What 630,000 Professionals Revealed About Decision Debt

Two weeks ago, getAbstract — the world's largest library of business book summaries — published its summary of The Decisive Edge and rated it 9 out of 10. I'm grateful for the rating. But the rating isn't the story.

The story is what happened next. getAbstract's recommendation engine pushed the summary to more than 630,000 professionals, matched to their individual interest profiles. Thousands downloaded it. And because their platform reports where those readers work, I got something authors almost never see: a map of exactly who reaches for a decision-making framework when one is put in front of them.

I expected my own industry to dominate. It didn't.

The map

Banking and finance led — by a wide margin. Healthcare and pharmaceuticals came second. Energy and utilities third, technology fourth. Insurance, the industry where I've spent much of my career, placed mid-pack. Behind those: professional services, aerospace and defense, manufacturing, education, government. Readers in more than forty countries, with the United States, United Kingdom, South Africa, India, and Germany at the front.

Sit with that spread for a moment. A banker in London, a hospital administrator in Johannesburg, a grid operator in Frankfurt, and a software director in Bangalore all reached for the same thing in the same two weeks: a system for making decisions.

What the spread means

If decision failure were an industry problem, the downloads would cluster. Industries have their own literatures — risk frameworks for banking, safety protocols for energy, clinical governance for healthcare. When leaders reach outside their industry's canon for something domain-agnostic, it signals the canon isn't addressing the failure they're experiencing.

Here's what I think that failure is. Every one of the leading industries in the data shares a structural feature: regulated, high-consequence decisions that are increasingly made alongside — or by — automated systems. Credit models in banking. Diagnostic and triage tools in healthcare. Load-balancing algorithms in energy. Deployment pipelines in tech. These are the places where the question "who decided this?" has a regulator, an auditor, or a plaintiff attached to it.

And these are exactly the places where Decision Debt compounds fastest. Decision Debt — the accumulated cost of deferred, misplaced, and degraded decisions — used to accrue at human speed. A leader could only avoid so many hard calls per quarter. But when analysis and recommendation are automated, deferral gets easier, ownership gets fuzzier, and the debt accrues at machine speed. The industries downloading a decision framework in volume are the industries feeling that acceleration first.

The uncomfortable version

There's a less flattering way to read the data, and intellectual honesty requires naming it: these readers weren't looking for my framework specifically. They were searching getAbstract for answers on business topics, and the engine matched them to a decision-making title. The demand signal isn't about the book. It's about the vacuum.

That's the finding. Somewhere between business school and the boardroom, almost nobody is taught a system for deciding — and the professionals who feel that gap most acutely are the ones whose decisions now involve algorithms, audit trails, and consequences measured in careers. The vacuum is cross-industry because the gap in leadership development is universal.

What to do with this

If you lead in one of these industries — honestly, if you lead anywhere — three moves follow from the data:

First, treat decision-making as a named capability, not an assumed one. Your organization has a framework for budgeting, hiring, and incident response. If it doesn't have one for deciding, the most consequential process in the building is running on improvisation.

Second, find out where your Decision Debt is accruing. Deferred calls, decisions with no named owner, recommendations accepted because they were formatted like conclusions. The Decision Debt guide covers the mechanics, and the free Leadership Assessment will show you where you personally stand in about seven questions.

Third, draw the human/machine boundary on purpose. If automated systems inform or make decisions anywhere in your operation, someone should be able to state — in a sentence — which decisions are delegated, which are augmented, and which are reserved for human judgment. If nobody can, the boundary is being drawn anyway, one convenient default at a time.

Six hundred thirty thousand professionals were offered a decision framework, and the ones who reached for it span every industry that runs on consequential judgment. The demand is not for cleverness. It's for a system.

Leadership first. AI second.