Think. Don't Code.
How analytics professionals must use AI to solve problems they were never supposed to solve alone.
by Carol Marmute
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A Note on How This eBook Was Written
I wrote this ebook with AI. Not "AI helped me edit it." Not "AI suggested some ideas." I mean: I brought the thinking, the experience, the judgment, and the stories. AI brought the structure, the speed, and the drafts. We iterated together until it said what I actually meant.
That's exactly what this ebook is about. So if you're reading this and thinking "did a human write this?" — yes. And also: that's the wrong question. The right question is whether the thinking is real. Whether the experience behind it is real.
The Question Is Not Whether You Can Write the Code
The question is whether writing the code is the best use of your time. If you know how to code, you've felt the hours lost to syntax, debugging, formatting — hours that could have gone to the actual problem. If you don't, you've felt certain analyses are just out of reach.
I spent years living between the tech world and the business world — too analytical for the commercial side, too commercial for the data side. For a long time, I thought that was a problem to fix. It wasn't. It was the advantage I hadn't figured out how to use yet.
The people who can move between both worlds — who understand the model AND the decision it needs to drive — are something neither side can replicate alone. That space is yours. This ebook is about how to use it.
What Actually Happened on the Project
A global consumer goods company had a supply chain optimization model running across 14 factories in 8 countries — EMEA, North America, Asia Pacific. At product level, across every trade route, it calculated duty rate differentials, lead time impacts, and cost deltas. The model was technically correct.
Factory Flow Analysis
Mixed Integer Linear Program — minimize unmet volume and active connections, with a hard minimum of 10,000 units per movement.
Market Redirect
Same product, same factory — but which market should receive it? Solved for redirecting output to a different geography.
Output
Six tabs in Excel, six tables in Databricks, topped with a Pareto ranking flagging the top 80% of total gain as priority actions.
The Division That Actually Matters
Every line of Python that built this was written by AI. But here's what that actually means:
AI Wrote
  • The data pipeline
  • Both optimization formulations
  • The MILP logic
  • The Pareto ranking algorithm
  • The Excel output and Databricks writes
I Built
  • The decision to run two approaches/scenarios instead of one
  • The 10,000-unit minimum threshold movement
  • The call to rank by profit opportunity, not volume using Pareto
  • The decision of what would be the MILP objective function
  • What the sourcing lead needed to say to her director
You are not competing with AI. You are deciding what to think about while AI handles everything else.
How AI Actually Works With You
Automation
AI follows explicit instructions and does the work. The Director's Brief (you'll see below in Step 2) in action: you describe precisely, AI executes. The Python pipeline, MILP formulations, Excel output — all automation.
Augmentation
You and AI build together. Upload. Run. Break. Debug. Refine. The output gets more precise with every exchange — until what comes out is something that can run in production.
Agency
AI works independently on your behalf. Once the pipeline was validated, it ran in Databricks without me — writing to Delta tables while I moved on to the next problem.
The Architect's Method
What I used on this project wasn't luck or experience alone. It was a method that turns AI from a tool you type at into a thinking partner that builds with you. Every analysis that matters follows this sequence:
01
Map the Room
Before any tool: who acts on this, what do they need, how do they think, what are the stakes?
02
Architect the Brief
Write the Director's Brief. Stage, Expert, Seat, Shape, Ask. This is where quality is decided — before AI writes a single line.
03
Iterate
Run it. Describe what's wrong precisely. Each iteration is not a correction — it is the method.
04
Stage the Logic
Ground Truth, Mechanism, Consequence, Decision. Make the reasoning visible. Make the decision unavoidable.
05
Stress Test
Find the holes before someone else does. Fix them. Ship something defensible.
STEP 1
Map the Room
Most people start with the data. The Three Disciplines start one step earlier — with a question: who is going to use this, and what do they need to be able to do with it? Answer these four signals before you build anything.
The Chair
Who acts on this output? A sourcing lead thinks in operational feasibility. A data scientist thinks in model assumptions. Design for the chair, not for yourself.
The Gap
What does this person need to DO after seeing your work? Build backwards from the decision your output needs to enable.
The Filter
How do they process information? Some need the business logic first. Some need three columns, not twelve. Deliver in their format.
The Stakes
What happens if they act on wrong information? High stakes means the reasoning needs to be visible and defensible — not just correct.
STEP 2
Architect the Brief
Most people talk to AI the way they'd message a new intern — short, context-free, assuming the other side fills in what's missing. The highest-leverage skill in AI-assisted analytics is knowing how to describe a problem precisely enough that AI builds something useful close to the first pass. I call it The Director's Brief.
The Stage
Full context: business situation, the problem, the data, the constraints.
The Expert
Cast the role with precision — not "data analyst" but a specific senior expert with relevant domain experience.
The Seat
Name the audience. A sourcing lead and a VP of Operations are not the same audience.
The Shape
The exact format: a Python script? A ranked table? A decision memo?
The Ask
The precise deliverable — not "analyze this" but a specific script, output, and rationale per move.
STEP 3
The Iteration with AI
The first AI output won't be perfect. That's not a failure — it's the process. Run it. See what breaks. Describe what's wrong and why. Each iteration sharpens your model of the real problem.
"The duty rate calculation is routing Factory A through the wrong port — reroute through Port Y. Also, moves 4 and 5 have a dependency: Factory B can't start until Factory A completes 60% of its reallocation. Add that constraint and rerank."
You are not debugging code. You are refining your understanding of the business — and letting AI keep up with you.
Step 4
Stage the Logic
A correct output that nobody can defend is a useless output. When the leader presents Move 1 and the director asks "why this one first?" — she needs a real answer. Not "the model said so." Staging the Logic means building your analysis in four visible layers.
Ground Truth
What the data actually shows. No interpretation. The baseline everyone agrees on before the analysis begins.
Mechanism
Why this move makes sense — the cost structure, the duty rate differential, the constraint that makes this the right sequence. Where most analyses stop too early.
Consequence
What happens if we act, and what if we don't. Concrete and specific: "each quarter of delay is a quarter of carrying 18% duty rate on already-contracted volumes."
Decision
One clear action. Not options. Not a recommendation with caveats. The single thing this person needs to do next.
STEP 5
The Stress Test
The ranking is built. The logic is staged. One final step: ask AI to attack it — not to validate, to find what doesn't hold up before the person in the room does. This is the move most people skip. It's also the one that saves you most often.
For Senior Leaders
Creation Diligence
Using AI well is not just about speed and output quality. The question that once drove tech — "Can we build it?" — has been replaced. The question that matters now is: "Should we build it?" That's a leadership question. Owned by you.
The System You're Using
Not all AI is the same. Before embedding any AI into a consequential workflow, know what it can and cannot reliably do. Using a system you don't understand is not innovation — it's delegation without accountability.
How You're Working With It
Automation, Augmentation, and Agency carry different levels of responsibility. When you grant agency, you're responsible for everything the system does in your name — including what you didn't anticipate.
The Impact That Comes From That Interaction
Ask before any significant AI-assisted work: What's the worst realistic outcome if this is wrong? Who could be harmed if it fails? Who is accountable for fixing problems before they become irreversible?
What AI Can't Do
It cannot know what leaders need to say to their director.
It cannot judge which transitions are politically sensitive this quarter.
It cannot decide that operational feasibility matters more than theoretical optimality.
It cannot tell when the data is technically right but operationally wrong.
AI handles the execution. You handle what matters. Think. Don't code.
— Carol Marmute
She writes about AI, analytics, and how to think better with both.
linkedin.com/in/carolinnemarmute
Written with AI. Thought without it. — 2026