A Forward Deployed Engineer (FDE) is the embedded technical engineer that activates and operationalizes AI capabilities—LLMs, Agentic AI, and vibe coding—connecting them to business systems, data, and workflows. They are the ones implementing AI governance across the key components of Strategy, Design, Build, Integrate, Go-live, and Run.
Enterprise leaders are desperately trying to wrap their heads around why AI isn't paying off — and how to claw their way out of what feels like pilot purgatory. They are not alone: fifty-six percent of CEOs say their companies aren't yet seeing a financial return from AI investments. Microsoft’s Frontier Firm Initiative puts it plainly: pilot-rich but transformation-poor.
HFS leaders Phil Fersht and Saurabh Gupta offer some solace. Their recent article, Stop treating FDE as optional: Your AI Flywheel will not spin without it, names the missing layer most AI operating models lack: Forward Deployed Engineers (FDEs).
FDEs are the embedded technical engineers that activate and operationalize AI capabilities—LLMs, Agentic AI, and vibe coding—connecting them to business systems, data, and workflows. They are the ones implementing AI governance across Strategy, Operating Model Design, Build, Integrate, and Run. And for businesses who want to scale AI, FDEs are non-optional.
Without them, even the most sophisticated AI capabilities stall at the threshold of real business impact. LLMs can reason and generate code but cannot connect themselves to governed data or act in regulated industries without human intervention. Agentic AI can orchestrate across systems but accumulates risk around decision rights and accountability without proper governance design. Vibe coding can produce working agents at speed but creates fragmentation, compliance exposure, and technical debt without standards and guardrails. FDEs are what close all three gaps.
The HFS core argument lands: the gap in AI transformation is not technical; it’s operational. We agree, but we would go one step further: most importantly, it is organizational.
The traditional BPO model — decades of people-intensive service delivery built on sheer headcount — is being replaced, and fast. HFS calls the alternative 'Expertise Dense': rather than throwing bodies at a problem, you deploy compact, highly AI skilled teams. We agree. It delivers better outcomes, faster, at lower cost. The disruption will come from next-generation SaaS providers like Palantir, from organizations building FDE capability internally, or both. Incumbent BPO firms are scrambling to reinvent themselves using AI, but that transformation is an uphill battle when your entire business model is addicted to a BPO playbook.
The HFS ideal FDE profile, who is fluent in LLMs, agentic platforms, and vibe coding, with genuine operational and industry depth, is real but rare. Think back to the late 1990s: organizations understood they needed people who could bridge web design, digital analytics, and domain expertise, but that talent pool took a decade to mature. We are at a similar inflection point. Until the FDE talent pool catches up, organizations need Forward Deployment Teams (FDTs): multidisciplinary squads that collectively cover the skill set, in the same way leading firms used cross-functional digital teams in the early internet era.
HFS positions the FDE as the site architect — the person who ensures the building goes up correctly. But who designed the building? Who reimagined the process the AI is being asked to run? Most organizations lack that capability in their current transformation teams. They define what they want the process to do based on how it has always worked, then ask the implementation team to make AI fit. Re-engineering the process to address fundamental issues, and redesigning it with AI in mind, yields far better outcomes than deploying into the existing structure.
The FDE model by HFS describes a world where the business users, process operators, and managers whose teams are being restructured around AI are passive recipients of a transformation happening to them. That is not how durable transformation works. Bain's recent analysis, Want More Out of Your AI Investments? Think People First, makes the case directly: organizations that treat AI adoption as a people and readiness problem outperform those that treat it as a technology deployment. The FDE operationalizes the AI. Someone has to bring the organization along. Building a next generation operating model is great, only if you enable employees how to use the additional functionality and horsepower.
HFS's call to action is the right instinct: treat FDE as a strategic capability, not an implementation afterthought. Here is how to act on it.
Audit the process before you automate it. If the process is broken, complex, or built around constraints that no longer apply, fix it first. AI applied to a bad process produces a faster bad process. Organizations that bring Ashling in with the mandate to redesign and implement — rather than just implement — consistently achieve better outcomes and better economics.
Assess your current skills inventory across LLMs, agentic AI, vibe coding, and operational depth. Identify the gaps. Build multidisciplinary teams to cover them now, while developing a longer-term plan to grow the next generation of true FDEs through mentoring, cross-skilling, and deliberate deployment.
Not every process needs a white-sheet redesign. Some benefit from staged AI renovation. Others can be rebuilt from SOPs using agents. A few warrant fully AI-native redesign from outcome constraints alone. Use the right approach for the right problem — and be honest about which category you are actually in.
Structure change management as a parallel workstream, not a post-launch activity. Engage business users early in the process planning. Design with human-in-the-loop check points. The organizations that get sustainable returns on AI are the ones where the people responsible for running the system understand it well enough to improve it.