Integration with legacy systems came up as a barrier to AI adoption in our recent market insight study with 20 senior claims leaders in the insurance industry. Many insurers operate on infrastructure that predates the digital age. Yet, despite this, AI integration is not only possible but increasingly practical.
AI’s potential in claims processing—streamlining workflows, boosting accuracy, and accelerating response times—is encouraging insurers to rethink legacy infrastructure. Traditionally, the task of connecting AI with outdated systems was seen as complex, requiring major technical workarounds. However, with Sprout.ai, this integration is far more achievable than you might think.
Sprout.ai is a claims automation platform that enhances traditional claims processing through AI. It functions as an intelligent overlay, enhancing claims processes without the need for extensive overhauls. Aligning with existing infrastructure, it bypasses the need for costly system replacements. Using advanced Optical Character Recognition (OCR) and Natural Language Processing (NLP), Sprout.ai effectively handles unstructured data from various sources, such as documents, emails, and images, and instantly converts it into structured insights. This enables insurers to modernise claims workflows without technical headaches, proving that AI integration is within reach, even for those working with legacy systems.
Read more: Art of the possible: How Sprout.ai transforms claims
Ultimately, this helps create a better customer experience. “In recent years, customer expectations have really changed, Laura Lazarus, Head of Personal Lines Home Claims at Aviva told us. “Using AI in claims can help speed up the process and allow our claims experts to concentrate on helping customers who may require more support or need more urgent assistance.”
Read the report: A Business Case for AI: Insights from 20 Interviews with Senior Claims Leaders
Key challenges in integrating AI with legacy systems – and how to resolve them
Technical hurdles
A commonly cited barrier is the rigidity of legacy systems. Many of these systems lack modern interfaces like APIs that would allow them to communicate easily with AI solutions. As a result, even simple data exchanges become complex tasks, often requiring customised integration processes. Moreover, older systems were never designed for today’s data-intensive workflows, limiting the potential for real-time or dynamic processing that AI enables.
However, with solutions like Sprout.ai, which act as overlays, AI can bypass many of these technical limitations. For insurers, this means they can add intelligent features without redesigning the core infrastructure, significantly reducing the perceived difficulty.
Some insurers have tackled integration challenges by creating specialised teams focused exclusively on data flow between legacy and AI systems. These teams often consist of IT and data experts tasked with building custom bridges, or interfaces, allowing for smoother exchanges and higher data compatibility.
Middleware is a key tool for bridging the divide between AI and older systems, serving as an intermediary that lets AI “speak” to legacy infrastructure without overhauling it. This lowers the difficulty by providing a way for AI to interact with legacy systems without extensive reconfiguration, making the integration a less daunting task. For many insurers, these solutions are a practical and cost-effective way to circumvent technical barriers and achieve integration.
Read more: 9 essential features to look for in AI claims processing platforms
Skill barriers
Integrating AI has traditionally required technical expertise and specialised teams, which adds to both cost and complexity.
However, with Sprout.ai, insurers no longer need to rely on highly skilled engineers to make AI work with legacy systems. Sprout.ai’s intelligent overlay simplifies the process, enabling insurers to bypass the need for in-house AI and systems integration specialists. This approach reduces technical demands and also makes AI adoption feasible for insurers of all sizes, regardless of their internal resources.
Structural barriers
Integrating AI requires not only technical solutions but also a rethinking of processes and roles. Staff accustomed to older methods must adapt, and workflows may need reshaping to make the most of AI’s capabilities. Without organisational buy-in and a willingness to evolve, technical integration alone cannot deliver its full value.
In the long run, however, it’s worth it. Legacy systems often rely on manual data entry or rigid formats, but AI makes it possible to process unstructured data—emails, PDFs, images—and convert it directly into structured, actionable information. This replaces complex, often labour-intensive data handling processes, making AI integration both faster and less disruptive.
Martin Turner, Chief Claims Officer at AXA XL told us: “AI can significantly reduce the number of manual touchpoints in the claims process… potentially saving months of claims handlers’ time and improving overall claim lifecycle performance. This will ultimately benefit the customer and could also improve operational efficiency.”
Clear communication, leadership support, and a phased approach to AI adoption can help organisations gradually reshape workflows and processes to maximise AI’s impact.
Read more: How AI is changing claims handling
Market complexities
Local nuances apply too. In the London Market, for example, integration challenges are intensified by the market’s unique subscription model. Unlike general or retail insurance, the London Market involves complex claims handling across distributed teams, third-party administrators, and multiple distribution channels. The integration of AI here requires a collaborative industry approach that aligns with broader digitisation efforts.
Initiatives like the Electronic Claims File (ECF) and the International Claims Orchestration Service (ICOS), part of the Blueprint Two programme, are essential to building a shared digital foundation. These initiatives allow AI to function effectively within the London Market’s intricate framework, enhancing efficiency while preserving market-specific workflows.
Automating FNOL at Allianz
Allianz recently implemented an AI solution to streamline the First Notice of Loss (FNOL) process, the initial intake and triage step when a claim is reported. Traditionally, this involves manually sifting through emails and attachments to extract claim details—a time-consuming task.
The new AI solution now processes these emails and attachments, automatically identifying and extracting essential information. Joachim Zaech, Head of Property Claims at Allianz, told us that within weeks of implementation, it was able to pull out complete, structured data in 50% of cases. This automation reduces the administrative load on claims handlers, allowing them to focus on higher-value tasks and improving efficiency and accuracy.
Is it worth integrating AI?
We might be biased, but yes. There are many substantial benefits that make integrating AI a worthwhile investment for insurers.
Over a fifth of customers want claims settled in hours, but 43% wait over two weeks. AI accelerates claims processing by automating repetitive tasks like First Notification of Loss (FNOL) and data ingestion, reducing delays and enabling faster, more efficient service. With AI, insurers can meet customer expectations for quick, transparent claims handling.
Customers value fair decision-making, with 62% rating trust over speed. AI-driven data analysis provides decision support that standardises claim handling, reducing inconsistencies and human error. This helps ensure fairer outcomes and builds trust in the claims process.
Additionally, AI can detect patterns and anomalies indicative of fraud, often missed by manual processes. With more effective fraud detection, insurers save on claims costs and focus investigative resources on high-risk cases. This is more important than ever, as 65 per cent of claims handlers have noticed an increase in fraudulent cases in the last year, and AI enabled fraud is on the rise.
Read more: Is AI the only solution to rising insurance fraud?
By analysing vast datasets quickly, AI reveals insights and trends that were previously inaccessible. These insights improve risk management, support more effective underwriting, and help insurers develop policies that better meet evolving market needs. AI allows claims teams to manage higher volumes without additional hires, helping alleviate the impact of talent shortages. By automating routine tasks, AI enables skilled staff to focus on complex claims that benefit from human judgement and empathy.
AI helps insurers move from reactive to proactive claims management. By identifying common causes and risk trends, insurers can create targeted strategies to prevent future claims, reducing overall risk exposure. AI’s scalability allows insurers to handle large volumes of claims more cost-effectively, improving operational efficiency. This makes it possible to maintain or enhance service levels even as claims volumes grow.
With AI handling repetitive tasks, claims handlers can focus on high-touch cases, improving customer experience. This aligns with customer demands for empathy and support, allowing insurers to provide a human touch where it matters most. By delivering these benefits, AI is helping insurers modernise and optimise their claims processes, ensuring they stay competitive in an increasingly data-driven market.
Conclusion
Integrating AI with legacy systems is easier than you might think. While barriers are often technical, successful integration relies on a mix of strategic planning, innovative workarounds, and industry collaboration. With tools like Sprout.ai, insurers are now finding ways to bridge the gap between legacy and AI systems, proving that with the right approach, legacy infrastructure doesn’t need to be a barrier to modernisation.
