What Is The Enterprise AI Growth Rate Excluding Engineering?
How does 10-15% sound?
That’s right. 10-15% Enterprise AI growth ex-software engineering. I wrote a piece earlier, but never assigned an Enterprise growth rate ex-engineering. Knowledge work outside of Engineering is not as verifiable, which is a headwind to Agentic AI deployment. But the biggest Enterprise deployment blocker for Agentic AI is siloed data, stove-piped legacy systems, and lack of organizational coordination around intended AI outcomes. These are the same blockers that existed 10-12 years ago when companies wanted to roll out the first iteration of machine learning. They called it “Big Data” back then.
Companies will have to integrate data, make sure public and private clouds talk to each other, develop a plan for porting data to data lakes and AI-friendly databases. This is why Microsoft Fabric is doing well. This Agentic AI rollout will be a multi-year, multi-decade process.
For now, the LLM companies will keep buying chips at a torrid pace, but Revenue growth will slow for Anthropic and OpenAI as Software Engineering becomes a smaller portion of Revenue.
Why do I say 10-15%? My view is that ServiceNow (NOW) is best in class on the integration/dashboard side and will help pave the way for Agentic AI. If ServiceNow is growing 18-20%, Enterprise AI spend excluding Engineering will be slower than ServiceNow’s growth rate.