Old delivery had a slow feedback loop. Friction built up over months. Slow test suites, review bottlenecks, unclear ownership, repeated governance steps. Someone eventually noticed and maybe it got fixed.
With AI-assisted delivery, that loop is too slow. The system moves faster, which means waste compounds faster. And the cost isn't just time anymore, it's tokens.
Every unnecessary rerun, every unclear instruction, every late evidence fix, every "let me inspect what happened" loop burns model time and money. The gains from AI can disappear quickly if the process around it becomes the new bottleneck.
But there's a second trap that's less obvious: don't fix every issue the moment it appears. Spot a process problem, stop the work, fix it, rerun everything, spot another issue, fix that, rerun again and suddenly the improvement loop is blocking the delivery loop.
The better pattern is to capture the data, finish the governed delivery if the reviewer surface is honest, separate blockers from improvement backlog, and improve the system deliberately between runs. Telemetry and self-improvement loops have to be part of how AI-assisted delivery operates from the start, not bolted on later.
And this isn't just an engineering problem. Any team running agentic workflows sales, ops, finance, support will hit the same wall. The organisations that pull ahead won't be the ones that adopted AI the fastest. They'll be the ones that built self-improvement into the workflow itself, at every layer, so the system gets better as it runs rather than requiring someone to notice the problem first.
Too little governance and quality suffers. Too much and speed disappears. No telemetry and you're guessing. No improvement loop and the same bottlenecks keep coming back.
The teams that get this right won't just ship faster. They'll build delivery systems that can see where they're slowing down, understand why, and improve without losing control.
With AI-assisted delivery, that loop is too slow. The system moves faster, which means waste compounds faster. And the cost isn't just time anymore, it's tokens.
Every unnecessary rerun, every unclear instruction, every late evidence fix, every "let me inspect what happened" loop burns model time and money. The gains from AI can disappear quickly if the process around it becomes the new bottleneck.
But there's a second trap that's less obvious: don't fix every issue the moment it appears. Spot a process problem, stop the work, fix it, rerun everything, spot another issue, fix that, rerun again and suddenly the improvement loop is blocking the delivery loop.
The better pattern is to capture the data, finish the governed delivery if the reviewer surface is honest, separate blockers from improvement backlog, and improve the system deliberately between runs. Telemetry and self-improvement loops have to be part of how AI-assisted delivery operates from the start, not bolted on later.
And this isn't just an engineering problem. Any team running agentic workflows sales, ops, finance, support will hit the same wall. The organisations that pull ahead won't be the ones that adopted AI the fastest. They'll be the ones that built self-improvement into the workflow itself, at every layer, so the system gets better as it runs rather than requiring someone to notice the problem first.
Too little governance and quality suffers. Too much and speed disappears. No telemetry and you're guessing. No improvement loop and the same bottlenecks keep coming back.
The teams that get this right won't just ship faster. They'll build delivery systems that can see where they're slowing down, understand why, and improve without losing control.