Six case studies from applied strategy work: scaling a field organization for a consumption-based data platform, building product and GTM strategy for a frontier AI infrastructure startup, pre-launch product-market fit for a vibe coding platform, fixing workforce ramp time inside a PE-owned industrial company's AI roadmap, getting a sales team to prioritize new products, and the engagement that became the Product Health Diagnostic.
How to close the booking-to-burn gap in a consumption-based revenue model — and what a next-generation field engineering operating model looks like when the field motion has to drive actual production utilization, not just deal support.
The field organizations that grew the fastest were the ones where engineers spoke the customer's business language. Financial services wanted people who understood trading infrastructure and risk workflows. Healthcare wanted practitioners who had worked inside clinical systems. Manufacturing wanted engineers who had seen a production floor.
The tech mattered — but credibility came from domain fluency. And credibility was what shortened the time from "interesting demo" to "production workload." The customers who expanded consistently were the ones where field teams could translate workloads, not just explain features.
Most field engineering organizations are structured like pre-sales support functions. They exist to win deals. But in a consumption-based revenue model, a signed contract with no usage is a liability, not an asset.
That creates the booking-to-burn gap: the dangerous lag between contract signature and actual production utilization. At one platform I worked on, customers had large contracts and were running a fraction of the workloads we had scoped together. The compensation model rewarded the signature, not the workload. The structure followed.
Three changes consistently move the needle on closing the gap between what the platform can do and what customers actually run in production.
Every enterprise is now moving toward autonomous workflows — systems that reason, recommend, and act continuously. When agents operate around the clock and consumption is no longer bounded by human activity, the economics transform. The field organization's ability to help customers safely operationalize agentic systems becomes the primary growth lever — not technical features or deal support.
That requires field teams who understand the execution boundary in each industry, can design for governance and identity constraints that determine whether an agent gets approved for production, and can translate capability into durable business outcomes rather than impressive demos.
In the first 60 days of any engagement focused on field-at-scale, three things create the most clarity, fastest:
Define two or three vertical patterns per industry based on real customer deployments — not theoretical use cases. These become the reusable architectures the field runs everywhere. Stand up a small specialist group focused on migrations and AI workflow patterns. Pilot a modified compensation model in one region to prove the booking-to-burn uplift before rolling it across the organization.
How to translate genuine scientific differentiation into a repeatable commercial motion — from first design partner to commercial scale in 18 months, for a company at the intersection of next-generation AI infrastructure and adaptive AI.
A frontier AI infrastructure company had completed early technical validation and secured its first design partner. The challenge: translating genuine scientific differentiation — a next-generation compute architecture with transformative energy efficiency and adaptive learning characteristics — into a repeatable commercial motion ahead of the next funding stage.
The specific differentiators were real and measurable: continuous learning without retraining cycles, temporal intelligence that understood causality, and energy efficiency orders of magnitude lower than equivalent GPU workloads. The gap was a commercial framework that could communicate those advantages to investors, partners, and customers simultaneously.
The strategy was organized around a Now–Then–Later product roadmap, with each phase tied to investor-relevant milestones rather than feature delivery. The key design principle: each phase had to produce a proof point credible to the next audience — design partner, then strategic investor, then ecosystem partner.
Positioning was anchored on three differentiators that held up against GPU incumbents and alternative compute architectures: continuous learning with minimal retraining, temporal intelligence that understood causality, and energy efficiency at a fundamentally different order of magnitude.
The partnership strategy was tiered deliberately. First, co-validation with AI infrastructure research teams at major cloud providers — to establish benchmark credibility and open co-selling pathways without requiring direct sales capacity the company didn't have yet. Second, academic partnerships for co-authored research and industry standard-setting. Third, design partnerships with early leaders in target verticals to co-develop pilots and publish results.
Rather than locking in a pricing model before the platform had been stress-tested in real deployments, the strategy proposed a flexible commercialization framework that evolved with maturity. Early engagements focused on paid pilots and research collaborations to validate performance and quantify business value — energy savings, inference cost reduction, adaptation cycles. Those insights would inform a long-term model blending platform access, hardware subscriptions, and enterprise co-development agreements.
Ahead of launch, Praxie needed to know whether the platform — turn a spreadsheet into a fully working web app in minutes — actually fit the ICP it was built for, not just whether the product worked. Advisory work that closed the gaps before launch, not after.
Praxie was preparing to launch its vibe coding platform and wanted an outside, technically fluent read on whether the product and the ICP it was built for actually lined up before the launch date locked in.
Reviewed the solution and the ICP directly, using broad enterprise product experience to find where the platform's positioning didn't match what the target buyer actually needed. Worked with the team to close those gaps: refined the solution, surfaced additional opportunities the launch plan hadn't accounted for, tested the product hands-on, gave direct feedback, and built demos the team used going into launch conversations. Used the product myself, for real use cases of my own, ahead of launch — the same test any real user would put it through.
Six months into an AI roadmap, IT was running multiple pilots, each justified in isolation, with no one able to answer what it all added up to. The real lever was underneath: a workforce built on seasonal contractors and slow, inconsistent ramp.
Leadership was invested in AI but cautious to disrupt a business that already worked fine. Six months into the journey, IT had been leading multiple pilots, each justified on its own and trying to prove its own ROI in isolation, with costs adding up. Encouraging initiative — but no one could answer a simple question: what did all of this actually add up to?
Not with the pilots — with the problems actually costing the business time and money. In this case: a workforce built on seasonal contractors, constant recruiting, training, and IT onboarding on repeat, where slow ramp times and inconsistent hiring quality were adding costs in the short term and slowing growth in the long term.
Ran it through the HR and Org Design Diagnostic. New contractor ramp time was slow, and it was hurting productivity — mostly because of skill gaps, training delays, or delays getting people access to the right systems and tools.
A problem most enterprises face: how do you get a sales team to prioritize new products, not just sell the shiny new one, or the easy one?
Well-defined sales training and enablement, not a slide deck. Clear account prioritization and swim lanes, so reps know which accounts get which push and who helps. And a real feedback loop between product, field, and customer — something closer to an FDE model, where someone owns adoption and success.
Tie compensation to outcome and consumption, not just bookings. Did this by pairing customer engineers to accounts post-sale and linking sales comp to whether the product actually got adopted.
The core challenge: consolidate a fragmented codebase and architecture while preparing to integrate AI and coding agents into how the team builds — and drive customer growth and adoption through the transition.
This looked like a roadmap exercise on the surface. It wasn't — that's what made it fun. Talking to the team surfaced what was invisible until you looked at the end-to-end system: unclear prioritization between new features and foundational work, no shared definition of done between product and engineering, customer feedback landing in tickets and going nowhere, and almost no telemetry on what customers actually used versus what they had bought.
Took the learning from this engagement and built the Product Health Diagnostic — six areas: strategy and discovery, build and velocity, portfolio decisions, validation and adoption, feedback loops, and telemetry. It answers one question: where is the workflow breaking, and which lever fixes it first?
This is the second framework in the operating toolkit. It connects to the GTM Motion Diagnostic published earlier — fixing the product system without fixing the go-to-market motion only solves half the problem.