Overview
- Companies report that AI often produces superficially polished but incorrect or shallow work—called “workslop”—which creates extra verification and editing that cancels out promised efficiency gains.
- Consulting firm Accenture has said it spent roughly $3 billion on data and AI and over $1 billion a year on training before introducing a strict per-employee AI budget of $29 per month to rein in exploding token and license costs.
- A YouGov survey for recruiter SThree found 38.4% of 817 German MINT professionals said colleagues left their teams in the past year for AI-related reasons, with cited causes including radically changed job tasks, poor development prospects, and higher workloads.
- At the same time, two-thirds of AI users report higher team productivity than two years ago and say time saved is being reinvested in strategy, training, or extra tasks, creating a mixed picture of benefit and burden.
- Researchers and practitioners urge clearer governance, defined use cases, better data and sustained training to avoid skill erosion and uneven adoption, noting that an IAB survey found about two-thirds of German firms had not used AI tools by the end of 2025.