As Head of Digital Products for Latin America at Gallagher, André Scudiere draws on over seven years of experience in analytics, AI and digital transformation to reshape the insurance landscape. Armed with an MBA in Data Science & Analytics from the University of São Paulo, he leads cross-functional teams in creating scalable, data-driven solutions that streamline operations, automate processes and deliver measurable value across one of the world’s leading insurance brokerages.
Through this interview, Scudiere highlights building a trusted, scalable data foundation across Latin America to drive value. He emphasizes cautious adoption of AI to improve insurance processes and decision-making. Looking forward, he focuses on AI-powered policy comparisons and automation to transform the industry.
Building Latin America’s Data Foundation
If I had to describe my journey at Gallagher in one word, it would be extraordinary. I’ve spent eight years in the insurance industry, always rooted in data. But nothing compares to what I’ve experienced since joining Gallagher three years ago, when we were just starting operations in Brazil. Then, we were a tight-knit team of 25. Today, we’re more than 600 strong. The growth has been astonishing, but the real story is what we’ve built behind it.
We didn’t inherit a legacy; we created one. In doing so, we proved what’s possible when strategy, execution and culture align.
Building from the ground up meant being hands-on with everything—from hiring and structuring teams to implementing systems and earning trust. We didn’t inherit a legacy; we created one. In doing so, we proved what’s possible when strategy, execution and culture align.
Last year, I was asked to lead data strategy across Latin America, which is Gallagher’s most dynamic and fastest-growing region globally. It’s a big responsibility and a thrilling one, because this is where data can transform how we deliver value.
Today, I’m focusing on building a strong, scalable data foundation based on trust in the quality of our data, the systems that hold it and the insights we generate in the region. In Latin America, this means aligning four very different countries with varying technologies, processes and market realities. We’re harmonizing data to unlock its full potential.
Why does this matter? Because we sit at the intersection of carriers and clients. As a broker, we’re uniquely positioned to provide value on both sides. We show carriers how they perform across the market and help clients understand how they compare to peers. For example, with an energy client, we can show what other energy companies are buying, the coverage limits they choose and where the client stands.
The past three years have been a wild ride, but I have no doubt the best is still ahead.
A Shift toward Smarter Decisions
AI is clearly the topic of the moment. A key reason is the rise of AI agents and multi-agent automation, shaping the near future. With advances in model context protocol models, context-aware systems and protocol servers, AI is entering a new phase in realworld applications.
These developments are steering the industry, especially in the U.S., where scaling AI is a priority. Competition between Google and OpenAI is accelerating innovation, particularly in generative models. Over the next two to three years, the focus will be on generating real value from AI agents and automating scalable, responsible processes.
But automation isn’t everything. Understanding AI decisionmaking, especially with generative models, is a major challenge. Observability and transparency about how models function and make decisions are vital for trust, especially in regulated industries.
In our organization, we’re experimenting with AI cautiously. We’ve launched small initiatives like AI-driven claims classification and data analysis. These aren’t fully automated due to internal restrictions, but early results—especially in classification—are promising for streamlining insurance operations.
The insurance sector faces its own hurdles. Traditionally conservative, it only began modernizing in the last five years and is still catching up. Foundational work remains.
That’s why we focus on delivering personalized, value-driven solutions. We’re exploring how AI can benchmark market conditions and ensure clients get the most suitable options. The goal is to use AI not just for automation, but to support smarter decisions and better outcomes. While AI advances quickly, we take a measured approach by balancing innovation with trust and experimentation with responsibility
Driving Insurance Transformation with AI-Enabled Policy Analysis
Looking ahead, we focus on improving smaller AI-driven projects, especially in areas like policy comparisons, which are critical to our business. We rely entirely on technicians to review client policies and assess the risks they're assuming. What we’re building now is a solution that brings together all open market conditions into a single platform. From there, we can ask: if a client is buying a Policy Type A in a specific region, is that policy truly offering the best conditions available? What exclusions or coverage details should we be paying attention to? And more importantly, how does the client’s unique operation align with the policy they’re purchasing?
The goal is to train AI to understand the client’s risk profile and business operations, then evaluate multiple industries, coverage types and carriers to recommend the best-fit solution. It’s about freeing up technician time while ensuring clients get the most suitable, competitive options. This is a key trend for the insurance industry in the coming years.
Beyond that, we’re also looking at automating client deliverables like customized invoices and other specific outputs brokers often handle manually. These are the main areas we’re investing in now. I believe they’ll define the next wave of digital transformation in insurance.
Focused Steps to Drive Growth
If there’s one thing to emphasize, it’s staying connected to the AI landscape. It’s evolving faster than anyone can fully grasp. The key is to begin with a small, high-impact project that addresses a real problem and has the potential to scale. Avoid going big from the start when so much remains uncertain. Instead, focus on how AI can help your team work smarter and faster. Implement something manageable, learn from it and build progressively with a clear understanding of risks and rewards. The goal isn’t to let AI make decisions for you, but to enhance productivity. That’s where the true value lies.
