The scope of opportunity in embracing artificial intelligence (AI) and data is both broad and deep, and critical to the continued success of the whole underwriting process, from broking engagement to settlement, according to a new study by Capco and Xyenta, Lloyd’s Blueprint Two: Building Blocks for AI and Digital Empowerment.
Successfully integrating and implementing data and AI strategies will enable insurers to benefit from enhanced customer and risk insight, increased revenues, operational efficiencies, and meet head-on the challenges from the Insurtech sector.
The paper examines industry conditions within the context of Blueprint Two (BP2) and considers the wider challenges of industrialising AI. It sets out why conditions are ripe for insurers to engage in enterprise AI, and provides an overview of the key challenges that insurers face in doing so. To realise these benefits, insurers’ AI strategies will need to consider not only the data foundations upon which AI is built, but also the way in which AI is delivered, governed, trained, and integrated.
Penelope Quah, Managing Principal and Alvin Tan, Principal Consultant, Capco, comment:
“With Blueprint Two as a cornerstone, insurers can use internal and market forces as a springboard from which higher quality business outcomes can be achieved by the strategic application of enterprise AI.
“But as insurers increasingly move beyond isolated AI use cases towards scaling AI deployments, it has become clear that significant challenges exist in leveraging AI appropriately and successfully, due to factors ranging from inadequate data foundations to failure to address the challenges of integration with existing operating models.
“To mitigate the risk of failure and realise returns on investment, it is necessary to understand how AI projects need to be integrated into the operational environment of a modern organisation. In contrast with traditional analytics, AI projects have specific challenges that require changes not only in delivery mindset and governance, but also in the way in which business users consume and utilise AI outcomes.
“From data quality to AI governance, the disruptive impact of AI means that the decision-making culture and data management habits of the organisation will need to be shifted, and operating models and processes will need to adapt.”