In this new Enterprise AI Field Notes deep dive, Srini Annamaraju (aka, 'the tech guy') and David Royle (who's 'the business guy') take the story past design into delivery — from the Target Operating Model (TOM) to the everyday reality of running AI inside the enterprise.

Through the lens of a real Bank's AI copilot rollout, name changed to "Albion Bank", they map how real transformation happens inside the Enterprise AI Honeycomb — a connected system of data, models, patterns, platforms, and guardrails that must all work in harmony.

💡 What we cover:

• Why do most AI “pilots” stall before production — and how to stop the fade?
• How data decisions shape every downstream fork in the journey?
• What “brains, behaviour, and nervous system” really mean in AI design?
• How to build hybrid platforms that stay compliant and fast?
• What does it take to shift from ad-hoc prompting to disciplined LLMOps?
• Why security, governance, and economics are the body’s immune system and heartbeat?

Srini breaks down the technical scaffold — how the ten cells of the honeycomb connect to deliver measurable ROI.

David probes rigorously from the business side — questioning trade-offs, accountability, and real-world friction.

Together they turn AI from a keynote fantasy into a hard-nosed operating reality.

AI dazzles in demos but stalls in the enterprise. In this deep dive with a new section on rapid-fire questions by Srini Annamaraju, the resident tech strategy expert on the No Effing AIdea podcast, David Royle , reveals why — and how a smarter Target Operating Model (T.O.M.) can finally bridge the gap between ambition and adoption.

This episode connects TOM blueprints to the people dimension: the rise of AI workflow architects, the tension between agents and humans, and the messy middle where governance meets innovation.

💡 What we cover:

• Why enterprises confuse AI projects for AI infrastructure
• How to design a T.O.M. that balances guardrails and greenlights
• The rise of new ‘hot’ AI roles — and which ones will fade fast
• What happens when test kitchens meet board-level control
• Why scaling AI isn’t a tech problem — it’s an operating model one

David brings creative clarity and a rare mix of strategic and operational design chops. Srini brings decades of enterprise GTM and technology experience.

Together, they unpack what it really takes to make AI run the enterprise — not just impress it.

Hosts Srini Annamaraju and David Royle are back for episode two – and yes, the feedback is in. Some said we were a bit too serious last time. We’ll try not to become a Sunday love songs show, but we’re working on upping the “gags per minute.”

This week’s conversation covers:

• Listener reach & feedback: Almost 100 plays already, with listeners tuning in from the UK, US, India, and even Slovenia. The appetite is real for discussions that go beyond hype and get into the messy middle of enterprise AI.
• Event notes from Big Data London: A buzzing show, but still very tech-heavy. We debate whether AI conversations are stuck in the IT lane, and why that’s a problem when the real impact is business-wide.
• NBER study on ChatGPT usage: 700M weekly users. Surprisingly, 70% of usage is personal rather than work. Heavy skew toward under-26s. We unpack what that means for adoption inside enterprises.
• US tech investment in the UK: Nvidia and OpenAI committing eye-watering sums (hundreds of billions over time). A rare bit of good economic news for the UK, with national implications for jobs, productivity, and independence from US/China dominance.
• Enterprise field news:
  • Citi experimenting with agentic AI for wealth advisors, using Claude and Gemini inside secure workspaces.
  • FT analysis showing CEOs hype AI on earnings calls, but get risk-heavy and muted in regulatory filings.
  • JLR cyberattack fallout: £3.5B revenue hit, no cyber insurance in place. Knock-on effects on suppliers and supply chain.
• The “three rooms” where AI decisions get made:
  • C-suite (value, governance, risk)
  • Technical teams (architecture, data quality, safe design)
  • Operations (ongoing management, compliance, usage quality)
• Traps to avoid:
  • The Whac-a-Mole Trap – hallucinations never disappear, they just reduce.
  • The Origami Trap – clever prompts aren’t a moat; without guardrails, they fold fast.
  • The IT-Only Trap – AI left to technologists will fail; business P&L owners need to lead.
  • The Corporate DNA Trap – over-automating risks erasing what makes your org unique.
• Shadow AI is real: Even if companies ban AI tools, staff use them on personal devices. Risks around leakage and compliance multiply.

We close with a look ahead:

• How frontier model labs (OpenAI, Cohere, Mistral, etc.) are approaching enterprise go-to-market.
• Real use cases from our own client work – what’s working, what’s not.

Next steps for listeners:
Got topics you’d like us to cover? Message us on LinkedIn. The more specific, the better – we’ll dig in and bring field notes to the next episode.

Episode 1 — No Effing AIdea! - AI ethics, job disruption, GenAI “failures,” the AI bubble, India SMB adoption, coding risks, consultants falling behind, biotech breakthroughs, and AI in education — all collide in our first episode of No Effing AIdea!

Welcome to the first episode. We are David Royle and Srini Annamaraju.

We set the tone with a stark cold open: new Stanford data shows a 13% drop in jobs for 22–25-year-olds in AI-vulnerable roles since ChatGPT launched. Then, in our Reality Check, we unpack the last two weeks of enterprise AI news with a fresh lens:

• MIT’s “95% failure” GenAI claim — and why that’s too simple.
• Why the so-called AI bubble might actually be good for business.
• Reliance & Meta’s $100M JV bringing enterprise AI to India’s SMBs.
• AI coding tools: 30% faster, but 2x more vulnerabilities.
• Big consultants left behind by in-house AI adoption.
• Stanford’s autonomous AI lab slashing drug discovery timelines.
• Khan Academy’s Khanmigo AI tutor bringing hope to 180M learners worldwide.

Our Deep Dive asks: why does AI suddenly get its own moral panic when cloud, ERP, and digital never did? We explore what “ethics” really means for enterprises today:

• How ethics shows up on the P&L — as fines, lawsuits, and PR disasters.
• Where ethics must live in the AI stack to avoid “governance theatre.”
• The trade-offs leaders underestimate — speed vs. trust, open vs. proprietary.
• A pragmatic three-step ethical readiness checklist for 2025.

Finally, in The Paradox Box, we tackle three dilemmas from the field:

• Boards demanding ROI and revolution at the same time.
• Compliance vs. engineers in the race for velocity.
• Customers rebelling against brilliance.

Listen in for pragmatic tactics, not theatre — and a candid take on why AI ethics isn’t an afterthought. It’s the seatbelt that lets enterprises drive faster.