies in response to AI and other advances. As the S-curve compresses, so do the capabilities required to navigate it. Where innovation, scaling, and efficiency once happened in sequence, today they increasingly need to coexist, often within the same teams and even the same individuals. Building the human advantage is now as critical as managing technology itself. That means not simply preparing workers for the future, but building a workforce that can continually learn, adapt, and reinvent in real time. Those that make bold, intentional choices to strengthen their human edge will set the benchmark for success. From tensions to tipping points: Choosing the human advantage
Chapter 1 — Getting human and machine relationships right
Getting human and machine relationships right - nearly 60% of workers now use artificial intelligence intentionally at work, according to a recent study by the Melbourne Business School, yet few organizations are intentionally designing for how humans and machines actually interact. Organizations routinely design human-to-human relationships, and increasingly machine-to-machine workflows as well. But many are still designing work for people and technology separately, rather than designing for both together. This lack of intentionality is leaving many organizations struggling to realize value from AI. While some organizations are seeing results, most aren’t realizing a return on their investments at the speed they need. Organizations can’t count on cohesive human-AI interactions to happen organically, considering that only 14% of leaders responding to our 2026 Global Human Capital Trends survey say they are adept at shaping those interactions. The problem, according to recent Deloitte research, is that most organizations (59%) are taking a tech-focused approach to AI. They layer AI onto legacy systems and processes, rather than reimagining how humans and AI interact, collaborate, and make decisions. This is similar to the way historic cities are often forced to add new infrastructure onto old foundations rather than redesigning for flow and connection from the ground up. But in a world where access to AI is rapidly democratizing, technology alone no longer sets organizations apart—people do. It’s how people interact with AI through intentional design that can make the difference. Gettinghumanandmachine relationshipsright To multiply human potential with AI, organizations can deliberately design human and machine interactions Nic Scoble-Williams, Sue Cantrell, David Mallon, and Stefano Besana
Getting human and machine relationships right Deloitte research shows that organizations are twice as likely to exceed their return on investment expectations for AI when they prioritize work design, thoughtfully redesigning human and machine interactions and roles. Consider the results when one European telecommunications company added an AI “expert” to customer service without changing roles or workflow and saw a small 5% productivity lift. But dedicating 90% of the full rollout budget to redesigning human-AI interactions—new workflows, trust thresholds, escalation paths, and robust training—unlocked a 30% productivity increase, as agents learned to partner with AI. Leaders increasingly recognize what’s at stake: Sixty-six percent acknowledge that the intentional design of human-AI interaction is important to organizational success. Yet only 6% say they’re leading in this area. Our analysis shows that organizations leading the way on intentional design of human-AI interaction are nearly
2.5 times more likely to report better financial results and twice as likely to say they provide meaningful work. Thescaffoldingforintentionalinteractiondesign Effective human and machine interaction isn’t intuitive; it won’t happen by accident or default. Organizations should intentionally design human-AI interactions at both the organizationwide macro level (including design principles, governance, and strategy) and the more granular micro level (specific interactions for particular work, workers, and teams). 66% recognize the importance, with 57% having efforts underway, and 6% making great progress
Getting human and machine relationships right For design to succeed at both levels, it needs to consider both hardwiring and softwiring. Hardwiring includes formal elements like redesigned roles, accountability, decision rights, and clear escalation protocols that dictate when work shifts from AI to a human. Softwiring includes informal elements such as leadership behaviors, culture, and psychological safety that give people the trust and confidence to question, escalate, experiment, and learn with AI. Design atthe macro level Organizations need a clear view of the macro dimensions of work design along with the hardwiring and softwiring choices that shape how humans and AI actually collaborate. Starting with a clear strategic ambition of the desired human and business outcomes is foundational. For example, Michael Ehret, senior vice president and chief people officer at Walmart International, highlights how the company brings the outcomedriven and human-centered design principles to life through its AI strategy. He explains, “We design the way our people work with AI so that it provides an outcome. Too many organizations treat AI as an adoption problem without first asking how you can achieve the outcomes desired. What’s really required is behavioral change—not technical training.” While 56% of surveyed leaders say they are designing primarily for business outcomes such as cost or speed, a growing number of leaders (40%) are designing for both business and human outcomes (such as well-being). Another key macro dimension is governance and accountability. As the dimensions of human-AI collaboration expand—spanning technology, people, process, risk, and culture—the C-suite should increasingly operate as a symphony. Business, information technology, human resource, finance, operations, risk, and legal each play their part, all following the same score. To move beyond traditional silos, some organizations are adopting cross-functional governance models. For example, Moderna has merged IT and HR to unify technology and people strategy; Skillsoft’s AI council enables cross-functional oversight; and Disney’s chief AI and collaboration officer focuses on enabling better collaboration across the business. Once the organization establishes governance, leaders can set overall design principles to guide teams in creating optimal human and machine interactions. These principles should be anchored in the enterprise’s values and mission, so they might vary by organization. Some design principles to begin with include:
Outcome-driven: Define the human and business outcomes to amplify, focusing on results that transcend what humans or AI could achieve alone.
Contextual: Tailor solutions to each workflow, team, risk profile, and human-AI relationship.
Transparent: Make roles, decision rights , trust thresholds, and accountability explicit, so everyone understands how human and AI contributions combine to drive superior outcomes. Dimension Strategic ambition How can the interactions between humans and machines improve business and human outcomes? Governance and accountability Who makes these design choices? Who owns the consequences? How are outcomes monitored and evaluated? Design principles What principles are going to guide yourdesign choices? Ethics and trust What ethical frameworks guide efforts? Infrastructure What physical and technological foundations are required?
Board orstakeholder governance
Strategic planning
Risk and organizational controls
Decision rights
Organizational structure
Technology stack
Partners, alliances, and ecosystems
Laborrelations
Culture
Leadership
Purpose
Brand Examples of hard wiring Examples of soft wiring
Getting human and machine relationships right
Adaptive: Design human-AI systems for continuous learning, feedback, and evolution, ensuring they sustain outcomes and adapt as needs change.
Human-centered: Elevate human agency, creativity, judgment, empathy, and leadership. AI should amplify and never diminish what makes us uniquely human.
Empowering: Design AI systems and culture so workers are confident to challenge, escalate, experiment, and learn from both success and failure. Save the Children demonstrates how choices around trust can accelerate adoption and impact. The organization’s early gen AI pilots delivered fragmented adoption. To address this, the organization worked on building a culture of curiosity, learning, and collaboration through a variety of mechanisms, including training, leadership engagement, and an ambassador network. The organization also established clear guardrails on when and how to use gen AI. This approach quickly doubled weekly usage (from 36% to 71%), and as fluency and adoption increased, the organization was able to apply AI to more value-creating use cases (quadrupling complex task application from 10% to 45%). Guardrail awareness increased from 42% to 70% and collaborative learning from 36% to 60%. Having strengthened its capabilities and culture, the organization is now positioned to redesign work and roles for greater impact. Design atthe micro level Beyond establishing the foundations at the organizational level, leaders need to consider how to design human and machine interactions that are optimal for each team and type of worker.
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For example, shifting routine tasks to AI when using AI as a direct report often leaves humans with more complex, demanding work, requiring greater problem-solving skills and new forms of recognition. Increased reliance on AI can also lead to worker isolation as intelligent technology replaces peer collaboration. Organizations should work to anticipate and address these silent impacts from the outset to ensure healthy, effective relationships between people and AI. Multiplyingpotentialbydesign Intentionally designing human and machine relationships does more than create efficiency; it opens new frontiers for value creation, human flourishing, and organizational resilience. The future may reward not the fastest adopters, but the most intentional designers: those who see AI as an invitation to multiply the people who make their organizations truly exceptional. Getting human and machine relationships right
Chapter 2 — Fact or fabrication? AI is blurring the line when it comes to people and work
Fact or fabrication? AI is blurring the line when it comes to people and work O rganizations are losing trust in data about workers and work at the very moment they depend on it most.1 For decades, data has been a fundamental substrate of modern organizations—an essential resource that powers decision-making, enables predictive insight, and sustains resilience. But data without trust has the potential to do more harm than good. Generative artificial intelligence and agentic systems can now produce content at a staggering scale, potentially blurring authorship, amplifying bias, and creating echo chambers where people are only exposed to information that reinforces existing beliefs. The challenge of distinguishing what is real from what is fake is growing, and this problem will likely worsen over time. For example, according to SEO firm Graphite, as of May 2025, more than half of new web articles were generated primarily by AI, 2 up from just 5% before ChatGPT.Though Graphite reports that 86% of the top-ranking pages on Google are still human-written,4 this synthetic wave could contaminate data quality for everything from SEO to model training. Factorfabrication?AIisblurring thelinewhenitcomestopeople andwork In the age of generative AI, it’s getting more difficult to know what is true, relevant, ormeaningful about people and work. How can we manage disinformation at scale?
Fact or fabrication? AI is blurring the line when it comes to people and work Many organizations are now questioning the legitimacy of data about both people and their performance at work. How reliable is the data we have on our workers’ skills and capabilities? Can we trust the resumes of job candidates? This is not a distant technological problem; it is a pressing business risk that could affect an organization’s brand, reputation, finances, and operational performance. Yet, according to Deloitte’s 2026 Global Human Capital Trends survey, only 5% of organizations are making great progress in addressing the decline in quality and trustworthiness of work and workforce data (figure 1).To address these challenges, organizations should consider expanding beyond cybersecurity to disinformation security and establishing a digital trust pact to protect worker-related data. ThecomingAIstorm Organizations have long worked to create a clean, single source of truth for their people data. Despite progress, the rise of AI may introduce new complications. The erosion of authenticity Trust in data or information comes from knowing that it’s authentic—that it’s unaltered and originates from a verified source. But AI-generated content is challenging authenticity, often making it difficult to distinguish between genuine human talent and sophisticated fabrications. 61% recognize the importance ... ... with 56% having efforts underway ... and 5% making great progress
Fact or fabrication? AI is blurring the line when it comes to people and work This is a particular problem in talent acquisition. Data in talent markets is more abundant than ever, but identity and capability are often unclear. Our 2026 survey reveals that 95% of executives are concerned about the accuracy of the data gathered on candidates’ skills and capabilities—and with good reason: Just over a third of workers admit they regularly use AI to embellish their personal profiles. AI-generated resumes can exaggerate a candidate’s job scope or skills, invent quantifiable results, or align content too perfectly with a job description, suggesting deeper expertise than the candidate actually has. Likewise, candidates can submit portfolios—designs, writing, or code—they didn’t personally create. Authenticity is further strained by synthetic identities. AI can now fabricate entire candidates, complete with deepfaked interviews. One security firm, for example, interviewed an AI deepfake candidate and discovered the deception in part by asking them to perform a simple physical gesture—waving a hand in front of their face—that the bot could not execute, according to reporting in The Register.5 A projection by Gartner suggests that by 2028, one in four job seekers could be artificial,6 raising not just hiring concerns but risks of malicious infiltration. Even when the candidate is a real person, interviews are becoming harder to trust. Employers report that AI-assisted responses mask applicants’ true capabilities, leading to disappointing performance on core human skills once hired.7 Some organizations, including Google, are considering bringing back in-person interviews to reassert authenticity.8 Meanwhile, automation has created a “bot-versus-bot” dynamic: candidates use AI to mass-generate applications and complete assessments,9 while employers use AI to screen them.10 The result is résumé noise and “hiring slop,” where indicators of genuine human experience are lost.11 The rise of ghost jobs—in 2024, 4 in
10 organizations posted jobs with no intention to hire, according to Resume Builder—only compounds the sense that talent markets are awash in inauthentic information.12 But the issue extends beyond talent acquisition in the workplace. Consider how AI-driven deception has already enabled multimilliondollar frauds, with cybercriminals, for example, using deepfake technology to pose as a company’s chief financial officer in a video conference, ultimately convincing a worker to pay out $25 million.13 While organizations are increasingly aware of external misinformation risks, internal data quality issues are often neglected. Nearly half (48%) of executives in our 2026 survey worry that AI may introduce misinformation directly into company datasets, where small inaccuracies can cascade into major operational and ethical failures. The erosion of agency As authenticity crumbles, so too does agency—the clear, unambiguous link between an action and its author. It is becoming more difficult to determine what work is human-created and what is AI-generated, fueled in part by a growing shadow economy of unregulated AI tools. Forty-one percent say they have used AI to automate part of their job, often without employer awareness. The result is a parallel data ecosystem in which AI obscures or simulates human contributions at work. Not surprisingly, 80% of executives in our survey are concerned that workers are using AI to appear more productive than they are. If we don’t know who did what, how do we reward and value workers? Agency is also eroding due to the evolution of the technology itself. AI is moving from a supportive tool to a co-author of work, blurring authorship. When AI-generated outputs become indistinguishable from purely human work, it can be challenging to accurately assess workers and their work. Should human and machine contributions be evaluated jointly? Should disclosure of who—or what—created key work products be required? Or does the distinction matter at all when the outcome is strong? The erosion of critical judgment Perhaps the most dangerous long-term threat is the erosion of cognitive capabilities. As workers increasingly rely on AI to perform tasks, there is growing concern that they may lose critical judgment14 and domain expertise,15 disempowering and deskilling themselves in the process. In our survey, 42% of executives say they are already concerned about employees becoming overly dependent on AI for essential cognitive tasks. “People are treating AI as a technology that provides answers. Rather, we need to see AI as a thought partner who might not always have 100% accurate answers—if we view it as a knowledge partner, then a light switch goes off,” says Michael Ehret, senior vice president and chief people officer at Walmart.16 Two major risks emerge under these conditions:
Workslop: Emerging research reported in The Wall Street Journal indicates that AI doesn’t always level performance— it amplifies it.17 Experienced workers can use AI to extend their expertise, while less-skilled workers are more likely to generate “workslop”: passable but shallow outputs that mask weak reasoning and slow their own development.18 Once this low-quality work enters organizational data, AI models begin learning from it, contaminating training sets in ways that later training can’t fully undo.19
Fact or fabrication? AI is blurring the line when it comes to people and work
The AI echo chamber: AI tools increasingly mirror a user’s past inputs, tone, and preferences.
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Modeled after nutrition labels on food packaging, each card clearly displays how AI was utilized in the creation of the content, including what the model did, what data was used, how the data is protected, and what safeguards were in place.29 Other organizations, like one large pharmaceutical company, are experimenting with putting labels on everything from emails to slide decks, disclosing on a spectrum how much of the content was produced by humans versus AI.30 Apathtowardmoretrustworthydata Distrust in work and workforce data will likely persist—even as organizations strengthen validation and authentication systems— because gen AI will continue to reshape how information is produced and perceived. The real challenge is not only technical but also foundational: preserving meaning, authorship, and accountability in an age of synthetic intelligence. Organizations that fail to address these shifts risk eroding the foundations of human judgment and organizational culture. Overreliance on AI without critical oversight can lead to decisions that are efficient but not ethical, fast but not fair, measurable but not meaningful. In such environments, human-AI collaboration becomes accountable by design: Technology accelerates insight, while humans remain the ultimate stewards of interpretation and judgment. Fact or fabrication? AI is blurring the line when it comes to people and work
Chapter 3 — AI and the future of human decision-making
AI and the future of human decision-making A tech company launches an AI résumé screener meant to speed hiring, only to find it has been quietly learning past biases and rejecting qualified candidates. A retail service bot makes promises that the company doesn’t want to keep. Clinicians in a hospital lean on a condition-alert tool that speeds treatment but degrades their ability to spot nuances the model isn’t trained to detect. An industrial manufacturer puts AI on the board to surface risks; the directors learn it could be manipulated for personal agendas. These stories are not science fiction. They can and are happening to organizations every day, and they raise important questions: What could have been done differently? How do organizations improve input and oversight when AI is involved in decisions? What is the best mix of AI and human in each decision, leveraging enough machine autonomy to improve speed, consistency, and scale while also maintaining sufficient human agency? AI has the potential to transform human decisionmaking. However, organizations should first treat this process as a strategic discipline and then design human-machine decision-making relationships accordingly. Do this well, and AI is more likely to sharpen human judgment, not crowd it out. Today’s cautionary tales can become tomorrow’s competitive advantage. AIandthefutureofhuman decision-making As AI transforms decision-making, how can organizations make quality decisions, anchored in human agency and trust? David Mallon, Julie Duda, Stefano Besana, and Maya Boda
AI and the future of human decision-making Runningwithscissors:AIanddecision-making Leaders today face a torrent of choices in conditions that are noisier, faster, and riskier than ever. Dashboards multiply and data streams expand, but leaders rarely stop to question where that information comes from or whether they can trust it.1 In a 2023 Oracle study,
85% of business leaders regret or question decisions they had made in the past. Additionally, 72% of those leaders say the volume of data and their lack of trust in data has stopped them from making any decision at all.2 Many are turning to AI as a solution. In our 2026 Global Human Capital Trends survey, 60% of executives now regularly use AI to support their decisions. Gartner projects that, by 2027, half of business decisions will be augmented or automated by AI agents.3 Even boards are beginning to use AI to inform decisions.4 But AI use in decisions may be racing ahead of organizational oversight. As a fundamentally new technology, AI brings distinct challenges. • Establishing clear chains of responsibility between decisions and consequences, which can be difficult with “black box” algorithms and the potential for biases or inaccuracies in model data5
People feeling less ownership over AI-made decisions6 and becoming more likely to be dishonest when delegating decisions to AI7
AI agents acting at vastly different speeds and scales from humans, blurring oversight and stressing traditional controls
Managers not being prepared to be “supervisors” of AI,8 and many executives lacking sufficient AI literacy to contribute to oversight9
Organizations lacking necessary ethical frameworks or struggling to translate them into practice10
Insurance companies not wanting to cover corporate use of AI because of the scale and unpredictability of potential risks11
Keeping up with complex and constantly changing regulatory requirements around AI12 Our survey data suggests that the issue of AI and decision-making is still emerging despite the risks: Nearly two-thirds (64%) of respondents consider it very important to their current success, and a similar number are taking steps to address it. However, only 5% consider themselves to be leading the way (figure 1). As organizations expand AI-enabled decision-making, many find AI to be amplifying existing deficiencies instead of solving them. Deloitte’s High Impact Decision Intelligence research has found that high-quality decision-making is a discipline that can be learned, improved, and scaled. Yet, more than half of organizations in that study (57%) operate at low decision-making maturity, with few teaching decision skills or providing the necessary tools to support decision-making.13 High-maturity organizations are far more likely to do both and to make decision strategies explicit.14 AI is reshaping organizational decisions, whether organizations are ready or not. To strengthen decision quality and mitigate risks, organizations should first hone decision-making as a discrete capability and then intentionally design how humans and AI interact as deciders. AI and the future of human decision-making
64% recognize the importance ... ... with 62% having efforts underway ... and 5% making great progress Elevatingthedisciplineofdecision-making AI-enabled or not, organizations that practice decision-making as a rigorous discipline consistently outperform peers.15 Simply thinking about decisions as a capability is a good start. Organizations can use decision frameworks to classify choices and pre-assign owners, data, guardrails, and speed for each category
(figure 2). Amazon’s one-way vs. two-way door model provides a simple example: Decisions that are difficult or impossible to reverse—one-way doors—require methodical decision-making, while easily reversible decisions—two-way doors—can be made quickly.16 This framework helps teams make reversible bets quickly, while giving irreversible moves higher scrutiny. AI and the future of human decision-making Design fordecision rigorand integrity Most organizations still treat decisions as by-products of meetings and dashboards rather than worthy of explicit design and focus. To add rigor and integrity, consider the following:
Surface the decisions that matter. Decision rigor and integrity start with surfacing the important decisions, clarifying owners and inputs, and making those structures explicit in workflows. The Massachusetts Institute of Technology’s concept of intelligent choice architectures is useful here: Instead of trying to predict a single “right” answer, leaders should intentionally shape the environment in which decisions happen so better choices are easier and more reliable to make.17
Encourage sound decision basis and culture. A strong decision basis (that is, data and hypotheses) is important to decision-making hygiene, and even more so with AI. Define what constitutes fit-for-decision evidence and how it will be used, in advance of using AI. Build norms that value candor, evidence, and decision quality over politics or hierarchy. Design fordecision rights and governance Deloitte research in organization design found that a surprising number of organizations lack clarity about decision rights.18 AI will likely increase the pressure here and muddy decision rights further. To achieve clarity, organizations can take the following actions:
Modernize decision rights for AI. Most organizations have already acknowledged that humans need to be on the loop when it comes to AI making decisions—having humans oversee the results. They are also working to involve humans in the loop by ensuring humans work iteratively with AI, passing work back and forth. In defining who does what, legacy, person-centric decision-rights models (for example, the classic RACI project management tool with four key responsibilities: responsible, accountable, consulted, and informed)
Element Defining question Implications Risk profile Frequency Urgency vs. importance Time horizon Certainty/risk/uncertainty Level of chaos (Cynefin)a Option-creating vs. commitment Guardrail-critical vs. value-seeking
Potential consequences and reversibility
Often made orone-off
Time sensitivity vs. strategic value
Timing of consequences
Knowns vs. predictable unknowns vs. true unknowns
Obvious connection between cause and effect
Expands orconstrains future choices
Degree of sensitivity, regulation, orspecial requirements fordecision makers
(i.e., certifications)
Higherrisk: Greaterdiligence and consultation
Frequent: Opportunities to experiment orautomate
One-off: Bespoke analysis and explicit leadership calls
Route as: Do now, schedule, delegate, orignore
Align KPIs, funding, and risk to the time horizon: win now vs. build forthe future
Knowns/quantifiable: Model risk
Uncertain: Use adaptive playbooks If the environment is ... • Clear: Use best practice
Complicated: Use expert analysis
Complex: Probe-sense-respond
Chaotic: Act to stabilize
Expands: Treat like experiments
Commits: Treat like capital investments
Guardrails: Use rules and audits
Value-seeking: Use creative risk-return calculus
AI and the future of human decision-making can be a first step.19 The challenge is that they presume static authority. With AI, rights need to be more dynamic, incorporating override privileges, escalation paths, and consensus rules engineered into the system so humans and agents coordinate who decides, when, and on what basis. Atlassian, for example, recognized that unclear boundaries between AI-led and human-led decisions were creating bottlenecks. Instead of creating a rigid rulebook, they treat decision rights as something that evolves, regularly revisiting where AI should handle routine tasks and where humans need to step in for higher-risk calls.
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Many users want AI to play some role in analytical, high-stakes domains (for example, fraud detection, weather forecasting, and drug discovery) but little or no role in more personal or value-laden decisions.38 Decision-makingthatworksforhumansandmachines Organizations that elevate decision-making as a discipline, improve decision-making skills, evaluate AI’s involvement in decisions, and design for human agency in decision-making can gain speed and quality without sacrificing trust. Those who don’t could risk opaque choices, diluted accountability, and the slow erosion of human agency at precisely the moment when clarity matters most. Evidence suggests the upside is meaningful: Technology can accelerate analysis and clarify uncertainty, but it cannot replace human purpose, values, and judgment behind choices. This is the path to AI as a trusted adviser—improving the speed, scale, and quality of decisions while keeping humans firmly in charge of the “why.”
Chapter 4 — Dealing with AI’s cultural debt
Dealing with AI’s cultural debt I n moments of uncertainty when cultural coherence may matter most, organizations often struggle to maintain it. Behaviors and norms may begin to diverge from an organization’s stated values; faced with more urgent matters, organizations may fail to address unresolved issues like poor communication or lack of psychological safety. This neglect can cause organizations to develop “cultural debt”—the negative consequences an organization accumulates by neglecting its culture, similar to how financial debt accrues interest. Such is the case as we begin 2026. Amid rising tensions in the worker-organization relationship, artificial intelligence is adding even more complexity as it transforms work in profound ways. However, much of the organizational focus of this disruption seems to center on how workers interact with AI rather than how AI impacts the human-to-human work interactions that shape organizational culture. In fact, 42% of workers in Deloitte’s 2026 Global Human Capital Trends survey report that their organization rarely evaluates the impact of AI on people—an indicator of mounting cultural debt. Culture is built on a foundation of trust, and AI is breaking that trust in many ways, as reflected by the statistic from our 2026 survey that 80% of leaders, managers, and workers are concerned their co-workers and teams are using AI to appear more productive than they are. As a result, workers are often quietly adopting or challenging norms, values, and behaviors in light of fundamental new questions that organizations are not addressing, like: Is it cheating if I use AI to do my work? What is hard work if AI is now doing the heavy lifting? Who is to blame if AI is wrong? If I don’t use AI, will I lose my job—or will AI take my job anyway? When organizations don’t answer these kinds of questions, workers are left to navigate new ethical and value-based decisions in ways that can accumulate cultural debt. DealingwithAI’sculturaldebt AI may be creating an unnoticed, steady accumulation of negative cultural behaviors. How do we manage it so culture can be an advantage in the age of AI? Jason Flynn, Yves Van Durme , Stephen Harrington, and Ashley Reichheld
Dealing with AI’s cultural debt While even the strongest cultures will likely need to be reinforced to weather the influence of AI, AI’s infusion into work can further degrade weak cultures, quietly eroding organizations from within. But organizations that intentionally nurture and evolve their cultures can unlock AI’s potential and create sustainable competitive advantage. TheculturalcostsofAIintheworkplace As we discussed in our 2025 Global Human Capital Trends report, AI is reshaping work in ways that affect people daily—increased workloads and stress, reduced well-being, higher levels of loneliness, and decreased autonomy. More broadly, AI is posing big-picture questions about employment. While many experts say that AI is more likely to transform jobs than to fully replace them, a report in Forbes indicates that, on Wall Street alone, industry analysts forecast that AI and automation will eliminate up to 200,000 jobs by 2028 or 2030.1 The World Economic Forum reports that 41% of employers globally plan to reduce their workforce due to skills obsolescence by 2030.2 And as a growing number of organizations brand themselves as “AI first,” workers receive implicit messages about their perceived value. If AI is first, does that mean human workers are second? These impacts are happening amid a broader power shift from workers back to organizations. The post-pandemic hiring surge has pivoted to an uncertain job market. Workers have moved from job hopping to job “hugging,” as data from the Federal Reserve Bank of St. Louis shows fewer US workers voluntarily quitting their jobs3
—a potential indication of declining confidence—while hiring has slowed substantially.4
57% recognize the importance ... ... with 53% having efforts underway ... and 5% making great progress
Dealing with AI’s cultural debt This confluence of changing power dynamics and AI’s impact on workers’ jobs has pushed us to a tipping point. Culture is now showing strain: A 2025 Gallup poll found that only 20% of US workers feel strongly connected to their company’s culture.5 And trust appears to be eroding in both directions. Edelman’s Trust Barometer found that trust in employers declined in 2025 for the first time since 2018.6 Leaders, likewise, are losing trust in their workers, according to survey respondents. The good news is that organizations are taking note of the problem. Our survey this year indicates that 34% of organizations recognize culture as a direct inhibitor to their AI transformation goals, and
65% of respondents believe their culture needs to change significantly considering the impacts of AI. So why do some organizations seem to be struggling to address the potential impact of AI on culture, connections, and trust? Our
2026 survey found that while just over half of respondents felt the impact of AI on culture was important or very important, only 5% are making great progress. Fromculturaldebttocultureasanasset Leaders should take stock of their existing culture and map that against where they believe culture could become a true competitive asset. How culture needs to shift is a case of “best fit” over “best practice” and may vary. Our research this year showed many organizations recognize they need to sustain their sense of purpose, mission, and belonging within their existing culture while looking to improve recognition, open communication, and commitment to innovation (figure 2). With an understanding of current cultural strengths and weaknesses and clarity on a desired future state, organizations can begin the important work of shaping their culture to help them thrive in the age of AI. To do so, organizations need to set a strong foundation for cultural evolution, build trust by activating the change in the flow of work, and recognize that AI itself can be a helpful tool for the journey ahead. 72% 28% Strong sense of purpose ormission
72% 28% Sense of belonging
63% 37% Respect forvaried perspectives and backgrounds
62% 38% Supportive teamwork and collaboration
56% 44% Recognition of individual and/orteam achievements
53% 47% Value foropen and honest communication
42% 58% Commitment to innovation and improvement Keep Change
Dealing with AI’s cultural debt Setthefoundation With a clear idea of the current state and desired future culture, organizations can put several foundational items in place. The basics still hold true: leadership alignment, anchoring on purpose and values, and communication. But understanding how we do those things is changing in the context of AI. While leaders alone cannot drive culture change, they play a pivotal role in creating the environment for desired changes to take hold and be sustained. “Leadership and culture are inextricably linked,” says Zach Parris, former director of organizational effectiveness at Atlassian. “The attitude and example set by those at the top have a significant impact on individual behavior throughout the organization. There’s a powerful connection between leadership’s comfort with technology and the tone it sets for company culture, adoption, and how deeply new tools are integrated.”7 Indeed, Cisco research found that employees are twice as likely to use AI if their leaders do.8 Mission, purpose, and values are also important. In fact, a strong sense of purpose and belonging is ranked by respondents to this year’s survey as the most important element of culture they are looking to sustain. Walmart describes its AI transformation as people-led and tech-powered—positioning AI as a tool to amplify human potential, rather than supplanting it.
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In this environment, clinging to the cultural status quo is not a neutral choice—the cultural debt AI creates is real, and it’s a risk. Acceptance of an outdated or misaligned culture may erode trust and sacrifice competitiveness. The alternative is to treat culture as a strategic asset that boosts productivity, drives innovation, and anchors workers navigating disruption. Looking ahead, the adaptivity most organizations strive for could come more through culture than process and structure. As culture becomes a competitive advantage, organizations that shape and deploy culture to harness AI’s potential are likely to drive better outcomes—for workers, for the organization, and for society at large. Those who do not may find themselves left behind, undone not by AI itself, but by a culture they failed to cultivate. Dealing with AI’s cultural debt
Chapter 5 — The orchestration advantage
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The orchestration advantage C ompetitive advantage today depends on more than what organizations own—large customer franchises, product portfolios, and supply chain capabilities. Increasingly, it depends on how they can steer intent into action, fluidly reconfiguring capabilities and capacity as business conditions, customer demand, or technologies shift. Scale still matters, but the edge is tilting toward speed and agility: Some
67% of leaders responding to our 2026 Deloitte Human Capital Trends survey say their primary competitive advantage over the next three years will come from being fast and nimble, while only
28% believe scale will be their main differentiator. Artificial intelligence is accelerating this shift by making previously scarce capabilities widely available and reshaping how work gets done. It is upending traditional assumptions around capability (the ability to effectively perform work), capacity (how much can be done and how fast), and the classic speed-quality-cost triangle of strategy. Where organizations once had to choose two of the three, AI is creating a new performance frontier where speed, quality, and cost can improve simultaneously. Scaling, for example, no longer automatically requires more people or more spending, as shown by many new ventures that aim to operate as primarily AI companies, simulating the operations of a large firm using AI with very small human teams doing high-leverage work. Rapid learning cycles can also convert speed into quality. Consider how close collaboration between doctors and AI can help detect diseases earlier and with higher accuracy than either humans or AI could achieve alone. It’s the difference between humans plus machines and humans times machines. Theorchestrationadvantage AI can enable quick intent-to-action. But turning speed into competitive advantage will likely come from learning to orchestrate capabilities and capacity in real time. Sue Cantrell, Stephen Harrington, Nic Scoble-Williams, Kevin Moss, and Russell Klosk
The orchestration advantage But the key to speed and agility is not just planning for a new math of capabilities and capacity and organizing or allocating resources into fixed structures. It is the ability to fluidly orchestrate people, skills, data and technologies around businesscritical outcomes—continuously sensing, assembling, and recombining the right elements as needs evolve. Allocation is assigning a musician to play a specific part. Orchestration is the conductor’s role, adjusting elements in real time to deliver the outcome. Consider Levi Strauss as an example of orchestration in action. The company increased sales in its loose fit jeans category by 15% in three months by rapidly bringing together the skills and expertise of people across functional domains (including designers, merchants, and marketing) and pairing them with AI to sense weak signals, identify the surge in demand for baggier silhouettes, and iterate quickly from insight to design to market response.1 An organization that effectively orchestrates its capabilities and capacity can become both big and fast, escaping the traditional zerosum relationship between scale and speed. It can simultaneously improve speed, quality, and cost—not just two of the three—and can consistently rewrite its own source code as the world changes. In the process, it can transform unpredictability from a source of risk to a source of opportunity. Our survey suggests orchestration is more than just a future aspiration; it offers present-day competitive advantage. Analysis of our
2026 research shows that organizations leading the way in this area are about twice as likely as their peers to report better financial results and to say they are providing meaningful work for workers. The ability to dynamically orchestrate work ranks No.1 among trends of importance this year, with 88% of leaders saying it is extremely or very important to accelerate how people, skills, and resources are orchestrated to get work done. Yet only 7% of leaders say they are making great progress toward this goal (figure 1). The
81-point difference between importance and action is the largest such gap in this year’s survey. Fouractionstoorchestratecapabilityandcapacity Orchestrating capability and capacity involves four critical actions. Together, these actions can help organizations not just adapt, but adapt at least as fast as the world around them is changing. Identify and create capability and capacity Organizations can start by defining the mission and outcomes they want to achieve, and then aligning the capabilities and capacity required to deliver against them. At Walmart, this has meant setting a clear focus on efficiency and encouraging leaders across its international business to explore how AI could unlock new capacity and reinvest that time into innovation and growth.2 This is the “bot” (or AI) strategy—a newly added “b” to the traditional build (train and develop internal talent), borrow (temporarily access capabilities through external sources like contractors or outsourcing), and buy (hire talent) menu of options to access capacity and capability. Fifty-six percent of leaders in our survey say they now organize and evaluate AI agents as digital workers and 60% say their teams have the right human and AI capabilities to effectively perform the work that needs to be done. The orchestration advantage
88% recognize the importance ... ... with 77% having efforts underway ... and 7% making great progress Although these four “bs” are foundational to accessing capability and capacity, there are some specific approaches organizations can take to multiply and extend them (figure 2). For example, organizations can blend human and AI capabilities. Instead of treating digital labor as a separate cohort, the blend strategy recognizes a new category, in which AI dramatically enhances workers’ productivity, performance, and creativity as an exponential multiplier of outcomes. This is an example of humans times machines. Already, just over half (51%) of leaders in our survey say they account for human and machine collaboration’s potential to unlock value when they plan the size and composition of their human workforce. We expect understanding and capitalizing on the human/ AI multiplier effect to grow in strategic importance. That seems to
The orchestration advantage be happening already: Of the one-third of surveyed leaders who exited workers due to AI, more than half (57%) say they have come to question their decision. For example, a year after claiming that its AI chatbot could do the work of 700 representatives, a financial tech company is now rehiring those people to work with AI, combining AI’s speed with human empathy to deliver better customer outcomes.3 As we discussed in last year’s trends, another way organizations can unlock capacity is to boost the productivity of their workforce by reducing nonessential work so workers can focus on what matters most.4 Once nonessential work is reduced, workers can better realize their potential and take on more value-added work. Only 50% of workers and managers say their organizations are tapping into their full potential. One multinational consumer products company practices another “b” of bridging, unlocking talent across organizational boundaries. After fully automating its plants, the company bridged production and warehouse workers into new roles monitoring the AI and performing quality checks.5 Bridging also can mean unlocking capacity by moving people into temporary assignments through internal talent marketplaces or into agile, mission-driven teams, all while workers remain in their current jobs. Megan Bazan, vice president of people at Cisco, explains: “The rise of rapid mobilization of cross-business squads—including humans working alongside machines or agents—means the static team is becoming a thing of the past.”6 Only 28% of organizations say they currently use dynamic teams organized at the point of need or by the problem to be solved. But more than twice as many (59%) say doing so will be important for their organization’s success in the next three years. Finally, break—redesigning work, roles, and organizations—is another approach. In responses to talent shortages in health care, for example, Cleveland Clinic’s workforce planning group turned to role design, breaking down tasks and asking whether each needed to be done, and whether they could be automated, performed remotely, reassigned, or rescheduled. For medical assistants, this analysis led to shifting most tasks (37 of 40) to lower credentialed or non-clinical staff and automating or augmenting others with technology. As a result, this approach created the capacity equivalent of 430 full-time employees and generated more than $2 million in cost savings, while boosting employee engagement by enabling staff to spend more time on patient care instead of paperwork.7 Rightpeople, rightdecision, righttime Orchestration requires quicker, more effective decision-making, placing the right decisions with the right people at the right time. Walmart exemplifies a new approach to decision-making designed to support the orchestration of capabilities and capacity (figure 3). To support this approach, Walmart uses a cross-functional leadership
Traditional workforce strategy Multipliers Build Develop internally Buy Acquire capability Borrow Access external capabilities Bot Use a machine Blend Combine humans and machines Boost Unlock human potential Bridge Unlock talent across boundaries Break Reimagine work
The orchestration advantage model that brings together people across functions including human resources, technology, finance, and procurement. Rather than working in silos, these leaders take a holistic view of work across different roles, capabilities, and delivery models to determine how work is best designed and supported in this new era.
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Other AI agents could track how this worker and others choose to reallocate resources in light of disruptions, signaling when workforce plans may need to be adjusted or when a role might be ripe for redesigning. Only 20% of leaders say they are currently using AI to monitor signals of workforce changes, inform decisions, and take action, even though 52% say doing so will be important for their success over the next three years. Orchestratingawayforward The rise of AI is rewriting organizations’ nervous systems. The cycle of planning, locking in resources, and execution can no longer keep pace with reality. Adaptive orchestration is the alternative. It enables leaders to continuously align people, processes, and technology, coordinating workflows that flex and adapt in real time. The future may belong not to the best planners, but the best orchestrators—those who can turn uncertainty into momentum and complexity into advantage. The orchestration advantage
Chapter 6 — Have organizational functions outlived their function?
Have organizational functions outlived their function? A CEO is preparing for a major expansion of the company’s product line. Speed is critical: Staying ahead of the competition means getting the newly designed product into manufacturing as quickly as possible. But the CEO still needs to develop an integrated financial, workforce, and supply chain plan, which requires onboarding people from multiple corporate functions. The challenge: Functional capacity is limited. Functional capability is mismatched, and end-toend data and processes are lacking. Ultimately, the functions aren’t set up to orchestrate across the business at speed. Meanwhile, functional headcounts and leadership layers have expanded, adding cost without enabling business growth. The CEO has a nagging thought: Is there a better way? This is just one scenario that points to the opportunity for organizations to rethink the very concept of corporate functions. Are they fit for purpose as they are currently configured? How can they work differently to help the business deliver on its strategic priorities with the speed, scale, and agility that today’s environment demands? The functional pillars that have long been foundational at many organizations increasingly feel outdated. In Deloitte’s 2026 Global Human Capital Trends survey, 66% of C-suite leaders agree that it is very or extremely important for their organizations to push beyond the boundaries of traditional organizational functions, but only 7% are making great progress in doing so. Have organizational functions out lived their function? The time has come to question the usefulness of traditional organizational pillars.
Have organizational functions outlived their function? Functions such as human resources, finance, information technology, legal, and procurement were originally designed for dependability, efficiency, and specialization. These days, traditional functions may be misaligned with the dynamic, multidisciplinary needs of modern organizations. For example, organizations today need expertise from multiple functions to realize value from artificial intelligence by redesigning work and optimizing human and machine interactions. The ability to adapt and be resilient in the face of a turbulent business environment now requires collaboration across functions to fluidly orchestrate capability and capacity. Sustainability and environmental, social and governance programs; innovation and new product development; and transformation and change all now rely on functions that work together, not separately. To meet this moment, organizations may need to rethink the very concept of functions. Rather than clinging to rigid silos, they have an opportunity to deconstruct traditional corporate functions and reassemble their capabilities around human and business outcomes. Functionsunderpressure A confluence of factors has contributed to the need for organizations to reimagine traditional functions. Organizations are under constant pressure to rein in the cost of corporate functions while also increasing speed to value. In the last three years, headcounts in areas such as HR, sales and support, and business management have shrunk considerably, and US public companies have cut white-collar workforces by 3.5%.
Have organizational functions outlived their function? Cost pressures and a push to increase operational efficiency have led many organizations to turn to global business services that provide shared and outsourced services in corporate functions. Over half of organizations using a global business services model include finance, HR, IT, and procurement in scope for shared services, and
58% expect to increase that footprint over the next three years. In Deloitte research, more than half of organizations with a global business service leader role reported savings of more than 20%.2 But with this focus on cost, functional leaders such as chief human resources officers, chief financial officers, and chief information officers are struggling to bridge the gap between the cost of services and the value they deliver. As work increasingly gets automated, augmented with AI, or consolidated into global business services, what’s left are domain experts who have specialist expertise but sit outside the core value chain. Increasingly, they will be challenged to broaden their expertise around business problems (for example, mergers and acquisitions, transformation, etc.), collaborating and connecting with one another to bring a multidisciplinary perspective on core business problems to realize outcomes. Our survey revealed that while over half of executives say that their corporate functions work together, more than half also say that those functions are in need of substantial reinvention in both capability and mission to meet rapidly changing demands in the future. Almost half of all respondents agreed that internal constraints, such as organizational structure, were the primary barrier to pushing beyond the boundaries of traditional organizational functions. Meanwhile, AI is increasingly making it possible to reimagine the way functions deliver value and how they are defined. In addition, end-to-end processes and integrated data that transcend organizational boundaries are prompting leaders to rethink their functional structures. Gettingintentionalaboutthefutureoffunctions In many cases, organizations are responding to these factors by tweaking at the margins; for example, building more dotted-line reporting relationships between functions, or reactively standing up ad hoc teams. But without a clear future strategy, these responses don’t necessarily create a long-term solution. What’s more, they can be counterproductive, creating an organizational model that introduces greater complexity and confusion—especially if they’re undertaken without a vision of why corporate functions exist and what value they need to deliver to the business. The accelerating pace of change is creating urgency for a more agile and adaptive approach. How can organizations ensure that transformation efforts are more sustainable and scalable? To truly transform an organization, leaders should consider reimagining those functions by breaking them down and building them up in new ways. Run the business versus grow the business The first step in this process is to separate those elements that support running the business day to day from those that support growing the business. The tables below illustrate some of the adjacent processes and capabilities across functions in both of those categories. While “run the business” activities may require unique domain knowledge, they also share many similarities. These include repeatable processes with the opportunity for substantial automation and enablement through technology and shared data (figure 2). For example, the handling of many routine workforce inquiries and transactions has been transformed by self-service technology and AI that can address the need for tier 1 support—whether that need arises from HR, IT, finance, or procurement. Increasingly, the data to satisfy those requests and the workflow technology supporting them are already integrated. Meanwhile, “grow the business” activities tend to be linked together by the business situations they support. They often involve a common set of stakeholders, project life cycles, and business objectives (figure 3). Leaders want professionals who can bring together various types of data to produce fully integrated forecasts that put the right people, materials, and technology in place to deliver products and services. They also need agile teams that can execute all aspects of a merger or acquisition, from diligence to integration. These teams need to bring not just functional expertise but also pattern recognition around how to drive results in that specific business context. Have organizational functions outlived their function? Common processes/ capabilities HR Finance IT Supply chain Risk and compliance Transaction handling Analytics/reporting Vendor management Employee data privacy, workplace safety, laborlaw compliance Internal controls monitoring and compliance Cybersecurity protocols, data protection Regulatory adherence, suppliercompliance Employee inquiries, data changes Invoice processing, expense management End usersupport Procurement orders, payment approvals Workforce analytics, turnovertrends Financial statements, forecasting reports System uptime analytics, incident reports Inventory optimization, logistics KPIs Benefits providers, recruiting agencies Banking partners, audit firms Cloud software and service subscriptions, consultants Raw material suppliers, logistics providers
Shared business scenario HR Finance IT Supply chain Planning/forecasting New market entry Mergers, acquisitions, and divestitures Product development and launch Workforce planning, succession planning Budgeting, cash flow forecasting Capacity planning, tech roadmap Demand planning, inventory forecasting Workforce strategy and sourcing, local talent acquisition, policy localization Market-viability assessment, funding review Infrastructure localization and support Local suppliersourcing, regulatory reviews Culture integration, retention planning, org design Due diligence, asset valuation, integration plans Systems integration, data migration Supply network rationalization, logistics realignment Training programs, change management fornew products Budgeting forproduct development, investment analysis Supporting technology, development environments Supplieronboarding, production-timeline planning AI deployment Role redesign, reskilling, workforce transition Value case development and tracking Enabling technology architecture and provisioning, data governance Technology and services acquisition, contingent laborplanning
Have organizational functions outlived their function?
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These changes should also leave room for constructive friction among leaders. Leaders hoping to reimagine functions must be clear-eyed about the human tendencies to protect turf and political power within an organization—regardless of what the most agile or cost-effective future structures may be. Bold leaders should create a safe space to work through these issues and present a vision for how those aligned to the organization’s goals and direction can be individually successful. Organizingtheenterpriseforamoredynamicworld Functions may have outlived their function. Organizations today have the opportunity to arrange themselves not around these traditional pillars, but in ways that provide greater fluidity, agility, and cohesiveness throughout the enterprise. Doing so will position organizations to act as dynamically as the world around them and create new roles and career paths for those who do their essential work. Have organizational functions outlived their function?
Chapter 7 — Staying relevant in a world that won’t sit still
Staying relevant in a world that won’t sit still O rganizations and workers alike are striving to adapt to the ever-increasing pace of change. Change management and corporate training and learning initiatives have traditionally been the go-to tools for staying relevant in a quickly shifting competitive environment. The problem: These rarely evolve fast enough to keep pace with what workers actually need as their roles and realities shift. Deloitte’s 2026 Global Human Capital Trends survey found that only 27% of respondents believe their organizations manage change effectively, and only 8% believe their organizations are highly effective at meeting the continuous, “always-on” learning needs of their workforce. But AI is flipping the script, upending change management and forgoing the need for traditional, top-down change techniques. AI is also disrupting traditional approaches to learning: Instead of pushing out content to workers in hopes they’ll absorb it, AI is now enabling workers to sense, practice, and apply new ways of doing things directly in the flow of work itself. Indeed, the words change management and training may now be outdated, no longer fit for purpose to drive human performance. We need an entirely new vocabulary focused on growth and adaptiveness to describe how organizations and workers can stay relevant at speed, where the pace of change only continues to accelerate. And we need to consider both change and learning together, given that both have the shared purpose of helping workers grow and adapt to stay relevant. Without a new paradigm, organizations face stalled transformations that fail to meet their intended Stayingrelevantinaworldthat won’tsitstill Real-time adaptability is emerging as a competitive differentiator. How can organizations build workerresilience on the fly? Sue Cantrell, Chloe Domergue, Allyson Dake, Jeroen Van Eeghm, Matt Stevens, and Ishani Purohit
Staying relevant in a world that won’t sit still return on investment. They also face talent disengagement and a widening relevance gap that can threaten their very survival. Meanwhile, workers face the risk of skill misalignment, reduced employability, stagnant career growth, and feeling exhausted by change—or simply feeling left behind. What marks an organization’s advantage today is how fluidly it can steer intent into action by developing adaptive capability in its workforce. Used well, AI can be a game changer: Embedded directly into the very core of the work itself, AI enables organizations and workers not just to operate and execute, but to adapt and grow. Creatinganadaptiveexperienceforworkers Creating an adaptive experience for workers is increasingly important. Respondents to our 2026 survey ranked it as the second most important trend this year, with 85% saying it is critical to develop the ability for the organization and the workforce to adapt at the speed required by today’s world. However, only 74% say they are making any kind of progress, and just 7% say they are leading in this area (figure 1). 85% recognize the importance ... ... with 74% having efforts underway ... and 7% making great progress
Staying relevant in a world that won’t sit still Changefulness goes beyond these traditional approaches and cultivates workers’ abilities to adapt, experiment, learn, and evolve as a daily muscle embedded in work, not as a disruption. Workers are being asked to adapt to changes at a dizzying pace. Our 2026 survey found that one-third of workers experienced 15 major changes in the past year alone, from evolving customer expectations to shifts in strategy or business models. The most frequently cited changes involve the work itself and the skills required to do that work, followed by artificial intelligence and other technology disruptions (figure 2). Any change will have an impact on workers. But as organizations respond to the accelerating pace of change through traditional approaches, the impact on workers can be negative. Our 2026 survey found that the steady cadence of organizational change has led to impacts such as decreased well-being (68%), increased workload (60%), and feeling less relevant or left behind (58%) (figure 3). Clearly, an intentional, more empathetic approach is needed—one that changes the narrative from “change exhaustion” to “changefulness.” Change exhaustion stems from traditional top-down change and learning approaches. By contrast, changefulness goes beyond these traditional approaches and cultivates workers’ abilities to adapt, experiment, learn, and evolve as a daily muscle embedded in work, not as a disruption. As one chief human resources officer put it, “We don’t need more change frameworks or reskilling programs.”1 Doing so can pay off: Our 2026 survey analysis reveals that organizations that successfully cultivate this adaptive approach are 2.4 times more likely to report better financial results and provide more meaningful work to workers. Although organizations have long sought to achieve this level of adaptiveness, it has only now become possible at scale, thanks in large part to advancements in AI. Leaders can consider the following four approaches to adaptiveness and growth. Create a surround-sound system Today’s workers increasingly expect change and growth to be a part of their work experience rather than an added obligation. However, in many organizations, they can still feel like something outside of daily responsibilities—activities that require stepping away from “real work.”
Staying relevant in a world that won’t sit still Instead, some leading organizations are adopting a strategy from their marketing playbook and creating an omnichannel experience for workers. This experience surrounds workers, meeting them where they are with a variety of adaptive experiences embedded in the work itself. Organizations have been making progress with real-life experiences. These include optimizing the team mix so workers can learn from one another, peer coaching, and creating opportunities for hands-on practice and experimentation. But AI is changing the playing field, enabling far more opportunities to embed learning and change in the flow of work itself (figure 4). What might those experiences look like in practice? Instead of offering traditional training to sales professionals, for example, organizations can now embed AI into the flow of their work to provide real-time coaching based on specific behaviors. Or they can use AI to help them role-play with customers and offer AI-powered micro-challenges that help identify ways to practice new behaviors on a daily basis. Workers say these types of experiences will help them adapt and learn in the flow of work (figure 5). The marketing team at The marketing team at one multinational consumer goods company now uses an AI tool that provides context-aware digital assistance in the flow of work that, with permission from workers, will track their work and offer suggestions, insights, questions, and the names of colleagues working on similar challenges.2 Surround-sound approaches apply equally well to change and learning. The vice president of global talent strategy and succession at one multinational company says: “We used to push a lot of formal learning, but now we make support accessible in the natural flow of work so leaders and employees can get timely help. We’re shifting the focus from traditional workplace learning to AI-enabled tools and AI coaching that meet people where they are, making the process more tailored and interactive.”3 Consider how one global pharmaceutical organization is forgoing its traditional change management playbook altogether when implementing a new customer relationship management system. Instead, the organization uses a variety of approaches to help workers adapt to and learn new ways of working, including in-app guidance, AI-powered adoption agents, and behavioral nudges. For example, if a sales representative hesitates while entering data, an
Decrease in well-being Less clarity in my role Increase in workload Feeling less relevant orleft behind Decrease in employability
68%
61%
60%
58%
56%
Staying relevant in a world that won’t sit still
Human-centric Tech-centric Hybrid Injecting new thinking from the outside Live learnings Hands-on practice Peer coaching networks Physical campaigns Physical immersion in new domains Experimentation Micro challenges In-the-flow-of-work skill building AI matching to experts/mentors AR/VR/XR AI coaches AI-personalized learning AI learning assistants Digital sandboxes Real-time feedback systems Personalized nudges Adoption bots and agents Personal digital twin Immersive side projects Digital reflective questioning tools to create new habits Optimized team mix Surround-sound adaptive experiences
Regular, short challenges served up to me by AI to provide chances to learn by doing In-the-moment guidance provided in all of the tools and systems I use
85%
84% Pairing me with others who have new or different perspectives and expertise
83% AI-powered talent marketplaces that provide me with opportunities to learn by taking on projects outside of my core job
80% An AI coach that provides real-time guidance, feedback, and support
80%
Staying relevant in a world that won’t sit still agent can prompt the next best action or share a short tip video, reducing time out of the field.
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This shift calls for moving away from top-down management and rigid hierarchies toward agile, networked teams.14 Apaththroughuncertainty In an environment where change and disruption are the rules rather than the exceptions, the greatest source of competitive advantage may just be the ability to organically adapt in real time. Change and learning, especially today, don’t happen in neat phases. They are unpredictable and require constant adaptation. Competitive advantage is built not by how quickly you move humans through programs, but by how organizations and workers can be rewired to rewrite themselves in real time. While AI is one of many disruptors facing organizations today, it also offers the chance to redefine how organizations adapt, shifting from static playbooks to dynamic, data-driven models that mirror human adaptability. Organizations should shift focus from control to curiosity, from training to experimentation, and toward a deeper recognition that adaptability isn’t new at all. It’s hardwired into us—a survival instinct that has always helped humans navigate change and uncertainty. Staying relevant in a world that won’t sit still
Chapter 8 — Decisions that echo
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Decisions that echo W e are living in a moment when confidence in institutions—public and private
—is eroding at an unprecedented pace. The 2025 Edelman Trust Barometer finds that 61% of people globally now feel a moderate or high sense of belief that government and business make their lives harder and serve narrow interests. Only 36% believe the next generation will be better off.1 Inside organizations, trust is faltering as well: According to HP’s 2025 Work Relationship Index, which surveyed workers across
14 countries, only 16% of knowledge workers trust their senior leaders to make the right decisions for their people—a double-digit drop in one year.2 Even foundational narratives are cracking: A recent Wall Street Journal poll shows that 70% of Americans believe that the “American Dream”— the idea that hard work reliably leads to upward mobility—was never real at all.3 These statistics are signals that the relationship between people, institutions, and work is under strain. They may also hint at something more profound: that the decisions of leaders, organizations, and boards now reverberate far beyond corporate walls. The boundaries between business decisions and societal consequences are becoming more porous, and major decisions now land not just on balance sheets, but also on communities, institutions, families, and the fabric of civic trust. At a time when confidence is slipping and norms are shifting, leaders and boards are faced with an important question: What future are they building—by design or by default—for the people their organizations affect? Decisionsthatecho Amid eroding trust and accelerating technology, board decisions reverberate beyond the organization. The question is whetherthose decisions will strengthen society—orstrain it. Julie Duda, Sue Cantrell, Yves Van Durme , Brad Kreit, and Corrie Commisso
Decisions that echo Theexpandingmandateoftheboard In the introduction to the 2026 Global Human Capital Trends report, we described the shift organizations are experiencing from navigating tensions in the worker-organization relationship (which we explored in our 2025 report) to confronting tipping points— moments at which inaction is no longer an option. The statistics cited above are evidence that leaders and boards are quickly reaching that tipping point. Those that look beyond traditional governance and bottom-line oversight to consider their broader impacts can transform their organizations and also the ecosystems around them. But those that ignore wider implications risk more than reputational damage. They risk real legal, financial, and strategic consequences—and more importantly, they risk doing harm. This is why human sustainability—the degree to which an organization creates value for people as human beings—matters so deeply now. Human sustainability is not simply about worker well-being or productivity, but whether individuals leave their work with stronger skills and employability, better health, and a deeper sense of belonging and purpose. It is about whether the organization contributes to human flourishing. Traditionally, boards have focused on strategy and vision, leadership and talent, financial performance, risk and compliance, governance and legal oversight, and stakeholder engagement. These responsibilities remain essential. Historically, board decisions have been oriented toward protecting the interests of the organization and its financial stakeholders. But boards should have a heightened awareness that any decision they make can have an impact on broader societal outcomes, such as:
Health and well-being: The mental, physical, and social health of people, including happiness, sense of purpose, and meaningful work; whether work enriches their lives or erodes it; and whether communities gain meaning and connection or experience stress and isolation
Labor market health: The ability of the workforce to grow and adapt—or to fragment into those with the right capabilities and those without—as obstacles arise to deploying skills and trustworthy employment practices, all influenced by whether technology bridges opportunity gaps or widens them
Truth and trust: The degree of transparency organizations maintain, the integrity of their data and the workforce’s access to it, and the public’s ability to believe in what both leaders and algorithms communicate
General economic health: The overall state and performance of the economy and whether business contributes to sustainable economic growth through broad prosperity and financial security Each of this year’s human capital trends identifies the tipping points where leaders have the opportunity to influence organizational outcomes. Here, we turn to a different question: How might those choices influence broader societal outcomes, and how can those outcomes in turn impact the business? The impacts can be subtle or profound. Below, we explore the questions each trend raises that can shape whether organizational choices strengthen or strain the societies around them. Thedecisionsahead:Howtoday’schoicesshape thefuture Across this year’s trends, boards face decisions that can ripple beyond the walls of their organizations. Choices made now—large and small—can accumulate into vastly different futures. For each trend, we’ve identified an immediate question that most boards tend to ask now regarding their business, and a longer-term question that considers potentially outsized impacts both within and beyond their organization. Getting human and machine relationships right Today’s question: How do we realize the returns on our AI investments? Long-term considerations: Without positive, intentional design of human and machine interactions, organizations can hollow out empathy, nuance, and contextual judgment. But when organizations design healthy human and machine relationships, it can become a force multiplier, deepening innovation and preserving the dignity of human experience in the work.
Later in the chapter
The future will likely be defined not by the technology we adopt, but by the judgment, values, and courage we bring to the decisions ahead. As boards increasingly find themselves in roles of greater influence, now is the time to ask difficult questions, widen the lens, and make choices that serve both organizational performance and human sustainability. The human advantage remains real and irreplaceable. The question now is whether leaders will use that advantage to shape a future that elevates people, strengthens institutions, and rebuilds trust. And that work begins with the choices we make today. Decisions that echo
Key takeaways, Chasm compliant
Design the human–machine relationship, not only the technology. Define outcomes, roles, decision rights, trust thresholds, escalation paths, and accountability.
Treat digital trust as an operating capability. Expand beyond cybersecurity to address misinformation, provenance, authorship, and confidence in workforce data.
Protect human agency in AI-supported decisions. Make clear when algorithms act, when people intervene, and who owns consequences.
Actively manage AI’s cultural effects. Reinforce norms for effort, ownership, fairness, connection, and accountability before cultural debt accumulates.
Orchestrate capabilities around outcomes. Move beyond static jobs and structures by combining people, skills, data, and technology dynamically.
Redesign functions for value creation. Reassemble multidisciplinary capabilities around business and human outcomes rather than preserving rigid silos.
Build continuous relevance into work. Enable learning, experimentation, and adaptation in the flow of work rather than relying on episodic change programs.
Make decisions with their echoes in mind. Evaluate immediate efficiency together with long-term effects on workers, trust, organizations, and society.
