In my research at Kyle & Co, I'm observing a trend in HR's AI transformation: Most organizations approach AI the same way they've approached every other software rollout. Find a use case. Procure a tool. Launch it. Hope it sticks.
Then wonder why adoption stalls six months in.
Here's the thing: It's not a technology problemâit's a people problem⊠And it always has been.
That's the central argument behind our latest ebook AI Transformation and Change Management, a new resource from the Human-Centric AI Council (HCAIC) and lead author Alicia Miller (a seasoned transformation leader with a background in I/O psychology and AI enablement). The guide is practical, grounded, and refreshingly honest about why AI adoption is harder than most change playbooks account for.
You can access it as part of our full transformation toolkit!
AI Is Fundamentally Different. Your Change Management Strategy Needs to Be Too
Traditional software came with a manual. You trained people on the steps, they produced consistent outputs, and you moved on.
AI doesn't work like that. The outputs are probabilisticâthey vary by user, by context, by how the question is asked, etc.
The models keep learning and changing, which means yesterday's best practice can be tomorrow's outdated prompt. And then there's the "black box" problem... when employees can't explain why the system said what it said, trust erodes fast.
That's before you get to the harder stuffâthe fear that AI is coming for jobs, the blurring of what "my role" even means anymore, the sheer volume of models and tools competing for attention.
The guide names these dynamics directly, because you can't design around a problem you haven't acknowledged.
The COM Model: A Change Framework YSK (You Should Know)
One of the most useful things in the guide is what Alicia calls the COM Model: Capability, Opportunity, and Motivation. It's a behavioral change lens applied to AI adoption, and it reframes where organizations typically get stuck.
Capability is the one most teams focus on (training programs, role-based learning paths, L&D roadmaps, etc.). That work mattersâbut it's not enough on its own.
Opportunity is about creating the actual space and conditions for people to learn and experiment. Even when employees want to engage with AI, they often don't have the time, the sandboxes, or the explicit permission to explore. The most experienced people (i.e. the ones whose judgment you most need) are also the busiest.
Without protected experimentation time and real access to tools... adoption stagnates.
Motivation is where the real complexity lives. People need to understand what's in it for themânot abstractly, but concretely. They need fears addressed directly, not managed around. And they need to see it working before they'll fully commit.
Skeptics don't respond to vision statements. They respond to evidence.
Surprise! Culture Is the Hidden Variable in Change Management
The ebook spends meaningful time on something most rollouts underinvest in: Cultural readiness.
AI-enabling cultures share specific traits: a tolerance for experimentation (including failed experiments), continuous learning norms, psychological safety, and genuine knowledge sharing.
That last one is interesting. Because AI outputs vary based on how you work with them, the people who've figured out what works have an edge... and organizations need them to share it, not hoard it.
If your culture punishes failure, people won't experiment. If it rewards individual performance over team contribution, knowledge won't flow. No training program fixes that.
From Pilot to Scale: Where Most Organizations Actually Get Stuck
One of my favorite parts of the ebook was the section on pilots. It covers something Iâve been thinking about a lot lately, and I think itâs particularly useful for HR teams being asked to prove ROI before getting broader investment (aka all of us).
A good pilot tests more than technical feasibility. It surfaces adoption barriers. It identifies what roles will shift and what capabilities are missing. It gives you the change management intelligence you need to scale responsiblyânot just the business case your CFO wants to see.
The guide walks through how to structure POCs and pilots to actually evaluate business value... including approaches like randomized control trials and quasi-experimental designs.
Suffice to say, this is not typical change management content. It reflects Alicia's background and the HCAIC's commitment to evidence-based practice. Iâm really proud of it!
A Resource Especially Relevant for the In Good Company Community
I think itâs super cool that our friends at HiBob built this community for people-first leaders who believe that how you treat employees is how you build companies worth working for. It's validating for me and for our work with the HCAIC, as itâs exactly the lens this ebook brings to AI.
The goal isn't to make your workforce "use AI." The goal is to help your organization evolve into new ways of working where AI is embedded, outcomes improve, and people remain at the center of how value is created.
That's not a soft position. It's a strategic one. And it requires HR to lead change, not just support it. The good news is that you donât have to go it aloneâbecause youâre in good company!