To meet the growing demands of a complex and highly regulated environment, insurance asset managers require a clear, technology-enabled approach to process improvement. This whitepaper – developed in collaboration with our partner Origins Capital – presents a structured and practical methodology for analyzing, standardizing, and optimizing processes with AI readiness in mind. Combining a proven framework with technical solution strategies and real-world use cases, it delivers actionable insights for building a process landscape that is both future-ready and scalable.
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- Assessing the Status Quo and Documenting the Process Landscape
The foundation of any sustainable process optimization initiative is a transparent and end-to-end documentation of the current state ("as-is") processes. In this first phase, the existing process landscape is systematically captured – ideally covering the entire value chain, from front to middle to back office, including all management, investment, and support processes. A proven starting point is to establish…
- Analyzing and Prioritizing Efficiency Potential
With a complete view of the documented end-to-end processes, the next step is a structured analysis of the current state – aimed at identifying concrete improvement opportunities and setting clear priorities. The focus lies on operational pain points such as bottlenecks, media disruptions, duplicated work, and manual efforts that hinder scalability and efficiency. Key questions at this stage include…
- Streamline and Standardize Processes
Before processes can be digitized or automated, they must first be critically reviewed and structurally optimized. In many cases, inefficiencies stem not from a lack of technology, but from unnecessary complexity, exceptions, or outdated process structures. That’s why Phase 3 intentionally begins with simplification – removing friction and streamlining workflows – followed by…
- Automation and AI-Driven Execution
With processes documented, streamlined, and standardized, a solid foundation is established to leverage technological efficiency gains. Phase 4 focuses on targeted automation, either through traditional methods or by harnessing modern AI technologies. Typical use cases include…
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