You're Building a Feature Factory, Not an AI Practice

"We implement AI tools." "We build AI solutions." "We help companies adopt AI."

I hear some version of this every week from agencies and consultancies trying to spin up an AI services practice. And every time, I think the same thing: that's not a practice. That's commoditized labor competing on price.

There's a massive difference between selling AI services and building an AI practice. One is a line item. The other is a business.

The Feature Factory Trap

A feature factory takes orders. Client says "we need a chatbot," you build a chatbot. Client says "we need document processing," you wire up an LLM to parse PDFs. You're a pair of hands with an OpenAI API key.

The problem isn't that the work is bad. It's that anyone can do it. And when anyone can do it, you compete on price. Gartner estimates 40% of consulting tasks are now automatable. The generic AI implementation work you're selling today will be even more commoditized tomorrow.

You can see it in the pricing already. AI implementation rates are getting squeezed. Clients are comparing your proposal against offshore teams, against SaaS tools that do it out of the box, against their own developers who watched a YouTube tutorial. That's not a market position. That's a race to the bottom.

What a Real Practice Looks Like

A real AI services practice has three things a feature factory doesn't:

A specific problem it solves. Not "AI implementation" but something like: "We help mid-market SaaS companies automate their customer onboarding workflow using AI agents." Or: "We build AI-powered knowledge bases for professional services firms with complex compliance requirements." The narrower, the better. Specificity is what makes you referable.

A methodology that repeats. If every engagement starts from scratch, you don't have a practice. You have a freelance gig. A real practice has a process: discovery, assessment, architecture, build, measure. Each engagement makes the methodology better. Each project teaches you something that makes the next one faster and more reliable.

People who want to work on that problem. This one gets overlooked. The best practices attract talent because the work is interesting and deep. When your team is solving the same category of problem repeatedly, they develop genuine expertise. They publish insights. They speak at conferences. They become the people clients want to hire. That flywheel doesn't spin when you're a generalist shop taking whatever AI work comes through the door.

The Numbers Back This Up

The consulting industry is already splitting along this line. Specialist consultants now command 30-40% fee premiums over generalists. Specialist hiring is up 20-35%, with forecasts suggesting 60% growth over five years. Meanwhile, generalist roles are projected to decline 10-25%.

The big firms see it too. McKinsey has deployed roughly 12,000 AI agents internally to support consultants and enable leaner project teams. Accenture merged five units into a new "reinvention services" line focused on AI-driven operational overhauls. They're not selling generic AI help. They're building specialized machines.

And here's the stat that should worry every generalist shop: by September 2025, client RFPs increasingly requested domain-specific AI experience rather than general AI knowledge. The buyers are already telling you what they want. They want specialists.

The Gap We Saw

At Last Rev, we didn't set out to be an "AI services company." We looked at the market and saw a specific gap: companies are being told they need AI, but they have no idea how to actually do it.

They've bought the wrong tool. They don't know what to build first. They don't have the team. They've been burned by a pilot that went nowhere. They've got a board asking questions they can't answer.

So that's what we solve. We help them think through all of it... what's worth building, what's not, what team they need, what the realistic ROI looks like... and then we implement in a way that sticks. We start with a structured readiness assessment, not a sales pitch. We build a phased roadmap, not a moonshot. And we measure outcomes, not just deliverables.

That's a practice. It has a methodology. It repeats. It attracts people who care about solving that specific problem. And it builds a business because the problem we're solving is specific enough to be valuable but broad enough to be a real operating model.

The Question You Need to Answer

If you're thinking about an AI services play, here's the honest question: do you have a specific problem you're solving, or are you just trying to sell AI services in general?

Because "we do AI" is going to get harder to sell every quarter. The tools are getting easier. The templates are getting better. The offshore teams are getting faster. If your value proposition is "we can implement AI," you're already losing.

But if your value proposition is "we solve this specific problem better than anyone else, and here's the methodology to prove it"... that's a practice. That has a business. That has a future. The AI services opportunity in 2026 is enormous, but only for firms that pick a lane.

Pick your problem. Build your methodology. Hire people who care about it. Everything else follows.