AI for Tech Teams (3rd edition).

From AI tools to AI-native delivery: what we've learned from transforming dozens of engineering teams

Wednesday, July 1st 2026 - 4:00 PM (WEST)

About this event

Most engineering teams are already using AI somewhere. Most have at least one developer on Copilot, a few experimenting with Claude or Cursor, some starting to look at agents.

Tool usage is not the same as delivery improvement.

We've been inside live engineering teams long enough to know where the plateau happens: usually not in the code. It's in requirements that aren't clear enough for AI to act on, codebases that aren't readable to agents, and adoption that's inconsistent across the team.

In this session, we'll show what it actually takes to move from scattered AI use to a structured delivery system: clearer requirements, repo-level context, AI-assisted specs, review, testing, QA notes, and release notes. With metrics to know whether it's working.

This is based on work we're doing inside real engineering environments right now.

What you'll learn:

Why most engineering teams plateau after giving developers access to AI tools, and where the real bottleneck usually is
How to structure a codebase so AI agents produce code that fits your conventions, not code you have to rewrite
What an AI-assisted delivery pipeline looks like in practice
Which metrics to track when AI changes execution speed
How to choose the right pilot so you can prove value without disrupting live delivery

Who should attend:

CTOs and VPs of Engineering who need to improve delivery without growing headcount, and want a concrete model for how AI changes the engineering system, not just individual productivity
Engineering managers and tech leads whose teams are already using AI, but inconsistently, with no shared standard for what good looks like
Product and engineering leaders dealing with unclear requirements, slow handoffs, testing debt, or review bottlenecks
CEOs and COOs who want an honest picture of what AI can actually change inside their technical organisation, and what it can't

Why join us

We build AI-enabled delivery systems inside real engineering teams. That means working with live codebases, existing habits, real backlogs, and the constraint of shipping while changing how the team works.

We've seen what creates genuine improvement and what looks good in a demo but slows teams down in practice.

If you're the one deciding whether to scale AI investment in your engineering organisation, this session gives you a framework to evaluate what's actually working before you commit further.

Can't make the live online call? Register anyway and we'll send you the recording and notes

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