Exadel

Based on Gartner’s research, a staggering 85% of AI projects fail to deliver lasting value. Despite significant investment and talent, many AI initiatives fall short due to avoidable missteps in strategy, execution, or alignment with business needs. In this talk, we uncover the most common mistakes made by managers, developers, and data scientists when building AI products, from flawed problem framing and poor data foundations to lack of user integration and unclear success metrics. Drawing from ten real-world case studies, we walk through products that failed across various industries and dissect the root causes of each failure. But this is not just a cautionary tale, it’s a roadmap for doing AI right. For each case, we provide actionable strategies to prevent similar pitfalls, focusing on improvements across the AI product lifecycle: from ideation and data collection to deployment and post-launch monitoring. We'll also touch on cultural and organizational factors that can make or break AI adoption. Whether you're building your first AI-driven product or trying to rescue one already in motion, this session provides a clear, experience-backed roadmap for turning AI ambition into real-world impact.

Target Audience: Product and project managers, decision makers Machine learning engineers

Prerequisites: no experience is required

Expected Outcomes: Know how to avoid mistakes when building an AI product Learn how to setup a due diligence meeting Transform your company into an AI company

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