A growing number of financial institutions need to rethink their go-to-market strategy. This surge of reinvention activity is a generational reckoning.

Fintechs, still stuck in the first gear of their product suite. Mature banks watching the erosion of net-interest-margin-based financial models. Mobile and web experiences crowded with undiscoverable features and undifferentiated from one to the next.

And then there’s the question of how best to incorporate artificial intelligence capabilities.

Before You Begin

Sound strategy work begins with financial objectives. Leadership at the institution needs to clearly articulate the desired economics over a given period of time; these objectives serve as the basis for exploring new go-to-market strategies.

Teams need to capture all known metrics and financial levers: cost of acquisition, front-of-funnel conversion percentages across stages, lifetime value, and similar data points. Clear objectives and key metrics give teams bounds for solution development and criteria for evaluating and prioritizing opportunities.

Starting From Where You Are Today

Once the organization has aligned on objectives, the next step involves some soul-searching. Most financial organizations are heavily siloed, even with abundant documentation, and lack an adequate end-to-end picture of how they deliver value to customers.

• What do customers value about your products and services?
• What issues do they experience?
• What are competitors doing differently?
• What does service delivery look like across people, processes, and technology?
• Are there underexploited assets the company has available, e.g., data?
• Does alignment exist on objectives and the metrics that will matter?

Every financial institution has gaps in its current-state knowledge. Aggregating what’s known in the organization through internal research, and supplementing that knowledge with an outside-in analysis, gives cross-functional teams a starting point for opportunity analysis.

Defining Opportunity Areas and Solutions

With a better picture of how the organization delivers value, and where value is lacking, it’s time to bring together cross-functional stakeholders. A well-coordinated working session (or series of sessions) to review the current state, analyze competitive and divergent approaches, and form customer-centric problem statements puts teams on a fast track to a research plan.

Using Multiple Forms of Research

The opportunities cross-functional teams identify may vary. If the goal is to enter a new market or offer new products or services, the team will need discovery research to understand user needs.

If reinvention is more focused on recasting existing experiences, or if teams feel confident in working hypotheses, generative research may be a better starting point. As teams learn and build confidence in a new value proposition, evaluative research can confirm desirability and de-risk delivery upfront.

Designing for Validation, Designing for Consensus

Steve Jobs once said, “It’s really hard to design products by focus groups. A lot of times, people don’t know what they want until you show it to them.”

Since 1998, when Jobs made that observation, design has become a bigger part of the research process. Capabilities have advanced, making it much easier to show users potential solutions and generate insights that shape the final product.

The key is matching the approach, whether low-fidelity sketches, working prototypes, narrative prototypes, or vision videos, to the purpose. Each approach carries benefits and tradeoffs depending on whether the aim is concept validation or internal consensus building.

Success Factors and Pitfalls

For over 25 years, Method has been helping companies define what’s next. The most common success factors include:

• Executive sponsorship and inclusion early and often in stage-gated decision-making
• Cross-functional team inclusion: Product, Design, Research, Engineering, Business, Legal, Risk, and Compliance
• Clear ownership of next steps and capacity to execute

When organizations stall in the process, it’s typically for one of these reasons:

• A lack of top-down or cross-functional organizational commitment
• Assumption-driven solutioning or copycatting
• A failure to map user needs or validate approaches

What About AI?

No conversation on reinvention can ignore artificial intelligence. But AI is a capability, not a solution in itself. It doesn’t replace the process outlined above; it fits within it.

The opportunity analysis and research phases are the right moments to evaluate where AI can add value. That evaluation follows the same discipline: start with financial objectives, validate against user needs, and prototype before committing at scale.

Ready to Define What’s Next?

Method partners with financial institutions to move from strategy to action across the full reinvention lifecycle. From defining financial objectives and mapping the current state to validating new value propositions with real users, our teams bring 25+ years of financial services experience to every engagement.

Reach out today to start the conversation.