The Forward Deployed Engineer's Missing Tool
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The Forward Deployed Engineer's Missing Tool
Forward deployed engineer postings on Indeed grew 729% year over year — from 643 in April 2025 to 5,330 in April 2026 (Christian & Timbers). At the start of 2026, only 5–10% of companies were planning to hire FDEs. By the end of the second quarter, 70% were (TechCrunch). OpenAI and Anthropic both launched dedicated FDE business units in May 2026. Microsoft committed $2.5 billion to embed roughly 6,000 people inside client organizations under the same model. Salesforce committed to hiring 1,000 FDEs. AWS launched a $1 billion FDE organization of its own (LinkedIn / Ishaan Sanji).
Every one of those numbers is a bet on the same belief: the bottleneck in enterprise AI has moved. It's no longer about accessing the best model. It's about deployment — making a capability functional inside one specific customer's messy, siloed, real environment (TechCrunch).
Which means the FDE's real job starts before any code gets written. It starts with a question nobody hands them the answer to on day one: out of everything this organization does, where exactly does AI create value?
The job is discovery before it's code
Palantir's original FDEs — internally called "Deltas" — spent their time embedded with government, financial services, and commercial teams, taking a raw product and making it functional inside environments that were "messy, siloed, and often classified" (Paraform). Shyam Sankar, now Palantir's CTO, put it more bluntly: the role "absorbs pain and excretes product" (Resolve AI).
Absorbing an organization's pain is a process problem before it's an engineering problem. Applied AI job postings now list customer discovery, problem decomposition, and AI product judgment ahead of pure coding skills (Perspective AI). Roughly 41% of AI engineers already spend over 30% of their time customer-facing (Perspective AI). The job has shifted from "build the feature" to "find the feature worth building" — and finding it means seeing the client's actual process, not the org chart's idealized version of it.
This is the same conclusion we reached writing about strategy execution: process is where the real organization lives. Financial results are a lagging indicator. Customer experience and employee performance are both downstream of how work actually gets done. If an FDE wants to know where AI creates value inside an enterprise, the model isn't the starting point. The process layer is — because that's the layer everything else sits on top of.
The manual version of use-case discovery doesn't scale
Here's the wall an FDE hits almost immediately: mapping an organization's real processes by hand — interview by interview, department by department — is slow. It means shadowing staff, reconciling three conflicting descriptions of the same workflow, and producing documentation that's already stale by the time it's approved.
An FDE with a 90-day engagement and a dozen high-value use cases to find doesn't have months to spend mapping process before writing a line of code. And a consulting-style discovery phase — the kind that produces a deck full of "current state" diagrams — doesn't leave the client with anything they can actually run. It leaves them with a PDF, and the FDE with a guess dressed up as a use case.
That's the gap ESSAM.AI was built to close.
How ESSAM helps an FDE find the use cases
ESSAM is an AI-native, end-to-end process transformation platform. It runs a structured cycle — baseline, analyze, redesign, document, deploy, collect feedback, re-optimize — fast enough to compress what normally takes a discovery team weeks into a single working session. For an FDE, that changes what day one to day thirty actually look like.
It surfaces the use cases instead of guessing at them. Rather than interviewing stakeholders one department at a time and hoping the real bottlenecks come up, ESSAM baselines the organization's actual processes and shows exactly where the waste, the rework, and the manual hand-offs live. Those are the use cases worth building an AI solution around — not the ones that sound impressive in a steering-committee slide, but the ones where removing the friction produces a measurable result the FDE can point to.
It gives the FDE a shared diagnostic language with the client on day one. ESSAM's framework — Eliminate, Simplify, Standardize, Automate, or Migrate — turns every conversation with a process owner into a structured triage: is this step waste that should be eliminated, complexity that should be simplified, inconsistency that should be standardized, manual effort that should be automated, or a capability that belongs somewhere else entirely? Each label points to a different kind of AI use case — and a different reason it will or won't pay off.
It ranks the use cases by where the value actually is. Not every broken process is worth automating. ESSAM's baseline shows volume, frequency, and cost of the waste in each workflow, so the FDE can prioritize the handful of use cases that move efficiency, speed, and value delivery the most — instead of spending the engagement on the loudest complaint in the room.
It produces the documentation the engagement needs to leave behind. Bank-ready policies, procedures, RACI matrices, OLAs — the artifacts that make a deployed AI use case sustainable after the FDE moves to the next account — get generated from the optimized workflow itself, not written up separately after the fact.
It keeps finding new use cases after the FDE leaves. ESSAM's closed loop collects staff feedback on how the redesigned process performs in practice and feeds it back into re-optimization. An FDE's engagement has an end date. The organization's next AI use case doesn't wait for the next engagement — ESSAM keeps surfacing it.
Better efficiency, speed, value delivery, and stronger strategy execution
The metrics an FDE is measured on — efficiency gained, speed to production, value delivered, and how much the AI initiative actually strengthens the client's strategy execution — all trace back to getting the use case right in the first place.
Efficiency comes from automating a process after removing its waste, not automating a broken process faster. Speed comes from skipping months of manual discovery and going straight from baseline to a ranked list of use cases. Value delivery comes from targeting the workflows the process data actually points to, instead of the ones that happened to come up in a stakeholder interview. And strategy execution — the thing every enterprise AI initiative is ultimately supposed to serve — only happens when the use case reaches the workflow the organization actually depends on, not a side process nobody was blocked by.
An FDE can ship a technically excellent AI solution and still fail the client, if it was aimed at the wrong process. The hardest part of the job was never the code. It was knowing, with confidence, which process to point the code at.
FDEs are being hired at a pace the market has never seen, to solve a problem the market has correctly identified: enterprises don't know where to point AI to get real returns. ESSAM is the ready-made layer that answers that question for them — continuously, at scale, without waiting for the next FDE to arrive and start the discovery process over again.
Visit www.essam.ai to learn more and schedule your demo →
Abdulla Al-Awadi is the founder of TheStrategist.me and ESSAM.AI, and Chief Strategy Officer at KIB. TheStrategist.me gives business leaders the strategy OS they need to develop a rigorous strategy. ESSAM.AI gives them the process transformation platform they need to execute it.