Financial Services

The Team Lab at Global ETF Provider

Global ETF Provider

The Story

The Challenge: The Adoption Impact Gap

The leadership team at an international ETF provider was stuck in the "Scale Gap." They had rolled out enterprise AI tools (Google Gemini) and encouraged their teams to use them, but there was little evidence that individual adoption was improving business outcomes.

A small group of power users was racing ahead, creating a widening gap in confidence and capability, while the majority remained cautious. "Pilots" were launching but not converting into true offerings; they lacked the rigorous governance with clear ownership, boundaries, controls, and success measures required to survive in a regulated financial environment.

Leaders could see the potential for automation across their recurring processes, but they lacked a structured method to redesign them. They were layering AI on top of broken workflows instead of rearchitecting the work itself.

Our Approach

The Approach: Capturing Value by Redesigning Core Workflows

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.

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.
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.
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.
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.

Outputs Created and Delivered

The engagement was designed around artifacts that would support immediate follow-through after the offsite. Key outputs included:

A short, prioritized set of workflow-based AI opportunities tied to measurable outcomes
Business cases for the top candidates, including value hypotheses and feasibility assumptions
Delivery-ready implementation plans with ownership, milestones, dependencies, risks, and success measures
Workflow definitions that clarified AI boundaries, decision points, controls, and required inputs
An additional outcome was the method itself: a repeatable way to take AI from "interesting" to implemented, without relying on individual heroics.

Outcomes

Team Outcomes: What Changed for the Frontline

The Lab fundamentally shifted the group's perspective. They stopped viewing AI as a personal productivity aid and started treating it as an organisational capability that must be governed through the operating model.

All team members left with stronger discipline around prioritization and a shared language for defining autonomy and controls. Most importantly, they redefined value. Success was no longer measured by how many people used the tool, but by operational metrics: reduced manual effort, faster cycle times, and improved consistency. They didn't just learn to use AI; they learned to manage it.