Episode Details

BONUS: Why More Code Doesn't Mean Better Software — And Where AI Actually Helps Your SDLC

Most teams are adopting AI to write code faster. But what if code generation isn't your bottleneck? Mooly Beeri has spent 25 years diagnosing where software organizations actually underperform — from Microsoft to Philips to automotive — and his message is clear: measure before you automate, and tie every AI investment to a business KPI.

The Pattern Debugger's Origin Story

"I've been identifying patterns way before AI was doing that. One of my first jobs was Microsoft, and I got the opportunity to work in engineering excellence. Every single simple improvement would make the lives of so many people better and the code better and the products better."

Mooly's career started at Microsoft in engineering excellence, where he discovered his passion for finding process areas that need improvement. From there he built the first software centre of excellence for Philips, spawned it into a separate business, and has been doing the same process excellence work across healthcare, telecom, and automotive ever since. His framework: understand where you're bleeding quality, revenue, or budget — then intervene there, not everywhere.

Improvement Doesn't Mean Progress

"There are too many efforts to improve too many things that don't really matter. The ability to tie a specific improvement to what actually means progress for a business — that, for me, is one critical component that's missing in many transformations."

Mooly's core insight applies directly to AI adoption: everyone has an improvement plan, but few can answer "how does this improvement improve business performance?" If you ask that one additional question, you can probably cancel half your improvement projects — the ones that make people feel good but don't move the needle on time to market, quality, or cost.

The Code Generation Trap

"It's like saying a book author is more productive because they write more words. The unit of work is not the number of lines of code they produce. The unit of work is a piece of code that works, that is tested, that is fully reliable, that meets a customer expectation, and eventually generates revenue."

Data from Faros AI shows individual developer PRs went up 98% with AI tools — but organizational delivery actually dropped 1.5%. More code, same or worse outcomes. Mooly explains why: most organizations invest in code generation not because it's the most effective thing to improve, but because it's the easiest step to automate. There are 35 steps in the SDLC. Picking code generation gives you a 1-in-35 chance of striking gold. As the saying goes: hope is not a strategy.

Where AI Actually Works in the SDLC

"The best usages would be in areas of the SDLC where there is a lot of data that needs processing and needs some detection of patterns — where AI is really, really good."

The most successful AI applications Mooly has seen with clients:

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