AI maturity in the insurance industry
Whitepaper
Is your insurance organisation ready to harness the power of AI and unstructured data?
Introduction
Artificial intelligence (AI) and unstructured data are beginning to reshape the insurance sector, offering opportunities to enhance customer experience and streamline operations. However, the sector remains early in its AI maturity journey, especially when compared to the banking and financial services sector. Despite this early maturity, insurers have begun successfully leveraging AI to deliver personalised customer journeys, with tools like AI automation and retrieval-augmented generation (RAG) playing a key role in their success.
Findings are based on research conducted by Iron Mountain alongside independent market research specialist Vanson Bourne. Data in this report is based on 195 IT and data decision-makers in the insurance sector, who have knowledge or involvement in their AI strategy.
You can read the global report here.
Three key takeaways:
Current AI adoption
In general, the insurance sector has a relatively even split of organisations across the AI maturity journey. However, there’s a much higher proportion in the early stages than the later, more mature AI segments.
In fact, the insurance sector is one of the most likely to be earlier in their maturity journey), as 43% of organisations in the insurance sector fall into the early AI maturity group. What’s more, just one other sector (manufacturing and production sector) has a higher proportion of organisations in the early maturity stage globally (46%). This early stage of AI adoption likely comes down to a more conservative approach to digital transformation, prioritising stability and compliance over rapid innovation.
Moreover, organisations in the insurance sector typically have more outdated legacy IT systems embedded within their operations. This sector also is heavily regulated with strict guidelines around risk management, confidential data, and data privacy, which can result in a much slower uptake in AI adoption, particularly for innovation.
To accelerate the adoption of AI and increase the value of AI initiatives, more than half (59%) of insurance organisations are focusing on allocating more resources for AI expertise and project development. Similar proportions (55%) want to focus on scaling IT capabilities to manage and process large volumes of data more efficiently, including improving the sourcing and preparation of relevant data sets for AI training and analysis (53%).
Additionally, 56% of organisations within the insurance sector have a desire to establish comprehensive data and model governance frameworks. They are the most likely sector to report this focus area, especially compared to the banking and financial services sector (39%). This highlights a critical barrier to AI advancement in the insurance sector: while organisations recognise the need to improve AI resources and maturity, the lack of comprehensive data and model governance frameworks is holding them back. The sector’s risk-averse nature amplifies this challenge, as insurers prioritise reducing potential risks and ensuring compliance before fully committing to AI adoption. As a result, trust in AI capabilities has yet to be established. Developing these frameworks will be essential for unlocking progress, enabling the sector to balance innovation with the stringent oversight required to build trust and drive AI maturity forward.
The insurance sector is most likely to use AI currently within IT and security, or marketing and customer service. They also have a unique focus on using AI in sales, currently reported by more in the sector than the global average (49% in insurance vs 38% average).
Of note, there’s a stark difference in AI use cases for the insurance sector versus the banking and financial services sector where the insurance sector is more likely to use AI in customer service, research and development, sales and marketing. It seems that insurance is harnessing the power of AI for customer touchpoints, where banking and financial services are placing more focus on operational benefits such as finance. Despite these small wins, overall AI adoption is still slower within the sector than in banking and financial services.
Trust is the factor hindering AI adoption overall, and is likely the same reason it is being selectively applied in specific areas. The insurance sector is customer-centric with a heavy focus on success based on building trust and maintaining long-term relationships with customers. By leveraging AI, this industry can gain improvements in customer-facing touchpoints through increased personalisation, speed and accuracy in decision-making though this is only a small portion of the potential benefits. By having frameworks in place, and closing the skills gap, the sector will find itself better equipped to harness AI’s full capabilities, driving revenue growth, competitive advantage, employee productivity and cost reduction.
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