Insurers are investing heavily in cloud infrastructure, artificial intelligence (AI) and digital transformation, but many continue to struggle to translate those investments into faster business decisions.
This was a central theme at a recent Insurtech Insights webinar on building the agile insurance enterprise, featuring Earnix vice president of global solutions Drew Lawyer.
Earnix’s analysis highlights the gap between technological investment and operational results, with connected decision-making emerging as a priority for insurers seeking to improve pricing, underwriting, profitability and customer experiences. Bupa head of strategy Roddy Barnes and Prudential engineering leader Rohan Rayarikar joined Lawyer to explore why digital investment does not automatically lead to greater business agility.
Rayarikar argued that the insurance industry’s biggest challenge is not a lack of technology, but a failure to make decisions efficiently. While cloud migrations and AI initiatives continue to expand, insurers still need to improve how quickly they respond to market changes, assess risk and deliver relevant services to customers and intermediaries.
He said the industry has traditionally treated technology as a separate tool rather than embedding it into everyday workflows. The conventional proposition was, “here is a system, here is technology that can help your life.” Rayarikar believes insurers should instead build an integrated path from customer intent to business decision, allowing information to follow customers through underwriting, pricing and engagement without requiring them or their advisers to repeatedly provide the same context.
The challenge is particularly significant for large organisations. Prudential’s field force includes more than 90,000 financial professionals, creating a substantial requirement for relevant information to be available at the point of decision. Fragmented systems can make this difficult, reinforcing the need to connect data, intelligence and workflows across the business.
Lawyer offered another perspective on improving insurance decision-making, drawing on his experience at a mid-sized insurer. He asked colleagues to consider what would happen if every underwriting rule were removed overnight. If market pricing already reflects risk accurately, he argued, underwriting could focus less on simply accepting or rejecting applications and more on assessing whether a policy is priced appropriately.
Under this approach, a married couple insuring two Toyota Corollas might pass through automated checks, while an 18-year-old seeking cover for three Ferraris would receive additional scrutiny. The broader principle is that underwriting can generate signals that inform pricing, customer engagement and next-best-action recommendations, rather than functioning solely as a gatekeeping process.
However, technology is only part of the challenge. Barnes, reflecting on Bupa’s experience with cloud adoption, described it as an “unlock that moves the bottleneck”. While infrastructure improvements can remove technical constraints, organisations must still adapt their structures and processes to realise the benefits.
This creates questions around governance and accountability. When pricing, underwriting and customer engagement teams all influence the same customer interaction, insurers need to establish who owns the decision and how competing priorities are managed. Simply increasing AI investment will not resolve these organisational challenges.
For Earnix, the answer lies in connecting intelligence with action while maintaining appropriate controls. Its AIOS platform is designed to bring together data, models, AI and workflows, while its pricing, underwriting and customer engagement solutions support decision-making across insurance operations.
The wider implication for financial services is that digital transformation must deliver measurable improvements in how businesses operate. Earnix’s analysis points to connected decisioning as a way for insurers to turn technological investment into operational results, linking customer context, analytical insights and workflows to improve responsiveness and risk assessment while preserving human judgement and regulatory oversight.
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