Track chairs

Dr Victoria Uren, Centre of Excellence for Enterprise AI, Aston University, Birmingham, v.uren@aston.ac.uk
Dr Sian Joel-Edgar, Computing Discipline, Northeastern University London, sian.joel-edgar@nulondon.ac.uk

Track call

Recent years have seen AI, including generative, predictive and agentive systems, widely adopted by enterprises. AI technology is robust, but many enterprises still struggle to advance adoption beyond the pilot stage and create strategic value. Shadow AI systems, such as unofficial, unapproved, or unmanaged use of AI tools, are sometimes used by employees to fill the gaps, running without effective governance guard rails. This track builds on the work of Silic et al., 2025 who identified that there are emerging socio-technical tensions between organisational governance and employees’ practical needs for flexibility, efficiency, and innovation. They also identified that shadow AI systems are not malicious by definition, but there is a gap in governance mechanisms to address rapidly evolving AI capabilities, and employees often adopt unofficial AI tools to support everyday work practices. This is particularly the case for AI mediated communication channels, with generative AI widely used to speed up drafting documents and messages, or to support workers who lack confidence in their own abilities. We invite papers that take a sociotechnical approach to the study of unofficial, unapproved or unmanaged, shadow AI adoption in enterprises in regard to both private and public sectors. Interdisciplinary and cross disciplinary work is welcome, including case studies, empirical research, action research, and design studies.

Indicative topics

  • Sociotechnical factors in the adoption of shadow AI
  • Authorised AI and shadow AI, balancing control and creativity in the adoption process
  • Aligning shadow AI to organisational strategy
  • Where shadow AI fits in enterprise processes and organisational cultures
  • The transition of shadow AI to formal adoption and governance
  • Upskilling employees for effective shadow AI use and recognition of its limits
  • Design of AI interfaces to promote safe and effective use of shadow AI
  • The intersection of shadow and authorised AI in organisations
  • The impact of unacknowledged AI use on communication practices
  • Building trust in shadow AI systems and their outputs

References

Silic, M., Silic, D. and Kind‐Trüller, K., 2025. From shadow it to shadow AI–threats, risks and opportunities for organizations. Strategic Change.