Why AI Adoption Is the Wrong Metric: Measure Work Outcomes Instead, Says ServiceNow's John Phillips

On the latest episode of the podcast You Should Know, co-hosts Ryan Leary and William Tincup are joined by John Phillips, Group Vice President of Employee Experience at ServiceNow, for a candid conversation about the true metrics of AI success. Phillips argues that the industry is measuring AI adoption the wrong way: counting tool usage misses the point entirely. What matters is whether jobs get done faster, with less friction, and with better outcomes for both the employee and the business.

Phillips is blunt about the current state of AI in the workplace: "Every system of record is now got their little AI agent and it's creating chaos for these practitioners," he says. "We're watching this like train wreck of productivity." He predicts a fast pivot in how success is measured: "We're going to quickly stop talking about AI adoption as tool usage, and we're going to start looking at the outcomes and jobs to be done."

The conversation covers several key themes. Phillips highlights the fragmentation problem: customers often arrive with eight purchased AI tools plus one they built themselves, none of which talk to each other. ServiceNow's approach is to layer an agentic companion across these systems rather than ripping and replacing them. He describes a vision of an AI control tower that stitches together 15 LLMs and 100 systems.

Phillips also emphasizes the two-sided value exchange between employee and employer. He asks what happens to the 23 hours a tool claims to save you, and stresses the importance of discretionary effort over traditional engagement surveys. "High performance has both extreme focus and extreme recovery," he notes. "It's in any environment, the highest performers in the world have those things."

Tincup revisits his long-standing critique of engagement surveys, pushing Phillips on whether discretionary effort is the truer metric. Leary shares a personal story about applying to Home Depot and never receiving an acknowledgment email, illustrating the disconnect between tools and human experience.

Phillips' perspective is shaped by time spent in refugee camps, where he learned that "skills and talent is universal and opportunity is not." This philosophy informs his approach to employee experience and the need for hyper-personalization.

The episode lands as CHROs face growing pressure to prove AI productivity gains across increasingly fragmented technology stacks. Phillips' message is clear: stop counting clicks and start measuring outcomes.