Turning Chaos into Coordinated Collaboration

When work stops staying aligned

At some point, things just start to feel off inside a program.

More meetings.
More updates.
More people pulled into every decision.

And still—the work isn’t moving the way it should.

I saw this play out on a program where yield came in lower than expected.

That one change triggered everything:

• manufacturing projections kept shifting
• engineering demand changed daily
• stakeholders needed updates constantly

Teams were working hard.

Really hard.

But the system wasn’t holding.

The supply team was rerunning their model multiple times a day.
Engineering was adjusting plans in real time.
Leaders were asking for updates from every direction.

Everything became urgent; it was a fire drill

But what was actually happening was simpler: work wasn’t staying aligned as it moved across teams

That showed up as:

• conflicting updates
• work coming back late
• constant plan changes
• pressure going up without real progress

When things start to feel like this, the instinct is to push harder:

• more meetings
• faster updates
• more data

But that usually makes things worse.

Because activity goes up, while alignment continues to drift.

What changed

We didn’t start by redesigning everything.

We started by looking at how the work was actually moving.

Where it was getting delayed.
Where it was coming back.
Where teams were no longer aligned.

A few things stood out right away:

• teams weren’t working on the same cadence
• decisions weren’t holding from one step to the next

The biggest driver was how often the system was being updated.

Every new data point triggered another round of:

• updates
• meetings
• decisions

>> more information was creating more instability

So we changed the focus.

Not “how do we update faster?”

But: how do we keep work aligned across teams?

Stabilizing how the work moved

That led to a few simple shifts:

• we developed a weekly cadence
• we put structure and discipline into decisions
• clearer ownership around three types of escalations: quality, customers, and current revenue

We also realized what looked like one escalation problem was actually three:

• customer impact: # of samples they would receive
• supply trade-offs: NPI (development material) vs. HVM (customer material)
• development constraints: units for testing to deliver to our milestone metrics for quality

Each needed different inputs.
Different decisions.
Different ownership.

Until that was clear, alignment wasn’t possible.

What changed (and what didn’t)

The constraints didn’t change.

Yield still had to ramp.
Supply was still tight.

But the system changed.

• fewer conflicting updates
• fewer rework loops
• fewer late surprises

And most importantly: work started staying aligned again

Once that happened, things became predictable again.

Why this shows up so often

This is how most execution problems actually begin.

Not as one big failure.

But as:

• alignment slowly breaking down
• activity increasing to compensate
• problems showing up later than they should, we knew the yield was late

I’ve seen this pattern across many programs.

Where AI fits (and where it doesn’t)

A lot of teams are now trying to address this with AI.

The idea is straightforward:

move faster
react faster
analyze faster

But if execution isn’t stable:

• AI doesn’t fix it
• It just accelerates the breakdown

More updates
More decisions
More change

• faster misalignment

The pattern that actually works

What works is simpler than most people expect.

Stabilize execution first

Make sure work:

• stays aligned across teams
• holds from step to step
• doesn’t come back late

Then: apply AI where it actually improves delivery

Final thought

Most of what drives outcomes doesn’t show up in the plan.

It shows up in how work actually moves.

If work isn’t staying aligned, that’s where to start