Here’s something I keep running into: developers and architects having passionate debates about whether AI-assisted development is “real coding” while their enterprise clients quietly reorganize around AI as a given.

The disconnect is jarring. And if you’re on the consulting side of this equation, it should worry you.

The Debate That’s Already Over

I get it. The “vibe coding” conversation matters. Understanding when AI helps versus when it hallucinates garbage is a legitimate professional concern. Senior practitioners should care about code quality, architectural integrity, and maintainability. These aren’t trivial issues.

But here’s the thing: while we’re having that debate, enterprise IT leadership has moved on. They’re not asking “is AI-assisted development legitimate?” anymore. They’re asking “how fast can we roll out Copilot to everyone?” and “what’s our Claude Enterprise policy?” and “which teams are getting the best results?”

The philosophical debate is still raging in consulting circles. Inside your client organizations? That conversation ended six months ago.

What Enterprises Are Actually Doing

I spent 14 months as CTO at Telos, and during that time I had visibility into how multiple enterprise partners were approaching AI adoption. What I saw wasn’t cautious experimentation. It was systematic rollout.

One Fortune 500 we worked with had already deployed Copilot to 80% of their development organization. Not as a pilot. As standard tooling. Their internal metrics showed a 30-40% reduction in time-to-first-commit on new features. Were there quality concerns? Sure. They addressed them the same way they address any quality concern: code review, testing, and architectural oversight.

Another enterprise client had built an internal “AI Center of Excellence” specifically to accelerate adoption across business units. Not to study AI. To deploy it. They were running internal hackathons, building custom GPTs for specific workflows, and measuring productivity gains at the team level.

These weren’t bleeding-edge tech companies. They were a retailer and an insurance company. The “conservative” enterprises that consulting firms often assume will move slowly.

They’re not moving slowly.

The Uncomfortable Math

Here’s where this gets uncomfortable for anyone in professional services.

When an enterprise deploys AI tools internally and sees genuine productivity gains, they don’t just celebrate efficiency. They recalculate. How many contractors do we actually need next quarter? Can our internal team handle that integration project we were going to outsource? What if we kept that work in-house?

I’ve talked to PS firm leaders who are still attributing soft pipelines to “economic uncertainty” or “budget tightening.” And look, those factors exist. But they’re masking the deeper shift.

Your clients aren’t just being cautious with spending. They’re building internal capabilities that reduce their dependency on you. And they’re doing it faster than most consulting firms realize.

The “Wait and See” Translation

When a client says “we’re going to wait and see on that project,” there’s a decent chance what they actually mean is: “we’re going to see if our internal team can handle it with their new tools.”

When proposals come back asking for fewer people or shorter timelines, that’s not always budget pressure. Sometimes it’s a client who genuinely believes their augmented internal team can carry more of the load.

The firms that recognize this shift are having different conversations with clients. They’re asking about internal AI adoption. They’re understanding what tools are already deployed. They’re positioning around the gaps that AI tools can’t fill rather than the commodity work those tools are absorbing.

The firms that don’t recognize it? They’re optimizing proposals and wondering why close rates are declining.

What Enterprises Actually Need Now

This is where I’ll offer something constructive, because just diagnosing the problem isn’t helpful.

Enterprises that have deployed AI tools are discovering new bottlenecks. The code gets written faster, but what about the architecture decisions that determine whether that code is maintainable? The integration patterns that connect systems? The data pipelines that feed AI models? The MLOps infrastructure that keeps everything running in production?

Your clients’ developers can write features faster. That doesn’t mean they suddenly understand your client’s legacy integration landscape, or the regulatory constraints on their data, or the architectural patterns that will scale.

The consulting value proposition is shifting from “we provide skilled hands” to “we provide expertise your tools can’t replicate.” That’s a fundamentally different conversation. And right or wrong, it requires acknowledging that the old conversation is over.

The Bottom Line

The bottom line is this: while you’re debating whether AI is legitimate, your clients have already decided it is. They’re deploying tools, measuring results, and quietly recalculating how much external help they actually need.

This isn’t a prediction about what might happen. It’s a description of what’s already happening inside the organizations you serve. The only question is whether you’re aware of it.

I’d challenge you to ask one of your best client contacts a simple question: “What AI tools has your development team adopted in the last year, and what impact are you seeing?”

The answer might surprise you. Or it might confirm what you’ve already suspected but haven’t wanted to face.

Either way, you’ll know more than you did. And in a market that’s shifting this fast, knowing beats guessing.

John Doucette is the founder of The Disruption Brief, where he writes about the AI transformation reshaping IT professional services. With 34 years in the industry - from developer to CTO - he’s focused on helping PS firms navigate disruption before it’s too late. Connect with him on LinkedIn.