How CIOs Can Prove AI Is Improving the Employee Experience

CIOs can assess AI's workplace impact by monitoring productivity, technology performance, employee satisfaction, adoption rates, and deployment expenses. The IT sector sees growing expenditure on AI as businesses integrate these tools into daily operations and upgrade employee devices to support new workloads. For CIOs, the focus is on determining if these investments yield quantifiable gains in employee productivity and technology performance.

Tracking usage data reveals which AI tools employees engage with and their frequency of use, but it does not necessarily indicate whether employees accomplish tasks more quickly, produce higher-quality work, or experience fewer technology issues post-deployment. A thorough evaluation goes beyond mere adoption statistics, as performance improvements might stem from the AI application, the device, or the workflow itself.

Key metrics to evaluate AI's influence on productivity, employee experience, and business outcomes include baseline measures for productivity before AI deployment. These measures could encompass average completion times, error rates, support requests, and employee feedback for a given workflow. Post-implementation, comparing these metrics across similar employees or teams can help ascertain whether AI deployment enhanced efficiency.

The impact of AI may differ significantly across job functions, so it's crucial to assess specific roles and tasks. For instance, local AI processing might offer more benefits to software developers than to employees primarily relying on email or browser-based applications. Evaluate whether employees complete tasks faster or with fewer errors post-AI deployment rather than relying solely on AI feature usage as a productivity proxy.

Speed should be compared alongside output quality, as AI-generated drafts might require additional fact-checking or editing. The same methodology should apply to AI PCs, with CIOs gauging improvements in task completion time or output accuracy. Continued usage serves as a stronger indicator of long-term value than initial adoption alone.

Early engagement might reflect training or curiosity, whereas sustained usage over several weeks suggests that the AI capability has become an integral part of regular work. Analyze adoption patterns by role and workflow, as company-wide averages can mask disparities among employee groups. A significant drop in usage for certain employees could indicate that the AI capability is more suited to specific tasks or that some personnel face obstacles in adopting it.

Factors such as inadequate training, unnecessary steps, or limited relevance to the workflow could contribute to low adoption. Monitoring usage trends can help identify the root cause, especially when employees initially experiment with a feature but subsequently reduce usage. Consider device performance when AI tools demand additional computing resources.

Declining usage due to slow response times or reliability issues may point to technical limitations rather than a lack of interest in the capability. Comparing adoption trends with device and app performance data can help CIOs discern whether technical issues are affecting employee experience and productivity. HP's Workforce Experience Platform (WXP) offers an example of integrating device health, app performance, and employee feedback into a comprehensive assessment of workplace technology.

The platform's Workforce Experience Score provides IT teams with an overview of how technology is performing in the workplace. HP's documentation also outlines measures such as crashes, freezes, long load times, and app errors at the application level, enabling IT teams to pinpoint whether problems originate within a specific app or reflect broader technology issues.

When introducing both a new AI app and an AI-capable device simultaneously, CIOs must determine which change contributed to any observed improvement in employee performance. A reduction in task completion time could result from software enhancements, device upgrades, or employee training.