I work with a talented group of SE leaders and individual contributors covering Datadog's largest enterprise accounts. Right now we're navigating AI adoption together — building new workflows, rethinking what we hire for, and figuring out what high-performance SE teams actually look like when AI is part of the equation. I write about what we're learning, including what isn't working.
A weekly newsletter for SE leaders navigating the AI transformation in technical sales. Practical. Honest. From someone running a real SE org.
Essays on Sales Engineering, AI transformation, and enterprise GTM
The real question isn't whether AI replaces SEs. It's which parts of the job change, which parts don't, and how teams navigate that transition together. Here's what we're learning in real time.
Read →Most POVs fail before they start. After working through hundreds of enterprise evaluations alongside sales teams, here are the patterns that show up every time.
Read →Most SE interview processes select for the wrong things. After rethinking how our team evaluates candidates, hire quality improved significantly. Here's what we changed and why.
Read →Practical prompts tested with real SE teams. New prompts go to newsletter subscribers first — then appear here the following week.
Generate a structured pre-call brief from your CRM data and a quick web search. Replace the full 30-60 minute manual research process.
I have a customer call in [X minutes] with [COMPANY NAME]. Using my Salesforce/CRM connection, pull: - Current opportunity stage, amount, and close date - Last 3 activities logged on this account - Any active POV records and their status - Products currently in scope Also search the web for any news about [COMPANY NAME] in the last 14 days. Return a brief with: deal status, 3 discovery questions tailored to their situation, top talking points, and one suggested next step to propose at end of call.
Works best with Salesforce or Snowflake connected via MCP. Adjust field names to match your CRM setup.
Draft a starting point for POV success criteria in the customer’s language, not yours. Use as a conversation starter in the kickoff meeting — not as a finished document.
I'm starting a POV with [COMPANY] for [PRODUCTS]. Their stated goals are: [PASTE WHAT CUSTOMER SAID] Their environment: [KNOWN TECH STACK/CONTEXT] Key stakeholders: [NAMES AND TITLES] Draft POV success criteria structured as: 1. Business outcomes (in their language, not ours) 2. Technical milestones (specific, measurable) 3. Timeline with key dates 4. Definition of done Avoid vague language like "improved visibility." Frame everything from the customer's perspective.
Bring this draft to the kickoff and edit it together with the customer. Criteria they help write are criteria they own.
I'm the Director of Named Account Sales Engineering at Datadog, where I work alongside a team of SE leaders and individual contributors covering the company's largest enterprise accounts across North America.
I've spent 25+ years at the intersection of engineering and enterprise sales — starting as a Software Engineer at Microsoft and working through product management, individual SE work, and team leadership at companies including Mandiant, Rapid7, Malwarebytes, Splunk, and Datadog. Every role has been a team sport.
Right now, the work I find most interesting is figuring out how AI changes the SE function in practice — building new approaches with my team, rethinking what we hire for, and trying to separate what actually matters from the noise. I write about what we learn along the way, including the things that don't go as planned.