EXL 2025

Ben Labay & Ryan Lucht – Opening

Thomas Fredell - The Leadership Platform: Powering Breakout Growth

What drives breakout business growth? Leadership that empowers teams and fosters innovation. In this dynamic keynote, Thomas Fredell, a seasoned founder and CTO/CPO with multiple exits and an IPO, shares how empowered, outcome-focused product teams, inspired by Marty Cagan’s Product Operating Model, outperform rigid top-down plans. Drawing from his experience building the first virtual data room for M&A, Thomas shows how leaders can shift from directing to enabling—coaching teams, setting clear vision, and creating psychological safety for rapid experimentation. Through examples of remote-first teams and continuous testing, he reveals how experimentation fuels learning, iteration, and better business outcomes. Expect practical insights to transform your leadership and unlock team-driven growth.

John Cutler - Your (Experimentation) Operating System as a Design Problem

How do you scale effective local team behaviors across an organization? This talk addresses this challenge, focusing on moving beyond top-down mandates for product operating models and experimentation cultures. The key is designing a thoughtful operating system to make change stick. We’ll explore rituals and language, drawing from service, game, learning, and human factors design. Operations should shape how people work, learn, and adapt, not just focus on process efficiency.

Moderated Table session #1

A special table session where folks are grouped by biz size, vertical, and roles to discuss implications of leadership and operations on their org’s growth.

Moderated Table session #2

Topic specific to each table.
You will be in hand-curated groups by interests, experience, etc.

Medha Umarji - From Scrappy to Scalable - Evolving Experimentation at Fanatics

This talk will focus on the challenge of evolving a basic A/B testing program into a fully scaled, self-sustaining experimentation ecosystem. We’ll dive into both strategic and operational components - managing an in-house testing tool, balancing roadmap bandwidth across teams, creating a flywheel of ideas that keeps experiments flowing, and designing a scalable data pipeline.

Diana Jerman - Lessons Learned from Scaling Experimentation at Disney+ & Hulu

A common question in scaling experimentation programs is "How do we develop high-velocity experimentation?" Through my experience leveraging and enabling experimentation, and through countless hours collaborating with teams across Disney, external industry peers, and thought leaders, I have identified three key areas for improving any experimentation program: (1) strengthening of the experimentation culture, (2) enhancing automation and tooling capabilities, and (3) implementing robust measurement frameworks.

Understanding the difference between teams with a high velocity of testing vs low is an effective way to identify challenges and develop actionable approaches for how we help teams improve their experimentation speed and efficiency.

Moderated Table session #3

Topic specific to each table.
You will be in hand-curated groups by interests, experience, etc.

Martin Tingley - Self-improving products: Experimentation + Gen AI

What if your digital product could learn and improve itself—automatically? In this talk, I introduce a five-level framework to understand an organization’s learning and product development capacity, from basic build-and-ship to fully self-improving systems. Each level on the ladder represents a step-change in how we generate, test, act on, and learn from ideas, and requires changes to both experimentation tooling and practice. This framework can help you identify where your team is today—and what it takes to climb to the next rung.

Elena Grewal - The story of Data Science at Airbnb

This talk will share the story of how Airbnb created a rigorous experimentation culture focused on the voice of the customer and was able to drive significant business impact as a result. Dr. Grewal was at the front lines of the rocketship growth of Airbnb’s business, joining as one of the first data scientists in 2012, and departing 7 years later as the Head of Data Science leading a team of 200 data scientists.  She will share her perspective on the key factors that led to a close link between experimentation and growth at Airbnb and how data science contributed to this. Find out what the very first experiment was that was run at Airbnb and the major changes at the company that came from the results. Then learn about the steps that leaders and data scientists took to spread experimentation to all departments and, critically, to invest in a rigorous experimentation platform that enabled that spread.

Moderated Table session #4

Topic specific to each table.
You will be in hand-curated groups by interests, experience, etc.

Moderated Table session #5

Topic specific to each table.
You will be in hand-curated groups by interests, experience, etc.

Elena Verna - Growth in the Age of AI

Building inside an AI company is like scaling a product that’s still being invented - by users, models, and some chaos. Growth isn’t about playbooks anymore; it’s about keeping up with a product that evolves daily and customers who expect magic. I’ll share what it’s really like to run growth in an AI-native world.

Alexey Komissarouk - Building an AI-first Growth Organization

Your engineers are already vibe-coding their experiments, so why stop there?  In this talk, we'll talk through the AIificiation of Growth, exploring a Growth organization moving at breakneck pace, decoupled from human bottlenecks.

Mansour, Makram (Intuit) - Testing Business Ideas: Journey from Ideas to a Viable Business

Enterprise teams excel at optimizing existing products through experimentation. But what happens when the goal shifts from optimization to launching entirely new lines of business?

Makram built the testing programs at Linkedin and Intuit, here he’ll speak to his new role in ‘emerging products’ and reveal how leading companies structure for sustained growth—connecting core product teams, the CTO office, M&A, and internal ventures. We’ll unpack how these groups rigorously test new business ideas using a “three fits” framework: on paper, in the market, and in the bank. You'll learn how a Shark Tank-style process and succession metrics guide ideas from concept to validation, and how AI accelerates this path by focusing resources on what’s truly viable.

You’ll leave with a practical blueprint for building a repeatable, data-driven pipeline for new business growth within your enterprise.