The AI Engineering Metric That Matters More Than Speed
Originally published on LinkedIn
𝗧𝗵𝗲 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗠𝗲𝘁𝗿𝗶𝗰 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗦𝗽𝗲𝗲𝗱 AI gives engineering teams an extraordinary ability to move faster. Code is written faster, features reach production sooner and SDLC bottlenecks shrink.
Most AI-first engineering teams will produce and release more code. But are they monitoring what happens next?
Delivery speed, deployment frequency and throughput are useful metrics.
✅ They tell you how quickly work moves through the system.
❌ They do not tell you whether that work is any good.
Teams could always move faster by removing reviews, reducing testing and bypassing controls, long before AI, AI simply makes it easier to justify with false confidence or optimism.
The difficult part is going fast while continuing to deliver reliable software.
That is why the metric I'd watch first is 𝗲𝘀𝗰𝗮𝗽𝗲𝗱 𝗱𝗲𝗳𝗲𝗰𝘁𝘀 as an early warning you have a problem.
❓ How many defects are reaching production and affecting customers?
❓ How severe are they?
❓ Is that number increasing as delivery speed rises?
Then measure what happens next.
🔹 𝗠𝗧𝗕𝗙 (𝗠𝗲𝗮𝗻 𝗧𝗶𝗺𝗲 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗙𝗮𝗶𝗹𝘂𝗿𝗲𝘀), how resilient your systems are.
🔹 𝗠𝗧𝗧𝗥 (𝗠𝗲𝗮𝗻 𝗧𝗶𝗺𝗲 𝘁𝗼 𝗥𝗲𝗰𝗼𝘃𝗲𝗿), how quickly your engineers recover.
Together, these metrics tell you whether you're shipping quality software, or just shipping faster.
A team might be deploying daily, shipping more features and celebrating higher velocity while a rain of support issues falls across the organization.
⚠️
The engineering team that moved fastest this sprint spends the next fixing problems that should never have reached production.
Customer trust declines, support demand increases, engineers are interrupted, roadmaps slip and servers catch fire. 🔥
That is not increased productivity. It is work pushed into the next sprint or to others at a much higher cost.
Delivery speed means nothing without production quality. If releases are accelerating while customer-impacting defects, MTBF is falling, MTTR is rising, rollbacks and support requests are increasing, the team is not becoming more effective. It is simply creating problems faster.
It is an engineers job to maintain quality while AI makes this harder to spot. The code looks convincing, tests pass, AI says it works.
When that happens, slow down. Examine why unsafe code is reaching production, then strengthen reviews, testing and release controls and have the engineers at the helm take a hard look at themselves..
Engineers remain accountable for every line they ship, regardless of who, or what, wrote it.
𝗚𝗼 𝗳𝗮𝘀𝘁. 𝗠𝗮𝗸𝗲 𝘀𝘂𝗿𝗲 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀.
✅
Delivery pressure is no excuse for poor-quality engineering.
How many engineering teams are measuring this today as they embrace AI? Because I'm seeing the problem increase and its not AI to blame it those recklessly using it.
#AI #AIEngineering #SoftwareEngineering #EngineeringLeadership #DevOps #SRE #SoftwareQuality