Tag: C-Suite

Companies are investing millions in AI tools and training employees across all levels to use them. Yet the most senior leaders — those who shape culture, set strategy, and control budgets — are often excluded from hands-on learning. The prevailing assumption is that executives only need high-level briefings, not practical training. They’ll grasp the details later, after all,  shouldn’t training focus on the people doing the “real work?” 

This thinking has created a costly blind spot. A recent MIT report found that 95 percent of AI initiatives fail to provide ROI. While much of the conversation has focused on technical implementation challenges, there’s a less obvious culprit: Senior leaders are making decisions about AI adoption, governance, and resource allocation without truly understanding how these tools work in practice. The result is a disconnect between what gets purchased and what actually gets adopted, leading to fragmented execution and missed opportunities. 

The leadership bar has shifted. Three years ago, being tech-aware was sufficient — following digital trends and relying on experts for execution. Today’s leaders must be tech-immersed.

They need to understand how AI platforms directly impact products, customers, operations, and value chains. They need data literacy, which means knowing how to work effectively with generative AI tools to interrogate datasets, surface patterns, and generate insights. And perhaps most critically, they need human-centered leadership skills — the ability to cultivate trust at scale, guide teams through the emotional complexities of change, and create environments where people feel supported as their roles get redefined. 

The real advantage in AI adoption comes from senior leaders who understand these tools well enough to model their use, ask the right questions, and create the conditions for genuine adoption across the organization. 

What senior leader training actually looks like 

Effective executive AI training bears little resemblance to the typical boardroom briefing. It’s not about consultants presenting frameworks on slides or walking through theoretical use cases. It starts with customization, training built around the actual data executives work with and the specific problems they need to solve. 

For a senior leader, this might mean learning how to prepare for client meetings by connecting the AI tool to CRM data or analyzing financial forecasts to spot trends and anomalies. It could involve board preparation or simply understanding how to ask better questions of the data. And that training may look like the inverse of what has been typical for so long. 

At one global financial services firm, a senior executive in charge of an entire region began meeting with a junior employee every two weeks for AI coaching. During one session, the junior employee suggested using the tool to analyze the tone of the executive’s emails. The executive discovered that during particularly busy weeks, her email tone became noticeably more abrasive. She hadn’t realized it before. Here was someone at the top of the organization chart being coached by someone near the bottom, and the value created rippled across the entire C-suite. 

This dynamic requires something many senior leaders aren’t used to: vulnerability. Using AI tools effectively means iterating prompts, admitting when something doesn’t work, and starting over. When leaders experience firsthand what it’s like to learn these tools, they develop empathy for what their teams are going through and a more realistic understanding of what adoption actually requires. 

Many organizations bring in outside firms to provide credibility and frameworks for thinking about AI strategy. That’s fine, maybe even useful. However, those high-level sessions need to be paired with practical, hands-on training that grounds abstract concepts in real implementation. Leaders need both the theory and the practice. 

The cultural shift and structure required 

Training executives to use AI tools is only half the equation. The other half is teaching them how to lead adoption across the organization, which means understanding that AI cannot sit in silos or remain the responsibility of technical teams. It must be embedded in decision-making at every level. 

Leaders can’t simply mandate adoption from the top. What actually drives adoption is when leaders model the behavior themselves, like judging hackathons, highlighting the most creative uses of AI each week, visibly using these tools in their own work, and talking about what they’ve learned. As automation takes over routine tasks, the human elements of leadership become differentiators. 

One of the most effective structures for driving adoption is a champion network — employees across different departments who become power users and help their colleagues. But many companies start too small. One global bank had 700 champions and wanted to know how to scale its AI usage. Our work with a range of enterprise companies shows that champion networks with roughly one person for every 25 employees create the diversity of perspectives necessary for genuine culture change. The bank actually needed to grow its base of champions before it could sustainably scale AI use. 

The goal is citizen development, empowering thousands of employees to create thousands of their own AI applications and workflows, rather than relying solely on a few large, centrally managed projects. When an HR manager builds a custom tool for screening resumes, or a sales team creates an application for client research, adoption becomes organic rather than mandated. 

The paradox is that leaders need to champion AI adoption, but they also need to know when to leave the room. Employees need space to be vulnerable, to try things that might fail, to iterate without fear of judgment. Effective AI adoption requires both top-down endorsement and bottom-up experimentation. 

The companies that will succeed 

Organizations that are finding success with AI aren’t necessarily the ones with the biggest budgets or the most advanced technology. They’re the ones where senior leaders understand these tools well enough to ask informed questions, model effective use, and create genuine space for experimentation and failure. 

This requires a kind of humility that doesn’t always come naturally to people who have spent decades building expertise. It means being willing to learn alongside, and sometimes from, junior employees. It means admitting uncertainty and being comfortable with iteration. 

If your executives haven’t received hands-on AI training using your actual data and real use cases, your adoption program is already behind. Learning the technology itself isn’t the bottleneck anymore — it’s understanding how to apply it effectively. The organizations that will win have leadership that advances from mere awareness to practical fluency. That doesn’t come from briefings and white papers; it comes from direct experience. 

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