For years, insurers have talked about becoming “data-driven”. By 2026, that ambition turns into a stark reality: Either you have the data foundations to support AI, cybersecurity and digital transformation, or you’re competing at a serious disadvantage.
Most insurance businesses still operate with a familiar mix of legacy policy systems, bolt-on applications, scanned PDFs and boxes of historical files. At the same time, the pressure is building to use AI for underwriting, pricing, claims, fraud detection and customer experience. The firms that pull ahead in 2026 will be the ones that turn this messy picture into a coherent, secure data estate.
Why data foundations matter for insurers in 2026
AI in insurance is only as good as the information you feed into it. That sounds obvious, but in practice it means tackling some long-standing issues: Policy and claims data spread across multiple systems and home-grown databases. Historic files sitting in off-site storage or as unsearchable image scans. Weak integration between underwriting, claims, finance and broker channels. Cybersecurity controls that have grown organically rather than by design.
The scale of the challenge is significant. Research suggests that only around one in ten insurers have modernised more than half of their systems[1]. Many core platforms are between 13 and 15 years old, creating escalating maintenance costs and integration headaches[2].
Insurers that want reliable AI-driven risk models, faster claims and better fraud detection need a fundamentally different approach to data management.
The role of AI, cybersecurity and migration in insurance
By 2026, we expect to see three big shifts in how insurers approach their data.
AI becomes part of the day job
Underwriters and claims teams will increasingly rely on AI-generated insights: Risk scores, loss predictions, propensity to claim, fraud likelihood. Those models need broad, deep data – not just the last few years of structured records, but also historic claims notes, engineering reports and broker correspondence.
The momentum behind this shift is clear. By 2026, over 90% of insurance companies are expected to have adopted AI technologies in some form, with the market projected to exceed $13 billion[3]. Underwriting is likely to see the strongest impact as insurers focus on automation, pre-populating information and achieving more real-time decisions[4].
However, to realise these benefits, insurers must move beyond pilots and experiments. As McKinsey has observed, creating lasting business value from AI requires setting a bold, enterprise-wide vision and fundamentally rewiring how they operate across underwriting, claims, distribution and customer service[5].
Data migration and integration move centre stage
The traditional “wrap the legacy system and hope for the best” approach will no longer be enough. Insurers will plan structured migrations away from older platforms, consolidating core data into cleaner, modern environments while preserving history and audit trails. APIs and data pipelines will connect policy, claims, billing and external data into a single, trusted view.
This is not merely desirable – it’s becoming essential. Legacy systems often cannot support the bi-directional APIs that AI agents require, nor can they handle real-time data exchange[6]. The good news is that modernising legacy systems can reduce IT costs per policy by around 40% and increase operational productivity by a similar margin[7].
A phased approach tends to work best, breaking modernisation into manageable steps and running new systems alongside legacy platforms to reduce disruption and validate changes before moving forward[8].
Cybersecurity becomes a board-level data issue
As AI, cloud and integration expand the attack surface, cyber risk will be inseparable from data strategy. Access control, encryption, monitoring and incident response will need to be baked into every migration, every data feed and every digitisation project.
The urgency here is real. Recent high-profile attacks have demonstrated just how vulnerable the insurance sector can be – a major breach in 2025 affected over 22 million individuals, exposing names, Social Security numbers and health insurance information[9]. Multiple insurers have been targeted in quick succession, with threat actors exploiting social engineering and third-party vendor relationships[10].
The cyber insurance market itself is projected to reach $22.5 billion by 2026, driven by the rise in both frequency and severity of attacks[11]. Insurers cannot afford to ignore these risks in their own operations.
Unlocking value from historical insurance data
One of the biggest untapped assets for insurers is historical data. Decades of paper policies, claim files, adjuster reports, loss control surveys and email correspondence sit in storage, or as flat images in content systems. In 2026, leading insurance businesses will scan these archives at scale, classify and index documents intelligently, capture key data points such as perils, limits, materials, cause of loss and parties involved, then link those records back to current policy and claims systems.
This matters because AI and machine learning models perform best when they can draw on rich historical context. Research has shown that combining structured data – claims histories, financial statements – with unstructured data like broker submissions and damage reports provides a far richer picture of risk[12].
Digitising historical records transforms a static archive into a searchable, analytics-ready data pool that can improve pricing, reveal long-term patterns, expose hidden accumulations and sharpen fraud analytics. Automation can reduce the cost of a claims journey by as much as 30%[13].
How Dajon supports insurance data transformation
At Dajon, we work with insurers to solve exactly these challenges: Scanning and intelligent capture of large volumes of historical insurance records. Controlled data migrations from legacy policy and claims systems. Integration approaches that make data available to AI and analytics while staying secure. Governance and cybersecurity woven through every stage of the data lifecycle.
If 2025 has been the year of AI pilots, 2026 can be the year you turn your scattered insurance data into a strategic asset. The starting point is simple: Understand what you have, digitise it properly, and move it into a secure, integrated environment where it can work hard for the business.
- Insurance Legacy System Transformation: Challenges & Trends Intellias[↩]
- Insurance Legacy Systems Challenges – 2025 Survey Insights Adacta[↩]
- AI in Insurance: 12 AI Tools Transforming 2026 OneAI[↩]
- Best Insurance Looks Ahead to 2026 Insurance Edge[↩]
- The future of AI for the insurance industry McKinsey[↩]
- A Practical Guide to Core System Replacement for CIOs Genasys[↩]
- Insurance Legacy System Transformation: A Complete Guide Astera[↩]
- Legacy System Modernization in Insurance nCube[↩]
- 22 Million Affected by Aflac Data Breach SecurityWeek[↩]
- Series of Major Data Breaches Targeting the Insurance Industry Crowell & Moring[↩]
- Cyber Insurance Statistics and Data for 2026 Security.org[↩]
- Precision at Speed: The Role of Data Analytics in Insurance Underwriting Intellias[↩]
- Why digitalization is important for the Insurance Industry Managed Outsource Solutions[↩]