As organisations accelerate AI adoption, a new productivity problem is emerging: “workslop”, which is low-quality work generated by or with AI that adds little value and requires additional human time and capacity to correct.
The promise of AI is to help employees produce more, faster. But when employees are pressured to increase output without the time or autonomy to ensure quality, AI can end up creating more work rather than reducing it. This is making it harder for organisations to realise the productivity and financial value they expect from AI.
Just 10% of CFOs have said their organisation had realised financial value from AI, according to Gartner research. At the same time, only 1% of layoffs in the first half of 2025 were driven by AI-related productivity gains, putting leaders in a difficult position as they make workforce decisions based on AI returns that have not yet been realised and, in some cases, may never materialise. By 2027, Gartner predicts that 50% of companies that attributed headcount reductions to AI will rehire staff to perform similar functions, but under different job titles.
For CHROs, the answer is not simply to increase AI adoption or reduce headcount. Instead, they must ensure AI is applied in ways that genuinely improve organisational performance and employee productivity.
Make AI Work for the Workflow
One way CHROs can tackle workslop is by identifying the tasks where AI can genuinely add value and providing employees with targeted sessions on how to use AI within those workflows.
AI tools can assist with specific actions that employees previously would have needed to outsource. As a result, employees may find themselves producing more work and absorbing responsibilities that previously justified additional support.
While more work being done at a faster pace can appear to be a productivity gain, it can also lead to weakened decision making, cognitive fatigue and greater instances of workslop.
Our research has found that employees who use AI experience an uptick in work friction, such as having to create new processes or workarounds for formal processes. However, when AI is applied selectively to appropriate tasks, workflows can unlock adaptability, increase human bandwidth and improve decision quality.
CHROs should therefore partner with business unit leaders and HR business partners to assess whether workflow changes associated with AI will genuinely add value or simply introduce unnecessary complexity. They should also consult employees about AI workarounds and points of friction, identifying where AI could help and where it may be hindering work.
Once tasks that would benefit from AI have been identified, CHROs should map how those tasks will change and what new skills the workforce will need. This means considering what new tasks and critical skill needs will result, whether employee capacity has changed, how jobs and workflows need to evolve to get the most out of AI investments and whether existing AI investments can be leveraged to automate tasks.
Organisations should then support these workflow changes with targeted AI task development sessions that teach employees how to use AI effectively within their specific workflows. CHROs should partner with learning and development leaders to create these sessions, tying them directly to workflow changes that employees helped design. The sessions should be prioritised according to the criticality of the skills required and the timeline for job transformation and measured through performance outcomes tied to new responsibilities.
Reward the Value AI Created, Not the Speed of Output
CHROs must also reconsider how they measure productivity in an AI-enabled workplace. AI investment remains a top priority for organisations, with roughly 95% of organisations already using AI.
As AI becomes more embedded in organisations, leaders may incorporate AI engagement indicators into individual performance metrics, such as how frequently an employee uses an AI tool or how quickly they produce work.
But while AI can influence the speed of task completion, speed alone does not demonstrate value. Engagement-based indicators can capture surface-level activity without accounting for outcomes such as reduced errors, accelerated decisions or process improvements.
This is particularly important in the fight against workslop. If employees are rewarded primarily for producing faster work, they may have less incentive to ensure that AI-assisted output is high quality and genuinely useful.
CHROs should instead redefine how AI value is understood and measured across the organisation, reducing the emphasis on speed when it comes at the expense of value creation. Rather than mandating AI usage through individual performance metrics, they should align performance metrics with organisational strategy and AI maturity using nonevaluative goals, support leaders by creating guidelines for AI usage norms and set base and stretch targets once there is evidence of repeated improvements, facilitating continued learning.
The challenge for CHROs is therefore not simply to encourage employees to use AI. It is to ensure that AI changes how work gets done in ways that create measurable value.
As organisations continue to make workforce and investment decisions based on expected productivity gains of AI, CHROs have a critical role to play in distinguishing more output from more value. The best CHROs will help their organisations avoid the workslop trap by focusing on saving employees’ effort, not just time, and ensuring AI delivers genuine performance gains rather than simply generating more work.
