In the latest podcast by Multimodal and Ankur Patel – “Commercial Insurance Meets AI” – Draivn’s co-founder, Felix Kühlmann, shares his insights from his work at Draivn on leveraging AI to transform commercial fleet insurance — and an honest look at where the industry stands today.
Transformation starts with
Fleet insurance remains largely unprofitable, not because the industry lacks data, but because it lacks usable, structured, and contextualized data. Outdated pricing models based on fragmented information simply can’t capture the dynamic, fluctuating exposures of modern fleet operations.
But the landscape is reshaping, influenced by several key shifts:
AI as a board-level imperative
Insurers’ boards are under pressure to adopt AI, driven by the expectations of investors, funding the trendiest new tech. It’s a typical technology adoption curve, where initial hype leads to on-paper implementation before meaningful change occurs.
Operational transformation requires embedding AI strategically into core business processes, with business KPIs – like improved loss ratios or faster underwriting – not innovation KPIs.
Addressing complexity with agentic AI
It can reason, adapt, and handle the variability of fleet insurance workflows. And it’s not just about automation – it’s about deploying intelligent systems, comprising:
- Autonomous agents: Handling repetitive, standardized tasks with high accuracy and low cost.
- Assistive co-pilots: Augmenting human underwriters with relevant context and insights, preserving critical oversight.
Master data management as the foundation
The biggest hurdle is not the AI itself, but the lack of "data readiness." High-quality, auditable, and well-structured master data is critical. Without reliable data pipelines, AI models will only amplify existing inconsistencies and produce unreliable results.
In governance we trust
Beyond technical deployment, strategic AI adoption requires robust governance. Insurers must address critical issues of consent, data ownership, auditability, and trust in AI outputs. Compliance with stringent privacy regulations must be designed into the architecture from the start.
Collaboration over disruption
Unlike personal lines, commercial fleet operations demand deep domain expertise. The winners will be those who combine it with technology and partner strategically. AI shouldn’t replace expertise, but amplify it.
At Draivn, we turn these principles into practice. By building quality into data foundations, investing into data governance, integrating AI into the process and enabling data as context for agentic AI, our platform helps insurers to be ready to leverage agentic AI. At the same time, Draivn enables data to improve results today.
Watch the full discussion on YouTube: https://bit.ly/437QTUj
