Pranay.
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2 min read

Identity Drift: Why AI Transformation Stalls

The most dangerous resistance to AI isn't loud. It's polite. And you probably won't even notice it happening.

Most leaders expect pushback. Debates. Objections. Strong opinions. That's what they prepare for. But that's not what shows up.

Let me describe how it plays out, and what we can learn in the process.

Initially, the engineers move with full enthu. Everyone starts playing with the Copilots:

  • Developers have AI-generated code
  • Some QA teams start AI-generated tests
  • Internal tools popping up across teams

Messy. But alive! On paper, the transformation is working.

In reality, the roadmap is still moving slowly! Senior engineers aren't resisting. They are explaining.

"Doesn't quite work for us."

"3GPP specs are too complex."

All valid. But also unusual! Because good engineers don't stop at why something won't work. They usually come back with how to make it work.

But when they don't:

  • Conversations get longer
  • Quality debates become… philosophical

Nothing breaks. But nothing moves faster either. You can hear some engineer say this almost as an aside: "I don't know what my role becomes here."

That's the moment!

This is what we miss addressing at the beginning of the transformation: Identity Drift.

When the system changes… and people don't know who they are in it anymore. They adapt - not by resisting. But by creating distance. Carefully. Intelligently. Professionally. In ways that slow everything down without ever saying "no."

So here's the uncomfortable truth: AI transformation doesn't fail at the tooling layer. It fails at the identity layer.

If you don't address this and redesign what good engineering looks like, what gets valued, and what roles evolve into… You don't get transformation. You get hesitation. At scale.

So before asking: "Why isn't adoption higher?" Try asking: "Do they know who they become if this works?"

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