How CIOs Can Prove AI Is Improving the Employee Experience

Enterprise AI spending continues to increase as more organizations deploy AI applications across daily operations and refresh employee devices to support emerging workloads. For CIOs, the priority is determining whether those investments produce measurable improvements in employee productivity and technology performance.

Usage data can show which AI tools employees access and how frequently they use them. However, it does not indicate whether employees complete tasks more efficiently, produce higher-quality work, or encounter fewer technology issues after deployment.

A complete assessment requires more than adoption data, since performance changes may come from the AI apps, the device, or the workflow itself. The following metrics can help evaluate AI’s impact on productivity, employee experience, and business performance.

1. Start with a baseline before measuring AI productivity

A baseline gives IT and business leaders a reference point for determining whether an AI deployment improves employee productivity. For a specific workflow, they can record measures such as average completion time, correction rates, support requests, or employee feedback before deployment.

After rollout, those same measures can be compared across similar employees or teams. The analysis should remain specific to individual roles and tasks because the impact of AI can vary considerably across job functions.

Local AI processing, for example, may provide greater value for software developers than for employees whose work primarily depends on email or browser-based apps. The value of an AI PC therefore depends on the software in use and the computing demands of each role.

2. Measure completed work instead of AI activity

CIOs should assess whether employees complete work faster or with fewer errors, rather than use AI feature usage as a proxy for productivity. One way to measure this is to compare the time spent on repeatable activities before and after AI deployment. Depending on the role, these activities may include preparing reports, summarizing meetings, analyzing data, or creating documents.

Evaluate speed alongside output quality because faster production may require additional review. An employee may generate a first draft more quickly with generative AI but spend additional time verifying facts or correcting errors.

Revision cycles, approval times, correction rates, and rework can show whether the overall workflow improved. These measures help CIOs determine whether gains in one stage are offset by additional work later in the process. The same approach should apply to AI PCs, with CIOs measuring whether deployment produces measurable improvements in task completion time or output accuracy.

3. Track whether employees continue using the AI tools

Sustained adoption provides a better indication of long-term value than initial usage alone. Early activity may reflect training or curiosity, while patterns over several weeks can show whether an AI capability has become part of regular work.

Examine adoption by role and workflow, since company-wide averages can hide differences between employee groups. If usage remains high in one group but declines in another, the capability may be more relevant to certain tasks, or some employees may face barriers to continued use.

Low adoption can result from insufficient training, unnecessary process steps, or limited relevance to the workflow. Usage trends can help identify the cause, particularly when employees try a feature initially but reduce their use over time.

Include device performance in the analysis when AI apps require more computing resources. If employees encounter slow response times or reliability issues, declining usage may reflect technical limitations rather than limited interest in the capability. Comparing adoption trends with device and app performance data can help CIOs distinguish between those causes.

4. Use workplace telemetry to identify technology problems

Workplace telemetry helps CIOs determine whether device or app issues are affecting the employee experience and contributing to changes in productivity. Device health, app performance, crash frequency, and load times can reveal where technical problems interrupt work, while changes in help desk volume or downtime can indicate whether the employee experience improved after deployment.

HP’s Workforce Experience Platform (WXP) provides one example of how this information can be combined with employee feedback. The platform incorporates device health, app performance, and employee sentiment into its Workforce Experience Score, which gives IT teams an overview of how workplace technology is performing.

The HP Workforce Experience Platform documentation also describes visibility into device performance, app usage, system health, and user sentiment. This information can help IT teams identify technical issues that affect employees during AI-enabled work and determine whether problems are concentrated in a specific device, application, or user group.

HP executives have also framed employee experience measurement as broader than endpoint performance alone. Faisal Masud, Division President of Digital Services at HP Inc., said, “CIOs need a platform that can manage the full workforce experience, not just PC performance.”

At the application level, HP’s Application Experience documentation includes measures such as crashes, freezes, long load times, and app errors. By reviewing those indicators alongside employee experience data, IT teams can determine whether a problem originates in a specific app or reflects a wider technology issue.

5. Separate the AI application’s impact from the AI PC’s impact

When an organization introduces a new AI app and an AI-capable device at the same time, CIOs need to identify which change contributed to any improvement in employee performance. If task completion time decreases after deployment, the result may reflect the software, the device, employee training, or a revised workflow.

The analysis should therefore distinguish software-related improvements from those associated with the endpoint device. Completion time, output quality, correction rates, and continued usage can indicate whether the AI app improved the work itself, while processing time, responsiveness, reliability, and local execution capabilities can show whether the endpoint contributed to the result.

HP has connected this type of device-level visibility with the management of AI-enabled endpoints. Manoj Leelanivas, President of HP Solutions, said WXP provides “unified visibility, built-in security, seamless manageability, and proactive remediation across the device fleet.”

Comparisons between similar employee groups can provide a clearer basis for attribution. Organizations may compare employees who use the same AI application on different device configurations or examine results before and after a hardware refresh. Controlled pilots can strengthen this analysis because fewer variables change during the evaluation period.

This approach can help CIOs assess whether the additional cost of AI PCs is supported by measurable gains. Some roles may benefit from greater local processing capability, while others may already have devices that meet their performance requirements.

6. Pair employee feedback with operational data

Employee feedback provides context that device and app data do not capture on their own, especially when CIOs need to understand how technical issues affect specific workflows. Short surveys may ask whether an AI capability reduces time spent on recurring tasks, how often employees need to correct its output, or which technical problems interfere with use.

Task-specific feedback usually provides clearer evidence than broad assessments of overall productivity. HP supports this type of measurement through Sentiment Pulses in its Workforce Experience Platform, which collects employee feedback about workplace technology and converts responses into a Sentiment Score that organizations can track over time.

When survey responses are reviewed alongside device and app data, IT teams have a clearer basis for determining whether a negative employee experience is associated with technical performance or with the AI-enabled workflow itself.

7. Include the full cost of the deployment

The financial assessment should account for the full cost of deployment, including devices, software licenses, implementation, training, and ongoing IT support. The analysis should also account for employee time and IT workload, since small time savings can add up across a large workforce.

Hardware-related savings may also contribute to the return. If a device refresh reduces performance issues or support requests, part of the financial benefit may come from lower IT support demand rather than from AI features alone.

At the same time, measurable gains should be weighed against the additional cost of AI-capable devices. Some roles may benefit from greater local processing capability, while others may already have hardware that meets their performance requirements.

An AI tool may reduce the time required for a task while adding licensing or support costs that offset part of the benefit. For that reason, the investment decision should reflect both the total cost of the deployment and the measurable improvements it produces.

Use the results to guide the AI roadmap

Results from each deployment should inform where additional investment is most appropriate. When employees consistently complete tasks faster, improve output quality, or experience fewer technology problems, the evidence may support expansion to comparable roles or workflows.

Other deployments may require adjustments before broader adoption. Changes to training, software configuration, or device assignment may improve outcomes, while differences across employee groups can indicate that computing requirements vary by role.

Those differences are especially important when evaluating AI-capable devices. Some employees may benefit from local AI processing or greater computing resources, whereas others may achieve similar results through cloud-based AI applications on their existing devices.

By comparing results across roles and workflows, CIOs can base future AI investments on measured improvements in productivity, employee experience, and technology performance. This evidence also helps identify where additional investment is justified and where current technology already meets employee requirements.

FAQs

How can CIOs measure whether AI is improving employee productivity?

CIOs can compare pre- and post-deployment metrics such as task completion time, correction rates, output quality, support volume, and employee feedback. Measures tied to specific workflows provide a more precise view of productivity changes than companywide productivity scores.

What metrics should CIOs track after deploying workplace AI?

Relevant metrics include task completion time, output quality, correction rates, adoption, help desk volume, downtime, employee sentiment, and total cost. The appropriate mix depends on the workflow being evaluated and whether the organization is assessing software, devices, or both.

What should CIOs prioritize when building an AI roadmap?

CIOs should prioritize AI initiatives that show measurable improvements in employee productivity, technology performance, or operating costs. Each deployment should produce evidence that informs where AI should expand and where existing tools already meet operational needs.