Original text here from Patrice Bernard (LinkedIn)
Like many large corporations, Generali France is attempting to demonstrate its technological maturity through five examples of artificial intelligence and robotic process automation (RPA) applications. However, this effort mainly highlights the company’s ongoing struggles to fix long-standing inefficiencies.
The initiative is presented as part of Generali’s strategic plan, “Boost 27,” which focuses on operational excellence and service quality. This vision is embodied in a dedicated AI and automation center of excellence. The primary goal? To reduce existing friction points and operational defects.
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Two initiatives stand out for their customer-facing impact:
The problem? These types of AI solutions have existed for over 20 years, often with mixed results. Generali’s approach aims to leverage the latest technology for improved outcomes, but current results fall short. Only 30% of customer requests are resolved via the voice bot without human intervention, meaning that two-thirds of calls still require additional steps that consume customer time, ironically undermining response time metrics. Similarly, traditional text analysis methods could likely achieve the same email-routing efficiency without requiring AI.
Generali’s AI-based claims management tool exemplifies the broader challenge. As with most RPA-based solutions, it functions as a temporary patch rather than addressing the underlying problem: the insurer’s inability to create a flexible, coherent IT system despite decades of costly digital investments. Ideally, such a system would enable seamless end-to-end customer journeys without the need for band-aid automation fixes.
The conversational agent deployed by La Médicale (a Generali subsidiary) for internal staff suffers from a similar issue. Its main purpose is to help employees navigate disparate and outdated information sources—product documentation, training materials, underwriting guides, and more. But the very need for such a tool reveals a deeper issue: the overwhelming complexity and inconsistency of these resources. Without structural improvements, the chatbot merely acts as a crutch for navigating a disorganized digital ecosystem.
Generali’s final AI tool stands out for its originality: analyzing policyholder relationship networks to combat dormant contracts, a growing regulatory concern. However, the real challenge here lies in locating and cross-referencing data across fragmented systems, a task that traditional data analysis methods might handle just as effectively without AI’s added complexity.
In summary, Generali—like many insurers—appears to be using AI and automation not to transform its core business, innovate products, or adapt to industry shifts, but rather to patch accumulated inefficiencies stemming from years of organizational and IT fragmentation. This approach wouldn’t be a concern if it weren’t overshadowing the urgent need for a deeper strategic rethink. To secure its future, Generali must reconsider its foundational systems instead of relying on AI-driven cosmetic fixes.
