Replay

Most AI Efforts Are Built Backward

AI adoption is moving at breakneck speed, but there’s a growing frustration in the enterprise: initiatives that look brilliant in a demo often fall apart in production. This "pilot-to-production chasm" usually happens when we fall in love with a shiny new tool before truly understanding the business problem it’s meant to solve.

In this fireside chat, Merav Yuravlivker and Dr. Quentin Reul strip away the hype to discuss what it actually takes to build AI that lasts.

Watch the Replay

Speakers from

Data Society ✦ expert.ai

What You'll Walk Away With

Practical Insights for the AI Era

This isn't a vendor pitch. It's an honest conversation between two leaders who have spent decades navigating data and technology change and know what it really takes to make AI work inside complex organizations.

Why starting with the technology leads to stalled projects—and how to pivot your strategy to focus on high-impact use cases instead.
Real-world tactics to bridge the "chasm" between a successful pilot and a robust system integrated into your enterprise workflow.
How to move past generic AI outputs by leveraging your proprietary data to create a sustainable competitive edge.
An honest look at how edge cases, data quality, and human behavior impact AI performance long after the initial launch.
What responsible and ethical AI actually looks like in practice, ensuring your systems remain trustworthy and scalable.

Who Should Attend

For Leaders Navigating AI in Complex Organizations

If you're responsible for data strategy, AI adoption, or leading teams through technology change, this conversation was designed for you.

Chief Data Officers & Data VPs

Driving data strategy and maturity across global organizations

AI & Analytics Leaders

Moving AI from pilot to production and proving real business value

C-Suite Executives

Navigating governance, risk, and workforce readiness in the AI era

Data & Technology Teams

Building the foundations that make AI trustworthy and scalable

Meet the Speakers

A Conversation Between Two Data Leaders

Host · Fireside Chat

Merav Yuravlivker

Chief Learning Officer, Data Society Group · Co-Founder, Data Society

Merav Yuravlivker is the Chief Learning Officer at Data Society, where she leads the organization's approach to AI and data education, workforce transformation, and learning strategy. She is passionate about helping enterprise and public sector leaders move beyond AI experimentation to build the skills, culture, and confidence needed for lasting organizational change.

In this fireside chat, Merav brings her signature ability to draw out the insights that matter most asking the questions other leaders are thinking but haven't said aloud.

Featured Guest

Quentin Reul, Ph.D.

Director of Global AI Strategy and Solutions, Information Services | expert.ai

Quentin Reul, Ph.D., is a seasoned AI strategist and Director at expert.ai with over 15 years of experience bridging the gap between theory and industrial reality. Known for his mantra to "fall in love with the problem, not the solution," Quentin argues that many AI initiatives stall because they attempt to retrofit "shiny" technology like Generative AI into undefined business problems.

To counter this, he specializes in navigating the "pilot-to-production chasm," helping organizations move beyond narrow experiments to build robust systems integrated into complex enterprise workflows. By focusing on high-impact use cases, he enables enterprises to transform their proprietary data into a strategic "unfair advantage," ensuring that AI solutions provide a genuine return on investment and a sustainable competitive edge in an increasingly automated world.

What to Expect

Inside the Conversation

Intro: The "Built Backwards" Trap

Merav and Quentin discuss why many organizations start with the technology instead of the problem—and how this common mistake sets AI initiatives up for failure before they even launch.

Part 1: Bridging the Pilot-to-Production Chasm

An honest look at why a "successful" pilot often falls apart in the real world. We’ll explore what actually changes during the transition to production and how to prepare for it.

Part 2: Navigating Data Realities

Beyond the clean datasets of a demo lies the reality of messy, incomplete data. Learn how these constraints shape what is actually possible and how to build systems that are resilient to "real-world" data.

Part 3: Responsible AI in the Wild

Moving beyond theory to discuss what accountability and ethics look like when a system is live. Quentin shares how leaders can navigate risk and evolving regulations without stalling innovation.

Q&A: Open Discussion

Your questions answered live — no vendor pitch, no fluff