the diagnostic · outcomes economy

The Bottleneck Taxonomy

A complete, mutually exclusive map of where enterprise value gets trapped: three failure types across five groups, fourteen cells, and one causal bottleneck at a time. Diagnose the right one before you spend a dollar removing it.

By Vin Vashishta · V-Squared AI

Run the diagnostic Then remove it: MAIT →

three failures, three tests

Every trapped dollar is one of three failures — and each has a test.

Name the failure precisely and the remedy names itself. The three run in sequence: a model must exist before it can move, and it must move before execution can align to it. So the tests run in order, and diagnosis stops at the first one that fails.

F1 · don't understand

A modeling failure — no accurate model of the group exists anywhere. Test: would two people, working independently from what the organization actually knows, describe this group's decision logic the same way? If not, F1 is present.

F2 · can't work with

A translation failure — the model exists but hasn't crossed from the person who holds it to the group that owns the decision. Test: does that group act on the model without its author in the room? If not, F2 is present.

F3 · can't align

A coordination failure — the model reached the decision-owner, but strategy, build, and execution still point elsewhere. Test: would a fully informed, fully empowered actor build what's being built today? If not, F3 is present.

The diagnostic rule — run the tests in order, stop at the first failure — sounds procedural. It's the taxonomy's most valuable edge.

Most problems present as alignment problems: the roadmap misses, the numbers disappoint, execution looks sloppy. But run the tests and you'll often find the coordination failure is downstream of a translation failure — the roadmap drifted because customer understanding never reached it — and that translation failure is downstream of a modeling failure, because the understanding that got stuck was itself a guess. Fixing alignment on top of a missing model just aligns everyone to the guess.

The causal bottleneck is the earliest failed test. Everything after it is noise until that gate opens.

The reverse error matters too. If your AI pilots convert to production at a healthy rate, modeling isn't your constraint — pouring more money into understanding is fixing an open gate while translation or coordination stays shut. The taxonomy isn't just for finding the problem; it prevents you from overpaying to fix the wrong one.

Five groups. Fourteen cells, not fifteen.

The failures recur across five groups — every population whose model governs whether enterprise value gets created. Technologists own the model of how technology is built and monetized. C-level leaders own the funding decision, and each executive runs a different decision model. Business units own the operating reality that corporate strategy makes assumptions about. Customers own the buying decision — the model most companies describe most vaguely. And the marketplace owns the external dynamics that decide which positions get rewarded.

The marketplace is also the taxonomy's one deliberate hole. There is no "can't work with the marketplace" cell, because a market is a system you read and align to, not a counterparty you work with. Fourteen cells, not fifteen — and the missing one is a claim, not an omission. Every bottleneck except one is ultimately a people-or-process problem; the single exception is the one force you calibrate.

the evidence · priced and documented

Each failure has a price tag — and a cautionary tale.

Three decades of research converge on the same point: these failures are almost never technological. They're modeling, translation, and coordination.

modeling failure

MIT's Project NANDA found roughly 95% of enterprise GenAI pilots delivered no measurable P&L impact in 2025 — a directional but blockbuster snapshot — and RAND put AI-project failure near 80%, about double conventional IT, with organizational rather than technical causes leading. Blockbuster is the story: its model of the customer was built on the late fee (~$800M in 2000), so it couldn't imagine a customer who hated the fee more than they loved the store. Netflix could.

translation failure

PMI priced it at $75M of every $135M at risk per $1B of project spend — put on the line by poor communication alone. Nokia is the story: its engineers knew Symbian was inferior, but organizational fear kept the truth from traveling upward while ~90% of the company's value evaporated. Boeing's MCAS risk never reached regulators or pilots — 346 lives and over $20B.

coordination failure

McKinsey found even high-performing companies leave ~30% of their strategy's value undelivered, tracing the gap to the operating model rather than the strategy. Target Canada is the story: strategy, an untested supply chain with up to 70% inaccurate product data, and execution never pointed the same way — roughly $2B gone, shelves empty above full warehouses.

from symptom to cause

Three steps to the causal bottleneck.

Diagnosis is mechanical once you run it in order.

Locate the group

Identify which of the five groups is producing the most friction: a decision that keeps stalling, a build that keeps missing, a relationship that costs more than it returns.

where is the friction

Run the tests in order

Apply the independence test, then the decision-owner test, then the informed-actor test. Stop at the first failure. That cell is the causal bottleneck.

stop at the first failure

Hand off to MAIT

The causal cell defines what MAIT's Model step must build: a shared model accurate enough to pass the first test, transferable enough to pass the second, operationalized enough to pass the third.

diagnosis → removal

The taxonomy classifies a state, not a permanent condition

Removed bottlenecks reopen. Customers shift, executives turn over, markets move — so every model decays back toward "don't understand" and every alignment drifts back toward "can't align." That's why removal is a cycle, not an event, and why the diagnosis hands off to a method built to run continuously.

from diagnosis to removal · mait

The taxonomy names the bottleneck. MAIT removes it.

Diagnosis is the input to the cycle. MAIT — Model, Apply, Improve, Transform — builds the shared model the diagnosis calls for and turns it into a compounding advantage.

Go to MAIT

one causal bottleneck at a time

Every cell, correctly named, is a located, priced, removable reason the next stage of growth hasn't happened yet.

Not a diagnosis of what's wrong with the business — a map of where the value is waiting.

Run the diagnostic V-Squared advisory