

馬特哈里森克拉夫 / Ikon Images
At the Community College of Philadelphia, generative AI tools didn’t magically free up people’s time or supercharge one productivity metric across the board. An analysis found that GenAI resulted in different workflow gains for key roles: decisiveness for executives, speed for operational leaders, and resolution efficiency for student-facing professionals. When judging GenAI’s success, examine how your organization’s work changed shape.
When leaders talk about generative AI tools, one promise comes up repeatedly: These tools will save time.
Fewer emails. Fewer meetings. Less administrative drag.
That expectation shapes how many organizations decide whether GenAI is “working” — and why they’re often disappointed when people’s calendars don’t suddenly open up.
But for our organization, time savings turned out to be the wrong place to look.
What we saw inside a large public higher-education institution, the Community College of Philadelphia, wasn’t less work, but work that changed form. When generative AI entered our organization’s everyday workflows in 2026, coordination didn’t vanish. It shifted away from meetings and toward writing, away from clarification and toward clearer first passes, away from back-and-forth deliberation and toward faster closure on decisions.
To understand what really changed, we looked at how three of the professional roles within one administrative unit — executive leaders, operational leaders, and student-facing professionals — worked during the same six-week period across four different years. What emerged wasn’t a story about automation replacing people. It was a story about how work gets shaped, completed, and passed along.
That distinction matters. Organizations that judge AI only by hours saved risk missing the real gains and feeling underwhelmed by AI, even when it’s quietly doing what it’s supposed to do.
Three Groups’ GenAI Gains
GenAI tools showed up in day-to-day work at our college in 2026. To understand the impact GenAI had on the three groups of professionals, we examined the same six-week window each year (February 1 through March 15), comparing work in 2026 with patterns from the previous three years.
We didn’t ask, “How fast did people work?”
We asked, “What kind of work were they producing?”
Because staffing levels and work hours remained essentially the same across all four years, any differences we observed reflected changes in how work was done, not changes in capacity.
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