Asaf Bord, Engineering Leader at Northwestern Mutual (a 160-year-old financial services company), shares his experience building a GenBI (Generative Business Intelligence) system in an extremely risk-averse environment. The company's motto, "generational responsibility," imposes absolute stability, making AI innovation difficult to sell and to deploy.
To succeed, Bord adopted a "small bets" (incremental rollout) strategy and a "Crawl, Walk, Run" approach. Instead of promising an all-knowing agent immediately, they started by targeting the BI experts themselves, then managers, using AI to accelerate their work rather than replace them.
The technical architecture reflects this caution. Rather than letting AI generate complex SQL across the entire database (risky), they built a pipeline of specialized agents: 1. Metadata Agent: Understands the context of the question. 2. RAG Agent: First checks whether an existing certified report contains the answer. They found that 80% of BI requests simply consisted of finding the right report. Automating this delivers immense value with minimal risk. 3. SQL Agent: Steps in only if no report exists, to generate targeted queries. 4. BI Agent: Formulates the final answer.
A key point of their success was the use of real, "messy" data from the start, involving business users in the research process. This validated real-world feasibility and created allies ("champions") within the company. In addition, the project was broken into 6-week sprints, each stage delivering standalone value (e.g., the metadata improvements for AI benefited the whole company), allowing management to keep control over the investment.
Bord concludes with an economic reflection: AI challenges the "per-seat" billing model of SaaS software, since a single user can now produce the value of ten.