How AI-first process design reshapes company processes from the ground up
How insurers can maximize value from core insurance system transformation
Outdated core systems are blocking insurers from AI-enabled performance and growth
Insurance carriers worldwide are confronting a critical strategic decision. Many core insurance systems (CIS) were designed for an era of stable, paper-driven operations. They were not built for continuous change, including the real-time data access required for AI integration. As digitalization accelerates and both customer expectations and regulatory demands intensify, legacy infrastructure is increasingly incompatible with the pace of change that the market now requires. For insurers, transforming the core system has shifted from a long-term ambition to an immediate operational necessity.
Key insights from the article
Delay is no longer a viable option: Legacy systems are not only an obstacle to AI adoption and faster product launches – they also make regulatory compliance more difficult. As modernization programs multiply, postponing transformation means higher costs and growing competitive disadvantage
Execution discipline matters more than platform selection: Around half of core system replacement programs fail to meet time or budget targets, often because governance is weak or complexity is underestimated. Success requires treating the program as a business initiative, not an IT replacement
Data modernization determines success: A new platform alone does not unlock AI-enabled performance. Fragmented legacy data transfers its complexity to the new core if left unresolved
"Core insurance system transformation is no longer an IT project – it is a business imperative."
The urgency behind core insurance system modernization
The volume of core insurance system modernization programs is set to increase sharply over the next five years, creating significant competition for specialist talent and vendor capacity across the market. Many of these programs will run in parallel, raising the stakes for organizations that delay action. Legacy systems – often still based on mainframe or AS/400-like infrastructure – accumulate technical debt over time, constraining the speed at which products can be launched and processes automated. The cost of inaction compounds as competitors accelerate their adoption of AI and cloud technologies.
At the same time, these programs involve substantial risk and investment, typically ranging from EUR 20 million to more than EUR 100 million over two to five years. Around half of core system replacement programs fail to meet time or budget targets. Root causes include underestimated data migration complexity and weak ownership. Unclear decision-making is another recurring issue. Understanding what drives both success and failure is therefore essential before committing to a transformation program.
Where transformation creates strategic value
A well-executed core system transformation delivers value across multiple dimensions, from direct IT cost reduction to strategic capabilities that were previously inaccessible. Modern cloud-based core insurance system solutions reduce the structural cost of legacy stabilization and support higher levels of straight-through processing. They also enable faster time-to-market for new products and regulatory updates.
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The strategic dimension of value creation is equally significant. Legacy architectures constrain real-time risk scoring and advanced analytics while limiting AI deployment at scale. A modern core system, by contrast, provides the data foundation and integration interfaces that make these capabilities operationally viable. Building a credible, comprehensive business case – one that captures both direct cost savings and indirect strategic returns – is one of the most critical steps an insurer can take before initiating a transformation.
Data and AI as the foundation for post-migration performance
Data modernization is a critical workstream that is frequently underestimated in core system transformation programs. Insurance operations depend on decades of policy, premium, claims, actuarial and financial data – much of it fragmented across systems and carrying embedded business logic and historical inconsistencies. If data quality issues are not resolved during migration, they transfer directly to the new core, limiting the automation and AI capabilities the transformation was intended to enable.
AI is also reshaping the migration process itself. Advances in generative and agentic AI are enabling faster discovery of legacy business logic and more automated testing and validation at a scale that was not previously achievable. These capabilities reduce dependence on scarce legacy expertise and improve delivery predictability. Beyond migration, a clean and well-governed data foundation unlocks AI-driven underwriting, fraud detection, predictive claims handling and personalized customer engagement.
Insurers that approach core system transformation with a clear target architecture and disciplined business case, supported by a data-first delivery model, are best placed to convert a complex, high-risk program into a durable platform for AI-enabled performance. Our report sets out how to make that happen.
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