AI rarely kills a “job that needs to be done.” But it's likely to kill standalone, single-step features that can be done “good enough” inside an existing platform (Microsoft 365, Google Workspace, Salesforce) or a general AI assistant.
what happens when an AI model misses something critical in a clinical trial, such as when a group of patients stops responding to a drug that looked promising in earlier phases? The consequences aren’t an annoying recommendation. They’re measured in human lives.
Generic AI agents sound like a shortcut: plug in a tool, connect a few apps, and let automation run. For many mid-market companies, that promise breaks down fast. The issue usually isn’t that AI “doesn’t work.” It’s that generic agents are built for the average workflow, the average data quality, and the average risk tolerance. Mid-market operations are rarely average. They’re complex enough to need real governance and integration, but lean enough that failures hit harder and faster.
Every entrepreneur dreams of launching the perfect product: sleek, powerful, and ready to scale from day one. But here's the brutal truth: that's rarely how successful startups actually start. In fact, your Minimum Viable Product (MVP) might be little more than spreadsheets, email automation, and manual labor disguised as "tech," as Michael Zalle discovered when he launched YellowBird.