Discover—Validate And Incubate new Product Ideas

Product Chōro was the first in a series of POCs tested in a non-production data environment, showcasing the potential of AI for the project management team.

Client: SMBC
Proposition: Product Discovery
Industry: Financial Services

Team & Timeframe:
Over a six-month period, I served as the client partner, leading the AI experience design workstream. My responsibilities included conducting user interviews, shaping experience strategies, managing the design team's execution, and ensuring seamless collaboration and alignment with client objectives.

Key Activities:

Facilitating executive workshops and ideation sessions.
Developing cross-functional team concepts.
Creating the GenAI Venture Framework.
Leading user story workshops.
Experience design development in collaboration with technology teams.
Delivering an executive funding pitch, including a video explainer.

RESULTS

70%

Early projections showed a 70% improvement in time efficiency.

03 in 06 mos

The launch of three POCs was pivotal for SMBC, positioning GenAI as a key driver for future growth.

Driving Product Discovery for SMBC

Leveraging unstructured data and AI to enhance business efficiency

From its origins as Japanese merchants to its evolution into a global financial powerhouse, SMBC has upheld a legacy of integrity and innovation for over four centuries. Over a century ago, SMBC expanded into the Americas, bringing with it a commitment to lasting relationships and a tradition of honorable service. The SMBC team continues to build trust and drive innovation for the stakeholders it serves.

Problem Space—SMBC identified an urgent need within its operational workflows across the Americas. Operations teams were relying on outdated systems that hindered agility, reduced competitiveness, and created inefficiencies. The question was no longer whether change was necessary, but how best to implement that change to unlock new value for the business.

One of the most significant challenges was the lack of a clear, deliberate framework for funding these initiatives. While there was strong ambition and ownership, the process for nurturing exploratory proof-of-concept ideas lacked definition. Without a structured approach to guide investment, these ideas risked being underdeveloped and unable to reach their full potential..

Figure 1: The solution was to establish a clear funding framework, where innovation budgets supported the seed funding of proof-of-concepts, and the business side funded their scaling once matured. By leveraging GenAI APIs like ChatGPT and Gemini, alongside synthetic data to model experiences, we ensured ideas were thoughtfully nurtured and seamlessly transitioned into scalable solutions.

Figure 1: AI Venture Development Funnel

Objective—Redefine these workflows—not through incremental improvements, but by completely reimagining how operations could contribute to the organization’s broader strategic efficiency objectives. Through discovery, validation, and incubation phases, I worked to uncover pain points, inefficiencies, and untapped areas for innovation. The goal was to create new product opportunities that could be tested, refined, and developed into MVPs, targeting a 20% increase in operational efficiency.

Outcomes—This initiative resulted in the launch of three proof-of-concepts (POCs), marking a major turning point for SMBC. These early successes set the stage for GenAI to become a driving force for the future. The immediate impact was projected to yield up to a 70% improvement in operational efficiency across critical workflows.

The process was quickly adopted in other regions, including EMEA and Tokyo, demonstrating the replicability and scalability of the framework

Product Strategy Approach—Figure 1.2 illustrates how we envisioned three integrated models to achieve the objectives. The first model, Product Discovery, identified the operations team as our customer, focusing on uncovering ideas and opportunities. This seamlessly connected to the second model, Product Leadership, which aligned with our strategic goal of operational efficiency.

In this model, Design elevated Discovery to a functional level, integrating it with platform engineering, product management, and data science. Lastly, the third model, Design Strategy, was guided by the strategic goal of collaboration. It emphasized research, ideation, evaluation, prototyping, and testing, ensuring each step was deliberate and thoughtfully interconnected.

Figure 1.2: Design and Discovery Framework

Figure 1.3: AI Venture Governance Gateways

As we defined the funding and maturation of ideas and identified a product discovery approach, we recognized the need for a framework that would establish process and governance. This framework would guide the team with clarity, ensuring that each activity and outcome generated the necessary data points to inform and evolve the long-term platform architecture.