Finding the few
The qualified pool is tiny and contested. Pipelines, assessment, and the profile that actually predicts embedded success — engineering plus discovery plus presence.
The FDE Practice
Forward deployed engineers don't come out of nowhere.
The hottest thing in AI adoption right now isn't a unicorn hire—it's an FDE (forward-deployed engineer). Behind every engineer embedded in an enterprise stands expert sourcing, active engagement management, and deliberate service design. That is our practice.
Intelligence
Labs
Partners
FDEs
Enterprises
While enterprise AI investment continues to surge, measurable business value remains elusive.
Organizations have funded pilots, licensed powerful models, and subscribed their teams to a growing list of AI tools. Yet the work queue keeps growing, while the promised productivity gains remain just over the horizon.
There is still no easy-button utopia. Real progress still requires redesigning workflows, connecting AI to real systems and data, managing risk, earning adoption, and proving economic results.
McKinsey's 2026 global survey exposes the gap. Nearly nine in ten respondents said their organizations regularly use AI, but only 44% reported scaling it across the enterprise. Just 37% attributed any positive EBIT impact to AI, and only 6% qualified as AI high performers.
~90%report regular AI use in at least one business function
44%report scaling AI across the enterprise
37%report a positive contribution to EBIT
6%qualify as AI high performers
The difference is execution. Nearly three-quarters of AI high performers fundamentally redesign workflows, compared with only one-quarter of other organizations. They do not simply add AI to the existing way of working. They connect technology, people, process, governance, and measurement around an operating outcome.
PwC found the same divide in its 2026 survey of 4,454 CEOs: 56% reported no significant financial benefit from AI, while only 12% reported both cost and revenue gains.
The forward-deployed engineer works inside this gap — turning frontier capabilities into production systems that fit the business, operate under real constraints, improve through use, and produce results leaders can measure.
Sources: McKinsey, The State of AI in 2026, 1,719 participants across 97 countries; PwC, 29th Global CEO Survey, 4,454 CEOs across 95 countries and territories.
FDEs don't exist in a vacuum.
The industry talks about FDEs like they materialize fully formed. They don't. An FDE practice is a highly specialized consulting practice, and it needs what every serious practice needs: a sourcing engine for scarce talent, practice management that keeps utilization and quality honest, methodology, enablement, delivery standards, and an economic model that survives contact with procurement.
The labs proved the role. Turning the role into a repeatable, governed practice — that's the unbuilt middle of this market, and it's what we are building.
Where we focus.
Running the bench
Utilization, staffing, quality reviews, enablement, and the operating cadence that makes a scarce specialist practice economically durable.
Designing the delivery
What the engagement is, how it's scoped, how embedded teams hand off, and how outcomes get measured — the service itself, engineered like a product.
Keeping the edge
Continuous learning, applied research, tool evaluation, and field-tested enablement that keep FDEs current as models, platforms, methods, and risks evolve.
Advancing the practice
Active engagement with labs, builders, researchers, and practitioner communities — turning field evidence into useful methods, informed points of view, and thought leadership that moves the practice forward.