AI is moving from experimentation to execution. Yet as adoption scales across the enterprise, one truth is becoming clear: tools alone are not enough. Success hinges on the people, processes, and leadership that bring AI into daily business operations.
On a recent episode of The AI Forecast, host Paul Muller sat down with Donna Beasley, Cloudera’s first-ever Chief AI Enablement Officer, to explore this newly emerging role, the challenges of scaling adoption, and what it takes to build organizational readiness when no blueprint exists.
AI’s Impact Relies on Operational Discipline
Paul: AI’s impact still hinges on the basic principles of operational discipline and execution. Yet a McKinsey survey showed that while 78% of companies use AI, only 17% saw a meaningful earnings before interest and tax contribution. Why is the impact not showing up in the results?
Donna: You can’t push this on people, nor can you hold people back. You’ve got to meet them where they are and then help them just take whatever that next step is. For many people, the first encounters with AI feel uncertain—even intimidating. Some worry it could replace their role, while others don’t know where to begin. I focus on creating space for employees to explore without pressure. The goal isn’t to force everyone into the same pace of change, but to ensure everyone can see how AI connects to their work. Progress comes in steady steps. When someone sees a colleague using AI effectively, they’re far more likely to return and be ready to try it themselves. That momentum—built gradually and reinforced through real examples—turns curiosity into measurable business impact.
The Chief AI Enablement Officer Role Has No Blueprint
Paul: You’ve stepped into a brand-new role at Cloudera, and unlike other executive positions there’s no predecessor, no set KPIs. How did you think about defining success in the absence of a blueprint?
Donna: There is no right or wrong answer to this. We’re kind of forging this path as we go forward. The advantage here is that Cloudera already had guardrails in place—an AI council, security guidelines, and governance. That foundation meant I could focus on putting tools in people’s hands, building confidence, and creating pathways from casual use to real innovation.
I approached success in phases. First, we ensured everyone had access to AI tools so they could start experimenting. Next, I focused on departments eager to adopt and show early wins. From there, the goal has been to build advanced learning paths for power users who want to go deeper. We track progress through adoption metrics and by looking at what people are creating—like the internal tools employees have started building for their teams. That phased approach ensures we’re not just experimenting, but turning new capabilities into practices that can scale across the organization.
Success Starts with the Right Departments
Paul: If you’re advising other companies on where to start, which departments provide the most natural footholds for AI adoption?
Donna: Marketing is absolutely the place where it makes a lot of sense to start. The outputs are already designed for public sharing, so you can sidestep some of the trickier data concerns. Once one team demonstrates success, others line up. Our CMO wanted marketing to be the poster child, and from there, adoption spread quickly to engineering, sales, and beyond.
Each department comes with a different level of complexity. Marketing provides the fastest wins because the work is already meant for public use, which lowers data risk. Sales bring strong opportunities, but require careful governance since customer information is involved. Engineering is a natural fit because developers already operate within strict guardrails and coding practices. That’s why I always suggest starting where adoption is easiest. Early successes create momentum, and once employees see tangible results, adoption expands naturally across the business without forcing it in areas with higher risks.
Organizational Change Requires Trust and Patience
Paul: One of the biggest challenges leaders face isn’t technical at all. It’s about trust, resistance, and change management. How do you help employees move past fear and skepticism?
Donna: It’s much more carrot than stick. If an idea or practice is powerful enough, the value will come through in the long run. Sometimes the tools don’t work perfectly at the onset, and I’m upfront about that reality. It takes the pressure off people. If they’re not ready today, that’s fine—because once they see colleagues using AI successfully, curiosity takes over. My role is to meet them at their pace and make adoption approachable.
Fear and resistance are normal reactions when people are asked to change how they work. I focus on building trust through transparency—acknowledging when tools don’t perform as expected and reminding teams that a human must always be in the loop. That openness helps take the pressure off and makes adoption feel less risky. I also use peer examples to create positive momentum: when small pilot groups demonstrate success, others naturally want to join. By letting curiosity and proof drive the process, adoption spreads more smoothly and scales in a way that feels approachable rather than forced.
Catch the full conversation with Donna Beasley on The AI Forecast on Spotify, Apple Podcasts, and YouTube.
