You’ve funded three AI POCs. None of them made it to production. Sound familiar?

I’m not guessing. I’ve lived it from the inside. And here’s what I can tell you: the problem isn’t the technology. It’s not your data. It’s not even your team’s capability. The problem is the model you’re using to get AI projects done.

The traditional consulting engagement wasn’t built for this. And until that changes, you’re going to keep watching promising pilots die on the vine.

The Use Case Graveyard

Let me tell you about a project I worked on recently. A client came to us with twenty use cases. Twenty. They’d done their homework, identified opportunities across the business, and were ready to move. This wasn’t a company dipping their toe in the water. This was serious commitment.

So we did what any good consulting team would do. We analyzed the list, mapped the revenue opportunities, assessed the technical complexity, and prioritized ruthlessly. We found the sweet spot: high impact, minimal dependencies, contained enough to actually ship something.

The solution was looking good. The data science work was solid. We had experience from similar engagements, so the path forward was clear.

And then we hit the wall.

The department sponsoring this work had pull, but not enough pull. They couldn’t command changes to the broader data infrastructure. They weren’t in a position to make IT work with them on their timeline. The production path required crossing organizational boundaries that our engagement simply didn’t have the authority to cross.

So we put that first use case on the shelf. Started working on the second one. Found a way to stay small enough to be contained within the department’s sphere of control.

Right or wrong, that’s how traditional consulting engagements work. You operate within the boundaries you’re given. You make progress where you can. You avoid the fights you can’t win.

But here’s the thing: that approach doesn’t get AI to production at scale. It gets you a graveyard of promising POCs.

Why Staff Augmentation Doesn’t Fix This

When enterprises hit these walls, the instinct is often to bring in more people. “We need a bigger team.” “We need specialized AI talent.” “We need consultants who understand our tech stack.”

IMHO, that’s exactly the wrong response.

The bottleneck isn’t headcount. It’s not even expertise. The bottleneck is organizational: the intersection of technology decisions, data governance, infrastructure access, and executive alignment that determines whether an AI initiative lives or dies.

Staff augmentation gives you more hands. What you actually need is more authority.

Think about it. When you bring in a traditional consulting team, you’re essentially renting skilled labor. They report to your project manager. They work within your org structure. They have exactly as much power as you delegate to them, which usually isn’t much.

That model worked fine when the job was “build this application to these specs.” The boundaries were clear. The integration points were defined. The politics were someone else’s problem.

AI projects aren’t like that. AI projects touch data from multiple systems. They require infrastructure changes. They need buy-in from stakeholders who don’t report to the same VP. They cross the boundaries that traditional consulting engagements are specifically designed to stay inside.

So you end up with talented people, doing good work, building solutions that never make it to production because nobody gave them the keys to the kingdom.

The Gap Nobody Wants to Talk About

Here’s what I’ve seen happen over and over: the POC succeeds in isolation, then fails in integration.

The data science team builds something impressive. The demo looks great. Leadership gets excited. And then someone asks: “Okay, how do we operationalize this?”

That’s when the trouble starts.

Who owns the ML Ops pipeline? Does one even exist? What about data quality monitoring? Model retraining schedules? Integration with existing systems? Who’s responsible when the model starts drifting and predictions go sideways?

Traditional consulting engagements aren’t structured to answer those questions. The statement of work says “deliver POC.” So that’s what gets delivered. The hard work of making it real? That’s someone else’s problem.

Except it isn’t someone else’s problem. It’s your problem. And your current consulting partners aren’t set up to solve it.

What Actually Needs to Happen

The enterprises I’ve seen successfully move AI from pilot to production share a few things in common. It’s not about finding better consultants or hiring more data scientists. It’s about fundamentally restructuring how external teams engage with the organization.

The teams that succeed have direct lines to executive decision-makers. Not “we’ll escalate if there’s an issue.” Direct access. The ability to get a decision in days, not months.

They have authority that crosses departmental boundaries. When they identify a data infrastructure blocker, they can actually do something about it. When they need IT to prioritize an integration, they have the standing to make that request stick.

They’re measured on production outcomes, not deliverables. Not “did you build the POC?” but “is this thing running in production and generating value?”

The model isn’t “tell us what you need, we’ll send you people.” The model is “let us help you figure out what’s possible, and then let’s make it real together.”

The Consulting Firm Opportunity

Here’s why I’m writing this on The Disruption Brief, not just for enterprises: the consulting firm that figures out this model will own the next decade.

Right now, most PS firms are still selling the old way. Staff aug. Time and materials. Deliverables-based SOWs. They’re optimized for a world where the job was building applications to spec.

But the job has changed. Enterprises don’t need more developers. They need partners who can navigate the organizational complexity of getting AI into production. Partners with the credibility and the engagement model to actually move the needle.

There’s a name for what I’m describing. It’s called Forward Deployed Engineering. And it requires a completely different approach to how consulting engagements are structured, staffed, and measured.

I’ll write more about what that looks like in practice. But the bottom line is this: if you’re an enterprise stuck in pilot purgatory, the problem isn’t your technology or your team. It’s the model. And if you’re a consulting firm watching your clients struggle to operationalize AI, the opportunity is right in front of you.

The old model was: tell us what you need, we’ll send you people.

The new model is: let us help you figure out what’s possible. And then let’s actually make it happen.

Which model are you buying? Which model are you selling?

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.