Simple AI Recommendations for Small Business Growth
This joint research project between the University of Warwick and Nanyang Technological University (NTU), Singapore, explores how artificial intelligence can be made more accessible to small and medium sized enterprises that do not have access to large datasets or expensive digital infrastructure. Led by Dr Yi Ding from Warwick Business School and Professor Vivek Choudhary from the Division of Information Technology and Operations Management at NTU, the project examines how firms with limited data and technical resources can still use AI driven recommendation systems to support digital growth and customer engagement.
Titled Levelling the AI Playing Field: Evidence on Low Data Recommendation Systems for SME Digital Growth, the research focuses on recommendation systems, the tools that suggest products or services to customers on digital platforms. These systems are now a central feature of online commerce, shaping what people see, click on, and buy. However, most existing recommendation tools rely on detailed customer histories and sophisticated algorithms that are costly to build and maintain. This places them out of reach for many SMEs, limiting their ability to compete with larger firms that have greater data and technical resources.
Dr Yi Ding’s research lies at the intersection of behavioural operations, data analytics, and digital platforms, with a strong focus on how data driven tools influence consumer behaviour and firm performance. Professor Vivek Choudhary is a leading expert in information systems and operations management, known for his work on digital innovation, platform strategy, and the economic impacts of technology adoption. Together, they bring complementary expertise in theory, data analysis, and real-world experimentation.
The project builds on a large-scale field experiment conducted in partnership with Careem, a major ride sharing and delivery platform operating across the Middle East. In this live commercial setting, the researchers compared two very different recommendation approaches. The first was a sophisticated, data intensive system that used detailed user histories to infer customer intent and personalise recommendations. The second was a much simpler strategy that required almost no customer data, rotating service options in a largely random way to encourage exploration.
The results were striking. As expected, the data intensive personalised system delivered the strongest overall performance. However, the simple low data approach also generated meaningful increases in customer activity and spending. Even without knowing anything about individual users, the lightweight recommendation system was able to prompt engagement and influence behaviour. This challenged the common assumption that effective digital recommendations always require large amounts of data and complex AI infrastructure.
These findings have important implications for SMEs. They suggest that smaller firms can still benefit from recommendation technologies by using simpler, low-cost approaches that nudge customers without relying on extensive data collection. The research aims to identify when and why these exploration-based strategies work well, and under what conditions they may even outperform more complex data driven systems.
Beyond measuring economic outcomes, the project seeks to develop a practical and transferable framework that SMEs can adapt to their own digital platforms. Rather than offering a one size fits all solution, the research provides evidence-based guidance to help firms choose recommendation strategies that match their resources and goals.
The collaboration between Warwick Business School, NTU, and Careem combines academic rigour with real world relevance. It brings together expertise in behavioural science, information systems, and field experimentation with industry insight into platform design and user engagement. The partnership is intended as the foundation for a longer-term research programme on inclusive digital growth, with a focus on helping firms adopt AI tools responsibly, cost effectively, and at scale.
By showing that simpler technologies can still deliver real value, this project will demonstrate how research can help democratise artificial intelligence. The project offers practical insight for SMEs, policymakers, and platform designers seeking to ensure that the benefits of digital innovation are not limited to only the largest and most data rich organisations.