Why Most Enterprise AI Projects Fail Before They Start
The gap between AI demos and production systems is where most enterprises lose millions. Here's what separates the 8% that succeed from the 92% that don't.
Rajesh Kumar is the Chief AI Officer at Plaxonic Technologies, where he leads the company's artificial intelligence strategy, research, and enterprise deployment initiatives. With over 15 years of experience at the intersection of machine learning and enterprise systems, Rajesh has been instrumental in guiding Fortune 500 organizations through their AI transformation journeys.
Before joining Plaxonic, Rajesh held senior data science and ML engineering roles at leading technology firms, where he built and deployed production AI systems serving millions of users across financial services, healthcare, and manufacturing sectors. His work has directly contributed to over $200M in measurable business impact for enterprise clients.
Rajesh holds a Master's degree in Computer Science with a specialization in Machine Learning from IIT Delhi and has published research on production ML systems, model governance, and enterprise AI architecture. He is a frequent speaker at industry conferences including AWS re:Invent, Google Cloud Next, and O'Reilly AI Conference.
At Plaxonic, Rajesh oversees a team of 50+ AI engineers and data scientists, driving innovation in areas such as generative AI, MLOps, autonomous agents, and responsible AI frameworks. He is passionate about bridging the gap between AI research and real-world enterprise value.