Part of the "AI Leadership in the Real World" series: how leaders turn AI from scattered pilots and executive excitement into governed, adopted, measurable business capability.
TLDR: 60 ideas on a board. 4 product teams to build them. $1.2M in annualized run cost for pilots that produced $340K in measurable value.
The week we made tradeoffs visible was the week AI stopped being a budget line item and started being a product strategy.
My AI backlog looked like a menu with 60 "top priorities" and no kitchen to cook them.
Every function had a smart idea.
Every idea came with urgency and a sponsor.
Customer support wanted a deflection chatbot.
Engineering wanted a code review assistant.
Sales wanted lead scoring.
Operations wanted anomaly detection.
HR wanted resume screening.
Finance wanted invoice reconciliation.
Each one was a good idea.
That was the problem.
When every idea is good, prioritization becomes political.
The loudest pitch keeps winning.
People start optimizing for being seen, not for being useful.
And the organization quietly trains everyone to be louder.
I run a product P&L.
I do not have the luxury of treating every good idea as a funded initiative.
My job is not to maximize the number of AI pilots.
My job is to maximize the return on the engineering capacity, infrastructure budget, and organizational trust I have been entrusted with.
Those are finite.
Every pilot I approve is a pilot I cannot fund somewhere else.
Every dollar of inference cost is a dollar that did not go to a product feature, a reliability improvement, or a person.
We were treating AI like a lottery ticket instead of a managed investment.
The P&L was telling us that before I was willing to listen.
What Changed: The Cost of the First Prototype Collapsed Two years ago, prototyping an AI use case took weeks — data pipeline, model, inference endpoint, UI, deployment path.
The cost itself was a prioritization mechanism.
Only ideas that survived a viability check got built.
That barrier is gone.
Today, a prompt, an API key, and an afternoon get you a working demo.
A LangGraph agent can be built in an evening.
A RAG pipeline can be running by lunch.
This is great for product.
I can test a hypothesis before writing a quarterly business case.
A product manager can answer "would this help our users?" in days, not months.
But it also means the filter is gone.
Now every idea can get a prototype, every prototype a demo, every demo enough excitement to justify keeping it alive.
And every live pilot has a run cost: API calls, cloud infrastructure, developer attention, security review cycles, roadmap slots.
The prototype is cheap.
The pilot is not.
The production system is expensive.
The distance between those three stages is where most AI budgets quietly bleed out.
HBR Noticed the Same Pattern I started seeing this pattern in my own portfolio before I saw it in print.
Then HBR published three pieces in six months that described exactly what I was living through.
In November 2025, Goutam Challagalla, Mahwesh Khan, and Fabrice Beaulieu (IMD/BCG) published "Stop Running So Many AI Pilots".
Subtitle: "Instead of testing lots of use cases across the company, pick one area and go deep." They used Reckitt as their case study.
Reckitt found use cases spanning the business — presentations, customer support, procurement.
Each guaranteed time savings.
But the executives realized "the effort wouldn't transform the company's strategy or create a meaningful advantage.
They were hoping for something more dramatic, not just marginal efficiency improvements." That sentence hit me.
We had pilots that worked, saved time, produced decent demos.
But they were not changing the product.
They were making the same product slightly faster.
The same month, Ania Masinter published "Prioritizing AI Investments That Create Real Value" as an HBR Executive Playbook.
Her argument: "It's time for companies to move from experimentation to disciplined, focused investment in AI." She referenced t