A recent IUMI industry poll shows a sector progressing in digitalisation yet held back by structural constraints. Insurers are focusing on underwriting, AI and workflow automation to enhance efficiency and pricing, while claims transformation attracts less priority despite its relevance for profitability and trust. AI adoption is widespread but largely experimental, with legacy systems and data quality – not cultural resistance – cited as the main barriers. Overall, the results point to a preference for incremental optimisation over fundamental system renewal.
Based on responses from a broad reinsurance and insurance ecosystem – with a strong bias toward insurers – the results highlight that digital transformation efforts are currently driven primarily by efficiency gains and pricing initiatives. More than one third of insurers identify underwriting as their top digital priority, well ahead of claims and finance functions. This focus reflects the central role of underwriting quality and speed in a competitive marine insurance market, where disciplined risk selection directly influences top-line performance.
The comparatively modest emphasis on claims transformation stands out. This may indicate that the operational and strategic potential within claims processes is not yet fully exploited. While underwriting determines premium income, it is the effectiveness of claims handling that ultimately shapes technical results and policyholder confidence. In contrast, HR and marketing continue to rank as secondary areas for digital investment.
When looking at initiatives already underway, AI implementation, workflow automation and customer-facing portals form the most common combination. Interestingly, modernising core production systems ranks only third overall, although it remains a top initiative among insurers. This suggests that many organisations prefer incremental efficiency improvements over fundamental system replacement, despite long-term dependencies on legacy infrastructure.
At the same time, legacy systems and insufficient data quality are frequently cited as key obstacles to effective AI deployment. The fact that core system modernisation does not top the priority list reinforces the impression that firms are opting for step-by-step optimisation rather than structural transformation. In other words, technological ambition appears to be advancing faster than the underlying system architecture that would sustainably support it.
AI adoption is widespread, yet still shallow. While a majority of respondents use AI tools in some form, only 31% report use beyond experimental purposes. Companies rely heavily on internal capabilities to drive transformation, raising questions about whether in-house resources alone will be sufficient as complexity increases.
The main barriers to AI adoption are not training or employee resistance but legacy systems and data quality. This stands in contrast to other industries where workforce scepticism is often cited as a key obstacle. In marine insurance, employees appear largely receptive to new technologies. Given that training and employee resistance do not emerge as leading barriers, the constraint appears less cultural and more architectural. AI adoption may therefore be heavily reliant on system modernisation and data quality improvement.
Self-assessed digital maturity averages 2.79 on a five-point scale, indicating a slightly above-average perception that may mask uneven progress. Operational efficiency dominates success measurement, with KPIs such as expense ratios, processing times and error rates emerging as natural candidates for deeper follow-up analysis.
