Try this Framework for AI adoption

At the 2026 CRM Playaz IRL event in Atlanta, one of my favorite sessions was on why AI adoption falls short, led by Dr. Stefanie Boyer.

When asked, the audience at the session was quick to volunteer challenges. No internal champion. Bad data. No humans in the loop. Lack of training. Unrealistic expectations. Use cases that never made sense to begin with. The list goes on.

But AI done right is powerful. It makes work easier for employees, improves customer experiences, and helps companies act faster and more strategically.

The SAFE AI Adoption Framework

Most AI projects don’t fail because of the technology. They fail because people are taking the wrong approach or overlooking key requirements. If you’re struggling with making AI work in your business, try Dr. Boyer’s SAFE framework.

S - Solve Problems That Matter

Start with a compelling business problem. The question is not “where can we use AI?” but “what’s hurting the business?” Adoption follows value. If your tool isn’t impactful, why would anyone use it?

A - Assess Alignment

Spend time upfront identifying project requirements and change strategy. Companies often underestimate what it takes to succeed, especially around training, data readiness, and having the right people involved.

F - Focus on Functionality

Many tools simply don't deliver on their promise or have consequences you never expected. Get feedback early to avoid those issues. Always do UAT. And pay close attention to what happens after go-live, so that you understand what’s working and what still has room to improve.

E - Ensure Data Safety

Without precautions, you are taking a real risk that users will expose confidential data or access unauthorized information. (💡Tip: Platforms like ServiceNow play a critical role in providing the visibility and control organizations need here.)

The main lesson for AI adoption?

Adoption is about much more than the technology itself. It requires identifying the right problem, creating a roadmap for change, getting real users involved early, and keeping data safety and governance top of mind. Otherwise, you set yourself up to fail.

Now your turn. Where have you seen AI initiatives struggle? Or even better - how have you helped them succeed?