01 / MODERNIZE
Modernize the stack
The tools are dated or disconnected, and the team works around them. You need a target picture and a sequence to get there, not a shopping list.
What I do · AI GTM leadership
AI in your go-to-market needs a leader, not another tool.
Growth leadership for B2B SaaS and tech companies modernizing their go-to-market stack, getting more from the one they have, or making AI adoption stick.
Get in touchThe point of view
AI adoption is a change management problem wearing a technology costume.
The tools are the easy part. Most of them work roughly as advertised. What decides the outcome is whether the data underneath is reliable, whether someone owns it, and whether the people using the tools see their role growing rather than shrinking.
That's a leadership job. It sits across marketing, sales and RevOps, and it rarely belongs to any one of them.
Where this starts
Three situations.
One discipline.
Different starting points. The same leadership job underneath.
02 / OPTIMIZE
Optimize a stack that works
The engine runs, but it costs more than it should and carries too much manual work. The question is where AI takes out cost and effort first.
03 / ADOPT
Make adoption stick
The tools are bought, and usage is shallow. The software isn't the problem. The change around it is.
Where you stand
Know your starting point before you set the ambition.
Stage 1
Individual experiments
People try tools on their own. Pockets of value, no shared standards.
Stage 2
Team projects
A few use cases inside one team. Results are real but hard to scale.
Stage 3
Coordinated program
Shared priorities, owners and measures across marketing, sales and RevOps.
Stage 4
Built into the work
AI is part of how the revenue team operates, governed and measured like any other system.
Most revenue teams sit between the first two stages and plan as if they were at the fourth. Closing that gap honestly is where the work starts.
How I lead it
Frame. Structure. Evaluate.
FRAMEWhy we're changing
- Outcomes before tools. Hold the business goal tightly and the method loosely.
- A bigger job, not a smaller one. Roles move from producing the work to owning the system that produces it. Say so explicitly.
STRUCTUREWho owns what
- Every workflow and dataset has an owner. Data problems are ownership problems first.
- Judgment stays inside the workflow. People review and decide, by design, not as an afterthought.
- Solve the real constraint first. Fixing one bottleneck exposes the next. Plan for where it moves.
EVALUATEWhether it's working
- Adoption, not just results. A tool that's bought but not used is a cost, not a capability.
- Watch the feedback loop. When people stop reporting what's wrong, that's a warning sign, not a success.
The model draws on Stanford's AI-Driven Leadership program, applied to revenue teams.
Where this comes from
Built in the field.
Grounded in a framework.
- Led AI-powered account prioritization rollouts at two B2B SaaS companies, and the adoption work that made sales trust the signals.
- Rebuilt a global revenue stack across three regions, balancing one shared system with each region’s tools, workflows and practices.
- Design partner to an early-stage generative AI vendor, shaping the product while rolling it out to the team.
Certificate, AI-Driven Leadership: Strategies for the Future. Stanford Online, Stanford Engineering Center for Global & Online Education. Generative AI management and implementation, generative AI workflows, predictive analytics management, and organizational culture and leadership.
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