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Manish Dasaur is a Managing Director at PwC with over 20 years in data and AI, having helped 100+ clients navigate AI disruption and extract real business value from data, AI, and agentic AI initiatives. In this episode, he breaks down why most enterprise AI programs stall — and the playbook the winners are using instead.

Huge thanks to PwC for supporting this episode!

💰 The 30% benchmark — What "good" actually looks like: real efficiency gains clients are reporting across engineering, finance, HR, and supply chain
🔄 Workflows, not use cases — Why isolated pilots and POCs never show up in EBITDA, and how end-to-end workflow redesign does
🧪 Champion vs. challenger — Running a control group against your AI-automated process so ROI is demonstrated, not guessed
📞 Why customer care agents are still freaking hard — Context, CDP integration, billing systems, and voice-to-voice latency
💸 Tokenomics & FinOps — Consumption-based cost surprises, model selection, prompt engineering, and enforcing cost-per-workflow budgets
🔍 Auditing agentic behavior — Using AI to test AI, the missing "SOC 2 for agents," and certifying agents for sensitive use cases
👤 Human in the loop as an evolving scale — From reviewing 50% of outputs down to 10% as trust builds
🧠 88% do AI, 33% scale it — Building a culture of innovation, and why AI usage is showing up in performance reviews
💼 Jobs, reskilling & the operating model reset — Why 75%+ of jobs will be reskilled, not replacedIf you're an AI leader, platform engineer, or exec trying to turn AI experiments into P&L impact, this one's for you.

Links & Resources:
Connect with Manish: https://www.linkedin.com/in/manishdasaur/
PwC AI: https://www.pwc.com/us/en/tech-effect/ai-analytics.html
PwC's 2026 AI Business Predictions: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html

Timestamps:
[00:00] AI Hype vs Business Value
[00:44] API Spend Tracker Widget
[02:18] Tokenomics and FinOps for AI
[06:19] Measuring AI Impact Objectively
[11:07] AI in Support Workflows
[18:05] AI Innovation Culture
[27:16] MCP Servers and SOC 2
[29:14] Human in the Loop in evolving scale
[35:44] AI and Workforce Efficiency
[39:59] AI Transformation and Mindset
[42:19] Wrap-up

In this episode, we're joined by Jeremiah Lowin, Founder & CEO at Prefect and the creator of FastMCP, to explore how one of the most influential projects in the MCP ecosystem came to be - and where the protocol is heading next.

We discuss the accidental origin of FastMCP, why Anthropic adopted it into the official SDK, what developers are getting wrong about MCP, and why Chris believes the biggest opportunity for AI agents isn't customer-facing applications, but internal enterprise systems. We also dive into MCP Apps, developer experience, protocol design, AI tooling, Python, and why building great abstractions is often more valuable than exposing more configuration.

Along the way, we explore the rapid growth of the MCP ecosystem, how FastMCP became the default way many developers build MCP servers, why "too much magic" can actually hurt developer experience, and what the next generation of AI-powered applications will look like as agents move beyond simple tool calling into rich, interactive experiences.

Prefect: https://www.prefect.io
Jeremiah Lowin: https://www.linkedin.com/in/jlowin
Demetrios: https://www.linkedin.com/in/dpbrinkm

Timestamps:00:00 Lost My Entire Talk00:47 The Story Behind FastMCP02:08 Anthropic Adopted FastMCP02:34 When MCP Took Off04:10 FastMCP vs The Official SDK05:43 Is MCP Actually Dead?06:42 What Everyone Gets Wrong About MCP08:11 MCP's Biggest Use Case10:25 Building Internal AI Systems12:00 Why FastMCP Exploded13:29 Making Complex Software Simple15:10 Can Software Be Too Magical?20:11 MCP Apps Explained23:42 Why Python Needed MCP Apps27:54 The Future of AI Interfaces34:18 AI Should Generate UIs40:11 AI Deleted My Presentation43:30 The AI Assistant We Actually Need48:00 Personal AI vs SaaS52:28 The Future of AI Agents55:06 Final Thoughts

In this episode, we're joined by Stephen O'Grady, Co-Founder and Principal Analyst at RedMonk, to explore one of the biggest shifts happening in software engineering: AI is making code dramatically cheaper to produce, but everything downstream is becoming the new bottleneck.

We discuss why SaaS isn't dead despite the hype, the explosive rise of MCP, why AI agents are overwhelming developer infrastructure, and what happens when every engineer suddenly has dozens of AI developers working alongside them. Stephen explains how package managers, code reviews, security, governance, and enterprise systems are all struggling to keep pace with AI-generated software.

Along the way, we dive into AI coding tools, MCP adoption, developer productivity, infrastructure scaling, enterprise software, open source, package repositories, governance, and why the hardest problems in software may no longer be writing code—but managing everything that comes after.

RedMonk: https://redmonk.com

Stephen O'Grady: https://www.linkedin.com/in/sogrady
Demetrios: https://www.linkedin.com/in/dpbrinkm

In this episode, we're joined by Matt DeBergalis, CTO and Co-Founder of Apollo GraphQL, to explore what happens when AI agents start interacting with enterprise systems that were never designed for them.

We dive into the collision between APIs, MCP, GraphQL, and agentic AI, and why traditional assumptions about trust, permissions, and security are breaking down. Matt argues that AI agents should be treated as untrusted actors by default, and explains why giving agents access to enterprise data creates entirely new challenges around governance, access control, and risk management.

Along the way, we discuss semantic APIs, enterprise data silos, citizen developers, agent permissions, security boundaries, and how GraphQL and MCP can work together to make enterprise systems more accessible to both humans and AI. The conversation also explores why companies are racing to deploy agents despite the risks, and what the future of enterprise software might look like when AI becomes the primary consumer of APIs.

Apollo GraphQL: https://www.apollographql.com

Matt DeBergalis: https://www.linkedin.com/in/debergalis
Alex Salkever: https://www.linkedin.com/in/alexsalkever

Timestamps:
[00:00] AI, APIs, and Trust
[01:16] MCP API Lessons
[06:16] GraphQL and MCP Integration
[12:55] API Security for MCP
[16:10] Linux Kernel Security Concerns
[19:09] API Design and Controls
[21:52] Trust in Autonomous Systems
[25:06] MCP GraphQL Wish List
[27:13] API Access Patterns
[28:44] GraphQL API Perspective

Matt DeBergalis, CTO and Co-Founder of Apollo GraphQL