Forward Deployed Executives: The Next Billion-Dollar AI Unblock

I’ll start and end this article with two recent stories (names changed to protect the awesome).

Story 1: Two Weeks vs Ten Minutes

Ben is a non-technical colleague that I respect, and I was livid that he was being taken advantage of.

His affordable, ~$30/hour dev was quoting him two weeks to deliver a feature that (I kid you not) was basically a single API query and exporting it into an HTML table.

I sat dumbfounded. I called bullshit.

This was May 2026, 6 months after the “holy shit” moment when Opus 4.5 turned the coding world upside down.

I asked Ben to let me screen-share while I fired up Claude Code. I then verbally brain-dumped a prompt.

“Make a dummy database from industry X. Then create a single HTML page. Then have that page call said database and convert it into an HTML table.”

We went back to talking. And somewhere between two and ten minutes later, the demo loaded on screen.

I turned to Ben. “So you’re telling me your developer with Claude Code is going to take two weeks to do that?”

This lunch meeting was the turning point for how his startup operated. Within days, they hired a new, AI-first developer to upskill the team. Within weeks, a client demo deadline that felt impossible was doable.

It took a technical, Forward Deployed Executive (FDX) to get the Ben over the hump.

Why I Hated the “Forward Deployed Engineer” Title

It’s ok to admit when you were wrong, and I was dead wrong.

The first time I heard about “forward deployed engineers” (FDEs), I thought it was yet another stupid marketing trick.

Isn’t this just software services?

Isn’t this just staff augmentation?

But it was fucking genius, because it solved a problem as old as time.

The Gap

In The Success Principles, mega-author Jack Canfield repeats the mantra of “closing the gap between where you are and where you want to be.”

The self-help, professional consulting, and executive coaching industries are built around this premise of gap closing.

And this gap is MASSIVE in the AI space right now. A few research examples (thank you, Perplexity!):

• 95% of enterprise GenAI pilots produced no measurable return. MIT’s Project NANDA looked at 300+ deployments, 52 case studies, and 153 leadership surveys; only 5% generated real financial value.
• 56% of CEOs saw neither higher revenue nor lower costs from AI in the past 12 months. Only 12% got both. That’s PwC asking 4,454 CEOs across 95 countries.
• 72% of CEOs now call themselves the primary AI decision-maker at their company, roughly double a year ago. Nearly all of them plan to keep funding AI whether or not they can prove it works.

Companies WANT to be at the bleeding edge, and yet most are feeling further and further behind… and their attempts at catching up just aren’t working.

Closing The Gap with FDEs

Palantir recognized the AI gap early. They knew the fastest way to close the gap, get customers to a win, and become indispensable for their clients was to embed the very same elite engineers that built the tech alongside their customers’ teams. Even better, these FDEs would keep identifying more opportunities to expand and grow the accounts, creating a virtuous loop.

So they coined a new job title and went all in on this approach.

I would argue that while the technique wasn’t new (it’s basically staff aug), the mindset was very different.

So how did FDE’s solve this?

Buying is Easy; Actualizing is Hard

Enterprise companies know that change is hard.

This is why sales cycles are typically 18-24 months, and most will not bring on more than 1-2 new tools per cycle.

You have to deal with things like…

• Data migrations
• Tooling integrations
• Code refactors
• Hiring new people
• Training existing people
• Capacity management
• Comms (internal, external)
• Coordination (internal, external)
• Board politics
• C-suite buy-in
• Budgets
• Mercury Retrograde (just checking if you’re still reading)

Oh, and ultimately achieve the goal or ROI you set out to achieve. Otherwise, WTF did you start this in the first place?!?

With this laundry list of challenges and headwinds, it’s no wonder why there’s a >90% failure rate for enterprise AI initiatives.

Add to that that the fact that the world is changing more rapidly than ever, sometimes on a quarterly, monthly, or weekly basis.

But all hope is not lost.

Solutions! Embedding Engineers AND Executives

The goal is to close the gap, but how?

FDEs were a critical FIRST part of that solution. They brought…

• Proximity. They sit inside the customer’s team and see the actual workflow instead of the sanitized version that showed up in the RFP.
• Speed. Something working in weeks (or days… or hours) rather than quarters, which breaks the pilot purgatory that produced that 95% number.
• Ownership of the outcome. They’re measured on whether the customer wins, not on closing the sale or watching usage metrics through a dashboard.

But IMHO, that wasn’t enough. Yes, this addressed the technical gaps. These SEAL Team Six-type engineers can largely address business objectives, but they’re still often too far away from the key decision makers that are critical for decisions and culture.

Two citations that emphasize this.

1. Dan Shipper of Every once said one of the strongest leading indicators of AI adoption is whether a CEO is an avid user of AI (and usually sharing the wins to the team). I’ve witnessed this juxtaposition personally.
2. PwC came at the same idea from the other direction. They found that CEOs whose organizations built strong AI foundations are 3x more likely to report meaningful financial returns. The difference between the 12% who win and the 56% who don’t comes down to foundations and embedding.

The last part is critical. You need an AI foundation, which requires AI champions at the top. And since companies can’t just rapidly hire and replace the entire C-suite in a month, they need outside help… they need a Forward Deployed Executives to help level them all up.

What exactly does an FDX look like? More on that in a minute.

Remember the story of Ben? He didn’t even know he needed an FDE-like resource! He first needed an FDX, an AI-first founder type that could identify the problem and get him over the mental/emotional hurdles that prevented him from making it a priority to hire an AI-first engineer.

The AI Bottleneck is Humans

The smart folks at Anthropic (I’m looking at you, Dario) told us that AI was going to start replacing 50% of white-collar work starting by 2030.

Whoops. It doesn’t look like it’s happening… yet.

We may get their eventually, but right now the bottleneck isn’t the models, the products, the agents, or the token costs.

The gap is how fast humans can successfully absorb AI practices, products, and methods into their companies.

And that’s just the mechanics. There’s also the psychology. Here’s a direct quote from an executive I work with.

“You’re just my security blanket, so I want you here.”

AI is scary and risky. Even if someone hands you a perfect answer, they may not take it unless they trust you.

Why? The rate of change and the potential risks of AI mistakes are so high that they create so much fear, uncertainty, and doubt (FUD). It’s so massive these days that even seasoned executives who have weathered the 2008 market crash and the 2020 COVID lockdowns are affected.

Hell, there’s an executive that I’ve heard about through a friend of a friend. He’s been suffering from regular panic attacks, even throwing up multiple times a week because he’s so afraid of falling behind. I’ve heard others in my direct network say they feel the same pressure.

These execs don’t need another LinkedIn article with 5 new tips. They need a trusted confident to describe their challenges and, most importantly, help them close the gap.

They need someone with both technical AND leadership experience that can help them navigate this chaos and complexity.

What Makes a Good FDX

History doesn’t repeat, but it does rhyme.

A decade ago, one of the hottest hires on the market was a “solutions architect.” This mythical hire had skills and experiences across 3 key areas.

• Technical (i.e. deep understanding of architecture, best…