Traditional metrics won't catch any of it. Deployment frequency might even go up while the real problem spreads.
I built a free guide that maps exactly where your delivery system cracks under AI load.
buff.ly/INlMIZ8
Does your team measure what happens after the code is generated? Most don't.
When you add AI without changing the system, 5 things break:
• Hallucinated code slipping past review
• Senior engineers trapped in the queue
• Duplicate tests that contradict each other
• Fake coverage confidence
• Devs lost between 3-4 AI tools
Your metrics look fine. The bottleneck just moved.
98% more pull requests. Still shipping late.
Faros tracked 10,000 developers using AI coding tools. PRs nearly doubled. Review times jumped 91%.
The tools did their job. Nobody fixed the pipeline around them.
AI coding tools move the bottleneck from writing code to reviewing it. Your devs ship 10x more code but your review workflow stays the same? Congrats, you just multiplied your backlog.
Your support team is drowning. Tickets pile up. Response times slip. So you hire more people. Six months later, you're drowning again. The process was broken. You just paid more people to do broken work. Fix the flow before you scale the headcount.
Your backlog has 20 "top priorities." Your team starts 10 things, finishes 2. Everyone's busy, nothing ships. You think it's a prioritization problem. It's the flow you're not seeing.
88% of AI pilots never reach production. Teams blame the tech: wrong model, bad data, integration issues. The real problem: they jump to solutions before understanding what they're solving. No baseline, no clear definition of success. That's not a tech gap. It's a capability gap.
I just published "What is Lean Tech? Understanding Digital Transformation’s Most Powerful Framework" medium.com/@jlcossi/wha...
If your support team is stuck in a similar backlog, I’ll give you a free 15‑minute audit of your request flow and a concrete “kill‑or‑continue” rule you can implement tomorrow. DM me or drop a comment “audit me.” #Lean #SupportOps
Result (after 3months):
- Email backlog ↓≈90%
- WhatsApp backlog ↓≈75%
- Average lead‑time halved (≈48h→≈22h)
- Citizen‑satisfaction ↑35%
The numbers aren’t perfect, but the trend is unmistakable.
– Introduce a pull system.
The team‑leader starts assigning tickets based on each person’s expertise, their current capacity, and the urgency of the customer’s request. No more “pick whatever you see first.” With the board we could see exactly how many tickets we could realistically pull each hour.
– Make the work visible.
We put up a simple board (whiteboard at first, later a shared Notion table) that tracked every request by channel (WhatsApp and email). Anything older than 24h turned bright red, turning the backlog into a problem the whole team could see.
“It takes forever to get help."That was the exact feedback I heard from the users of a six‑person tech‑support team. When we looked closer, there were 415 pending requests and most callers waited 2–7days for a solution. #LeanTech
Faster code ≠ faster delivery.
AI speeds up coding.
But lead time stays the same.
The real blockers?
Handoffs, approvals, unclear priorities.
AI boosts output, but stuck output is still waste.
Tools don’t fix systems. People do.
AI isn't the productivity booster most teams think it is.
Teams celebrate 'faster coding' while their delivery times stay the same.
The bottleneck was never how fast you write code.
It was the handoffs, approval loops, unclear priorities between writing & shipping.
Tools don't fix systems. People do
The Leadership Shift
• Month 1: We need better tools to track our tickets.
• Month 2: We need more people to handle the volume.
• Month 3: We need to fix how the work actually flows.
That evolution, tool-thinking to flow-thinking, changes everything.
Same team. Same tools.
94% backlog reduction.
Your team isn't unmotivated. They're unclear.
- Don't know what "done" looks like
- Can't see their impact
- Solving the same problems repeatedly
Motivation follows clarity, not the other way around.
In Lean, we remove obstacles that make work frustrating.
#LeanTech
AI-assisted code can be a gift or a trap.
The difference isn’t the tool.
It’s whether your team is thinking together, or outsourcing the thinking.
In Lean, we slow down just enough to see.
Not to delay but to stay intentional.
A few anchors help:
— Ask “who is this for?” before every prompt
— Review as a team, not alone
— Track back to the user problem, not the code
AI isn’t making coding worse.
It’s making bad habits invisible.
You stop questioning.
You skip review.
You assume it works.
That’s not speed. That’s drift.
And drift is how teams lose touch with the why.
The age of AI demands new discipline:
• Mandatory “why” prompts before accepting AI code
• Human validation checkpoints: AI writes, humans explain
• Trace-back rules: every function links to a user story
• Context-first prompts: start from the customer problem
The real danger of AI-assisted coding isn’t bugs.
It’s accelerating what was already broken: detachment.
Teams were losing touch with the “why” behind the code. Now they’re just doing it faster.
You don’t fix Developer Experience with emojis and surveys.
You fix it with structure, facts, and follow-through.
That’s what A3 gives your team:
→ A voice
→ A process
→ A result
A3 = the antidote to vibes-based DevEx.
It gives structure to:
• Identify root causes
• Align your team
• Track real metrics
You don’t need a task force.
You need a method.
Most Developer Experience problems aren’t mysterious.
They’re just uninvestigated.
– Slow builds
– Confusing reviews
– Poor tool choices
Want better DevEx?
Use A3 to stop guessing and start fixing.
The real danger of AI-assisted coding is detachment.
You stop thinking.
You stop reviewing.
You stop understanding.
Code becomes output, not design.
That’s how technical debt piles up quietly, and fast.
Vibe coding feels fast.
Until you’re drowning in technical debt.
Duplicated logic. Inconsistent patterns.
Nobody knows what the system really does anymore.
AI can help, but only if you stay in control.
Lean Tech keeps speed from becoming chaos.
Most teams aren’t lazy or incompetent.
They just can’t see the work.
No flow visibility. No shared standards.
No idea what “good” looks like.
Fix that—and performance jumps.
One support team increased productivity by 72% in 8 weeks—no new hires.
Within 2 months:
📉 Backlog = 0
📈 Productivity +72%
😊 Customer satisfaction +35%
Sometimes, the best fix isn’t complex.
It’s clarity, ownership, and structure.
We implemented:
✅ Daily check-ins
✅ A dispatcher role
✅ Visual management
✅ Productivity targets
✅ Balanced workload
✅ Team training
No AI. No buzzwords. Just Lean fundamentals.
