The Role Your AI Strategy Needs, and How to Get the Outcome Without the Title
Your AI investment is not stalled because the technology is broken. It is stalled because no one inside your business has the time, the authority, and the cross-functional view to adapt the technology to the way your business actually runs. That gap is exactly what a Forward Deployed Engineer fills.
The Forward Deployed Engineer (FDE) role is reshaping how AI gets implemented at the largest companies in the world. Palantir built the role in the early 2010s. OpenAI, Anthropic, Salesforce, Stripe, and a long list of AI-first companies now hire FDEs to embed inside customer environments and make the technology produce real results. Job postings for the role grew more than 800% in 2025.
The problem for most growing businesses is simple. You cannot hire one.
What a Forward Deployed Engineer Actually Does
An FDE works inside the customer’s environment, not from a distance. The engineer writes code, untangles data, adapts workflows, and translates between leadership and technical teams. The role exists because enterprise software, and AI in particular, rarely fits a real business environment without significant adaptation.
A typical FDE engagement looks like this. The engineer embeds with the customer’s team for weeks or months. They map the actual workflow. They prototype a solution inside the customer’s systems. They surface edge cases that no discovery call would have caught. Then they build the version that works in production.
The output is a working system that moves the metric the business cares about.
Why Mid-Market Companies Cannot Hire a Forward Deployed Engineer
The FDE model is built for 8-figure contracts. Most FDEs at OpenAI, Palantir, and Anthropic earn total compensation between $300K and $700K. They are deployed at enterprise accounts where the contract value justifies the engineering investment.
Companies between $5M and $50M in revenue feel the same need. AI is not producing the results the sales pitch promised. The team is busy. The tools do not talk to each other. The customer experience suffers in places that are hard to measure. The math, however, does not support a $400K hire, and these companies are not the size of account that an OpenAI FDE will fly in for.
The result is a gap. Growing businesses see what the technology can do. They cannot find anyone close enough to their business to actually make it work. That is the exact gap the Operations Optimization Advisory was built to close.
What the FDE Outcome Actually Looks Like
Strip away the title and the salary range, and an FDE delivers four things:
- A diagnosis of where AI, automation, and systems actually fit inside the business.
- An adaptation of those tools to the workflows that already exist.
- A translation layer between leadership, internal teams, and outside builders.
- A vendor-neutral perspective that is not selling a specific platform.
Those four outcomes are what growing businesses need. The title matters less than the outcome.
The Operator-First Version of the FDE Role
The FDE concept assumes the engineer enters the customer through the technology. Most growing businesses do not need that entry point. They need someone who enters through the business first, then prescribes the technology where it fits.
That is the role I built the Operations Optimization Advisory to fill. The work happens the way an FDE engagement happens, with one important difference. The entry point is operational diagnosis, and AI is treated as one of several possible answers, not the question.
Three things make the model work:
Operator experience. Twenty years inside Marketing and Sales Operations across companies of every size. The patterns of operational waste repeat across industries, which is why the diagnostic translates from one business to the next.
An active AI practice. I run AI inside Content Monsta, my own services business, and use it to deliver real client work. The recommendations made inside the advisory are tested in production before they show up in a deliverable.
A vendor-neutral position. No software resale, no hidden referral structure. The role exists to help you make the right call, which is only honest if there is no financial reason to steer you toward a specific tool.
See a full background on this approach here.
What an FDE-Style Engagement Looks Like Without Hiring an FDE
The Diagnostic Pilot mirrors the structure of an FDE engagement. It starts with a paid diagnostic. The work happens inside your real operating environment, not at a distance. The deliverables are built so your team can act on them, with or without further help.
A typical pilot runs three to six weeks. It produces a prioritized list of operational drag points with estimated dollar impact, a fix-first recommendation, a 30-60-90 day execution plan, and a decision matrix that classifies each issue as fix, automate, delegate, outsource, or stop.
The decision matrix is the operator-first equivalent of what an FDE produces at the end of a deployment. It tells you what should be built, what should not, and what should never have been a software problem in the first place. That protects the business from the most expensive mistake in AI adoption, which is building the wrong thing well.
What Happens After the Diagnostic
An FDE engagement at OpenAI ends when the customer’s AI is operationalized. The advisory works the same way, with four paths after the pilot.
You can take the plan and run it with your internal team. You can engage me to oversee the partners who build the recommended fixes. You can bring me on as a fractional operations advisor across AI, automation, and systems decisions. You can hold the plan and act when the timing is right.
The path is decided after the pilot, not before. Nothing is forced and nothing is bundled. The full breakdown of options is on the Engagement Options page.
Why This Matters for Companies Operationalizing AI
Most AI initiatives stall in the same place. The pilot succeeds inside a controlled environment. The rollout fails when it meets the real workflow, the real data, and the real team. The pattern is so common that AI-first companies built an entire role to solve it.
The lesson from how Palantir, OpenAI, and Anthropic deploy AI is worth repeating. Software does not finish in the codebase. It finishes inside the customer’s operation. That is why these companies put their best engineers in the same room as the customer.
A $5M to $50M business does not need a 700K hire to apply that lesson. It needs someone who can sit close enough to the business to do the work an FDE would do, at a scope and a cost that fits a growing company.
To sum things up: the FDE role exists because the gap between AI capability and AI in production is wider than most companies expect. You can fill that gap with a $400K hire from OpenAI. You can also fill it with an operator-first advisor who has spent twenty years inside the seat where the gap lives.
The Fit Call Is the Entry Point
Thirty minutes to confirm whether the advisory is the right move for your business. If it is, the pilot gets scoped on the call. If it is not, you will leave the conversation with a clearer view of what is.
