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Ualá

Scaling AI with Purpose: Leadership Lessons from the CDAO Seat

As Chief Data & AI Officer at Ualá, one of Latin America’s most influential fintech’s, Pablo A. Guzzi drives AI, machine learning, and Generative AI strategies that push innovation forward, reshape how users experience financial services, and scale solutions that make a real impact across the business. Over the years, Guzzi has rolled out ML and GenAI models in multiple markets, building up a culture where data and AI don’t just sit in the background but shape everyday decisions and tie directly into business growth.

Through this article, Guzzi highlights how data and AI must be embedded into a company’s DNA as drivers of innovation, trust, and growth. The narrative also outlines a practical playbook for scaling AI beyond technology, focusing on mindset, visible impact and building belief across teams.

Embedding Data into Ualá’s DNA to Drive Growth

My day at Ualá begins by looking at end-to-end strategy on how data and AI can unlock new ways to innovate, scale solutions, and improve the experience for every user. The focus in twofold:

• Build capabilities – from robust data infrastructure and governance to machine learning models and self-service tools.

• Turn capabilities into impact – ensuring everything we build moves the business forward. My team works across two tracks:

● Data Driven: empowering teams to make better, faster decisions through accessible tools and a strong self-service culture.

● Data Boosted: leading initiatives where data and AI create direct competitive advantage like UaláScore for credit, AI-driven fraud prevention, and our GenAI-powered virtual assistant, Uali.

Beyond the tech, I’m deeply involved in talent strategy, regional alignment, and integration with product and tech, so data isn’t a silo, it’s part of the DNA of everything we build.

Lessons learned?

● Impact over hype: fancy models mean nothing if they don’t change the business.

● Trust first: without it, data doesn’t get used. Quality, proximity, transparency — non-negotiable.

● Balance today and tomorrow: deliver now, invest in what’s next.

● Care for the team: a motivated, purpose-driven team scales better than any architecture.

● Speak business: when data meets real context, it becomes unstoppable.

Shaping strategy, Not Just Reporting Past

The shift has been radical. We went from reporting the past, to predicting the future, to shaping the strategy itself. Data leaders are now expected to be strategists and innovators, not just enablers. This is fueled by an explosion in demand for AI — not just to optimize, but to create new products, business models, and value streams. Data is now at the center of every major conversation. That raises the bar:

● Connect data directly to decisions.

● Accelerate time-to-action, not just insight.

● Drive AI adoption as a source of competitive edge, not a trend.

● Inspire and manage diverse teams — from engineers to product thinkers.

● Safeguard trust, governance, and responsibility as AI spreads.

Today’s CDAO has to build, strategize and innovate and make those dimensions work together to push the company forward.

Make AI Feel Close, Usable and Exciting

Scaling AI is less about technology, more about mindset. You can’t just “throw models over the wall” and expect magic. AI has to become part of how the company thinks, decides, and builds.

My playbook:

Get the foundations right: rock-solid infrastructure, governance, real-time monitoring and responsible AI by design: explainability, bias checks, human-in-the-loop when needed.

Solve visible problems: start with high-impact use cases, fraud, credit scoring, automation, co-built with product, tech, and business.

Build belief: tools alone don’t scale AI. Belief does. We run demos, trainings, internal showcases — making AI feel close, usable, exciting.

When teams start asking, “Could AI help here?” without us prompting, that’s when you know it’s scaling.

Celebrate Small Wins and Make Employee Contagious

Upskilling isn’t about handing people a course; it’s about giving them permission to try, to question, to fail smart. Four things that work:

Safe space to explore: internal hackathons, AI playgrounds, side projects with real business backing. Some fail, some spark waves. Learning in context: tie training to real problems.

  • Data-driven field moves fast. You don’t have to outrun it; you have to shape it and bring others along. Do that and you won’t just grow your career; you’ll help define the future

Learning sticks when it’s applied immediately.

Celebrate small wins: share stories across the company, a two-day process cut to two hours with AI becomes contagious energy.

Make learning part of the job: innovation is not “extra work”— it is the work.

People don’t resist change; they resist feeling alone in it. Show them support, trust, and recognition, and they’ll surprise you every time.

Lead Before You Feel “Ready” and Own the Messy Parts

Forget being the smartest person in the room. Instead:

● Obsess over impact — translate complexity into business outcomes.

● Build trust before systems — it’s the real platform everything runs on.

● Stay in learning mode — not chasing every trend, but staying relentlessly curious.

● Lead before you’re “ready” — take ownership early, even when it’s messy.

● Bring people with you — the best leaders make others smarter, stronger, more capable. Data-driven field moves fast. You don’t have to outrun it; you have to shape it and bring others along. Do that and you won’t just grow your career; you’ll help define the future.

The articles from these contributors are based on their personal expertise and viewpoints, and do not necessarily reflect the opinions of their employers or affiliated organizations.