Balancing Individual AI-Augmentation with Organisational Stability
Short description
Employees are building personalised AI workflows, sometimes outside formal organisational systems. This project studies how individual experimentation, personal digital infrastructures and “shadow AI” affect collaboration, oversight and organisational resilience. It compares bottom-up adoption with top-down rollout and follows consequences for trust, disclosure, handovers and continuity.
Longitudinal cases will combine observation, digital diaries and interviews with collaboration-network analysis. The research aims to explain the trade-offs between individual augmentation and collective stability, and to develop guidance on disclosure norms, versioning, audit trails and workflow portability that helps organisations retain the benefits of experimentation.
Applications closed
The application period ended on 10 October 2026. Applications are being reviewed.
What the project involves
- Examine how employees build personalised AI workflows and how these affect collaboration, oversight and resilience.
- Compare individual experimentation with organisational rollout, tracing consequences for trust, disclosure and continuity.
- Combine longitudinal cases, observation, digital diaries and interviews with collaboration-network analysis.
- Develop guidance on disclosure, versioning, audit trails and workflow portability.