The conversation around AI in the workplace has been dominated by adoption metrics, but John Phillips, Group Vice President of Employee Experience at ServiceNow, argues that this approach is fundamentally flawed. In a recent episode of the podcast 'You Should Know,' Phillips discussed why counting tool usage misses the point entirely. What truly matters is whether jobs get done faster, with less friction, and with better outcomes for both employees and the business. This perspective comes at a critical time when CHROs face mounting pressure to demonstrate AI productivity gains across increasingly fragmented technology stacks.
Phillips paints a stark picture of the current AI landscape, describing it as a 'train wreck of productivity.' He points out that every system of record now ships its own AI agent, creating chaos for practitioners. 'We're watching this like train wreck of productivity,' he told hosts Ryan Leary and William Tincup. The problem is that these agents do not communicate with each other, leading to a disjointed experience that undermines efficiency rather than enhancing it.
The solution, according to Phillips, lies in shifting the focus from adoption to outcomes. '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,' he said. This shift is essential because the ultimate goal of AI in the workplace is to improve work outcomes, not just to increase the number of tools employees interact with. By measuring the real impact on productivity and employee satisfaction, organizations can better assess the value of their AI investments.
Phillips also highlighted the importance of human performance, noting that 'high performance has both extreme focus and extreme recovery.' This principle applies to any environment, and it underscores the need for a balanced approach to work that prioritizes well-being alongside productivity. The episode delved into the two-sided value exchange between employee and employer, questioning what happens to the time saved by AI tools. Phillips emphasized that the benefits of AI should be shared, not just absorbed by the organization.
ServiceNow's approach to addressing the AI fragmentation issue involves layering an agentic companion across existing systems rather than ripping and replacing them. Phillips described customers arriving with eight purchased AI tools plus one they built themselves, none of which talk to each other. By stitching together 15 LLMs and 100 systems, ServiceNow's AI control tower vision aims to create a cohesive, integrated experience that reduces friction and improves outcomes.
The discussion also touched on the collapse of work boundaries post-COVID and the resulting burnout, as well as the internal dialogue of 'am I enough' that many employees experience. Phillips connected these issues to a broader philosophy shaped by his time in refugee camps, arguing that 'skills and talent is universal and opportunity is not.' This perspective informs his belief that AI should be used to democratize opportunity and enhance human potential, not just to optimize processes.
Co-host William Tincup revisited his long-standing critique of engagement surveys, pushing Phillips on whether discretionary effort is a truer metric. Phillips agreed, suggesting that hyper-personalization beats one-size-fits-all pulse data. Co-host Ryan Leary shared a personal saga of applying to Home Depot and never receiving an acknowledgment email, illustrating the kind of friction that AI could help eliminate.
As the episode concluded, the message was clear: the time for measuring AI adoption is over. The future lies in measuring work outcomes—how AI helps employees achieve their goals faster, with less effort, and with greater satisfaction. This shift is not just a matter of metrics; it is a fundamental rethinking of what success looks like in the age of AI.
