Most organizations are spending millions on AI and getting little back — not because the technology is broken, but because they never defined what problem they were solving in the first place. Sameer Ranjan has seen it from every angle: underground mines, McKinsey boardrooms, healthcare systems, and now as a builder of AI tools that actually measure what matters.
In this episode:
• Why AI adoption keeps failing in legacy organizations — and what mature adoption actually looks like
• How a US-patented algorithm turns 84 soft skills into measurable, career-relevant data
• The difference between AI augmentation and AI application — and why confusing them costs companies millions
• Why human-in-the-loop is non-negotiable in knowledge-based industries like localization
Sameer Ranjan is CTO and Director of Data Science at Catenate, a workforce intelligence company built around the philosophy that careers are a chain reaction. He holds a US patent (12,198,213 B2) for an algorithm that quantifies soft skills and personality patterns to improve career direction. His path spans underground mining engineering in India, a silver medal in academics, a master's in business analytics at UT Dallas, consulting at McKinsey, and AI product development at the American Heart Association before founding Catenate.
Connect with Sameer Ranjan:
LinkedIn: https://www.linkedin.com/in/sameer-ranjan
Website: https://www.catenate.ai
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Credits
Host: Robin Ayoub, Founder, Localization Fireside Chat
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