Back Imagine an insurance company receiving hundreds of claims every day. Customers submit invoices, photographs and documents, while employees review information, verify policies and manage individual cases. Now imagine introducing artificial intelligence capable of extracting and analyzing this information within seconds. It sounds like a major step forward. But what happens if employees still have to transfer the results manually between systems, verify the same information again and resolve the same operational bottlenecks?
The AI works. But the process has barely changed.
This is becoming one of the biggest challenges facing the insurance industry in 2026: moving beyond promising AI experiments and turning technology into measurable business results.
AI adoption is accelerating. But where are the results?
According to a February 2026 report by EIOPA, 65% of the 347 European insurers surveyed are already using generative AI, with another 23% planning to adopt it. However, most reported applications remain at the proof-of-concept stage. Insurers are exploring what AI can do, but many have not yet fully integrated these capabilities into their core operations.
A separate KPMG study published in September 2026 revealed another interesting finding: only 11% of the insurance leaders surveyed reported having a very clear understanding of their AI return on investment. Although the study involved a limited sample, it highlights an important question for the industry: are companies measuring the value their AI investments actually deliver?
For CIOs, Claims Directors and operational leaders, the question is no longer simply “Where can we introduce AI?” but rather “Which processes can we actually improve, and how will we measure the difference?”
An impressive demonstration does not automatically translate into faster claims processing, lower operational costs or better customer experiences.
Why do promising AI projects struggle to deliver real value?
The problem is often not the AI technology itself, but the environment in which it needs to operate. Insurance companies typically manage information across multiple systems: policy administration platforms, claims management applications, document repositories, customer databases and legacy software developed over many years.
An AI tool might successfully extract information from a document, but that information still needs to reach the correct system, be checked against the policy and support the next step in the process. If these connections are missing, automation may simply create another isolated tool.
There is also the question of business logic. Insurance processes involve coverage conditions, exceptions, approval procedures and regulatory requirements. An AI model may understand a document, but it does not automatically understand every business rule determining how a claim should be handled.
Finally, many projects focus on demonstrating technological capabilities without defining clear operational objectives. If nobody measures how much time, effort or rework a solution eliminates, how can the company know whether the investment is worthwhile?
The challenge is therefore not just to implement AI, but to connect technology, existing business processes and measurable outcomes.
Claims processing: where automation can make a real difference
Claims management offers significant opportunities for automation because it combines repetitive administrative tasks with decisions requiring specialized knowledge.
Consider a typical claim. A customer submits documents that must be reviewed, relevant information must be extracted, policy conditions checked and the case assigned to the appropriate workflow. Some claims are straightforward, while others involve missing documentation, unusual circumstances or complex coverage questions.
A well-designed automation process can help distinguish between them. Document recognition and AI can support information extraction and classification. Business rules can check relevant information against existing policy data, while connected systems can transfer information without repeated manual entry.
Straightforward cases may then move through predefined processing steps automatically, while exceptions are directed to employees with the expertise to review them.
The objective is not to eliminate human involvement from every decision, but to reduce unnecessary manual work so specialists can focus on cases that genuinely require their attention.
A February 2026 study by Adacta, involving 110 senior insurance decision-makers across European markets, found that only 17% reported advanced levels of claims automation. More than half identified legacy infrastructure as a major obstacle.
The opportunity is considerable, but achieving results requires more than introducing new technology.
Five questions insurers should answer before scaling AI
Moving from a successful pilot to reliable operational automation requires a clear strategy. Before expanding AI initiatives, insurance leaders should consider five essential questions:
1. Which business problem are we solving? Identify where employees spend the most time, where delays occur and which tasks could realistically be simplified or automated.
2. Can our systems work together? Determine whether the solution can access the necessary policy, customer and claims information. Existing applications may need better integration or targeted modernization.
3. Where is human judgment essential? Define which cases can follow established rules and which require expert review, particularly when decisions are complex or have significant consequences for customers.
4. How will we measure success? Establish a baseline before implementation. Useful indicators include average processing time, cost per claim, the percentage of cases completed without manual intervention, error rates and customer satisfaction.
5. Can the solution operate reliably at scale? Consider data protection, security, monitoring, system availability and ongoing maintenance. A solution that works with a small test dataset must also perform consistently in real operations.
These questions shift the focus from technological experimentation to operational performance and help organizations avoid investing in solutions that look impressive during a pilot but fail to deliver sustainable improvements.
What measurable automation looks like in practice
A useful example comes from AGILA Pet Insurance, where insinno supported the automation of veterinary invoice processing. As the volume of incoming invoices increased, AGILA needed to process claims more efficiently without compromising quality or placing additional pressure on its teams.
Using insinno’s iCore platform, process automation and text recognition, invoices are processed digitally, relevant information is matched with existing customer and policy data, and cases are transferred into the management system. The documented workflow achieved 80% processing without human intervention, while special and complex cases continued to receive manual validation.
Importantly, this result comes from structured process automation and text recognition, not from a generative AI implementation. That distinction reveals an important lesson: the objective should not be to use the newest technology everywhere, but to combine the right technologies to solve the right business problems.
Sometimes generative AI is valuable for interpreting unstructured documents or supporting employees. In other situations, established automation tools, clear business rules and reliable system integration may be more appropriate. The real value comes from making the entire process work better.
From experimentation to sustainable transformation
For insurance companies, the next stage of AI adoption requires a broader perspective. Instead of introducing another isolated tool, organizations need to understand how information moves through their systems, where manual work creates bottlenecks and which processes can be redesigned safely.
This is where software engineering, business process knowledge and system integration become essential.
At insinno, we approach these challenges by examining existing processes first, identifying opportunities for automation and connecting new capabilities with established enterprise systems. Our experience combines process automation, custom software development and the modernization of existing applications, helping organizations address both individual tasks and the technical dependencies that may prevent improvements from scaling.
Equally important is maintaining control. In its October 2026 Insurance Risk Dashboard, EIOPA continued to identify elevated digitalization and cybersecurity risks. As insurers expand their use of AI, operational resilience, data protection and effective oversight remain essential considerations.
Successful transformation is not simply about automating more. It is about automating reliably, measuring the impact and keeping the business in control.
Conclusion: the next competitive advantage is measurable value
The insurance sector is moving quickly towards AI adoption, but the next phase will not be defined by how many tools companies introduce or how many pilots they launch. It will be defined by how effectively they turn those investments into better business processes.
Faster claims handling, fewer repetitive tasks, improved operational capacity and better customer experiences are meaningful objectives. Achieving them requires understanding the business, connecting systems and ensuring that technology delivers measurable improvements.
The question for insurance leaders is no longer whether AI can transform their operations. It is whether their organizations are prepared to turn that potential into real results.
At insinno, we believe successful digital transformation begins with understanding the processes that create business value and then applying technology where it can make a genuine difference.
Is your company experimenting with AI but struggling to turn promising pilots into operational results? The next step may not be another AI tool. It may be rethinking the process behind it.
