Technology is making it easier for everyone to move faster. The important question is who will move in the right direction?
New technologies—including AI and automation—are quickly becoming indispensable teammates that can draft, summarize, analyze, and accelerate the work that keeps organizations moving. I see most individuals on my team using AI and automation to complete some tasks in a fraction of the time, allowing them more time to focus on relationship-building, innovation, and value creation.
When the use of AI and automation becomes widespread, it will stop being a performance differentiator. Differentiation will come instead from the people that use them with judgment, clarity, and accountability, and that’s where leadership matters.
As new technology spreads, human capabilities contribute to high performance
In Deloitte’s new research on high-performing teams—based on an external survey of 1,394 US working professionals—respondents were asked to think about teams they have been a part of that consistently meet or exceed expectations over time. We found that surveyed high-performing teams are more likely to use AI in their day-to-day work (78% versus 54%) and more likely to report stronger outcomes including efficiency, problem-solving, and collaboration.
Technical know-how certainly matters. But the research was also clear that high performance in the AI era is human-led and AI-powered. Members of surveyed high-performing teams are 2.3 times as likely to feel trusted by their team leader, 2.3 times as likely to feel respected and appreciated by peers, and nearly 1.5 times more likely to report feeling included. They also cited emotional and social intelligence as the top success factor for their team. While AI can generate options quickly, it can’t act on what matters most, set expectations for excellence with others, or own the consequences of outcomes. That’s human work, and it is leader work: staying close enough to provide direction and reinforce judgment, not just measure speed.
Despite the importance of human capabilities in maximizing the benefits of AI, most organizations are investing almost exclusively in technology. According to Deloitte’s 2026 Tech Trends, roughly 93% of surveyed organizations’ AI-related budgets are being spent on technology, and only 7% on people. That imbalance may determine which organizations translate AI investment into sustained performance and which simply deploy more technology. It can also signal a broader pattern: investing in technology without investing in the leaders and teams that make them useful.
What sets high-performing teams apart
Deloitte’s research found teams that say they consistently achieve high performance tend to demonstrate capabilities such as)—curiosity, resilience, divergent thinking, and emotional and social intelligence. While these capabilities are not new, they take on greater importance in the age of AI. They help shape how teams navigate uncertainty, exercise judgment under pressure, and apply technology responsibly in real-world decisions.
High-performing teams don’t emerge only because AI was deployed. They’re built through leadership choices, often small, repeatable ones, that build durable capability over time. Here are five moves leaders can implement now to help teams build a culture of high-performance while using AI.
1. Clearly define expectations
Expect that work is verified, define in clear terms what “good” looks like, and set standards for, and role model, ethics and integrity. In many ways, this is no different than the expectations a leader sets for all work and how team members hold each other accountable.
2. Continue to invest in human capabilities
AI will continue to evolve, and investment in technology will continue to rise, but the next competitive advantage isn’t expected to be defined by technology alone. It will likely be defined by who also builds teams that consistently demonstrate enduring human capabilities.
3. Embed curiosity into workflows
Rotate a “second viewpoint” role in key meetings and reviews, or someone explicitly responsible for asking what’s missing, challenging assumptions, and surfacing trade-offs. Make that role responsible for challenging tool-driven conclusions, not just human ones.
4. Use strategic check-ins
Our research shows leaders are more likely to perceive their team as high-performing than their team members. A consistent 10-minute check-in—what changed, what’s unclear, what decision is needed, what help is required—can help speed up decisions and impact. Consider using it to spot where technologies are creating bottlenecks, rework, or hidden risk.
5. Cultivate a culture of continuous learning
Normalize peer demos, “show your work” walkthroughs, and quick prompt/output feedback. When learning flows laterally, skillsets expand—and AI adoption becomes more effective and consistent. Broaden apprenticeship from skills to craft—how the team thinks, decides, and delivers quality when technology accelerates the work.
In our research, 63% of all respondents said enduring human capabilities will increase in importance in the AI era, yet only 33% of all respondents strongly agree their organization is developing technical and human capabilities equally. That gap should be a concern among leadership.
As AI scales across the enterprise, executive responsibility scales with it. Leaders can treat AI as a technology initiative or as an opportunity to build enduring human capabilities to achieve and sustain high performance, whatever comes next.