Season 1 Finale

AI isn’t coming. It’s already here.

In this season finale of The AI Storm, we step away from theory and research — and speak from lived experience. From building AI POCs inside a global enterprise… To launching an AI startup without a large team… To navigating fear, trust, security, job anxiety, and speed-to-market pressures…

This episode is about what really happens when AI meets real organisations and real people.

You’ll hear:

• Why speed-to-market is no longer about team size or funding
• How AI reshapes cognitive load — not just productivity
• Why job fears increase even as opportunity expands (Jevons Paradox)
• What leaders get wrong about readiness, trust, and control
• How one POC changed the way Krishna thought about building products forever

This is not a prediction episode. This is a reflection episode. And it closes Season 1 of The AI Storm — before we move into a new chapter of video conversations and interviews.

🎙️ Listen now.
🌩️ Stay thoughtful. Stay curious. Stay ahead of the storm.

AI can decide faster than humans.But it cannot decide responsibly.In this episode of The AI Storm, we explore the most critical question facing leaders as AI systems become more autonomous:👉 Who should decide — the machine or the human?As organizations move into 2026, AI reasoning is no longer experimental. Systems are already making decisions across logistics, cybersecurity, finance, and operations. But not every decision should be automated.In this episode, we cover:

• What “AI reasoning” really means (and what it doesn’t)
• Where AI decisions outperform humans
• Where human authority must always remain
• Why leadership is shifting from managing people to orchestrating intelligence
• A simple decision framework every executive should use before trusting AI autonomy

This isn’t a technical conversation.It’s a leadership one.🎧 Listen now and stay ahead of the storm.

Why Enterprise AI Breaks After the Demo

AI rarely fails because the technology is weak.

It fails because it’s deployed inside workflows that were never designed for intelligence.

In this episode of The AI Storm, we explore The Automation Gap — the space where AI pilots succeed, demos impress, and real-world execution quietly breaks down.

You’ll learn:

• Why automating broken workflows amplifies chaos instead of fixing it

• How poor data quality, missing context, and fragile integrations cause AI to fail silently

• The difference between human-in-the-loop and human-on-the-loop — and why leaders must design for both

• Why most AI failures are operational and leadership problems, not model problems

• The four workflow design questions every executive must answer before scaling AI

This episode isn’t about better models.
It’s about better workflow design.

If AI in your organisation “works… but only sometimes,” this episode will help you understand why — and what to redesign next.

🎧 Episode 7 — AI on the Edge: Why Smaller Models Win on Cost and Speed

For the last few years, the AI conversation has been dominated by scale. Bigger models. Bigger budgets. Bigger infrastructure. But quietly, a different story is unfolding.

In this episode of The AI Storm, we explore why smaller, faster, edge-deployed AI models are increasingly outperforming large, centralized systems—on cost, speed, reliability, and control.

This isn’t a technical deep dive. It’s a leadership conversation.

You’ll learn:

• Why many real-world AI use cases don’t need massive models
• How edge and smaller models are being used in retail, manufacturing, security, and operations
• What “training,” “fine-tuning,” and “retraining” actually mean in practical business terms
• Whether companies should buy off-the-shelf models or invest in building their own
• The new roles and skills emerging around edge AI and model operations
• How leaders should think about ROI, governance, and long-term sustainability

• This episode is about designing intelligence for reality, not for demos.

If you lead teams, build platforms, or make decisions about AI strategy, this conversation will help you rethink where intelligence should live—and why smaller may be smarter.

🎙️ Hosted by Krishna Goli
🌩️ Finding direction and decisiveness in the storm of AI

AI is no longer a technology decision. It’s a leadership behaviour.

In this episode of The AI Storm, let us understand what executives, founders, and senior leaders actually need to know about AI in 2025 — beyond the hype, beyond pilots, and beyond experimentation.

You’ll learn:

• Why most AI initiatives never escape “pilot purgatory”
• How leading organisations measure real AI value (not vanity metrics)
• The executive behaviours that separate AI leaders from AI experimenters
• A practical 30-day action plan to move from trials to impact

This is not a technical deep dive. It’s a leadership playbook. If you’re responsible for strategy, teams, or outcomes — this episode is for you.

🎧 Next episode: Why Smaller AI Models Are Winning on Cost, Speed, and Scale

What happens when AI stops waiting for instructions… and starts pursuing your goals?

In this episode of The AI Storm Podcast, Krishna Goli explores the rise of Agentic AI — systems that can plan, act, reflect, and collaborate with minimal supervision. From autonomous debugging to multi-agent teams that produce full strategic documents, this episode dives deep into the moment where AI shifts from answering to accomplishing.

You’ll hear real stories of agents solving problems end-to-end, understand the architecture behind their reasoning loops, and learn the practical challenges — cost, brittleness, looping, and the gap between what you say and what the agent interprets.

We also look at everyday-life examples:

• Agents fixing broken flows while you’re in a meeting
• Tools like Devin, OpenDevin, LangGraph, AutoGen, and CrewAI already working in the real world
• Personal agents reshaping your schedule, drafting emails, and preparing talking points
• Small-business automations running customer workflows end-to-end

In this episode, you’ll discover:

• What makes an AI system truly “agentic”
• How agents reason using observe–plan–act–reflect loops
• Real examples from engineering, research, productivity, and small businesses
• Why autonomy introduces risk — and how to manage it
• What happens when multiple agents collaborate
• How leaders should prepare for the age of autonomous workflows

Try this tonight:
Pick one workflow you repeat each week — weekly reports, lead qualification, or bug triage — and run it with an AI agent for two weeks. If you’re technical, try LangGraph or CrewAI; if not, start with the agent features inside your CRM or helpdesk.
After a week, ask: Where did it save you time? Where did it surprise you? Where did it make you nervous?

Agentic AI isn’t the future of automation.
It’s the future of collaboration.