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Many organizations want to explore how AI can change their business, but their success depends not on the tools, but on how well people accept them. This change requires a different kind of leadership rooted in empathy, curiosity and intentionality.
Technology leaders need to guide their organization with clarity and attention. People use technology to solve human problems, but AI is no different. In other words, adoptions must be as emotional as they are technical and must be organizationally inclusive from the start.
Empathy and trust are not options. They are essential to expanding changes and fostering innovation.
Why do you feel that this AI moment is different?
Over the past year alone, we have seen AI adoption accelerate at a fierce speed.
First, it was a generative AI, then a copilot. Now we are in the age of AI agents. With a new wave of AI innovation, companies are rushing to adopt the latest tools, but what is the most important part of technological changes that are often overlooked? people.
In the past, teams had time to adapt to new technology. Operating systems or enterprise resource planning (ERP) tools have evolved over the years, giving more room for users to learn about these platforms and acquire the skills to use them. Unlike previous technological shifts, this with AI does not come with a long runway. The change arrives overnight and expectations continue just as fast. Many employees feel they are asked to accommodate a system that doesn’t have time to learn, let alone trust. A recent example is to reach ChatGpt 100 million monthly active users Just two months after the launch.
This creates friction – uncertainty, fear, and departure – especially when the team feels left behind. It’s not surprising 81% of staff Still, don’t use AI tools in your daily work.
This highlights the emotional and behavioral complexities of adoption. Some people are naturally curious and experiment with new technologies quickly, while others are skeptical, risk aversion, or worried about job safety.
To unlock the full value of AI, leaders must meet people where they are and realize that adoptions appear different for every team and individual.
AI adoption 4 E
A carefully considered framework is required for successful AI adoption. This is where the “Four Es” appear.
- Evangelism – Inspire through trust and vision
Before employees adopt AI, they need to understand why it is important to them.
Evangelism is not a hype. It helps people care by showing how AI can make their work more meaningful and less efficient.
Leaders need to connect the dots between organizational goals and individual motivations. People should prioritize stability and attribution before conversion. The priority is to show how AI supports it rather than disrupt a sense of purpose and place.
Use meaningful metrics like Dora Or improved cycle times to show value without pressure. When done with transparency, this builds trust and promotes a culture of high performance that is clearly based, not fear.
- Enabling – Empowering people who resonate
The success of recruitment depends heavily on emotional preparation as well as technical training. Many people handle the confusion in a personal and often unpredictable way. Empathic leaders recognize this and build an enablement strategy that provides teams with space to learn, experiment and ask questions without judgment. The gap between AI talent is real. Organizations should actively support them in bridging it with structured training, learning times, or internal communities to share their progress.
People leave if they feel that the tool is unrelated. If they can’t connect today’s skills to tomorrow’s system, they tune. Therefore, the enablement must feel tailored, timely and transferable.
- Enforcement – coordinate people around shared goals
Enforcement does not mean command and control. It is about creating alignments through clarity, fairness and context.
People need to understand not only what is expected of them in an AI-driven environment, but why. Skipping straight to the results without removing the blocker will only create friction. As Chesterton fence If you don’t know why something exists, you shouldn’t hurry up and delete it. Instead, set realistic expectations, define measurable goals, and make progress visible across the organization. Performance data motivates, but should not be called only if it is transparently shared, framed with context and used to lift people up.
- Experiment – Create a safe space for innovation
Innovation thrives when people feel safe to try and fail and learn.
This is especially true for AI. With AI, the pace of change is overwhelming. When perfection is a bar, creativity suffers. Leaders need to model the idea of progress beyond perfection.
In my own team, I found progress, not Polish, gaining momentum. Small experiments lead to big breakthroughs. The culture of experimentation values curiosity as much as practice.
Empathy and experimentation are held hand in hand. One gives the other strength.
Leading change and first human
Adopting AI is not just a technical initiative, it is a cultural reset, challenging leaders to show up with more empathy than just expertise. Success depends on how leaders can inspire trust and empathy across the organization. Adoption 4 E offers more than just a framework. They reflect a leadership mindset rooted in inclusion, clarity and care.
By incorporating empathy into the structure and using metrics to light progress rather than pressure outcomes, teams are more adaptable and resilient. When people feel supported and empowered, change is not only possible, but it becomes scalable. That’s where the true possibilities of AI begin to take shape.
Rukmini Reddy is the SVP of Engineering Pagerduty.
