Measuring Workplace AI by Employee Outcomes With DXC Technology

What if the most useful measure of workplace technology was not the number of tickets closed, but the amount of productive time returned to employees?

In this episode of Tech Talks Daily, I speak with Kelly Candler, Global Offering Lead for Workplace and Business Process Services at DXC Technology, about moving digital workplace services away from activity metrics and toward employee and business outcomes. Kelly has worked on both sides of the managed services relationship. Before joining DXC, she was a customer of the company and several of its competitors, giving her a practical view of what large enterprises expect from workplace technology and where support models continue to disappoint.

Kelly begins with a simple distinction. Traditional IT reporting often focuses on ticket volumes, response times, and calls answered. Employees care about whether they can do their jobs, how much time they lose, and how quickly they can return to productive work. When the outcome becomes the starting point, support is judged by its impact on the employee rather than the amount of activity generated behind the scenes.

That matters because many companies already have crowded workplace technology estates. Years of investment have produced overlapping tools, legacy applications, automation, device platforms, and service channels. Adding another AI product can increase cost and confusion if it replaces nothing and connects to little. Kelly argues that organizations should understand the technology they already own, identify the employee outcomes they want, and use orchestration to connect those investments rather than discarding them automatically.

DXC positions its Workplace Services offering as a people-centered, AI-native service enabled by DXC OASIS, its orchestration platform for Human+ agentic AI workflows. In the interview, Kelly describes Human+ as a partnership in which AI handles repetitive work while people retain creativity, judgment, and accountability. The intention is to make support more proactive, provide employees with help through familiar channels, and allow experienced teams to concentrate on work that requires human knowledge.

Device performance provides a practical example. In a reactive model, employees discover that a laptop has become slow, unstable, or unusable after productivity has already been lost. Kelly explains that operational data and automation can identify patterns such as declining battery health, storage problems, and application