A recent survey of senior managers reveals that while AI adoption has sparked excitement about boosting productivity, it is also making it harder for managers to assess employee contributions accurately and manage work effectively.
- More than half of managers find employee evaluation harder post-AI adoption
- Managers spend more time reviewing AI-aided work
- Nearly half use AI to help inform decision-making and performance reviews
What happened
A survey commissioned by Monday.com and conducted by Nielsen, involving 1,700 directors and senior managers across seven countries, found that over 50% of managers report difficulty in evaluating employee work after deploying AI tools. These tools often produce polished work outputs that obscure how much contribution came from employees versus AI systems.
Consequently, nearly half of managers say they now invest more time reviewing and guiding their teams’ output than before AI adoption. Meanwhile, 94% of managers expressed enthusiasm for AI’s promise to improve productivity, though many acknowledge that organizational processes have yet to fully adapt to the rapidly evolving technology.
Why it matters
The integration of AI into workflows challenges traditional performance evaluation methods. Seventy-eight percent of surveyed managers have altered their evaluation criteria to emphasize skill in prompting AI, deciding task delegation to AI, and innovating new working methods. This shift raises questions about fairness since managers struggle to separate employee effort from AI-generated content.
Use of AI to assist managerial decisions is growing, with 46% of managers leveraging AI for decision support and 39% employing AI to draft performance reviews. However, this reliance risks embedding existing biases in AI models into judgment processes, potentially reinforcing unfair assessments along social and demographic lines.
What to watch next
Organizations face the urgent challenge of adapting managerial frameworks, evaluation standards, and AI governance to address transparency and fairness issues in employee assessment. Companies will need to develop clearer guidelines and tools to differentiate human work from AI contributions to maintain trust and accountability.
Additionally, managers are navigating increased complexity in software ecosystems, with 57% reporting more tools in use and 51% noting greater interdependence among them. The anticipated consolidation of software has not materialized, suggesting ongoing efforts to streamline AI tool management and oversight will be critical in 2027.