Episode Summary

In this episode, we explore how AI is moving from experimentation to real-world execution. From sovereign AI initiatives struggling with organisation and culture, to India’s RationalGO enabling autonomous agents to complete tasks end-to-end, to FedEx embedding AI into logistics, and Backboard.io solving memory bottlenecks in agentic AI this episode highlights the systems, strategies, and infrastructure that make AI truly operational.

What You’ll Learn in This Episode

• Why many sovereign AI projects fail despite massive investments—and how organisations can address data, expectations, and cultural challenges.
• How RationalGO is taking AI from intelligence to execution, enabling autonomous agents to handle real-world tasks.
• The role of AI in enterprise logistics, including predictive tracking and automated returns at FedEx.
• Why memory is critical in agentic AI, and how Backboard.io is setting new benchmarks for long-term, multi-agent coordination.
• Key principles for scaling AI successfully: integration, discipline, and human-in-the-loop approaches.

Key Quotes from the Episode

• “The problem isn’t infrastructure it’s organisation. Data, expectations, and culture determine whether AI projects succeed.” — Ruchir Puri, IBM Research
• “Agentic AI doesn’t become meaningful because you label something an agent. It becomes meaningful when agents can remember, coordinate, and operate over time.” — Rob Imbeault, Backboard.io
• “Speed of learning, speed of iteration, and speed of execution is the real MOAT in AI today.” — Praveen Yadav, RationalGO
• “Small improvements in prediction accuracy in logistics can reduce support calls, lower refund rates, and improve trust across supply chains.” — FedEx AI Team

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• Host: Manish Balakrishnan
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