Why do so many AI projects fail when they move from a demo to the real world?
In this episode, AI product leader Hemang Upadhyay joins host Boris Agranovich to break down the critical gap between controlled pilots and messy, unpredictable customer journeys. Learn how to maintain clear accountability and build trust as automation begins to influence high-stakes business decisions.
Hemang shares his 16-year journey from engineering to AI leadership, offering a unique perspective on why technical success does not always translate to business value.
The conversation dives deep into the necessity of a strong operating model, the importance of clean data, and the reality behind the hype of current AI adoption rates.
Key topics covered in this episode include:
🚀 Transitioning AI from controlled environments to massive production catalogs
🚀 Defining clear ownership across technology, data, and business teams
🚀 Creating recovery paths and human review points for high-risk decisions
🚀 Moving beyond adoption metrics to measure true customer trust and outcomes
🚀 The future of agentic AI and its potential impact on industry standards Whether you are a product manager, a business leader, or a risk professional, this discussion provides a roadmap for scaling AI responsibly while avoiding the common pitfalls that lead to project failure.
Subscribe to the Global Risk Community for more insights into risk management and leadership strategies. You can also visit Hemang's website to explore his open-source projects for small and medium businesses.
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