I've operated in these,
not just consulted.
The pattern matching that makes this seat valuable comes from having done the job in more than one place. Here is where I've run product, engineering and AI myself, and the revenue models underneath them. An agent that routes an order and an agent that drafts a campaign fail in completely different ways; you only learn that by having shipped both.
Industries I know
from the inside.
Four have pages of their own because I've held the seat in them. The rest are sectors I've delivered in, each linked to the work itself.
SaaS
Twenty-five years in SaaS and enterprise software: scaling engineering orgs without breaking them, and making delivery predictable.
EdTech
CPTO of an online learning platform, running product, engineering and its AI transformation.
HealthTech
Former CTO of a healthcare EMR platform. SOC 2 Type I & II and HIPAA-grade compliance without freezing the roadmap.
E-commerce
Platform scale on the busiest day of the year, plus margin-finding AI agents across fulfillment, in both B2B and B2C.
Logistics & Supply Chain
3PLs, freight brokerages and shippers. I built a cloud-native TMS with live telemetry and shipped agents that run order flow and exception routing.
Manufacturing & Distribution
Quoting, order intake and invoice matching for manufacturers and wholesale distributors. The office work, sequenced ahead of the plant floor.
Transportation & Logistics
A cloud-native transport management system with live GPS and telemetry across a public-bus fleet, re-platformed from on-prem without a service gap.
Supply Chain & Order Fulfillment
Sourcing across owned inventory and drop-ship suppliers, carrier selection, landed cost and a margin floor no order may breach.
Event Management
An event purchasing and management platform, built end-to-end.
Marketing Automation & Strategy
Lead qualification tied back to the campaign that produced it, and an autonomous marketing system that researches, writes and schedules.
Manufacturing & Finance
Digital products delivered end-to-end in both, including compliance-heavy environments.
And the models
underneath them.
Industry tells you the vocabulary. The revenue and distribution model tells you where the money actually moves, which is what decides whether an AI project is worth funding. These are the ones I've operated inside.
B2B
Long cycles, named accounts, procurement and security review. AI pays first in qualification, quoting and the paperwork around a deal rather than in the pitch.
B2C
Volume, thin margins and no patience. The wins are in service response, merchandising and the cost of fulfilling a single order.
B2B2C
You serve a partner who serves the end customer, so you own the experience without owning the relationship. Data access is the constraint, and it is usually contractual rather than technical.
Marketplace & multi-brand
Supply and demand on one platform, or several brands on one commerce architecture. Catalog quality and trust are the product, and both are AI-tractable.
Subscription & SaaS
Retention is the whole business, so the AI questions are churn signal, expansion and the cost to serve an account rather than the demo.
Wholesale, DTC & drop-ship
The same catalog sold three ways with three different cost structures. Landed cost per channel is where the margin hides, and where agents earn their keep.
Industry experience isn't trivia. It's the difference between learning your problem on your dime and recognizing it on day one.
Working in one of
these industries?
If your problem looks like one I've solved before, I can come in fast and skip the ramp.