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.
Practical, shareable, occasionally fun. Built by a Sales Engineer for the SEs, AEs, and managers who run enterprise deals. Tools to sharpen your instincts, assess where you stand, and unstick the deal in front of you.
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 →Every SE team has a version of the same deal in their pipeline. It started well and somehow stalled. Here is why ramp takes so long, and what the fastest-ramping SEs do differently.
Read →Most SEs treat discovery as a box to check before the demo. The ones with the highest win rates treat it completely differently.
Read →The best demos aren't the most thorough ones. They're the ones where the customer feels genuinely understood.
Read →Every enterprise deal has someone who will fight for you when you're not in the room. Or someone who won't. Here's how to build the real thing.
Read →The objection is rarely about the technology. Here's how the best SEs figure out what's really being asked, and what to do about it.
Read →Competitive deals aren't won by attacking the competition. They're won by being the SE the customer trusts most.
Read →The quality of this relationship often determines the ceiling on what's possible in a deal. And when it's great, it's one of the best things about this job.
Read →SE stands for Sales Engineer. Big S, big E. The companies that get comp right understand that, and build their programs accordingly.
Read →The transition from top SE to SE manager is harder than most people expect. Here's what actually matters in the first three months.
Read →The tools that are actually changing how SEs work today, not the ones that might matter someday. Practical, specific, and field-tested.
Read →The SE is the only person in a QBR with the credibility and context to make it genuinely valuable for the customer. Here is what the best ones do with that.
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.
The two prompts above are a sample. The complete SE AI Prompt Library includes 20 prompts covering POV strategy, competitive positioning, executive engagement, hiring, and more, with guidance on when to use each and how to automate them.
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 Fluke Networks, 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.