Most AI initiatives don’t fail because the models are weak - they fail because organizations never design for reality.

In this episode of Intelligent Insights, we unpack why 80 - 95% of enterprise AI pilots never make it to production, and what separates scalable AI systems from endless proof-of-concepts. Drawing from industry research and real-world engineering patterns, we explore the hidden blockers behind “pilot purgatory” — including verification tax, MLOps immaturity, technical debt, and misaligned incentives.

We break down a practical roadmap for scaling AI responsibly, starting with high-control, low-agency systems and gradually increasing autonomy as trust is earned. You’ll learn why Human-in-the-Loop (HITL) frameworks, disciplined data foundations, and cost-aware hosting strategies matter more than choosing the latest model.

This episode is not about hype. It’s about shipping AI that survives contact with production.

If you’re a product leader, engineer, founder, or executive trying to move AI from demos to durable business impact - this one’s for you.

Do you really need a trillion-parameter model to solve enterprise problems?

In this episode, we unpack why Small Language Models (SLMs) are gaining momentum across enterprise AI. We explore how techniques like knowledge distillation and quantization allow smaller models to deliver competitive performance - while significantly reducing cost, latency, and energy consumption.

We also discuss why SLMs are a natural fit for agentic AI, enabling multi-step reasoning, on-device and on-prem deployments, and stronger data privacy in regulated environments. The takeaway: the future of AI isn’t just about bigger models, but smarter architectures built for real-world production.

AI is entering a new phase.
In 2026, the conversation is no longer about basic automation or copilots - it’s about agentic AI: autonomous systems that reason, decide, and execute work across functions.

In this episode, I break down six defining AI trends for 2026, drawing from multiple industry reports and real-world adoption patterns. We explore why AI agents promise massive productivity gains - and why trust, data quality, and governance remain the biggest blockers to scale.

You’ll learn why context engineering matters more than prompt engineering, how successful teams combine AI autonomy with human oversight, and what practical strategies actually reduce legal, ethical, and operational risk.

If you’re building, leading, or investing in AI systems - this episode is about what works in practice, not what sounds good in demos.

This episode is for product leaders, engineers, and executives navigating real-world AI adoption in 2026.