Organizational Friction Model of AI

Most companies think AI isn’t paying off because the tech isn’t good enough yet, but research on AI adoption increasingly shows the biggest barriers aren’t technical — they’re organizational.

I think about it in terms of these 4 layers:

1️⃣ Capability friction
“Our AI tools simply aren’t good enough yet.”
Example: Engineers focused on hallucinations / biases and what risks they pose.
**AI Transformation teams are currently fixated here!**

2️⃣ Workflow friction
“AI doesn’t fit into our existing processes.”
Example: Tools exist but people don’t know when or how to use them consistently.

3️⃣ Incentive friction
“There’s no clear expectation to adopt AI.”
Example: Workers pursue status quo delivery over AI adoption.

4️⃣ Identity friction
“What is my value if I can be replaced?”
Example: Experts resist tools that appear to undermine their expertise.

If teams are struggling to move from experimentation to real adoption, it may be worth looking beyond the technology itself. I'd be happy to compare notes!

Where do you see the biggest challenges when it comes to AI adoption in your team?

Sources:
https://arxiv.org/abs/2512.02048
https://thedecisionlab.com/reference-guide/management/organizational-barriers-to-ai-adoption
https://www.researchgate.net/publication/388661927_OVERCOMING_BARRIERS_TO_ARTIFICIAL_INTELLIGENCE_ADOPTION