A VP Sales Built His Own AI Tools. Your RevOps Team Stopped Doing RevOps. Both Are True.

A Reddit thread titled "some revops teams have stopped doing revops" formed a provocative take on people chasing the AI train instead of focusing on the foundations of the day job.

The consensus here is that people who built the GTM tooling stack are abandoning pipeline hygiene to build AI tools instead without thinking about downstream workflows or maintenance costs. It's an interesting take on whether the AI move is partly tons of us investing a ton of time and energy here in order to not be left behind.

Al Chen at Galileo fed Claude Code his entire production codebase and customers noticed the quality lift. My first read was "so risky, what about all your proprietary data, but the grounding and ROI makes sense." That hedge is the correct frame. Full context beats scoped prompts, and the quality lift is real.

But here is what the article is actually about: Galileo had the engineering discipline to feed a structured codebase to an AI model, manage the output risk, and ship customer-visible improvements. The lesson transfers. The execution prerequisites do not.

The part nobody is writing about yet is the shadow AI angle. Al Chen had structure, governance, and engineering oversight. Most operators feeding production data to Claude Code at two in the morning have none of those things. When employees feed customer data, deal threads, and internal communications to AI tools without a governance layer, you get a data exposure surface your security team cannot see and your procurement team has not scoped. Yash Tekriwal at Clay built a custom Slack inbox the same way and reports it was easier than expected. That is the point. The barrier to feeding production data to an AI tool is now low enough that it is happening whether your org has a policy or not.

Every growth-stage B2B company this quarter is having the same argument: vertical product variants, or horizontal product with verticalized GTM? SaaStr's breakdown of how Databricks sells to dozens of industries without a single vertical product is the best piece I have read on why the reflexive "build vertical" answer has more nuance than you would think.

When a buyer asks "do you know our industry," they are asking a risk question: what blows up in my face if I buy this and your team does not understand my regulatory environment, my data flows, my customer pattern? Databricks answers that risk question directly. They demonstrate that the platform handles the buyer's risk surface through reference architectures and composability of the underlying data layer. The reframe from "let me show you our healthcare logos" to "here is how the platform addresses your specific risk" is teachable at any scale.

Databricks' horizontal GTM works because their data architecture is genuinely composable, something that is not universally a given.

An eleven-year software engineer caught himself unable to debug without AI and called it the scariest thing he had seen in the industry. A separate thread surfaced practitioners feeling anxious about deviating from what AI tells them to do.

Collectively we're going to need to figure out how to set aside time to keep doing "the work", even as what "the work is" undergoes dramatic transformation.

At the same time and again showing the all-in vs. definitely-not split, eighty percent of white-collar workers are refusing their employers' AI adoption mandates. On the surface it sounds crazy, but it actually has some merit given skill decay patterns.