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AI in London's hospitality and retail SME sector

Funder: UK Research and InnovationProject code: 104756
Funded under: Innovate UK Funder Contribution: 249,678 GBP

AI in London's hospitality and retail SME sector

Description

"The 'AI for SMEs' programme will trial two methods of increasing productivity in London's SME retail and hospitality sectors, through the adoption of AI 'chatbots' and marketing automation systems. These are products which can supplement and improve the customer experience, increase their marketing reach and effectiveness, and which are tried and tested means of converting enquiries/leads into paying customers.They are particularly effective technologies for retail and hospitality SMEs, which have similar routes to customer discovery, marketing and converting leads into paying customers. Moreover, though every company is different, the technologies themselves augment the value of existing employees and existing workflows, rather than replacing the need for human staff. Using a RCT, we are testing the effectiveness of two alternative methods of increasing the adoption of AI chatbot and marketing automation technologies. Stream 1 will use a market convening methodology, which involves matching SMEs with AI vendors via a series of events, and allowing the supplier market to explain the opportunity through case studies and live demonstrations. This light touch method is based on 'letting the tech speak for itself' in order to encourage uptake. Stream 2 is a more targeted intervention, which will provide SMEs with a £1,000 innovation voucher, which they can use to install, test, or deploy the AI technology, and access an expert caseworker. This approach is based on the idea that expert technical and business advice is needed by the SME to assess the suitability of AI as a solution to enable the SME to calculate the ROI and to assist with implementation. By providing external expert support we will see if we increase uptake of AI amongst SMEs, and to realise the productivity potential of the technology. A control group will be established, who will receive basic information about the relevant technologies as an incentive to participate, but no further support. They will be tracked and used as a baseline to assess the effectiveness of the interventions on technology adoption, perceptions and performance. Lessons from this RCT will provide valuable insight into the most (a) effective and (b) cost effective means of driving adoption of AI; whether education and convening is sufficient to drive adoption or whether a degree of 'hand holding' is needed when seeking to drive adoption of perceived cutting edge technologies. Moreover, the findings from this experiment will have value for other geographies, technologies and sectors across the UK."

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