What's happening
Job postings for roles focused specifically on AI adoption have tripled in the past 12 months. These aren't AI engineering roles or data science positions. They're roles like AI Adoption Coach, AI Enablement Specialist, AI Change Lead. The job descriptions focus on helping teams integrate AI into their daily work, building internal capabilities, and managing the human side of AI rollouts.
Companies are creating these roles because they've learned something the hard way: buying AI tools doesn't mean people use them. And when people don't use them, or use them poorly, the investment doesn't pay off. Someone needs to bridge that gap, and it turns out that requires a specific skill set that most IT departments don't have.
Why this is interesting for change professionals
If you look at what these roles actually require, it reads like a change management job description with an AI layer on top. Understanding stakeholder dynamics, designing capability building programs, facilitating behavioral change, working with resistance. The difference is the domain: you need to understand AI well enough to have credible conversations about it, and to help people figure out where it fits in their specific workflow.
That's a different kind of knowledge than being able to build an AI model. It's more about understanding what AI can and can't do in practical terms, how it changes the way people make decisions, what happens to team dynamics when a tool starts doing part of someone's job. These are questions that require a systems thinker who can also work on the behavioral level.
What this signals: The market is creating roles for people who can combine change expertise with AI understanding. If you're a change professional, you already have the foundation. The question is how to build the AI layer on top of it, credibly and practically, so you can step into this space with confidence.
Something to consider
There's an important nuance here. Some of these new roles are being filled by people from tech backgrounds who are learning the people side as they go. That works sometimes. But the complexity of organizational change, the systemic thinking, the ability to design interventions that actually shift behavior: that's hard to pick up on the fly. It takes years to develop.
Change professionals who add AI expertise to their existing skill set are probably better positioned than tech people who try to learn change management from scratch. The foundation matters. What you need is the confidence and the vocabulary to work in the AI adoption context, and a way to demonstrate that you understand the specific dynamics at play.
That's what we're building at CHNG: a path for experienced change professionals to develop that AI adoption layer in a structured, practical way.