105017 - What works in AI Adoption within DCMS Sectors
Department for Culture, Media and Sport · United Kingdom
Key facts
- Estimated value
- £1,500,000
- Contract ends
- 07 Mar 2030
- Buyer type
- Central government
- Source
- Find a Tender
Description
DCMS are seeking to commission "what works" research into the impact and effectiveness of AI adoption interventions within DCMS sectors. We are interested in two broad sets of questions. The first is analysis of how businesses within DCMS sectors that are in the process of adopting AI achieve this, the challenges they face and the opportunities and benefits realised. We anticipate this will be explored through an ethnographic study that observes how individuals and teams actually use AI tools in their jobs. The second set of questions is what policy interventions work best to support businesses that wish to adopt AI, which we anticipate will be answered by testing hypothetical interventions through randomised control trials (RCTs). Bidders will be able to propose alternative methodologies if they think they suit these questions better; the core need for this project is to produce direct, robust evidence of what works, as opposed to what is self-reported (e.g. through interviews, focus groups, surveys, and similar) to work. This project will be commissioned either through a Competitive Flexible Procedure under the Procurement Act 2023, or through the RM6126 Research and Insights framework. This is part of the DCMS Science and Analysis R&D Programme, which supports R&D capability within the department. This project will run concurrently aside three other large projects across 2027-2030, these include; mapping the specific "user pathways" audiences take to find, consume, and value information; a continuation of previous research to develop data-intensive approaches for capturing engagement at unticketed cultural, heritage, and sporting events/activities/locations, e.g. using mobile app data to model how people participate in our events; and a continuation of previous research to design and improve methods for holistically tracking the long-term impact of major events, tested on a major event.
Categories (CPV)
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