Editorial
Artificial intelligence may be the most energizing and exhausting transformation in modern business. The recent EY U.S. Agentic AI Workplace Survey captures this duality in full color: 84% of U.S. desk workers are eager to use AI, yet 61% are overwhelmed by the constant influx of new tools and information. Enthusiasm is abundant; organizational readiness is not.
EY’s findings reveal a workforce hungry to experiment and a leadership class still learning how to lead in the agentic era defined not just by automation, but by AI systems capable of reasoning, planning and acting with autonomy.
The Upskilling Gap
Perhaps the most striking insight is that AI learning is happening in spite of organizations, not because of them. Eighty-five percent of desk workers say they’re learning how to work alongside AI agents outside of work, and only 52% of senior leaders report a fully deployed agentic AI training or upskilling initiative.
In a recent virtual conversation with Kim Billeter, EY’s Global People Consulting Leader, I explored the story behind the data and what these findings reveal about how organizations can turn AI enthusiasm into lasting capability. She described the current landscape as “a perfect storm” of misaligned learning and leadership. Many employees use consumer AI tools rather than enterprise ones because they’re rarely given access to personalized learning experiences that meet them where they are on their enterprise tool learning journey. Generic, enterprise-wide training on prompt engineering has its limits.
The organizations getting this right are:
- Defining an AI learning strategy linked to business goals and specific skill needs.
- Measuring outcomes, not just participation.
- Rewarding learning, so employees feel recognized for upskilling.
In short, AI upskilling must move from individual initiative to institutional intent.
The Communication Breakdown
EY’s data show that twice as many employees below VP level (21%) as those above (9%) say their organization hasn’t clearly communicated its AI strategy.
This misfire often happens in the middle. Managers, already stretched thinly, struggle to translate lofty AI ambitions into practical relevance for their teams. Billeter advised leaders to communicate in “micro moments.” Explain what changes in someone’s job next quarter, what new skills matter and how success will be measured.
This is not a time to lead from the ivory tower. Executives must ensure their messaging covers not just the enterprise AI strategy but the “what’s in it for me” for employees.
Where communication is clear, EY finds, adoption and business outcomes improve. Communication, in other words, is not a memo; it’s infrastructure for change.
The Overload Dilemma
Sixty-one percent of desk workers feel overwhelmed by the constant flow of AI information, and 64% of those who already use AI at work say they’re overwhelmed by the sheer number of new tools being introduced. Every week seems to bring another app or platform, leaving employees unsure of what’s relevant, safe or even approved.
This isn’t just digital fatigue; it’s a design problem. When organizations roll out tools faster than they help people integrate them into daily work, learning becomes fragmented and anxiety grows.
Billeter offered a practical remedy she calls “thrive time.” When AI saves employees hours of work, she argues, leaders should intentionally give some of that time back for learning, reflection, experimentation or even rest, making it part of the employee value proposition.
Creating this breathing space signals that learning is real work, not an extracurricular activity. It also helps employees see AI as a way to reclaim capacity, not lose control. In short, leaders must treat capacity as a change management issue, not just a productivity metric.
From Adoption to Absorption
Billeter argues that most companies haven’t yet reached absorption — the stage where leveraging AI becomes as second nature as using email or spreadsheets. Absorption happens when employees view agents as colleagues, not just tools, and when workflows, oversight and accountability naturally blend humans and AI.
To get there, organizations need trust. People must believe leadership will treat them fairly as roles evolve. “Before we can get to absorption,” Billeter said, “we need to get culture and trust right. People need to trust that when they make this leap, they’ll still have jobs they want to do.”
A Three-Part Model for AI Advantage
EY’s new Work Reimagined 2025 study shows that reaping value from AI investments requires mastering the tensions between talent and technology. Organizations that fail to do so risk a 40% decline in AI productivity. Success depends on three intertwined levers: skill set, toolset and mindset.
| Dimension | What It Means in Practice | Why It Matters |
|---|---|---|
| Skill set | The capabilities that enable people to use AI well (prompting, analytical reasoning, data literacy) | Accounts for nearly 50% of the “AI Advantage” score; training depth predicts time savings (14 hours/week saved by employees who receive 81+ hours of training per year) |
| Toolset | The technologies available (copilots, governance, data infrastructure, accessibility) | Even skilled employees underperform if tools are fragmented or unsanctioned; 23–56% use personal AI tools because corporate ones fall short |
| Mindset | The culture of curiosity, safety and trust around AI use | Determines speed of adoption and resilience; people adopt faster when they believe AI augments, not threatens, their jobs |
Skill set unlocks capability, toolset provides capacity and mindset sustains momentum. When all three align, organizations move from pilots to performance.
Rethinking the Manager’s Role in Human–Agent Teams
EY’s survey shows 53% of managers doubt their ability to lead AI-augmented teams, and 63% of non-managers hesitate to pursue management for the same reason.
The new manager playbook looks different:
- Lead from the front. Work with your own agent. Model curiosity. Admit what you don’t yet know.
- Balance empathy with ability and learn in public.
- Redesign roles. Define which tasks belong to humans, which to agents, and how feedback flows between them.
As Billeter put it, “You’ll know transformation has taken hold when agents are viewed somewhat as colleagues.”
She also warns against the “myopic” view of cutting entry-level roles. Automation may eliminate some grunt work, but forward-thinking organizations continue to invest in entry-level talent with the long-term goal of building future talent pipelines. Her advice is to build a “dumbbell-shaped workforce,” strong at the top and bottom, with apprenticeships that pair junior employees with human and AI mentors.
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AI Innovation, Beyond Efficiency
EY urges leaders to push past the doom-and-gloom narrative and focus on market-defining growth by using AI to create new products, services, and models. This aligns with a useful distinction between Efficiency AI and Opportunity AI.
- Efficiency AI is about continuing to perform the same work but faster, better or cheaper.
- Opportunity AI explores what was previously impossible: reimagining business models, redefining customer value and designing entirely new work systems.
Neither is superior; both are essential. The danger lies in equating AI strategy with headcount reduction. When efficiency becomes synonymous with role elimination, leaders decimate trust precisely when engagement matters most.
When restructuring is necessary, fairness and transparency are non-negotiable. But when cost-cutting becomes the primary motive rather than the outcome, leaders pit people against bots and lose the narrative.
The Human Inflection Point
EY’s research reminds us that agentic AI is rewriting the rules of work, but the script is still unfinished. Technology won’t decide the ending; leaders will.
The choice isn’t between efficiency or opportunity; this is a both/and play. It’s whether leaders can hold both truths at once. Organizations that master that balance won’t just deploy AI successfully; they’ll earn the trust to lead in the era of human-agent collaboration.
Editor's Note: Catch up on more thoughts on how leadership must adjust to the current moment:
- Lead or Get Run Over: A CEO's Field Guide to AI You Can Use Now — Real AI leadership means clarity over chaos. Start small, educate everyone, run ethical pilots and scale what works. Show proof, not PowerPoints.
- 5 Questions Every Leader Should Ask Before Building AI Solutions — AI isn’t the enemy — or the magic fix. Most failures come from leaders skipping the hard questions. Here are 5 that separate hype from real impact.
- People-Centric Leadership and AI: Can We Have Both? — AI can support people-first leadership, if we use it with care.
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