Over the past few months, I’ve had the opportunity to speak with a wide range of professionals—coworkers at Microsoft, consulting partners, CIOs, and CTOs—about the evolving landscape of AI adoption, particularly generative AI. One theme keeps emerging: organizations tend to fall into two distinct camps when it comes to implementing AI.
Camp 1: The Moonshot AI Innovators
These are the bold visionaries. They come in with big ideas game changing, business altering AI projects that promise to revolutionize how they operate. Their ambition is inspiring, and they’re not afraid to fail fast and learn quickly.
But here’s the catch: many of these organizations haven’t laid the groundwork necessary for success. They often lack:
- A clear understanding of their data estate
- Proper governance frameworks
- Change management plans
- Training programs for employees
- Strategies for user adoption
Without these foundational elements, even the most promising moonshot can struggle to get off the ground. Still, these companies are in the game, and that’s a win. They’re learning by doing, and sometimes failing, which is a critical part of innovation.
Camp 2: The Toe-Dippers
On the other end of the spectrum are the cautious adopters. These organizations are experimenting with AI in small, isolated use cases. They’re dipping their toes in, trying to understand the technology before diving deeper.
While this approach minimizes risk, it often lacks vision. These companies:
- Don’t think big enough
- Fail to connect small wins into larger workflows
- Struggle with user adoption because they don’t teach users to chain tasks together
For example, imagine an employee using AI to recap a meeting. That’s great, but what if they also used it to schedule the next meeting, create an agenda, research the topic, build a presentation, and send follow-ups? That’s the kind of end-to-end thinking that unlocks real productivity gains.
The Hybrid Model: A Tale of Two Strategies
Interestingly, some organizations are trying both approaches simultaneously. One part of the business is shooting for the moon, while another is cautiously experimenting. This hybrid model can work—but only if there’s alignment, communication, and a shared vision for how AI can transform the organization.
Final Thoughts
Whether you’re aiming for the stars or just testing the waters, success with AI requires more than just technology. It demands:
- A strong data foundation
- Clear governance
- Thoughtful change management
- Empowered users who understand how to leverage AI holistically
AI isn’t just a tool—it’s a new way of working. And the organizations that recognize this will be the ones that thrive.
Craig Jahnke 
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