From Google to Fintech: How Small Teams Can Outmaneuver Giants

By Natalia Corobco, Co-founder & CEO, Francis

After ten years at Google, I left and focused on my fintech startup full-time. Having been a founder before and after Google, I'm now bringing together lessons from both worlds. Success comes from knowing which big tech lessons to apply and which ones to ignore.

The Network Advantage

One of the most valuable lessons in big tech for me was that knowledge lives in networks, not documentation. At Google, you couldn't accomplish anything without leveraging colleagues' institutional knowledge. In a startup, building the right external network becomes critical since your small team can't have all the answers.

The key difference is calibration. At Google, I could explore every option. In a startup, you need broad awareness but must quickly categorise opportunities by relevance to your current stage. 

This network-driven approach becomes especially important when you're targeting massive market opportunities.

Three Trends, One Opportunity

Today fintechs represent only 3% of global finance, so the opportunity is enormous. There is a market gap in solutions that combine long-term financial planning with truly personalised advice. Most people still rely on spreadsheets or disconnected apps. This approach breaks down as financial lives become complex with multiple income streams, investments and goals.

This is where AI can finally deliver personalisation at scale. It can help consolidate data from multiple sources and provide insights that adapt to your specific situation.

Three trends are converging: democratising access, better automation and deep personalisation.

The future is that app- and AI-first design moves us beyond "spreadsheet mode" into interfaces that make financial management intuitive rather than a year-end chore. AI optimises tax and investment costs in the background, handling complexity that users shouldn't need to think about.

The result is holistic personal finance management that moves beyond simple expense tracking toward tailored insights, without requiring users to become prompt engineering experts.

Why haven’t existing players already captured this opportunity? The answer lies in understanding what’s holding back both traditional institutions and established fintechs.

The Market Window

Large fintechs face repositioning challenges when shifting business models - a payments company expanding into financial advice encounters different user expectations and regulations than their core business. Traditional institutions struggle with legacy systems and incentive structures that prioritise sales over user outcomes.

The window exists because this isn't just about better technology - it's about aligning business models with user success. Incumbents optimise for assets under management or product cross-selling. There's space for new fintechs to succeed when users achieve financial goals, regardless of which specific products they adopt.

Capturing this market window requires more than just identifying the opportunity, it demands solid execution. Here's how we're approaching AI implementation to move faster.

AI Implementation Framework

For us AI has been most valuable in accelerating development: prototyping interfaces, generating documentation and debugging frontend issues. We're also using AI for team onboarding, reducing the time spent bringing new developers up to speed, which is crucial when competing against incumbents.

As an AI-native fintech building with LLMs, we focus on new or transformative user experience and defensible integration. You can't build a sustainable product around AI features that could easily become standard LLM capabilities. 

The most interesting challenge right now is creating AI-powered interfaces that don't feel like chatbots. Users want insights and actions, not conversations with their financial app. 

Having a clear AI framework is one thing. Knowing when to completely change course is another. 

Pivoting at Startup Speed

Google taught me to think strategically about technology trends and opportunities, but startups demand different execution speed. We experienced that earlier this year when AI capabilities evolved faster than we expected. Originally, we planned a nine-month development timeline with AI integration post-MVP. But when we saw what became possible with foundation models, we realised waiting would be a mistake.

The most interesting challenge right now is creating AI-powered interfaces that don't feel like chatbots. Users want insights and actions, not conversations with their financial app.

Instead of layering AI onto existing plans, we fundamentally redesigned our MVP to integrate AI from day one, cutting some features and reducing the development timeline. For small teams, the critical adjustment is being willing to completely rethink product architecture when big trends emerge.

Of course, building the right product means nothing without a smart distribution strategy.

Distribution is King

Having spent five years at Google Play, I've seen app stores evolve significantly. That evolution is likely to continue in response to competitive headwinds. It's no secret that economics is a big challenge for fintechs today. Sustained platform fees should come with a distribution value guarantee. But organic discovery is harder than ever for fintech apps competing against established players with big marketing budgets. 

We are, of course, relying on app stores but also thinking how not to be entirely dependent on them. Fortunately alternative billing, web apps, direct distribution through financial institutions and embedded finance partnerships are creating new paths to users.