The engagement was built around a practical readiness sequence designed to move from "scattered experiments" to "engineered workflows". We focused on two aims: identifying where AI could actually drive value, and designing the automation with enough specificity to move directly into delivery.

1. Establishing a Common Foundation: We began by leveling the playing field. We brought leaders to a common baseline on the platform's capabilities and aligned on a definition of "acceptable use". In financial services, fear of risk often paralyzes adoption; by defining the boundaries early, we turned compliance from a blocker into a design constraint.
2. Generating and Selecting High-Value Opportunities: We shifted the conversation from "What can the tool do?" to "Where is the drag?" Use cases were drawn from real, recurring work — processes plagued by manual effort and rework. Using a structured prioritization lens, we narrowed the backlog to a shortlist of high-impact opportunities. Teams then used a custom Gemini "Gem" to generate initial estimates for value, feasibility, and time-to-impact, validating their hunches with data.
3. Redesigning for an AI-Enabled Operating Model: This was the pivot point. Selected workflows were mapped end-to-end and redesigned. Teams had to make explicit decisions: Where does AI add value? Where must human judgment remain? To accelerate this, we built a custom "Gem" to generate the first draft of the workflow design, allowing the team to focus on refining the controls rather than starting from a blank page. This surfaced the hidden constraints — data sensitivity, integration needs, and decision rights — that typically derail pilots months down the road.
4. Translating to Delivery: Finally, we converted the designs into action. Using another custom "Gem," teams generated first drafts of implementation plans right in the room. This compressed a typical half-day planning exercise into 30 minutes. Owners were named, measures defined, and risks made explicit.