What I Wish Existed My First Year as an SE
My first year as an SE, I spent more time than I’d like to admit just trying to get educated fast enough to be credible.
Scrambling to understand the product. Piecing together answers from docs, Slack threads, asking senior people questions I worried made me sound green. Trying to look field-ready before I actually was.
Last month I was doing competitive research — checking what the big players were doing with AI on their websites. So I opened one of their product pages and started using the chatbot. Not casually. I asked the kind of questions I used to panic about as a new SE: architecture edge cases, integration complexity, failure modes, compliance requirements.
The answers came back in seconds. Detailed, accurate, well-structured.
This tool would have changed everything for me in year one.
And then the other thought followed immediately: buyers are using this same tool right now. They show up to calls with your SE already knowing the product — educated by AI before the meeting was even booked. The SE who shows up to deliver information is walking into a room that no longer needs them for that.
That’s not a future risk. It’s already happening.
AI isn’t coming for Solutions Engineers. It’s coming for a specific version of the role: the one whose entire value is answering product questions. And if that’s still the job description, the problem isn’t AI. It’s that the bar moved and the role didn’t.
Two markets are emerging from this shift, and they look nothing alike.
The first: AI as replacement. Automate the demo, generate the proposal, answer the RFP. The SE becomes unnecessary because AI handles information delivery faster and cheaper.
The second: AI as elevation. Use AI to handle the commodity work — demo prep, environment setup, RFP responses — so SEs can spend more time on the work that actually wins deals. Discovery. Architecture design. Whiteboarding. Building trust with technical buyers who’ve already done their homework.
The first market gets the headlines. The second gets the enterprise contracts.
The skill that separates good from great isn’t product knowledge. AI will always know the product better. It’s not demo fluency — AI can generate a personalized demo in minutes.
The irreplaceable skill is live technical problem-solving with a human being in the room.
The whiteboard session where a customer describes a migration that doesn’t fit any reference architecture, and the SE sketches a solution in real time. The moment in discovery where the SE catches that the stated problem isn’t the actual problem. The ability to say “honestly, this isn’t the right tool for that use case” — and gain trust instead of losing the deal.
That requires situational judgment, technical creativity, credibility through vulnerability, and human connection. None of which the chatbot has.
The question isn’t how to teach SEs the product faster. AI already does that better than any onboarding program.
The question is how to develop SEs into practitioners who can diagnose, architect, and advise at a level that makes AI a tool — not a replacement. Scenario-based practice instead of product walkthroughs. Live feedback instead of self-paced modules. Competency that shows up in a real customer conversation, not a certification score.
I think about what my first year would have looked like with AI handling the knowledge ramp — and me spending that time instead on the skills the chatbot can’t teach.
That’s the version of readiness worth building.
What would have changed for you as a first-year SE if you’d had access to tools like this? I keep wondering what we’re still leaving on the table.