Welcome to Intelligent Insights, the podcast where we explore the latest advancements in artificial intelligence, machine learning, and data science. Join us as we dive deep into the world of Retrieval-Augmented Generation (RAG), language models, and cutting-edge AI applications transforming industries from education to technology. Whether you're an AI enthusiast, tech professional, or just curious about the future of intelligent systems, Intelligent Insights brings you clear explanations, expert interviews, and practical insights to help you stay informed and inspired.
From Demo to Production: Where Should AI Reasoning Happen?
Gartner expects about 40% of agentic AI projects to be canceled by 2027. Only around 17% of organizations have actually deployed agents, even though more than 60% plan to.
This episode covers the first three parts of my series, Enterprise AI in Production, and looks at why a great agentic demo so often stalls before it reaches production.
We cover:
Predictability. Can we audit it, reproduce it, and put an SLA on it? These are enterprise software questions, not AI questions, and they decide what goes live.
Cost. An agent that keeps reasoning keeps spending tokens. Why cost per decision matters, and why paying to re-solve problems you already solved is the hidden waste.
Trust. Enterprises buy confidence, not capability. We walk through the four questions that decide whether something ships: explainability, reproducibility, auditability and accountability.
The pattern that connects all three: AI should discover, and deterministic systems should execute. Once AI reliably solves a class of problems, turn that into a rule or workflow. It then runs cheaply, the same way every time, and a regulator can inspect it.
Capability gets you the meeting. Confidence gets you the deployment.
Where have you drawn the line between reasoning and execution in your systems? I'd like to hear.
#EnterpriseAI #AgenticAI #AIinProduction #AIGovernance
18 Aug 2026
Reasoning Is Expensive. Execution Should Be Cheap
AI agents can plan, re-plan, call tools, reflect, and keep reasoning. That flexibility is powerful—but every reasoning step costs money.
In Part 2 of Enterprise AI in Production, I look at one of the biggest challenges in taking agentic AI from demo to production: unpredictable cost.
The important question isn't simply how much an AI model costs.
It's:
What does one business decision cost—and can we bound that cost?
When an agent repeatedly reasons through a problem it has already solved hundreds or thousands of times, we're paying premium token costs to rediscover something the system already knows.
A better pattern is to let reasoning earn its retirement.
Use AI for genuinely new, ambiguous, or difficult problems. Once a behavior becomes stable and repeatable, convert it into a deterministic rule, workflow, cached decision, or ordinary code.
You don't lose the intelligence. You bank it.
In this episode:
• Why autonomous agents can create unpredictable operating costs
• Why cost per decision matters more than token cost alone
• The hidden cost of repeatedly solving the same problem
• How to identify workflows that should move off the reasoning path
• Why reasoning should be a phase—not a permanent state
• How hybrid AI architectures can improve enterprise ROI
The goal isn't to use less AI.
It's to make sure we're paying for reasoning only when reasoning is creating new value.
30 Jul 2026
AI Should Discover. Deterministic Systems Should Execute.
Agentic AI can look impressive in a demo. Production is where the difficult questions begin.
Can the system be audited? Can its decisions be reproduced? What happens when the underlying model changes? Can it meet compliance requirements, control costs, and guarantee reliable execution?
In this episode:
• Why autonomous-agent architectures struggle at production scale
• The difference between AI reasoning and system execution
• Why predictability matters more than adding more AI
• How hybrid architectures balance intelligence and control
• When AI-discovered solutions should become deterministic workflows
Next episode: Why reasoning is expensive—and execution should become cheap.
Reach and audience
Public platform figures. Ratings count people who left a rating, not total listeners.
Apple Podcasts (US)
1.0 / 5
1 ratings
Spotify
5.0 / 5
2 ratings
Podcast Authority Score: 18 / 100
A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.
Quality
36
Social presence
0
YouTube
0
Engagement
0
Contact Intelligent Insights
Guest appearances
Does not typically book guests
Based on episode analysis; this does not confirm that the show is currently accepting guests.
Host of Intelligent Insights?
Claim your podcast to manage its listing and keep your show details accurate.
Pod Engine is an independent podcast discovery and analytics service and is not affiliated with or endorsed by this podcast. Artwork and show content belong to their owners. Full legal notice.
Explore this show Podcast research with Pod Engine
