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AI Series Hub

Know the AI risk. Mitigate it

In this four-part series, CEOs, CTOs, board members and the engineering leaders running agentic AI at scale explain the technical debt risks, the accountability gaps, the cost, and the framework that lets you move fast without it costing you later.

Why this matters on the top

Is your organization AI-ready and how do you avoid the risks?

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At least 60% defect risk when agents operate on unhealthy code.

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35-45% token waste when agents operate on unhealthy code.

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Avg. CodeHealth of enterprise codebases is 5.15, far from AI-ready.

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What you'll get

  • See the quantified AI risk you're not measuring, before you scale your AI strategy. From wasted token spend to rising defect rates as agents take on more of your code.
  • Understand why AI coding failures are a business, board and compliance issue, not just an engineering one. And who should actually own AI validation.
  • Hear from the pioneers at loveholidays and Alfa Laval, two enterprise success stories in scaling AI. Plus the research and framework to get there yourself.

 

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"The real challenge here is trust. A lack of trust in what we produce prevents us from extracting the highest value from agentic development."
Gustaf Eklund
Partner & Senior Consultant, System Verification

What's inside the hub

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The AI Risk you're not measuring yet

Panel discussion from industry leaders on the AI risk, we dig in research and data behind unhealthy code, token waste and defect risk. Proving that the problem is live, systematic and expensive. With Hamdija Jusufagic (CEO, CodeScene), Peter Caron (CEO, 3T Software Labs), Burak Karakan (CEO, Bruin), Daniel Webb (Founder, CTO, NeoSee).

When AI adoption actually costs your business

Board level discussions: Why technical debt stopped being an engineering problem and became a governance one. With Hamdija Jusufagic (CEO, CodeScene, board member), Peter Caron (CEO, 3T Software Labs, board member), Magnus Bergman (Founding Partner, Luminar Ventures, board member), Daniel Webb (Founder, CTO, NeoSee).

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Agentic coding at scale: the missing framework

The framework: how code health, deterministic quality gates and human-on-the-loop checkpoints turn AI speed into something you can trust — and why QA's job shifts from running tests to being the one who signs off on going to production. With Gustav Ekelund (Team Manager, System Verification), Erik Järdemar (COO, System Verification), Marcus Borg (Principal Researcher, CodeScene), Daniel Webb (Founder, CTO, NeoSee).

Scaling AI: Real-World Success Stories

Two companies, two different paths: loveholidays scaled to 80% agentic code while reaching elite code health, and Alfa Laval shipped an internal AI platform to 25,000+ employees in 10 weeks. With Stuart Caborn (Distinguished Engineer, loveholidays), Fredrik Nilsson (AI Business Architect, Alfa Laval), Daniel Webb (Founder, CTO, NeoSee).

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Evidence, not opinion

Every claim in this series is backed by data, peer-reviewed research, and live production numbers from teams running AI at scale.

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Built for the boardroom

This series is built to help CTOs and CISOs translate technical debt into the language a board already understands: risk, revenue and compliance.

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Proof it works, not just theory

loveholidays scaled to 80% AI-generated commits while pushing code health to elite industry levels. Alfa Laval deployed an enterprise AI platform to 25,000+ employees in ten weeks. Both are in the series.

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Unlock the full series — the talks, the research, the success stories, and the framework to make it work for you.