Transformative AI: Redefining Workflows for Success

About half the tasks in a typical workday could already be done faster by AI—yet most teams still work as if it doesn’t exist. A sales rep trusts an AI draft more than her own, while her manager forbids it. So who’s really in charge of the workflow now: the org chart or the algorithm?

McKinsey estimates that up to 70% of what we call “work” today can be automated or augmented by existing tech—yet most teams still bolt AI onto old processes like a gadget, not a gear. The real shift happens when AI stops being a tool you occasionally consult and becomes a silent partner built into how decisions, handoffs, and feedback loops actually run. That’s what turns scattered experiments into measurable gains: GitHub’s Copilot changing how code reviews are structured, or UPS’s routing AI reshaping how dispatch, drivers, and customer promises fit together. In these cases, the org didn’t just add AI; it rewrote who does what, when, and with which information. This episode is about that redesign: how to move from “using AI” to re-architecting workflows so your team plans, executes, and learns in fundamentally new ways.

Some of the most interesting gains from transformative AI aren’t coming from tech-first companies, but from places that once looked “too physical” or “too human” to change. Hospitals are routing patients based on live capacity and risk scores rather than static schedules. Marketing teams are running dozens of creative variations per campaign, learning in days what used to take quarters. And in customer service, Gartner expects generative AI to handle most new interactions within a few years. The pattern isn’t replacement; it’s redistribution—who prepares, who decides, who reviews, and when they step in.