Happy Triple Threat Thursday.
Here’s one Signal to notice, one thing to Spark growth and one Shift to consider.
This week’s theme: Why building systems that work feels so hard and why it has to come before AI.
I chose this topic because across many of the businesses I work with, AI isn’t the problem showing up. What shows up first is hesitation followed by confusion, resulting in work that technically “moves forward,” but never quite lands in practicality and execution.
That’s not an AI issue, it’s a systems issue.
📡 Signal — What’s Changing
Why AI exposes weak systems
For a long time, most companies ran on informal systems.
Things worked because capable people filled in the blanks.
They knew what “good” looked like, when to bend a rule, and who to ask when something didn’t fit.
None of that was written down.
It didn’t need to be.
AI changes that dynamic immediately.
It doesn’t know which exception is reasonable, when speed matters more than quality, or whose judgment wins when priorities collide.
So the moment teams try to introduce AI, the conversation slows.
Not because the tech is confusing, but because no one can clearly explain how the work is actually supposed to run.
From the outside, it looks like an AI adoption problem.
Inside the business, it feels like friction everywhere.
That’s the signal.
AI isn’t failing.
It’s exposing how much of execution depends on people compensating for unclear systems.
If you’re struggling to explain how work actually gets done, AI will struggle too.
That friction isn’t a failure, BUT it is a signal telling you where a system doesn’t exist yet.
⚡ Spark — What to Try This Week
How to draft a system before you automate it
Most leaders try to use AI to build systems.
That rarely works.
What does work is using AI to reflect the system back to you as it actually exists.
Pick one messy workflow.
Something important. Something that regularly creates debate.
Don’t clean it up or overthink it.
Drop in notes, a transcript, a Slack thread, or a rough explanation of how the work gets done today.
Then have the GPT do one job only.
Draft the system as it is.
Surface:
What starts the work.
What “done” actually means.
The steps as they really happen.
Where judgment shows up.
Who decides when things don’t fit.
What can’t be violated.
The output won’t be elegant.
It will be usable and that’s the point.
Once the system is visible, leaders can react.
They can decide what to lock in, clarify and stop.
AI isn’t doing the thinking for you, but it’s lowering the cost of seeing clearly.
You can try this here: System Draft GPT
Before you fix anything, draft it.
Make the system visible as it really is, then decide what deserves to stay.
No one applauds systems, but everyone notices flawless execution.
🔄 Shift — How to Rethink It
Why system-building feels uncomfortable for leaders
Most leaders say they want systems because they want scale.
What they don’t always anticipate is what systems demand.
Systems force you to decide what matters when tradeoffs show up.
They force you to define quality instead of relying on taste and to place judgment somewhere other than people’s heads.
That’s uncomfortable.
Before systems, flexibility hides uncertainty.
After systems, priorities are visible.
That’s why many teams jump straight to AI.
It feels like progress without commitment.
But AI doesn’t create clarity. It locks in whatever already exists.
If the system is loose, AI amplifies the looseness.
If judgment is implicit, AI makes that painfully obvious.
The real shift isn’t technological, it’s leadership.
Systems aren’t about efficiency.
They’re about deciding how execution should work when you’re not in the room.
System-building feels hard because it removes ambiguity.
And ambiguity is often the last place comfort hides in growing organizations.
📚 Worth A Look
AI fails in most organizations because the underlying work isn’t systemized. When inputs, outputs, and decision ownership aren’t clear, AI amplifies confusion instead of reducing it.
🔗 Why bold AI strategies stall without aligned operating models
A January–February 2026 HBR piece shows that even breakthrough AI innovations can fail when the organization’s operating model can’t support them.
🔗 AI adoption in 2026 hinges on redesigned workflows and operating models
Insights on how most AI initiatives stall not because of tech but because organizations don’t redesign decisions, behaviors, and work structures first.
🔗 Executive leadership must guide AI adoption, not just deploy tools
A January 16 2026 Forbes piece argues that AI only “sticks” when guided by people and integrated into familiar decision and execution practices.
📈 TL;DR
Draft your reality before you automate it with AI.
📈 One Question
What part of your business only works because someone “just knows” what to do?
Thanks for reading Triple Threat. See you next Thursday with another Signal, Spark, and Shift.
— Alexandria Ohlinger
p.s. If this helped you think sharper or move faster, share it with someone who builds the way you do. And if you want more practical insight between issues, connect with me on LinkedIn.