The pitch from every major consultancy right now: "We've deployed AI across our practice. Our teams are more productive than ever."
Translation: we're billing the same rates to do mediocre work faster.
The math doesn't work for clients
Traditional consultancies built their entire business around headcount. More bodies, more billing. Partner compensation, office footprint, recruiting pipelines—everything scales on the assumption that revenue equals hours times rate.
Now they've handed their associates AI tools. The associates are faster. But the firms aren't cutting rates or shrinking teams. They're pocketing the margin and calling it "transformation."
The client still gets the same deliverable. The same 80-slide deck. The same "digital roadmap" that sits in SharePoint until everyone forgets about it. It just arrives sooner, which means the consultants can move on to the next engagement sooner, which means more revenue for the firm.
None of that is savings for you.
Ultra-specialization is the trap
Here's what the big firms won't tell you: their teams are structured wrong for AI to actually help.
They've spent decades building practices around ultra-specialization. You've got your data engineers, your ML engineers, your DevOps people, your architects, your project managers, your business analysts, your change management consultants. Each one owns a narrow slice of the work. Each one bills separately.
Give each of those specialists an AI copilot and what happens? Each one does their narrow slice 30% faster. But the handoffs are still there. The coordination overhead is still there. The meetings to align the data team with the DevOps team with the business analysts—still there.
You've made an expensive, slow process slightly less slow. Congratulations.
What the difference actually looks like
Here's a typical "AI-powered" enterprise engagement: 12-person team, 6-month timeline, $1.8M budget. Discovery phase, architecture phase, build phase, testing phase, change management phase. Weekly status calls. Monthly steering committees. A deployment date that slips twice.
Here's a Proof Sprint: 3 engineers, 10 days, fixed price. No phases—just building. You get a production system with telemetry, documentation, and a handoff your team can actually maintain. If it doesn't work, you've lost ten days, not two quarters.
The traditional model isn't just slower. It's structured to be slow. Each handoff is a billing opportunity. Each phase transition is a change order. The complexity isn't accidental—it's the product.
You have to rebuild from the ground up
Real savings don't come from accelerating the existing assembly line. They come from questioning why you have an assembly line at all.
We don't staff projects with specialists who hand off to other specialists. We run small teams of engineers who own the entire delivery—from data pipeline to deployed product to observability. AI doesn't make each role faster; it makes roles unnecessary.
When one person can do what used to require three, you don't need coordination meetings. You don't need a project manager to track dependencies between teams. You don't need a business analyst to translate requirements into tickets that get translated again into code.
You just build the thing.
The safer bet
We know why enterprises pick the big firms: cover. Nobody gets fired for hiring McKinsey. If the project fails, at least you chose a name everyone recognizes.
But that calculus is changing. A $2M engagement that delivers a roadmap and a reorg recommendation isn't safe—it's a way to spend two years not solving the problem. The risk isn't picking the wrong vendor. It's spending 18 months in "discovery" while your competitor ships.
A 10-day Proof Sprint is lower risk precisely because it's smaller. You see working software before you've committed to a multi-year contract. If we're wrong about feasibility, you find out in two weeks, not two quarters. The scope is capped. The outcome is binary: it works or it doesn't.
That's not reckless. That's how you actually derisk delivery.
Why they can't copy this
We're not worried about publishing this. The big firms can't fix their model even if they wanted to.
Their economics depend on headcount. Cut the team size by 60% and you've cut revenue by 60%. Partners don't vote for that. Shareholders don't vote for that. The whole incentive structure points toward more people, not fewer.
They'll keep talking about AI transformation while protecting the billing model that AI should have made obsolete. They'll release case studies about "efficiency gains" that somehow never show up in client invoices.
Meanwhile, we'll keep shipping production systems in ten days with three-person teams. The math is simple. The work speaks for itself.
If your current vendor is charging you the same rates they charged five years ago and calling it AI-powered delivery, ask them where the savings went. You already know the answer.