Many insurers aim to embed AI into their operations, but poor data quality and legacy systems pose significant barriers, risking project failures and slower decision-making.
In insurance, the signs of poor data readiness are rarely dramatic. More often, they appear in the routine: a report that changes depending on who runs it, a pricing adjustment that triggers another round of checks, or a simple request from leadership that turns into a manual reconciliation exercise. The result is less a technology failure than a drag on everyday decision-making.
That pressure is becoming more visible as insurers race to apply artificial intelligence. AM Best found that many carriers and MGAs see data readiness, security concerns and legacy integration as the main barriers to adoption, even though nearly 60% expect AI to reshape their business models within the next one to three years. Insurance Thought Leadership has reported a similar gap between ambition and execution, with many firms expecting AI to define the sector’s future but only a small share having fully embedded it into financial operations.
The problem is broader than AI itself. If teams cannot trust the figures behind underwriting rules, claims triage, fraud alerts or customer communications, they cannot automate with confidence, and they struggle to explain outcomes when auditors or regulators ask for the logic. Captive.com has noted that AI depends on clean, structured data, while older systems and security worries continue to slow implementation.
Insurers often describe the readiness gap in three parts: decision-making, ownership and proof. The first is speed, or the ability to answer a core business question without hours of manual work. The second is clarity, meaning someone is accountable when definitions conflict. The third is evidence, or the ability to show how a number was produced and which controls shaped it. A SAS survey found that poor data quality is the biggest obstacle to robust decision-making for 41% of insurers, while lack of collaboration and unclear ownership were each cited by 36%.
The most effective insurers, according to the reporting and commentary, are treating data more like a managed product than a by-product of operations. That means assigning ownership in core areas such as claims, policy, billing, customer and fraud, and defining what “good” looks like in business terms rather than only technical ones. It also means building routines around review, escalation and impact analysis so that upstream changes do not quietly break downstream reports.
For many firms, the practical starting point is not a wholesale transformation programme but a single high-value workflow. Claims intake, underwriting appetite, fraud detection and customer service are all common candidates. Once one process has clear definitions, visible quality signals and traceable data lineage, the same pattern can be expanded elsewhere. That matters because most insurers cannot stop day-to-day operations while they modernise, and several reports suggest that underdeveloped data frameworks and legacy systems remain widespread across the sector.
The larger lesson is that data readiness is not a back-office abstraction; it is what makes the business faster, safer and easier to govern. When answers are trusted, teams spend less time reconciling numbers and more time improving outcomes. In that sense, the real test of AI readiness in insurance is whether the organisation can already rely on its data when the pressure is on. According to the companies and industry surveys cited here, that is still where many insurers are falling short.
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Source: Noah Wire Services
Noah Fact Check Pro
The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.
Freshness check
Score:
8
Notes:
The article was published on Insight Global’s blog on March 25, 2026. A search for the article title reveals no earlier publications of substantially similar content. However, the topic of data readiness in insurance has been discussed in other recent articles, such as ‘Compliant Modernization for Data and AI in Insurance’ published last month. ([insightglobal.com](https://insightglobal.com/blog/compliant-modernization-in-insurance/?utm_source=openai)) This suggests that while the specific content is original, the subject matter is currently being actively discussed in the industry.
Quotes check
Score:
7
Notes:
The article includes several statistics and findings, such as ‘nearly 60% expect AI to reshape their business models within the next one to three years’ and ‘78% of respondents believe data readiness is the biggest challenge in getting value from artificial intelligence (AI)’. A search for these specific statistics reveals that similar figures have been reported in other sources, such as the LIMRA and Equisoft report. ([insurtechinsights.com](https://www.insurtechinsights.com/limra-and-equisoft-report-finds-data-readiness-a-major-hurdle-for-ai-adoption-in-life-insurance/?utm_source=openai)) This raises concerns about the originality of the data presented. Additionally, the article does not provide direct links to the original sources of these statistics, making independent verification challenging.
Source reliability
Score:
6
Notes:
The article is published on Insight Global’s official blog, which is a corporate platform. While Insight Global is a reputable staffing and services company, the content is self-published and may have inherent biases. The article references external sources, such as the LIMRA and Equisoft report, but does not provide direct links to these sources, making independent verification difficult. The lack of direct citations to external sources diminishes the overall reliability of the information presented.
Plausibility check
Score:
7
Notes:
The article discusses the challenges insurers face with data readiness and AI adoption, citing statistics like ‘nearly 60% expect AI to reshape their business models within the next one to three years’ and ‘78% of respondents believe data readiness is the biggest challenge in getting value from artificial intelligence (AI)’. These claims are plausible and align with industry trends. However, the lack of direct citations to the original sources of these statistics makes independent verification challenging, which affects the overall credibility of the claims.
Overall assessment
Verdict (FAIL, OPEN, PASS): FAIL
Confidence (LOW, MEDIUM, HIGH): MEDIUM
Summary:
The article presents plausible claims about data readiness challenges in the insurance industry, citing statistics that align with industry trends. However, the lack of direct citations to original sources and the self-published nature of the content raise concerns about its reliability and verifiability. The absence of direct links to external sources makes independent verification challenging, and the self-published platform may introduce inherent biases. Therefore, the overall assessment is a FAIL with MEDIUM confidence.