What’s Covered?
This report introduces a practical framework for categorising how AI is—or could be—used across business sectors, with a special focus on UK industries targeted by the BridgeAI programme: agriculture, construction, creative industries, and transport/storage. Rather than proposing theoretical classifications of AI systems, this guide zeroes in on real-world use cases, offering companies a structured way to think about adoption before they even start building.
The core of the framework is built around four dimensions:
- Organisation – What is the AI system supporting? A process? A product? Customer experience?
- AI System – What type of system is being used? Predictive models, generative tools, hybrid architectures?
- Data – What kind of data does the system rely on? Is it internal or external, static or real-time?
- Sector – What economic context does this apply to?
It’s designed to help companies match business needs with technical solutions and decide where AI might offer value, based on their unique constraints and capabilities. Importantly, the framework doesn’t assume the reader has already deployed AI—it’s a planning tool aimed at helping teams structure their exploration of AI opportunities.
The authors also reviewed and trialled the framework using 40 real-world use cases across the four sectors, later narrowing that to 20 for detailed sector briefings. These examples helped stress-test and refine the model through input from researchers, policy stakeholders, and BridgeAI partners.
The report is part of a five-part briefing series, with this document acting as the conceptual backbone. Follow-up briefs break down five key use cases per sector, explain the business context, and offer lightweight strategic takeaways.
💡 Why it matters?
There’s a flood of AI opportunity, but very little clarity. This framework helps bridge the awareness gap between knowing AI could help and knowing how AI could help. It makes it easier for early-stage adopters to align their business goals with the kinds of AI systems that might be a fit—without needing to have all the answers up front.
What’s Missing?
This version stops short of linking AI use cases to specific risk profiles—something planned for a later phase. That limits its utility for compliance officers or AI governance leads looking to stress-test operational risks. It also lacks quantitative adoption benchmarks or investment indicators, which would help businesses understand how mature certain use cases are in the market.
Another gap is the absence of tooling or templates—for example, worksheets or decision trees to apply the framework in workshops or planning sessions. And while sectoral differences are acknowledged, the report doesn’t offer guidance on cross-sector learning or how transferable use cases might be.
Finally, the paper is UK-focused and funded by BridgeAI, which means international applicability is somewhat implied but not directly addressed.
Best For:
SMEs and mid-sized businesses beginning their AI exploration. Especially relevant for innovation teams, consultants, and non-technical leaders who need a structured way to frame discussions with technical partners. Also useful for regional policymakers and accelerators looking to guide responsible AI adoption in sector-specific contexts.
Source Details:
Title: Business Applications of Artificial Intelligence: A Framework to Categorise AI Use Cases (2024)
Authors: Arcangelo Leone de Castris, Shakir Laher, and Dr. Florian Ostmann
Affiliation: The Alan Turing Institute (AI Governance & Regulatory Innovation Team)
Publisher: BridgeAI, funded by Innovate UK
License: Open publication with citation required
The lead authors are affiliated with The Alan Turing Institute’s Public Policy programme, working at the intersection of responsible AI adoption and regulatory readiness. They’ve drawn on both academic research and lived experience with BridgeAI partners to craft a tool meant to help businesses take the first serious step toward operational AI—without being overwhelmed.
