New research explores the AI development process in FinTech SMEs and uncovers key factors that may help accelerate product deployment, so often encumbered by a resource-intensive development stage.New product development (NPD) is essential for both the survival and success of firms, particularly for those in fast-paced competitive markets. One major impediment to swift, successful deployment of a new product is the concept-product gap.
The concept-product gap is the time it takes an organisation to deliver a fully deployable product from the moment of its conception. The process can be laborious, plagued with uncertainty, and is often unsuccessful.
The challenge can be even greater when it comes to AI innovation, where practitioners find it particularly difficult to successfully deploy beyond pilot programmes (the field of consumer internet business being one exception).
Due to its significance, the problem of the concept-product gap has been studied extensively, and several approaches for accelerating NPD have been developed, as have guidelines for bridging the gap. However, most NPD studies have focused on large organisations. Empirical research on NPD practices in SMEs, particularly in technological SMEs, is in short supply, even though World Bank data finds that SMEs constitute the predominant portion of businesses worldwide, contributing significantly to job creation and global economic development.
Responding to this gap in research, and recognising the difficulties faced by AI innovators, the authors of Bridging the ‘Concept–Product’ Gap in New Product Development (NPD): Emerging Insights from the Application of Artificial Intelligence (AI) in FinTech SMEs have explored AI product development and innovation processes in FinTech SMEs. This phenomenon is of particular interest today due to the significance of AI and FinTech for the global economy. The study is also timely for although finance has been an early adopter of AI, academic research into AI in FinTech (AIFT) innovation is still at the nascent stage.
The study focuses on Montreal, Canada; a city that has established itself as one of the main global AI knowledge hubs, and seen a surge in high-tech start-ups, particularly in FinTech.
Data was primarily gathered from:
- semi-structured interviews with AIFT industry experts, including senior executives from six AIFT companies, supplemented by email exchanges and post-interview phone calls.
- key insights from Canadian FinTech forum including interviews and other exchanges with selected thought leaders.
- the websites of 28 AIFT companies, and
- relevant news, public research, and media reporting on these firms.
What emerges from the data collected is a new typology of innovation development processes that can serve as a guide to understanding the factors contributing to differences in process configurations. SMEs interested in developing AI-based products can use this typology to understand their current development stage, their goals, and how to arrive at them. It could also serve as a diagnostic tool for more effective utilisation of AI innovation in the FinTech sector. A more agile approach toward the development of AI in SME product portfolios is advised. Firms that demonstrate greater agility in the implementation of AI innovation are more likely to reduce the concept-product gap.
The study also develops a new theoretical framework to explain how AI innovation development unfolds in FinTech SMEs and the rationale for different implementations. It identifies key factors contributing to different process configurations and distinguishes between resource-driven and goal-driven process behaviours that are contingent on the investment level in AI and organisational agility. It shows that AI innovation development processes are fundamentally different from other innovation types, as the role of technology is increasingly superseded by the importance of data.
Finally, this study offers empirical observations on the current state of AI in FinTech. Despite the enormous potential of AI to offer SMEs the tools to challenge larger firms, this promise has not been realised in the financial industry, where dominant financial institutions have been quick to adopt AI and subsquently benefit from efficiency gains and improved product customisation. The study explores why AI’s potential for creating new services and disrupting incumbents via digital start-ups has not been fully realised, even with significant investment and support from public and private business development programmes.
Overall, this study provides a useful guide for practitioners and will contribute to their understanding of AI product development and innovation processes in FinTech, so that they can reduce the concept-product gap and improve the productivity of their business.
Bridging the ‘Concept–Product’ gap in new product development: Emerging insights from the application of artificial intelligence in FinTech SMEs is published in ScienceDirect