The insurance industry faces a key question: How can insurers successfully industrialize their AI initiatives?
Scaling AI requires a distinct approach relying on: compliance, security, and traceability. Not meeting these requirements prevents projects from moving beyond the early stages, especially when they lack clear governance, performance indicators and risk management.
AI itself is not the factor impeding or slowing down the crucial passage from POC to fully deployed projects. The real issue lies in the approach and environment in which AI initiatives are developed and industrialized. Projects are still approached from a very traditional perspective rather than as potential business use cases, overlooking the need to factor in elements such as IT infrastructures, operations and, crucially, data management.
In this case, the distinction lies in the way data is being managed. Organizations with poorly structured and scattered document silos struggle with the technological debt of outdated systems including legacy enterprise content management (ECM) platforms and archives. Data management makes a difference when it comes to successfully industrializing insurers' projects as well as being one of the main difficulties they can encounter.
Scaling up with strong decision making
To succeed, organizations must rely on clear strategies driven by high-value business use cases that show immediate and significant affect in areas that are key to the business, for example the automation of claims processing.
They should also increase the focus on modernizing the existing ECM platforms while refraining from launching a complete overhaul. As counterintuitive and challenging as it might sound, this balance is essential to success and can be achieved by intelligent information management and keeping up with the latest AI implementations.
Finally, they take into account challenges related to governance and compliance from the very beginning: data traceability, model explainability, and compliance with regulatory frameworks.
The key to success: a strong information foundation
What differentiates insurers that have successfully scaled their AI projects from others is the way they approached the issue: they started with data rather than starting with AI. Insurance is a document-driven industry; its value lies in leveraging its content: policies, claims files, contracts, correspondence, broker communications, loss reports, medical records, underwriting submissions, and regulatory documentation.
Some insurers are still dependent on legacy systems, traditional ECM/DMS platforms which are now showing their limits and slowing access to information. Their lack of flexibility, combined with the proliferation of repositories, make the use of information difficult.
To overcome these difficulties insurers must rely on technological solutions incorporating AI to automate the creation of a unified, structured, and accessible information environment. But in order to be truly impactful and bring long-lasting innovation, this can't simply be merely a new layer added on top of an existing system: what is really needed is a thorough modernization of native platforms, contextualized in real-time thanks to advanced AI tools.
Inspired industry leaders are those who know how to prioritize long-lasting sustainable industrialization over short-term and rapid changes. Integrating AI solutions is a starting point, but not the solution itself. The ideal conditions for large-scale deployment have to touch all assets of the business, from talent acquisition to fill the new skill gap to investing in research and development and, especially for a consumer-facing industry like insurance, transparency and the ability to explain the benefits stemming from the technology upgrades.
Organizations that can't align with this approach are likely bound to be left behind in the no man's land of unrealized POCs, while others successfully scale up projects and introduce innovations.
