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Most AI pilots never make it past the demo phase. AWS Machine Learning Lead Praveen Jayakumar has seen plenty of promising AI projects get stuck between a successful demo and production. Teams often define what success looks like without deciding what failure looks like, leaving underperforming projects alive long after they should have been shut down. As Praveen puts it, they become “zombie” AI projects.

This video shows how Astera ReportMiner extracts structured data from PDFs in two ways: a reusable template for consistent layouts, and an AI-driven pipeline for documents that vary. Working from a mix of PDFs, digital and scanned, that vary in layout and quality, you can build a custom extraction logic in ReportMiner: Whether a PDF arrives with a known layout or a new one, it runs through the same pipeline into the same output, with fewer manual exceptions and less setup.

Human biology is extraordinarily complex, and researchers often have only fragments of information to work with. Brian Martin compares it to looking at a skyscraper through a keyhole: you can see something clearly, but only a tiny piece of the whole. Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with Brian Martin, CTO of Applied AI at Cloudera and co-founder of Rare Hopes NFP, to explore what one of the world’s most data-intensive industries can teach us about AI and decision-making.