What Still Works When You Build AI Products and What Needs to Change

You can’t build AI-driven features the same way you build traditional software. Try it, and you’ll end up with wasted time, frustration, and a product that doesn’t deliver real value.

In this session, a product manager and a data scientist will break down what actually changes when you build AI products, what still holds true, and how to make smarter decisions along the way. We’ll cover:

• Where AI requires different investments and how to avoid expensive mistakes
• When to bring in product, data science, and engineering to keep teams aligned
• Why traditional MVPs don’t work for AI and what to do instead
• Why launching an AI feature is just the beginning and how to keep it delivering value

Whether you’re shipping your first AI-powered feature or trying to level up your process, you’ll leave with practical, no-nonsense advice to help you and your team build AI products that actually work.

About Jessica and Pri:

Jessica serves as the Chief Growth Officer at OpsCanvas. With over 20 years of experience, Jessica specializes in driving innovation, building momentum, and delivering growth through strategic Product, Design, and AI leadership. She bridges the gap between business, engineering, and data science to create strategies that transform teams and deliver exceptional results.

Pri Oberoi is currently the Principle ML Data Scientist at Atlassian. Pri is a hands-on machine learning leader who has built impactful, use-case-focused statistical, machine learning, and deep learning solutions across consulting, start-ups, government and non-profits over the past decade. Most recently, Pri built the ML practice at Axios HQ from the ground-up, authored their patent, and established a data flywheel as the customer base grew to > 700 companies. Pri manages data science portfolios with an emphasis on user experience, close collaboration with SMEs, automated task-evaluation, and monitoring pipelines. Throughout their career, Pri has prioritized finding ways to make tech a more welcoming space for queer folks, women of color and other underrepresented identities.

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