Maggie Schroeder-O’Neal is a senior principal, analyst in the Gartner HR Practice.
As organizations accelerate AI adoption, a hidden threat is impeding expected productivity gains: “workslop,” or low-quality work generated by or with AI that produces flawed results, adds minimal or negative value, and often requires extra human oversight to correct. In other words, the very tools meant to improve productivity can also drain it.
Often, workslop is a result of employees being pressured by their organization to produce more work faster, leaving no time or autonomy to do proper quality checks. This is a key reason why many organizations are struggling to realize value from their AI investments. In fact, a March 2025 Gartner survey of 137 CFOs revealed that only 10% said their organization has realized financial value from AI.
Combatting the workslop paradox is not about stopping employee laziness and does not require technical solutions. Instead, CHROs have a unique opportunity to reverse the workslop trend by revisiting the strategy for how employees apply AI in their work and how they are incentivized to behave.
Two practical steps that CHROs can take to prevent workslop are:
- Identify AI-friendly tasks and provide targeted use sessions
- Follow through with performance metrics that support future workflows
Identify AI-friendly tasks and provide targeted use sessions
Because AI tools have been able to assist with specific actions that employees previously would have needed to outsource, many organizations find that employees are not only producing more than ever, but absorbing responsibilities that previously justified additional support.
While on the surface the notion of more work being done at a faster pace is certainly appealing, what is perceived as enhanced productivity can lead to weakened decision-making and cognitive fatigue, resulting in greater instances of workslop.
For example, a 2025 Gartner survey of 2,986 employees reveals how employees who use AI experience an uptick in work friction, such as having to create new processes or workarounds for formal processes. However, that same survey found that when AI is applied to tasks selectively, workflows can unlock adaptability, increase human bandwidth, and improve decision quality.
It is incumbent on the CHRO to partner with business unit leaders and corresponding HRBPs to carefully consider whether workflow changes that align with AI use will genuinely add value or merely introduce unnecessary complexity. CHROs should also consult their employees about AI workarounds and points of friction and discuss where AI could potentially be helpful to them or where it has hindered work.
Once the tasks that would benefit from AI involvement have been identified, it is important to map out how each task will change and what new skills your workforce will need moving forward. CHROs must ask questions like:
- What are the resulting new tasks and critical skill needs?
- Has capacity changed?
- How will jobs/workflows need to change to get the most out of AI investments?
- Can we leverage our existing AI investments to automate tasks?
To support workflow changes, organizations should roll out targeted AI task development sessions to teach employees how to best use AI in the context of their workflow. CHROs should partner with learning and development leaders to begin creating targeted development sessions.
These sessions must be tied directly to the workflow changes that employees helped design as well as prioritized by how critical the skills are and the timeline for job transformation. Lastly, these sessions should be measured through performance outcomes tied to new responsibilities.
Rework performance metrics to reward value over volume
AI investment remains a top priority for organizations, with roughly 95% of organizations already using AI, according to a December 2025 Gartner survey of 110 CHROs. As a result of heavy AI implementation across organizations, leaders will often incorporate AI engagement-based indicators within individual performance metrics. This can include things such as how often an employee is using an AI tool as well as time-to-production goals.
While it is important to examine how AI impacts the speed of task completion, that doesn’t mean speed should be incentivized in performance metrics at the individual level. More often than not, these engagement-based indicators only capture surface-level activity, failing to account for things like reduced errors, accelerated decisions or process improvements.
CHROs have an opportunity to define how AI value is understood and executed. To do so, CHROs must update performance metrics standards across the organization, reducing the emphasis on speed at the cost of value creation. CHROs should avoid any mandated AI usage performance metrics, by instead:
- Aligning performance metrics with organizational strategy and maturity using nonevaluative goals
- Supporting leadership by creating guidelines for usage norms
- Setting base and stretch targets once there is evidence of repeated improvements to facilitate continued learning
Moving forward, the best CHROs will be the ones who help lead their organizations out of this workslop trap by focusing on saving employees’ effort, not just time.
