Artificial intelligence is no longer a theoretical opportunity for the insurance industry. It is already being tested across claims processing, underwriting, fraud detection, customer service, contract analysis, and operational workflows.
Despite the explosion of AI projects, one reality stands out: the vast majority does not make it past the experimental stage. Gartner has reported that at least 50% of GenAI projects were abandoned after proof of concept by the end of 2025, while RAND cites estimates that more than 80% of AI projects fail overall. This raises a question: what distinguishes the insurers that successfully industrialize their projects?
From enthusiasm to standstill
Some blockers are “universal”, commonly seen across the board when it comes to take projects through all the stages of development: while not exclusively related to AI, these factors can still impact a successful POC process. First and foremost, any project needs economic support: funding from investors, or in some cases grants and public initiatives can provide the financial backing necessary to turn ideas into reality.
This kind of external support usually comes from people and organizations who believe in the idea and trust the experts to develop. That is why the second essential factor is having the right team: innovation requires talent, people with the skills and knowledge to deliver change. Any company should be able to recognise how much a purpose-built workforce can added to the business on different levels.
The issue doesn’t stem from AI itself, but from the environment in which it is deployed. Projects are still approached from a primarily technological angle, focused on models rather than business use cases. This results in a disconnect between IT, data and operational teams, slowing down large-scale adoption.
Added to this is a major challenge: data management. In many organizations, information remains poorly structured and scattered across document silos, including legacy Enterprise Content Management (ECMs), and archives. When combined with the technological debt of outdated systems, the nature of the bottleneck becomes much clearer.
Finally, scaling up faces the industry’s requirements: compliance, security, and traceability. Without clear governance, performance indicators, and risk management regarding bias, most projects remain in their infancy.
Rationalization of the information foundation: a true key to success
Insurers that have successfully scaled their AI projects approached the issue from a different angle: they do not start with AI, but with data. Insurance is a document driven industry: policies, claims files, contracts, correspondence, broker communications, loss reports, medical records, underwriting submissions, and regulatory documentation all contain business-critical information. The value lies in the ability to effectively leverage this content.
However, legacy systems are now starting to show their limits. Traditional ECM/DMS platforms are often too rigid, and the proliferation of repositories slows down access to information and its utilization.
The key therefore lies in using technological solutions often relying on IA themselves to automate the creation of a unified, structured, and accessible information foundation. These platforms enable content to be centralized, contextualized and made usable in real time by AI tools. In other words, using AI solutions to transform fragmented document assets into a strategic “AI-ready” asset.
POC and beyond: strategic decisions to scale up
Organizations that make the leap rely on clear strategies. Firstly, they prioritize high-value business use cases that deliver rapid and measurable results, such as automating claims processing or contract analysis.
They also rely on technologies capable of modernizing their ECM without disrupting services. Above all, they adopt intelligent information management, allowing them to leverage existing data and build a scalable foundation in sync with the rapid evolution of AI.
Finally, they integrate governance and compliance challenges from the outset: data traceability, model explainability, and respect of regulatory frameworks (GDPR, US NAIC Model Bulletin etc…). These are the essential conditions for sustainable industrialization.
Ultimately, industry leaders are not just implementing or adopting AI: they rely on tools that enable them to create the conditions for its large-scale deployment. By focusing on a strengthened control over their assets, they make their organizations truly “AI-ready”.
For those who can’t get on the same path, the risk is clear: remaining stuck at the POC stage while the pioneers transform an experiment into success.
AI is a fast moving world, with new applications being discovered every day. Therefore, the question is not quite if this technology can transform a specific industry or sector, but rather if businesses and organizations are equipped to integrate it and develop it effectively. Insurance companies are increasingly investing in this resource, but like anyone entering the “AI game” they can’t simply look to jump on the trend wagon and underestimate what is required to do so.
Any meaningful implementation of a new technology or tool in a company’s process starts with assessing their existing information strategy: only then can they truly take the first step in creating an AI-ready environment.
