Traditional SaaS companies are workflow tools, but AI demands data apps. This shift is an extinction event for 99% of engineering cultures—and a forced evolution for founders who think they can stay non-technical.
Traditional business application software from 2000-2022 (e.g. software sold into HR, Sales, Marketing, etc.) has been primarily workflow automation tools — Salesforce started with SFA (Salesforce Automation), ServiceNow started with IT Workflow Automation, and Workday is HR workflow automation.
The core value proposition was coordinating large groups of people around a process.
When you wanted to do complex data analysis — crunch lots of numbers, combine data from different data sources, etc. — you would push that data into a data warehouse (Teradata in the 2000s, later replaced by Snowflake) and layer it with a BI solution (like Tableau or, more recently, Sigma/Looker).
SaaS apps that lived in the world of Security and Infrastructure were more likely data/infra apps from the beginning — companies like Rubrik, Crowdstrike, and Datadog fit this mold.
The type of engineers and cultures you attract to build workflow apps versus data apps is materially different.
AI requires a higher percentage of SaaS apps to become data apps.
99% of apps will die in this process because they have to build a fundamentally different engineering culture. Only 1% will survive.
Rippit realized this three years ago and embarked on this journey — partly through our instinct of how SaaS needed to transform, and partly through a little bit of luck.
This also transforms my role, and I’m going through my own journey of survival. As a non-engineer, being the founder of a workflow app is significantly less technical and more S&M-oriented than being the founder of a data app.
My Monday morning meeting used to be the Go-To-Market kickoff and now it’s the Engineering kickoff. I’m working very hard to become as effective as I can at lower levels of product decisions — my ceiling here directly affects Rippit’s ceiling.
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