The Department for Digital, Culture, Media & Sport (DCMS) and the Office for Artificial Intelligence commissioned Oxford Insights and Cambridge Econometrics to evaluate which channels are the most effective at transforming AI R&D into marketable AI products and services in the UK. This includes understanding the role played by AI technical standards as a channel for AI R&D commercialisation.

The research report identifies the most prevalent ways, or routes, by which AI R&D is commercialised in the UK:

• university spinouts: businesses that grow out of a university research project, which attempt to transform research into a commercial product or service;
• startups: businesses in the early stages of operations, exploring a new business model, product or service;
• large firms that commercialise AI R&D: Big Tech firms and other large technology companies, such as ARM, Graphcore, IBM, Netflix and Twitter; and
• direct hire and joint tenure arrangements: relationships between industry and academia that allow for a back-and-forth flow of AI talent between the two.

The report explores the main enablers, barriers and challenges for AI commercialisation through these specific routes, and generally across the commercialisation process as a whole. For each of these routes the researchers investigated the key “enabling institutions” (such as The Alan Turing Institute and universities’ Technology Transfer Offices), the role of public funding (Innovate UK, UKRI, etc.) and private funding (from “angel” and venture capital investors).

Other issues that received particular focus include:

• the role of Standards Development Organisations (SDOs): the development of technical standards for AI, though at a nascent stage, may potentially have an impact on the commercialisation of AI R&D in a similar way to the impact technical standards had on other digital and emerging technologies, such as mobile; and
• AI in healthcare and the life sciences: UK businesses have seen particular success with applied AI products in this area, despite the manifest challenges presented in such a “high stakes” sector.

The researchers derived insights through four strands of research:

• the development of a taxonomy of the ways in which AI R&D is commercialised, or “commercialisation routes”;
• analysis of data sources giving insight into the activity of UK AI businesses;
• subsequent comparison with AI commercialisation activity in eight other countries: the United States, Canada, China, France, Germany, Israel, Japan, and South Korea; and
• over 40 interviews conducted with representatives from ten categories of stakeholder.

The researchers identified the following key themes:

• the value of AI comes from its application to an existing problem; AI techniques are rarely products in themselves, but create value when applied to a problem in a business or industry sector;
• AI businesses require a broad set of commercial and sector-specific skills in addition to technical AI skills;
• the UK has successfully commercialised AI in challenging “high-stakes” sectors;
• universities’ equity share in their AI spinouts presents an important commercial barrier;
• there is a significant flow of AI talent from universities to large technology firms;
• private and public funding are associated with differing outcomes for businesses;
• securing intellectual property protection for AI A&D is difficult; and
• without trust, attempts to commercialise AI will be ineffective.

To access the research report, click here.