TL:DR; Forward-Deployed Engineers (FDEs) are product-grade engineers embedded with customers to close the “last-mile” gap integrating platforms, hardening features, and driving adoption so AI/agentic solutions actually hit production and deliver P&L impact. Traditional models (pure product, consultants, or DIY) often stall; FDEs fuse product, service, and support into one accountable motion. insightfactory.ai runs an FDE-first, vertically integrated model (Insight Factory platform + delivery methodology + managed service), using a Recon/Front-Line team structure and AWAIRE mission planning to iterate fast, measure value, and keep solutions live and improving. Reported outcomes include 5,000+ FDE days, >95% of solutions in production, and hundreds of thousands of monthly task executions—far above an industry where <5% of AI initiatives reach production. Bottom line: don’t just buy software... deploy engineers to your frontline.
In data and AI, a powerful shift is unlocking value on the front lines, and it’s called Forward Deployed Engineering. Whilst this is not a new concept, it is a powerful one that many leading technology vendors and customers alike are starting to realise is the most power vehicle to get AI & agentic enabled solutions to production in a truly impactful way.
“A Forward-Deployed Engineer (FDE) is a product-grade engineer embedded with the customer who adapts and operationalises a vendor’s platform in the customer’s environment, integrating systems and data, building and hardening features for production, and driving adoption to ensure the promised outcomes are realised.”
Despite the narrative on LinkedIn, product vendors or consulting houses, having a clever model, solution accelerator or polished product just isn’t enough. According to the recent MIT NANDA report called the GenAI divide less than 5% of AI initiatives reach production or have any form of P&L impact. The reason so many initiatives stall at the pilot isn’t capability or data, it’s the last mile… implementation, integration and adoption in messy, real-world environments. That’s where Forward-Deployed Engineers (FDEs) matter. Sitting on the front line with users, systems and constraints, FDEs close the gap between a powerful platform and operational reality.
In this piece I challenge traditional services models and show how insightfactory.ai has been using FDEs to change the way AI & Agentic solutions are delivered and adopted. We’ve built insightfactory.ai as a vertically integrated, FDE-first business: platform, product, people, methodology and fully integrated support in one motion. To date we’ve delivered more than 5,000 FDE days, ship continuous improvements through FDE feedback loops, and evolve our agentic product suite, AgenticMigration.AI, AskIF.AI, Factory.MDM and more, alongside ever maturing delivery frameworks. Most importantly, over 95% of our solutions reach production, and we currently supporting hundreds of thousands of real task executions every month.
The Rise of the Forward-Deployed Engineer
Palantir first pioneered the FDE concept in the 2000s, literally deploying engineers to military bases and corporate offices so they could work side-by-side with users. These were the original Forward-Deployed Engineers they were tech experts embedded on the client’s frontline, solving problems in real time and tailoring solutions on the fly. The value of this approach became undeniable, it is believed that Palantir at one point even had more FDEs (which they called “Deltas”) than traditional software developers, shaping a culture where end-user impact trumped all. Fast-forward to today, and what was once a niche strategy is quickly becoming mainstream.
Startups and scale-ups seem to be on a hiring spree for FDEs and with good reason. An FDE is essentially a hybrid role part translator, part engineer, part product manager, part consultant, part delivery lead. An FDE bridges the gap between what a product can do and the solution a customer actually needs, iterating rapidly between the two worlds.
Several trends have converged to fuel the rise of forward-deployed engineering. The first is the increasing complexity of enterprise environments, another is the evolving scrutiny on the extremely low conversion rate for impactful (P&L impact) enterprise implementations of AI solutions. The recent MIT NANDA report on the GenAI divide highlighting that less than 5% of solutions get to production or have any form of meaningful impact on the P&L.
Implementing enterprise AI isn’t just about building a model or deploying a tool it’s about orchestrating an end-to-end journey that delivers measurable impact on the P&L, management of risk, or unlocked blue oceans. To achieve that, many things must converge.
First, you need to fully understand the problem you’re trying to solve, not just in technical terms, but in the context of how people work, and the processes being automated, augmented, or enhanced. From there, you have to translate that understanding into a highly aligned specification, that will ultimately integrate many varying forms of data, AI and Agentic capability operating in coordinated workflows.
Then comes the real engineering. That means having the right technology stack in place to ingest, store, govern, and secure data; integrate models (including CV, ML), agents, workflows; build orchestration and semantic frameworks; and deliver outcomes through integration, dashboards, applications, or front-end experiences. It requires the ability to test rigorously, move seamlessly into production, and establish monitoring, support, and continuous improvement mechanisms. And finally, and perhaps most critically, it requires driving adoption or accepted augmentation with end users, ensuring the system isn’t just functional but actually used, delivering measurable business value and continuous feedback. The reality is that all of this almost never converge, and most projects die in the Pilot phase or some form of Frankenstein is built that is incredibly difficult to sustain.
This is why Forward-Deployed Engineering is the perfect model for organisations implementing enterprise AI. FDEs work on a product they know inside-out, within an environment that has a clear path to production and sustainment. Around that product, they bring a delivery framework, methodologies for problem definition, solution building, testing, adoption, and value measurement. The vertical integration of product expertise, embedded engineering capability, and delivery framework creates a model where everything required for success is in one place: the infrastructure, the engineering talent, the methodology, and the accountability.
At insightfactory.ai, this philosophy underpins how we’ve built our entire company. We challenge our forward-deployed engineers to not just be problem solvers, but first and foremost outcome owners. They align deeply with clients, adapt our solutions built on the Insight Factory to fit complex environments, and ensure that these solutions deliver sustainable value.
In many organisations, what the end-users truly need is far more complex and nuanced than most standardised products deliver. Every organisation has its own behaviours, processes, and edge cases that shape how solutions must work in practice. Off-the-shelf products often don’t align with these realities, and while custom solutions can bridge the gap, they demand both a robust infrastructure capability to provide a path to production and support and a deep understanding of how the organisation actually operates to drive adoption and impact.
This is exactly why the Forward-Deployed Engineering model is so critical. It puts skilled engineers directly alongside the end-users, enabling them to capture those nuances, translate them into technology, and integrate solutions into the way the organisation already runs. Rather than forcing the business to adapt to the software, the FDE model ensures the solution adapts to the business, embedding alignment, driving adoption, and delivering the outcomes that matter.
At insightfactory.ai, our managed service has been deliberately designed to provide the perfect environment for this. With the Insight Factory platform as the infrastructure backbone and FDE’s as the connective tissue, we combine product, capability, and close alignment with client end-users. This vertical integration means we’re not just delivering a platform, we’re ensuring it works in the client’s world, tuned to their requirements, and continuously improved as their needs evolve. It’s how we take the FDE philosophy and make it real at scale.
Breaking the Mold of Traditional Services
This FDE shift is genuinely disruptive. It challenges the classic models of delivery in data & AI programs in a fundamental way. For years, enterprises have swung between extremes: buying “out-of-the-box” products and hoping for adoption, engaging armies of consultants for months on end, or trying to build everything internally with their own teams or do combinations of all three. Having been on all sides as a consultant, a product builder, and a buyer, I’ve seen firsthand that all three approaches carry challenges.
Off-the-shelf products often end up underutilised or abandoned, because clients lack the technical expertise or capacity to integrate them properly. Traditional consulting can deliver a demonstrable MVP, but the solutions are usually one-off, fragile, and difficult to sustain. Too often, consultants leave behind systems built on technology the customer doesn’t own (or doesn’t know how to use), relying on specialist skills the client doesn’t have (AI engineers, for example), with no clear path to production, support, or scale.
The third path of building internally can also fall short. Organisations may try to hire their own portfolio of capability (translators, engineers, data scientists, etc.) and build customised fit-for-purpose solutions. In theory, with the right people this can produce strong outcomes. But in practice, non-technology-first companies struggle to find and retain the highly skilled talent required. Even when they succeed, they face the same hurdles: having the right platform to get solutions into production, integrating with end-users to drive adoption, and building the organisational muscle to support, monitor, and scale solutions over time. More often than not, this leads to hybrid models, a patchwork of purchased products, consultants, and internal teams, which rarely deliver the seamless, sustainable outcomes the business expects. The statistics around successful AI impact in the enterprise is reflective of this reality.
Forward-Deployed Engineering breaks this cycle by fusing product, service and support into a single model. An FDE isn’t an external consultant working at arm’s length, nor are they a typical engineer with no exposure to the customer. They are integrators and translators, working directly on enterprise-deployed infrastructure with a clear path to production and sustainment. By sitting alongside the customer, they can understand the problem deeply, then build, deploy, and operate highly aligned solutions within that deployed environment. This ensures that the outcomes delivered aren’t just technically sound, but also practically impactful, designed in the context of the customer’s real operations, with continuous feedback loops.
This FDE approach also accelerates the evolution of the Insight Factory product itself. Instead of writing throwaway custom code in every mission, our FDE’s contribute constant user feedback for the core platform roadmap. In practice, they become conduits for customer-driven innovation, surfacing recurring pain points, identifying feature needs, and feeding them into the product roadmap to be generalised features for all of our customers. Palantir’s trajectory proved this model’s power: their FDEs co-built solutions with customers, and that constant cycle of learning made the platform stronger while driving more efficient delivery. The result is a virtuous cycle, better implementations improve the product, which in turn enables easier, faster, and more impactful future implementations.
Managing FDE Driven Outcomes
Coordinating delivery inside large organisations means confronting a difficult reality, building complex data, AI, or agentic-enabled solutions is inherently uncertain. At the outset, there are unknowns around data quality, integration points, process nuances, regulatory constraints, and end-user adoption. Against this backdrop, the idea of locking into a rigid Statement of Work (fixed steps, fixed timeframes, fixed costs) is highly risky for both vendor and client. This model, popularised by traditional consulting, creates an illusion of certainty. But in practice, how many of those projects ever make it to production or deliver meaningful outcomes for the business?
From years of delivering these solutions, I can say with certainty that no AI project follows a perfectly linear path. Once you start, deviations and unforeseen requirements are inevitable. Under an SOW, either we (the vendor) absorb the cost and see margins collapse, or the client is hit with endless change requests and variation fees. Either way, trust erodes, and outcomes suffer.
That’s why we’ve built our FDE model differently. Inspired by Palantir, we use a dual-team structure:
- Recon (Echo) Team – immerses deeply with end users, mapping processes, uncovering value drivers, and translating these into a living specification.
- Front Line (Delta) Team – technical builder who design, build, and deploy solutions in close collaboration with Recon and the client.
To guide delivery, we’ve adopted the Afterburner fighter pilot mission-planning framework (see AWAIRE). Fighter pilots developed this approach to plan, coordinate, execute, and debrief complex missions in high-stakes environments. It’s the most effective framework we’ve found for managing the uncertainty of enterprise AI delivery.
With AWAIRE, we begin by setting High-Definition Destinations (HDDs) the clear business outcomes and impacts the organisation is aiming for. These are broken down into short-term Objectives with specific Actions allocated across the team. Progress is managed through validation cycles and regular debriefs that surface deviations, recalibrate plans, and ensure alignment to the HDD. This cycle of planning, execution, and debriefing drives continuous learning while keeping delivery anchored to the outcomes that matter most.
The combination of our FDE model (including AWAIRE) provides clients with a fully integrated capability across engineers, infrastructure, and methodology, all working in cycles towards defined outcomes. It balances flexible execution (FLEX) with disciplined governance, making it far more effective and cost-efficient than SOW-driven delivery.
Ultimately, the FDE + AWAIRE model embraces uncertainty rather than denying it. It manages variation without penalty, ensures transparency, and keeps both client and provider focused not on ticking off outputs, but on achieving sustainable outcomes in complex, unpredictable environments.
The New Standard for AI Projects
The forward-deployed approach is particularly disruptive in the AI space, where outcomes matter more than ever. AI projects live or die by adoption and tangible impact. No executive cares how elegant your model is if it never makes it into production or fails to align with operational needs. By embedding technical talent directly with the stakeholders, FDEs ensure AI solutions aren’t just science projects they become integrated into business processes, embraced by end-users, and tuned to deliver results.
Consider what this means in practice, imagine a large organisation adopting an AI-driven approaches to streamline its operations. Without FDE’s, a product vendor might provide a tool and some documentation, perhaps even a few weeks of onboarding, but the client’s own team is left to figure out how to mesh it with their legacy systems and unique processes. A consultant might provide a Statement of Work to deliver an MVP in a confirmed period of time for a fixed fee that has no clear path to production. Now picture the forward-deployed model, skilled technical talent (from Recon and Front Line) from day one, are on-site (or virtually), working within the client’s environment. They don’t just drop in and give advice, they roll up their sleeves and build the integrations, tailor the models to the client’s data, set up dashboards that the client’s users actually find useful.
From my perspective, having worked across both consulting and product organisations, this is a truly provocative shift. It calls out a hard truth: Enterprise customers don’t want software, they want results. Forward-deployed engineers force us, as solution providers, to take ownership of those results. It’s not about logging billable hours or shipping features and saying goodbye, it’s about staying on the hook to make sure the solution actually delivers value in the real world. That level of accountability can be uncomfortable for those used to the old ways, but it’s exactly what separates modern, disruptive tech partners from the old guard. In fact, we’re seeing the truly progressive (and successful adopters of AI) enterprises are starting to expect this level of partnership. They’ve been burned before by vendors who over-promise and under-deliver. Showing up with forward-deployed engineers, people who will eat lunch in the company cafeteria, learn the acronyms, and not leave until things work is a powerful differentiator
Creating the Perfect Environment for FDEs
Embracing forward-deployed engineering is not as simple as giving someone the job title. It requires a deliberate environment and model to unlock the full potential of FDEs. Over the past few years, our team has thought deeply about what makes this model succeed (or fail), and we’ve oriented our entire managed service offering around getting it right. In our experience, a few key elements are essential:
- A Robust, Ready-to-Deploy Platform: Forward-deployed engineers need a solid foundation to build on. We equipped them with the Insight Factory platform a full “Insight Factory” can be deployed in a client’s cloud in a day. This means our FDEs aren’t wasting time reinventing data pipelines or MLOps from scratch for each project. They have an assembly line (or production line) of proven components at their disposal, so they can focus on what truly matters for the client. The platform handles the heavy lifting of data ingestion, storage, model deployment, monitoring, etc., which accelerates integration and gives FDEs more time to solve business-specific challenges.
- Top-Tier Engineering Talent with Domain Insight: Not every great engineer can be a great forward-deployed engineer. We select “Factorians” (our nickname for our team members) who not only have strong technical skills but also the right mindset, they are curious, adaptable, and comfortable working collaboratively with non-technical stakeholders. FDEs must be as confident debugging a machine learning model as they are explaining its value to a CFO. We invest in training our engineers to develop strong communication and domain knowledge. When an FDE walks into a client’s environment, they need to learn & speak the language of that industry and earn trust quickly.
- Vertical Integration and End-to-End Ownership: This is vital. At insightfactory.ai, we organize our teams to provide full vertical integration meaning the same delivery squad handles everything from understanding the problem, to building the solution on our platform, to deploying it, and even operating it long-term. There is no handoff where “implementation” is someone else’s problem. This structure creates the perfect habitat for forward-deployed engineers: they have the authority to do what it takes at each step, and they feel ultimate responsibility for the outcome. It also means fewer silos and bottlenecks, the FDEs can quickly loop in our product developers if a new feature is needed, or our Data Science experts if a novel model is required. Everyone is aligned on one goal: delivering value to the end-user, continuously.
- Feedback Loops Between Customer and Product: A forward-deployed engagement is a goldmine of insights… if you capture them. We make sure that our FDEs are in constant contact with our core product team. In practice, this might mean daily check-ins where field feedback is discussed, or pulling in a UX designer to observe how a customer interacts with a dashboard we built. Improvements get rolled into our platform’s roadmap at light speed through what we call Fast Features. This tight feedback loop prevents the drift that often occurs in long enterprise projects. It also keeps our product evolving in sync with real-world needs, which benefits all our clients. Essentially, each forward-deployed project not only solves one client’s problem, but also makes our overall product and methodology better for the next client, a compounding advantage.
These elements collectively create what we believe is the ideal environment for forward-deployed engineering. It’s no coincidence that a lot of what we do, the product design, the team structure, the processes is geared toward developing a leading FDE model. In fact, our mission from the start was to build the “Insight Factory” as a vertically integrated product-service ecosystem that embodies this philosophy. By having a product and a skilled team working in unison, we aim to eliminate the friction that plagues conventional data & AI projects. Our forward-deployed engineers can hit the ground running (since the platform is already in place), they can iterate rapidly (since they’re empowered to make changes across the stack), and they remain accountable (since we continue to manage and optimize the solution as a service). It’s a holistic approach, one that clients find both “sharp and professional” in execution, yet undeniably disruptive to the slower, siloed models they were used to.
The Future Belongs to the Fast and Integrated
The implications of the forward-deployed model go beyond just our company or our clients. I believe this is a glimpse into the future of enterprise technology delivery. As AI and data solutions become ever more central to competitive strategy, organizations will demand outcomes, not just promises. They will favour partners who are willing to co-own the outcome. That’s inherently provocative because it upends the comfortable arms-length vendor relationship. But it’s also pragmatic and it works.
We see evidence of this shift all around us. Investors are praising FDEs as strategic assets, and virtually every top AI startup is building some form of forward-deployed or “customer engineering” team. Customers, for their part, are getting accustomed to a higher level of service. Once they experience a forward-deployed engineer who can deliver a custom feature in a week, or who can seamlessly integrate a product into their tangled legacy systems, they don’t want to go back to the old model. It’s a competitive advantage for both the provider and the client.
However, making this work requires commitment. For companies (like us) considering adopting an FDE approach, be prepared to rethink your business model. You will need to invest more in people and accept lower short-term margins than a pure software sale – but the payoff is durable growth and customer loyalty. Forward-deployed engineering can be a slower path to scale than a one-size-fits-all product, but it builds deep moats. When you’ve solved hard problems hand in hand with your customer, you form a partnership that is not easily displaced by a competitor’s brochureware.
At insightfactory.ai, we’re all-in on this vision. We often talk about being provocative and disruptive in the market, not for the sake of hype, but because we genuinely believe the era of passive solution delivery is ending. In its place comes a model where technology providers are fully engaged in delivering outcomes. Forward-deployed engineers are the vanguard of this change. They operate with a mindset that the job isn’t done when the code is delivered; it’s done when the value is delivered. That ethos is what we strive to embody every day.
Closing Thoughts
In summary, forward-deployed engineering represents a powerful convergence of product, service and support excellence. It challenges us to be sharper and more accountable, while also enabling us to move faster and be more innovative. This model is shaping insightfactory.ai’s journey as we create what we consider the perfect environment for FDEs to thrive. But more broadly, I see it shaping the entire industry’s future. Those who embrace it will set the pace, and those who ignore it may soon find themselves disrupted by those who can build, deploy and operate better and faster.
For organizations seeking to truly leverage the promise of data and AI, the message is clear: don’t just buy a product, deploy the engineers. Put your technical talent on the front lines, or partner with providers who will. The closer you can get to your end-user’s reality, the more likely you are to create something of real, lasting value. In the end, that’s what forward-deployed engineers deliver, solutions that work in the real world.
In my view, there’s nothing more compelling or more necessary in a world being rapidly reshaped by the single biggest revolution of our time.
