Leading the Human-AI Organization: How Top CHROs Are Building the Skills That Matter Most in the Age of AI
AI is reshaping how work gets done — and the pressure on HR leaders to respond has never been higher. At HBR's 2026 Leadership Summit, three of the world's most senior HR executives gathered to answer the question leaders everywhere are grappling with: how do you build a human-AI organization when the technology, the risks, and the workforce anxieties are all moving faster than your playbook? This conversation — featuring Daisy Auger-Domínguez (CHRO, Digital Asset), Monique Herena (CHRO, American Express), and Daniela Seabrook (CHRO, Adecco Group) — produced one of the most grounded, practical discussions of AI leadership development published in 2026. Here is what they found.
How CHROs Should Reimagine Their Roles in the AI Era
The traditional CHRO role — overseeing hiring, compensation, compliance, and leadership development — is being stretched in two directions at once. On one side, AI is automating administrative HR tasks at scale. On the other, the human complexity of managing a workforce through technological disruption is intensifying. The net effect: CHROs must operate as strategic partners deeply embedded in business transformation, not as functional administrators managing people processes from the outside.
All three CHROs agreed that the AI era demands a fundamental shift in what HR owns. Rather than waiting for business leaders to define AI strategy and then implementing the talent implications, today's CHRO must be in the room where AI strategy is set — shaping which capabilities the organization needs to build, which roles will evolve, and how to sequence the transformation in a way that maintains trust and engagement. This is not an incremental expansion of the HR mandate. It is a redefinition of where HR sits in the power structure of the organization.
How People Management Changes With Agentic Bots in the Workforce
Agentic AI — systems that can autonomously plan, execute, and adapt to achieve complex goals — is moving from research labs into real organizational workflows. For HR, this creates an entirely new category of management challenge: how do you manage a workforce that includes both humans and AI agents performing work?
The panelists noted that this is not a future question — it is a present one. Organizations including American Express and Adecco are already navigating the practical implications: how to assign work to human-AI teams, how to evaluate performance when outcomes are co-produced by people and bots, and how to ensure that human judgment remains at the center of high-stakes decisions even as AI handles more execution. The critical governance question is not just "what can AI do?" but "what should only humans do?" — and answering that requires CHRO involvement from the outset.
How AI Uncertainty Affects Employee Anxiety and Burnout
One of the most consistent findings from 2025 and 2026 workforce research is that AI uncertainty — not AI itself — is a primary driver of employee stress. Workers who have clear information about how AI will affect their roles, and who have access to training and support, report significantly lower anxiety than those left in ambiguity. The ambiguity is the problem, not the technology.
The three CHROs described a consistent pattern: employee anxiety peaks at moments of strategic announcements — AI adoption announcements, restructuring news, or leadership statements about automation — and drops when organizations follow up with concrete, individualized support. The implication for HR leaders is that communication strategy and workforce support must be designed in tandem with AI rollout plans. Announcing AI initiatives without simultaneously launching visible reskilling and transition support reliably generates the anxiety and disengagement organizations are trying to avoid.
Burnout is a separate but related challenge. When AI augments productivity, the temptation is to expand workload rather than reduce stress. Organizations that use AI efficiency gains to increase output expectations — rather than to create more space for creative, relational, and strategic work — report higher burnout, not lower. Building a sustainable human-AI workplace requires explicitly protecting human bandwidth, not just optimizing it.
Employee Pessimism and Backlash — Reframing and Root Causes
Despite significant investment in AI communication and training, many organizations are experiencing meaningful employee skepticism or outright backlash. The CHROs at the panel were direct about the root causes: backlash is usually a trust problem, not a technology problem. When employees see AI investments as primarily benefiting shareholders rather than workers — through efficiency gains that translate to layoffs rather than better work conditions — their resistance is rational, not emotional.
The reframe these CHROs offered is consequential: rather than treating employee backlash as a communication failure to be managed, treat it as meaningful signal about whether the AI strategy is genuinely designed with workers in mind. Organizations that co-create AI deployment plans with employees — that ask workers which tasks they find draining and want AI to handle, rather than deciding this from the top — report dramatically higher engagement and lower resistance. The difference between AI-as-threat and AI-as-partner is largely determined by who controls the framing from the start.
What Separates Companies That Successfully Reskill From Those That Don't
Research from HBR on large-scale reskilling programs consistently identifies five differentiators between organizations that build genuine AI capability in their workforces and those that invest heavily but see little change:
- Commitment from the top — reskilling efforts owned by the CEO and CHRO produce lasting capability change; those delegated to L&D as a compliance exercise rarely do
- Role-specific, not generic training — programs tied to specific job contexts and concrete workflow changes outperform general AI literacy curricula by wide margins
- Learning in the flow of work — embedding AI skill development in day-to-day processes rather than pulling employees into separate training events dramatically improves retention and application
- Psychological safety for experimentation — organizations where employees can try AI tools and make mistakes without performance consequences develop AI capability faster
- Measurement connected to outcomes — tracking actual changes in how work is done, not just course completion rates, allows organizations to iterate and improve
Managing the Non-Linear Nature of AI Learning Journeys
One of the most practically useful insights from the panel was the recognition that AI learning is not a linear progression. Employees do not move smoothly from beginner to intermediate to advanced AI competency. They advance rapidly in some areas, plateau in others, regress when tools change, and often need to unlearn established habits before developing new ones.
This creates a significant challenge for organizations that design reskilling programs as linear curricula. A more effective approach treats AI development as an ongoing, adaptive journey — with frequent reassessment of where individuals are, personalized support for specific gaps, and organizational patience for the non-linear nature of capability development in a rapidly evolving environment. The CHROs emphasized that this must be communicated transparently to both managers and employees, or inevitable plateaus will be misread as individual failure rather than a normal feature of AI learning.
Maintaining Trust and Clarity Through Organizational Change
Trust is the currency of human-AI transformation. When employees trust that leadership is being transparent about what AI means for their roles and careers, they are dramatically more likely to engage productively with change. When trust breaks down — through inconsistent messaging, broken promises, or visible disconnection between what leaders say and what the organization does — AI adoption stalls and talent attrition accelerates.
The CHROs identified three specific trust-building practices that consistently differentiate high-adoption organizations: radical transparency about uncertainty (acknowledging openly what is not yet known, rather than projecting false confidence); visible follow-through on commitments made during AI rollouts; and managerial empowerment — ensuring that middle managers have the information and authority they need to answer their teams' questions rather than deflecting upward. Trust cannot be communicated from the top; it must be built at the team level, which means investing in manager capability as a non-negotiable component of any AI transformation.
How to Upskill the HR Function Itself
There is an uncomfortable irony in many AI transformations: the HR function — tasked with reskilling the entire organization — is often itself significantly behind in AI fluency. The panelists were candid about this. HR must be a credible example of AI adoption, not just an administrator of it.
Practically, this means HR teams need to develop fluency in how AI tools work, where they add value in HR workflows, and how to evaluate and implement AI-powered HR products responsibly. It also means building the analytical capability to use data from workforce analytics, skills assessments, and AI system outputs to make more informed talent decisions. The CHRO who cannot articulate how their own function is using and governing AI is poorly positioned to lead the broader organizational conversation. HR upskilling is a prerequisite for credibility, not a luxury.
Who Should Own the AI Strategy Structurally and Culturally
A recurring debate in organizations is whether AI strategy should be owned by technology leadership (CTO/CIO), business leadership (CEO/COO), or people leadership (CHRO). The CHROs at the HBR Summit were clear: no single function can or should own AI strategy alone. But the CHRO has a non-delegable responsibility to own the human dimensions — the cultural, capability, and trust architecture that determines whether AI adoption succeeds or fails.
In practice, the most effective structures the panelists observed were joint ownership models in which the CTO or CIO owns the technical infrastructure and the CHRO owns organizational and capability readiness, with both reporting jointly to the CEO on AI transformation progress. Organizations that assign AI strategy entirely to technology leadership consistently underinvest in the people dimensions, while those that assign it entirely to HR underinvest in the technical and ethical governance required for responsible deployment at scale.
Building Judgment and Critical Thinking at Scale
As AI handles more execution-level tasks, the premium on human judgment — the ability to evaluate AI outputs, identify edge cases, and make decisions in genuinely ambiguous situations — is rising sharply. The challenge: judgment is one of the hardest capabilities to develop at scale. It is not taught through compliance training or AI literacy curricula. It is developed through experience, feedback, and the deliberate practice of making consequential decisions under uncertainty.
The CHROs described a range of effective approaches: structured decision-review processes where managers debrief high-stakes choices; scenario-based leadership development that forces leaders to navigate ambiguity without obvious right answers; and organizational norms that actively encourage constructive disagreement with AI recommendations rather than defaulting to whatever the algorithm suggests. Building a workforce of discerning AI collaborators — people who work effectively with AI while maintaining the critical perspective to catch its failures — is the defining capability challenge of the current era.
The Middle Manager Problem — Compression and Existential Threat
No group in the organization faces a more acute AI challenge than middle managers. From above, they are squeezed by senior leadership's demand for AI-driven efficiency and direct data access — reducing the information-relay function that has historically justified layers of middle management. From below, they face teams that are anxious about AI, need more coaching and support, and expect managers to be further along the AI fluency curve than many currently are.
The CHROs were direct: the middle manager role must be actively redesigned, not just supported. Organizations that treat middle management compression as a natural reduction opportunity — gradually eliminating layers without replacing the value those layers provided — are creating a coordination vacuum that will cost them in execution speed and employee engagement. The more constructive path: explicitly redefine what middle managers exist to do in the AI era (coaching, sense-making, team culture, navigating ambiguity) and invest in developing those capabilities, rather than simply measuring managers by output metrics that AI can now optimize automatically.
Conclusion
The human-AI organization is not a destination — it is a continuous process of adaptation. The CHROs at HBR's 2026 Leadership Summit made clear that this process requires HR to operate at a fundamentally different level of strategic engagement: shaping AI strategy rather than just implementing its talent implications; building trust infrastructure rather than just managing communications; and developing judgment at scale, not just deploying AI tools. The organizations that will navigate this transition most successfully are those that treat it as a human challenge with a technology dimension — not a technology challenge with a human dimension.
