Enterprise AI: A Strategy for Modernising Internal Banking Processes
I led the discovery and define phase for an AI initiative for a large universal bank, identifying where AI could meaningfully transform daily processes, assisting with high-volume information processing, and increasing employee satisfaction.
When the project was happening, AI design tools were not as widely available for project work yet. Due to client confidentiality, I'm also limited in how much real project work I can show publicly. So, while building this portfolio website, I took the opportunity to recreate a couple of the designs with AI — to explore new possibilities and showcase what I would produce if the projects were happening today.
Project Summary
I led a comprehensive user research and synthesis initiative for a complex multi-departmental platform, helping a large universal bank uncover strategic opportunities where AI could safely create value for employees. The project identified practical use cases to reduce repetitive manual work, improve efficiency in information-heavy tasks, and better support employees in their daily workflows. It concluded with an initial prototype, prioritised use cases, and a small roadmap to guide future validation and implementation.
Employees were drowning in repetitive, data-intensive busywork
The bank's employees were spending significant time on repetitive, data-intensive tasks such as summarising large volumes of information and manually extracting data from dense documents. This increased cognitive load, created risk of human error and left less time for strategic or customer-impacting work.
One-size-fits-all wasn't going to work
Engaging directly with end-users revealed that the initial project hypothesis, of a one-size-fits-all solution, was only relevant to 50% of the departments. The interviews surfaced both universal pain points and department-specific challenges tied to varying compliance rules or the number of clients each team managed.
We uncovered four high-impact opportunities: optimised client management, streamlined market monitoring, human-led AI-assisted report generation, and enhanced data extraction & analysis.
Navigating Preconceived Solutions
During interview preparation, there was a strong business drive to accelerate the timeline by immediately presenting a pre-defined solution to users for validation. The challenge was to advocate for foundational discovery, ensuring we fully understood the employees' pain points before exposing them to any specific concept.
Hybrid Interview Structure
To balance the need for unbiased discovery with stakeholder expectations, I proposed a hybrid interview structure. I led each session with open-ended, foundational questions to map the users' true workflows, reserving the final few minutes to present the pre-defined screen. This approach protected the integrity of the research while maintaining crucial stakeholder buy-in.
The Core & the branch
Departments didn't overlap as much as initial interviews had suggested, so a single unified journey wouldn't work. We transitioned from a linear journey to a modular, capability-driven setup — a shared "Core" journey applicable to all departments, with diverging "Branch" workflows in the form of departmental modules.
Discovering Divergent Workflows
To validate our findings, I presented the initial user journey map back to the employees for feedback to ensure we had mapped their experience correctly. During these sessions, one department pushed back, pointing out that their specific processes were not accurately represented. After cross-referencing with other teams, we realised some of the workflows didn't overlap nearly as much as our initial interviews suggested, meaning a single, unified journey map wouldn't work.
The Missing User Journey Workshop
While two days of user interviews were enough to identify high-level overlaps between the departments, in this kind of highly complex ecosystem it would have been beneficial to additionally conduct a user journey workshop with all involved departments and define the core journey that is the same for everyone and reveal at which point it needed to branch out in different directions.
For future engagements of this scale, I would advocate for complementing the user interviews with this participatory approach as it would help map varying user needs more efficiently as well as helping the different departments build a shared understanding of each other's needs, priorities and constraints.
Modular Approach
We couldn't build a separate app for every department, nor could we force users down a single funnel if they have varying needs. Our solution was to transition from a linear user journey to a modular, capability-driven setup.
We defined a "Core" journey (applicable to all departments) by deducing the steps they all take and added diverging workflows in the form of departmental modules.
One use case, every team, immediate value
From the research we extracted one use case shared across all departments — the one with the greatest potential to save time and lift morale at once. It became the primary focus of the strategy roadmap: a tangible daily time saving of 1–2 hours per employee, a highly scalable MVP, and a foundation for more tailored departmental workflows to follow.
A foundation for scalable, AI-driven transformation
The project concluded with the foundation for a scalable AI-driven transformation: validated use cases, an actionable product strategy and roadmap, and an initial prototype focussed on creating immediate value by removing manual processes and enabling intelligent, data-driven workflows.
My Personal Highlight
Designing for AI in a highly regulated environment required careful consideration of security, reliability and user trust. The project deepened my understanding of how emerging technologies can be shaped through human-centred design to create meaningful, responsible and practical outcomes.
