The Rise of the Forward Deployed Engineer
Why 95% of AI pilots fail and what the winning 5% do differently.
The numbers don't lie, but they do hurt.
According to S&P Global Market Intelligence's 2025 survey of over 1,000 enterprises across North America and Europe, 42% of companies abandoned most of their AI initiatives before they reached production. That's up from just 17% the previous year. The average organization scrapped 46% of AI proof-of-concepts before they ever saw daylight.
RAND Corporation's analysis puts it even more bluntly: over 80% of AI projects fail, double the failure rate of non-AI technology projects.
The pattern repeats itself everywhere. MIT's research found that 95% of generative AI pilots at companies are failing to deliver meaningful revenue impact. IDC reports that 88% of AI proof-of-concepts never transition into production. These aren't rounding errors. They're a systemic breakdown in how enterprise AI gets implemented.
Here's what's strange: the technology works. The models are getting better every quarter. The platforms are maturing. Yet companies keep getting stuck in what I call pilot hell, a purgatory where promising demos turn into abandoned Slack channels and awkward quarterly business reviews where nobody wants to talk about "that AI thing we tried last year."
The winners, though, are doing something fundamentally different. They're not sending better slide decks or running longer discovery calls. They're embedding engineers directly inside business-facing organizations. Not consultants who architect and disappear. Not sales engineers who demo and hand off. Real builders who move in, build agents, untangle data disasters, navigate political minefields, and don't leave until the agent actually works.
This is the Forward Deployed Engineer. And it's becoming the critical capability that separates enterprises that actually deploy AI at scale from those stuck running perpetual pilots.
Why Traditional Implementation Dies on Contact
The old model worked fine when you were buying software that lived in a box. Purchase the licenses. Send your team to a training session. Maybe bring in a consultant for a few weeks. Hand it off to IT and move on to the next project.
AI breaks that model completely.
First, there's the data problem. Every demo runs on clean, normalized, beautifully labeled data that somebody spent three months preparing.
Enterprise data is a dumpster fire.
It doesn’t matter how many naming convention conversations you have been through. There are always fields that should be timestamps and are stored as strings. Product SKUs or reference numbers that should match across systems but don't.
Critical information lives in PDFs, Excel macros, and somebody's brain who retired last Spring.
You can't fix this from a conference room in your headquarters. You need someone on-site who can look at the actual databases, talk to the actual humans who use them, and build the actual pipelines to make them usable.
Second, there's the integration conundrum.
Your AI platform needs to talk to your CRM, which was customized by a vendor that went bankrupt in 2018. It needs to pull from your data warehouse, which runs on a version of Snowflake from 2019 that nobody's allowed to upgrade because it might break finance reporting. It needs to trigger workflows in Salesforce, where half the fields are named things like "Custom_Field_237" and nobody remembers what they do.
This isn't a technical specification problem. It's archaeology.
Third, and this kills more projects than anything else, there's organizational resistance.
The people whose jobs might change don't trust the AI. The IT team doesn't trust the vendor's security model. The compliance team doesn't trust how the platform handles data. The VP who sponsored the pilot just left for another company.
You're not going to overcome this with a better PowerPoint. You need someone who can sit in their meetings, understand their concerns, build trust over weeks and months, and prove value one workflow at a time.
Remote implementation teams can't do this. They aren’t part of the hallway conversations where someone mentions that "the sales team will never use this because it doesn't integrate with the tool they actually use."
They're not in the 4 PM emergency meeting when the head of a department suddenly needs to see how the AI will affect headcount planning. They're not around when the intern mentions that all the good data is actually in a different system that nobody told you about.
What Makes FDEs Different From Everyone Else
Sales engineers show up for the demo. They're good at it. They know the platform inside and out and can make it dance. But once the contract is signed or the project is green lit, they're gone. Their incentive is to sell a vision and close deals, not to make implementations succeed.
Contractors show up for the project. They write requirements documents and system architecture diagrams. They might even build something. But they’re billing by the hour, and their incentive is to extend the engagement, not to transfer knowledge and get out.
If they do decide to build something, they're usually stitching together legacy systems and patchwork integrations to create a custom solution that technically works but becomes a maintenance nightmare. When they leave, you've got something fragile that nobody internal understands how to fix when it breaks.
Which leads me to IT.
Your internal IT team is stretched thin. They’re managing existing systems, handling security patches, dealing with infrastructure issues, and trying to keep the lights on.
They don’t have bandwidth to become experts in a new AI platform while also maintaining everything else. They want to help, but they’re juggling ten other priorities that all feel equally urgent.
Your business analysts understand the processes that need automation, but they can’t write the code to make it happen. They can document requirements and validate outputs, but they’re stuck waiting for someone technical to build the actual implementation. The gap between “here’s what we need” and “here’s working software” never closes.
FDEs are something else entirely. They combine the technical depth of a product engineer with the customer empathy of a consultant and the urgency of a sales engineer whose commission depends on the customer going live.
They're embedded long enough to understand the actual problem, not the one described in the RFP. They can build production agents and workflows, but they're also willing to spend a Tuesday afternoon sitting with the operations team to understand why they don't trust the AI's recommendations.
Palantir pioneered this model in the early 2010s when they realized that enterprise customers, especially governments and massive corporations, didn't need more features. They needed engineers who could make the features work inside fragmented data systems, legacy workflows, and high-stakes operational constraints.
So Palantir embedded engineers, called “Deltas” back then, directly inside customer teams. Not as consultants, but as builders who could write code, untangle data pipelines, adapt workflows, and uncover constraints that no discovery call ever reveals.
The role looks like a startup CTO job.
You work in small teams and own end-to-end execution of high-stakes projects. One day you’re building workflows at scale. The next day you’re in a conference room explaining to a skeptical operations manager why the AI won’t accidentally order 10,000 extra widgets. The day after that you’re debugging why the data pipeline failed at 3 AM.
Here's the key difference: FDEs improve the product.
Traditional consultants build around the product. FDEs see what's actually broken in the field and feed that back to product teams. They build custom extensions when necessary, but they're building on the platform, making it better for the next team who is scheduled to participate in a project kick-off meeting next week.
The Dirty Work Nobody Talks About
Let's get specific about what FDEs actually do when they're embedded inside a business organization. This is the work that doesn't make it into the case studies.
They fix the data.
Not "data engineering" in the abstract sense. They're in the database at 7 AM because the batch process failed overnight and the business unit’s daily dashboard is blank.
They're writing Python scripts to backfill missing records because somebody's Excel export dropped 3,000 rows and nobody noticed for six months.
They're building validation rules to catch when a product code gets entered as "N/A" instead of null because that's how the legacy system handles missing values.
They navigate the politics.
The VP of Sales wants the AI to prioritize leads differently than the VP of Marketing. The engineering team is terrified the AI will make them look bad if it suggests technical solutions they didn't think of. The legal and security teams needs proof that the AI won't expose customer PII before they'll sign off on anything.
The FDE becomes a translator, a mediator, and occasionally a diplomat. They learn when to push and when to wait. They figure out who the real decision-maker is, which is never the person with "decision-maker" in their title.
They build trust through reliability.
When something breaks, and something always breaks, the FDE is the person who picks up the phone. They're the one who says "I'll figure it out" and then actually does. They build credibility not through presentations but through showing up every time things go sideways.
This work is exhausting. It's why FDE roles have high turnover at companies that don't support them properly.
You're context-switching constantly between deep technical work, business team hand-holding, internal escalations, and putting out fires. You're working too much. You're on calls across time zones. You're personally responsible for whether a multi-million-dollar project turns into a case study or a cautionary tale.
But it's also why FDEs become the most valuable people in an AI-fluent company. They know what actually matters to the business teams. They know which features are table stakes and which ones are marketing fluff. They know where the product is strong and where it's held together with duct tape. They're the bridge between "this works in our demo environment" and "this works in production handling real business problems."
The Economics of Embedding Engineers
Let me address the obvious objection: this is expensive.
FDEs earn top-tier salaries because they need software engineering skills plus business-facing abilities plus domain expertise plus the willingness to operate out of all of the office types your enterprise may provide, including “field offices”.
According to recent market research, FDEs in the US earn between $180,000 and $300,000 depending on seniority, company, and where they live. In Europe, enterprise-grade FDE contractors often bill £600-£700 per day.
Companies need to hire selectively because the role demands creativity, judgment, and business-facing charisma on top of technical ability. You can't just hire smart engineers and throw them at business leaders. Many brilliant engineers have zero interest in the business-facing parts of the job. Many great business-facing people can't debug a data pipeline at scale.
So why would a company, let alone an IT department, invest this much in a services-heavy model when they're trying to build a scalable agentic workforce? Because the alternative is worse.
A single failed AI implementation can waste $200,000 to $1 million in sunk costs, not counting the opportunity cost of delayed automation.
When projects fail, it's rarely because the vendor's product was fundamentally broken. It's because implementation hit a wall that internal teams couldn't get past.
Maybe the data integration was too complex for IT to handle alongside their existing workload. Maybe the business users never adopted it because nobody had time to properly train them. Maybe the project lost executive sponsorship because it took too long to show value.
An internal FDE prevents exactly these failures by owning the implementation end-to-end instead of hoping the vendor and your overstretched teams can coordinate effectively.
The math gets better when you realize FDEs don't just prevent churn. They accelerate time-to-value, which is how you expand successful use-cases. Instead of a six-month pilot that ends in "we need more time to evaluate," you've got production workflows running in eight weeks.
Instead of the business team using 10% of the platform's capabilities, they're using 60% because someone showed them how.
There’s also the competitive moat angle. Once you’ve embedded an FDE who’s built critical workflows on your platform, who’s integrated with all your systems, who’s earned trust across the organization, switching costs become a much larger concern.
It’s not just “replace the software.” It’s “replace the software and rebuild all the custom integrations and retrain everyone and lose the person who knows where all the bodies are buried.”
That’s why Palantir, at one point, had more forward-deployed engineers than traditional product engineers. They were building near-unchurnable accounts.
Who’s Winning With This Model
Palantir remains the archetype. They embedded engineers with governments, airlines, and banks to make Foundry and Gotham work in impossibly complex environments.
Their FDEs weren't there to sell, they were there to build production-ready workflows in close collaboration with the customer's teams. This model let Palantir solve problems faster, prove value earlier, and create adoption curves that competitors couldn't match.
OpenAI recently went all-in on forward deployed. Colin Jarvis, Head of Forward Deployed Engineering at OpenAI, initially joined as a Solutions Architect in 2022. By early 2025, he'd built a case for a dedicated FDE team.
That team has grown from two engineers to over ten. They work on the company's highest-stakes implementations, the ones where generic ChatGPT integration isn't enough and the customer needs bespoke workflows built on the OpenAI platform.
Salesforce deploys FDEs for its most complex AI implementations. When the deal is big and the environment unpredictable, they send engineers to ensure the outcome lands.
Stripe built a Revenue & Financial Automation FDE team to partner with strategic enterprise users and close product-market fit gaps that would otherwise derail deals.
The pattern is consistent. Enterprises that successfully deploy AI are building internal FDE capabilities because traditional implementation approaches can't handle the complexity.
You can't implement AI the way you implemented CRM software. The technology changes too fast. The integration points are too messy. The organizational resistance is too high. You need dedicated people who can build, teach, navigate politics, and stick around long enough to make it work.
It’s worth noting that smaller companies and startups often can’t afford the full FDE model. The costs are too high and the hiring bar is too difficult. But they’re adopting elements of it. They’re having engineers join customer calls instead of just account managers. They’re doing paid proofs-of-concept where they embed someone for a month to build the first workflow. They’re making implementation success a core part of the sales process, not an afterthought.
What This Means for Product Teams
First, product development needs to incorporate field feedback systematically. FDEs see what’s actually broken in a way that support tickets never capture. They know which features teams ask about in demos but never use in production. They know which limitations cause business teams to build workarounds.
You need a formal process for bringing those insights back into the roadmap. Otherwise, you’re just building features based on guesses about what the business teams need.
Second, you need to design for customization without creating technical debt. FDEs will extend your platform in ways you didn’t anticipate because every enterprise has unique requirements. If your platform isn’t architected to handle that gracefully, you end up with a mess.
You need APIs that are well-documented and in largely adopted specifications. You need extension points that are officially supported. You need ways for FDEs to build custom functionality that won’t break when you ship the next version.
Third, you have to hire differently. You’re not just hiring software engineers or data scientists. You’re hiring people who can thrive in ambiguous, business-facing, high-pressure situations.
They need technical depth, communication skills, a tolerance for grinding through problems and context-switching that would drive most engineers crazy. That’s a narrow hiring pool, and you need to pay competitively for it.
Fourth, you need to support FDEs properly or they’ll burn out and leave.
That means:
Reasonable workloads provided by an ingestion pipeline via governance and prioritization.
Career paths that don’t force them to become managers/directors if they want to advance. Think Enterprise-level Agentic Architects.
Recognition that they’re doing work that’s just as valuable as product engineering even though it doesn’t show up on a project management dashboard somewhere.
What This Means for Those in the Market
If you’re evaluating AI platforms for your enterprise, the presence or absence of forward deployed capability should be a major factor in your decision.
Ask the vendor directly: do you embed engineers on-site for implementations? For how long? What’s their role versus our team’s role? How do they handle issues that come up after go-live?
If the answer is “we provide implementation guides and our support team is available via email,” that’s a red flag for complex deployments.
You’re going to hit problems that can’t be solved asynchronously. You’re going to need someone who can look at your actual data, talk to your actual users, and build actual solutions, not send you links to documentation.
If the vendor offers FDEs but charges separately for them, do the math. The upfront cost is higher, but the risk of failure is lower. A $200,000 FDE engagement that gets you to production in three months is cheaper than a $50,000 pilot that fails after six months and sets your AI initiative back a year.
Look at customer references and ask specific questions. Did they have an embedded engineer? How long did implementation take? What percentage of the work did the vendor’s team do versus internal teams? How quickly did they get to production? If the references are all “we’re still in the pilot phase” six months in, that’s a warning sign.
The Future Is Already Here
The forward deployed model isn't new, but its importance is accelerating because AI is harder to implement than traditional software. The technology is newer, so there's less institutional knowledge. The capabilities change faster, so last year's best practices are obsolete. The failure modes are different, so traditional implementation playbooks don't work.
Enterprises that build internal FDE capabilities are actually getting AI into production.
Those that rely solely on vendor support teams and their existing IT staff are getting stuck in pilot purgatory, no matter how good the underlying technology is. The difference isn't the model architecture or the feature set. It's whether you have someone who can actually get the thing working in production.
This creates a weird dynamic where the most sophisticated enterprises are investing in what looks like old-school professional services hiring.
They're bringing on engineers to do implementation work that, in theory, the vendor and existing IT teams should be able to handle together. It feels inefficient. It doesn't look like the lean, automated future everyone talks about.
But it works. It's the difference between a 95% failure rate and a 67% success rate for enterprise AI projects, according to MIT's research. Those aren't minor variations. That's the difference between a technology investment that mostly fails and one that mostly succeeds.
The irony is that this model was pioneered by companies like Palantir over a decade ago, long before the current AI wave.
They figured out that complex enterprise software couldn't be implemented the traditional way. You had to embed people who could make it work in the chaos of real organizations, navigating the politics, the legacy systems, and the resistance. Now enterprises are learning they need these same capabilities in-house.
You can't buy an AI platform and expect your existing teams to figure it out while managing their day jobs.
You need dedicated engineers who are willing to own the implementation, write production code, navigate internal politics, fix data disasters at 3 AM, and stick around until the thing actually delivers value.
That's not the future of enterprise AI. That's the present. The organizations that have figured this out are seeing ROI. The ones that haven't are drowning in failed pilots and abandoned proof-of-concepts.
Forward deployed isn't a nice-to-have. It's the difference between winning and losing in enterprise AI.