When leaders create a narrative to connect AI to business strategy and company values, it helps drive understanding around the “what, why, and how” of the AI journey and overall purpose. This clarity makes AI part of “how we win,” instead of just another tool in the tech stack or a separate strategy. A compelling narrative includes answers to these questions:

• How can AI better enable us to deliver on our overall purpose and strategy?
• Who owns each piece of AI ideation and implementation?
• How will the organization ensure AI is used safely, accurately, and ethically?

One of the biggest barriers to AI enablement is fear of using it; training that makes it seem more complicated than it needs to be is a common complaint. Leaders should use clear, concise language to demystify AI, such as comparing learning to ride a bike to learning AI. At the beginning, both are unstable, and falls happen. But over time, you gain freedom and the ability to go places.

To make AI accessible, companies will benefit from knowing their leadership team’s overall comfort level with AI tools.³ With that knowledge, leaders can then build safe spaces to experiment — and provide the time to do so. This setup also creates opportunities for leaders and employees to co-create tools and processes with clarity, consistency, and conviction. “People need time and opportunities to develop natural relationships with AI,” Leidos’ Chief AI Officer Ron Keesing, told us.

It’s also important for leaders to shift their mindsets around AI. The pace of AI change makes specific tool training quickly obsolete, requiring leaders to eschew mastery in favor of continuous learning. AI literacy will soon, we believe, be as foundational an expectation for leaders as digital dexterity has become. By fostering a culture of experimentation, iteration, and curiosity, they can begin to see AI as a place for discovery versus perfection. Leaders can morph from striving to be “know-it-alls” to “try-it-alls” while simultaneously envisioning the future and delivering it today.⁴

“If you’re doing this right, you’re recognizing that no one has an advantage here,” Susan Youngblood, an operating advisor at Bessemer Venture Partners, explained. “We all learned about ChatGPT at the same time.”

If proof is needed that AI is critical to performance today, consider this: 31% of AI leaders now report to the CEO — near double the share only two years ago, according to our annual survey of AI and data leaders.⁵ It’s no longer enough for executives to have AI talking points. They must visibly and consistently role-model AI use, endorse it, and normalize curiosity, experimentation, and failure within senior leadership groups. Susan Youngblood told us, “Bessemer Venture Partners’ leaders use it regularly.”

Once a company moves beyond pilots, building enthusiasm for the potential is crucial. For example, a senior director of AI and digital strategy at a global logistics company partnered with a university to develop an AI education course for company executives. Once the leaders finished the program, they became sponsors, which led to more than 2,000 employees wanting to take the course. This approach engaged diverse stakeholders and leveraged team members to spread momentum across functions. It also showed leaders being vulnerable as they learned something new and tied AI to enterprise values, organizational purpose, and personal and professional growth.

In addition, leaders can identify early adopters to become champions for AI across the organization, and empower them with visibility, tools, support, and autonomy. One executive told us about a director who regularly shines a spotlight on the employee who does the coolest thing with AI that is impactful for their job. By spotlighting AI usage, expectations and AI literacy spread across the organization.

Too often, we’ve seen companies try to start by embedding AI usage, aligning incentives, and celebrating wins. But only once leaders are aligned, equipped, and activated for success can an organization truly succeed with embedding AI. If the desired behaviors aren’t integrated into culture, then linking AI to performance metrics or KPIs won’t work.

As part of performance feedback, leaders should elevate role models who exemplify AI fluency or experimentation and, just as importantly, openly discuss failures so employees can learn from mistakes. As Sophia Vestagui told us, “Celebrate the skinned knees. That’s how you learn to ride the AI bike.”