Four Best Practices for Leading Through AI Adoption

Chris Henley is a Microsoft Trainer, part of a community of professionals at Microsoft Global Skilling, working with customer and partner leaders to help them build the skills required to drive their organization’s AI strategy.

Where AI conversations are shifting for leaders

I’ve been working with executives and company leaders for several years, and it feels like the AI conversation is finally shifting from “What can AI do?” to “How do we move forward with AI in a way that creates real value?” That shift often shows up as organizations move beyond isolated pilots and begin integrating AI across the business: into processes, employee experiences, customer engagement, and innovation. Microsoft refers to this broader shift as Frontier Transformation, where AI becomes a strategic priority and changes how intelligence operates inside the organization, not just which tools people use.

From what we’re hearing from executives, one thing becomes clear: AI adoption rarely comes down to a single decision. Progress unfolds through small experiments, sharper priorities, and measured results that reveal what’s working and what to do next.

What seems to help drive adoption progress isn’t a rigid plan. It’s returning to a few best practices that show up regularly in real business discussions:

• Reframe: recognizing that AI is not just another tool rollout, but a shift in how work is structured, how decisions are made, and where intelligence shows up across the business.
• Focus: identifying a specific business priority where AI can create measurable value, rather than spreading experimentation across too many disconnected pilots.
• Assess: taking an honest look at whether the organization is ready to move forward across data foundations, leadership alignment, team capabilities, and governance.
• Commit: selecting a defined AI initiative, assigning ownership, and establishing how success will be measured over a clear timeframe.

 These aren’t meant to be a strict sequence. Leaders often move between them as they clarify strategy, prioritize investments, and decide what to do next.

The following sections take a closer look at how each of these best practices show up in real leadership discussions about AI adoption.

1. Reframing how to think about AI in the organization

AI often begins framed as a tool rollout, but leaders I work with frequently find that this narrows the discussion too quickly. Many have shared that the most useful shift happens when the question moves beyond “How do we use this?” to something broader: “Where could AI change how our business actually works?”

I was recently delivering a training session with one of our customers, a consulting firm that had rolled out Microsoft 365 Copilot. Early wins were familiar: faster emails, cleaner summaries, better documentation, and the team was energized. But in one session we paused and asked a tougher question: if AI is now part of the business, should client reporting and analysis still look the same?

The focus shifted from incremental productivity gains to rethinking how insights were created and delivered. You start to see where work should be redesigned—not just sped up. The technology remained the same, but leadership perspectives evolved.

2. Choosing where to focus before moving forward

Another common situation we’re seeing is companies trying to use many different AI solutions in hopes of finding an area where AI might have an impact. Pilots are running across the business, but their intended business impact is unclear. Activity isn’t the same as progress. Momentum usually picks up when leaders choose one area where AI clearly connects to a meaningful business outcome.

The experience of one of our customers, a global automotive manufacturer, is a good example of this. Rather than trying to use AI everywhere, they pinpointed a bottleneck that was slowing down their accounting workflows. So, they applied AI document intelligence to that problem first. That targeted focus reclaimed thousands of hours of manual work.

You can see what’s working and what it tells you about your organization. Investment conversations become easier, because you’re not funding “AI.” You’re funding a business outcome.

3. Assessing organizational readiness as aspiration meets reality

One of the most useful shifts happens when leaders pause to examine how ready the organization actually is for AI. It’s easy to assume you’re “AI-ready,” but that closer look often reveals where ambition is moving faster than capability.

In one executive discussion, a leader paused and said, “I thought this was an IT implementation. I didn’t realize how much AI would change how my leadership team operates.” That moment shifted the conversation from infrastructure and deployment to the real question: Was the organization ready to operate differently with AI? The team shifted its attention to making better decisions, ownership, and leadership readiness.

You can see whether AI is tied to business priorities, whether teams have enough hands-on capability, whether the culture supports experimentation and learning, and whether risk and accountability are clearly defined.

4. Committing to shape your AI strategy through action

Here’s something I see a lot: Leadership teams often agree AI is important, but progress stalls when no one defines the next step or who owns it.

In one session, an executive team had been reviewing use cases for months. Mid-meeting, someone finally said, “We’ve been talking about this for a while. What have we accomplished?” That simple question immediately shifted the conversation from exploration to ownership. The team aligned on one outcome and how they’d measure it, and that’s when momentum finally started.

The goal isn’t to have the perfect strategy upfront. It’s to commit to a focused, informed, effort that starts to transform your business in a meaningful way. The efforts that gain traction are usually clearly defined, have senior leadership behind them, and everyone on the team is aligned on how to measure progress.

Time to roll up the sleeves and get to work

In the end, leading through AI adoption isn’t about getting everything right from the start. What I see work most often is leaders building the skills to navigate it as they go, and learning how to judge where AI really matters, where to begin, and how to move forward through focused action.

If you’d like a practical way to start that process, check out our new Develop AI Leadership Skills guide for a structured starting point. It’ll help you think about your organization's next steps with AI and it’ll give you a clear framework for prioritizing actions and moving ahead confidently.