From board presentations and earnings calls to offsite executives and coffee machine conversations, the topic of AI is everywhere. The opportunity is enormous: to reinvent work, unleash creativity and expand what organizations and individuals can do. The pressure too.
In response, many organizations are deploying tools and launching pilot projects. Some of this activity is necessary. However, much of this text misses the deeper point. Too many leaders are wondering: how will AI change us? The better question is: what kind of leadership will we build to guide AI?
This distinction is important because technology alone does not determine outcomes. This is the case with leadership decisions—the systems, standards, and capabilities that organizations choose to build and apply to their work.
Here are three ways to strengthen what people can bring to the AI age.
Don’t let fear diminish ambition
The promise of AI lies in bold experimentation. However, even in the most sophisticated organizations, fear quietly constrains them. So there are tensions. Leaders are asking their people to experiment fearlessly with AI, while launching efficiency programs that employees interpret this as a harbinger of job cuts. When people feel exposed, they play small. Revolutionary ideas give way to micro use cases and companies refine today’s model instead of creating tomorrow’s.
What to do: Leaders can reduce fear by creating a protected space for AI experimentation, free from pressures for short-term efficiency. Research has shown that such psychological safety is essential to performance. Teams that feel safe identify problems sooner, question assumptions more freely, and learn faster. If leaders want bold thinking, they need to reduce the perceived cost of doing so. Alternatively, AI could improve efficiency while the time to reimagine passes.
History proves it. When Siemens And Toyota reinvented their production systems, they explicitly protected jobs. What companies gave up in short-term savings, they gained in long-term innovation. People were encouraged to take risks because they believed productivity gains would be shared, not weaponized.
Creating opportunities for people to learn is another way to help reduce fear and free people to think beyond the possible. That was the thinking behind CEO Satya Nadella’s Efforts to Instill a “Learn Everything” state of mind to Microsoft; This took the edge off of not knowing everything already and contributed to breakthroughs in product and strategy. Another approach is to provide regular time for generative work, such as Google’s “20% time” practice, in which engineers were encouraged to explore personal projects that could help the company. AdSense and Google Newsamong other products, started this way.
Use AI as input, not default
From the steering wheel to yesterday’s AI agent, every invention has increased or replaced human actions. The danger is when people rely on the tool so much that they stop thinking.
As access to AI models and computing power becomes more widespread, analytical advantages erode. This makes even more valuable the distinctive human ability to interpret context, weigh trade-offs, understand stakeholder impacts, and question outcomes. Stanford’s Human-Centered Artificial Intelligence The institute found that teams combining AI recommendations with expert supervision consistently outperform fully automated systems. Or, as my son’s first grade teacher said: being smart is knowing that a tomato is a fruit. Being wise means knowing not to put tomatoes in a fruit salad.
What to do: Design decision-making to ensure AI informs judgment rather than replacing it. For important decisions, leaders should require teams to document the human reasoning behind AI-informed decisions, making the logic explicit so it can be tested. Over time, this strengthens discernment and institutional memory, and ensures that people take responsibility for their calls, rather than blaming role models. Teams can also foster structured dissent as a counterbalance to AI-induced overconfidence by asking questions like: “What would have to be true for this to be valid?” »
Keeping people at the center of value judgments
Ethical leadership in the AI era is about deciding, explicitly and repeatedly, where optimization should stop and human responsibility should begin. Among the questions to ask: What decisions should algorithms be allowed to make? Who is responsible when an AI-based decision causes harm?
What to do: It is important that leaders clearly define the limits that will never be crossed. Integrate governance into workflows, ensuring people make the most important decisions; train managers to weigh what is possible and what is responsible.
Judgment, ethics and values cannot be outsourced to AI. These capabilities must be built, then nurtured, so that they become second nature, starting at the top but integrated throughout the organization. In business, compromises are inevitable; in the age of AI, they must be intentional.
Successful leaders in this moment won’t deploy AI tools just because they can; they will do so in a way that leverages psychological safety, human judgment, and ethical clarity. Efficiency without empathy this is not progress. Innovation without judgment is not leadership.
AI will not decide the future. Leaders will – and history will not forgive the difference.
The opinions expressed in comments on Fortune.com are solely the opinions of the authors and do not necessarily reflect the opinions and beliefs ofFortune.
This story was originally featured on Fortune.com
