The taxonomy names the bottleneck. MAIT removes it — a four-step cycle that turns a diagnosed bottleneck into a shared model, a measured outcome, and, run continuously, an advantage that compounds faster than the market can erase it.
The taxonomy describes the bottleneck. It doesn't remove it. Removal is MAIT.
The causal cell the diagnosis produces is exactly what MAIT's first step must build: a shared model of that group, accurate enough to pass the independence test, transferable enough to pass the decision-owner test, and operationalized enough to pass the informed-actor test. Diagnosis without removal is a report; removal without diagnosis is motion. MAIT is where the two meet.
The first three are the flywheel. The fourth scales it.
Build the shared model of the group, goal, or technology you don't understand, can't work with, or can't align to — accurate enough to pass the independence test, transferable enough to pass the decision-owner test, operationalized enough to pass the informed-actor test.
APut the model to work with the group that owns the decisions and actions, and measure the outcome. A model that isn't applied is just a document.
IIterate the model until the outcome the business needs shows up — not just removing the bottleneck, but delivering higher value more efficiently.
TScale the proven model across the enterprise, so the advantage isn't local. This is where a fixed bottleneck becomes a capability.
Run those three in a managed, iterative process and every group starts deciding from the same current model instead of from divergent private guesses. The business operates on more accurate models than the competition, so it delivers better results more consistently.
It has to run continuously because of the taxonomy's decay property: no removed bottleneck stays removed. Customers shift, executives turn over, and markets move, so every model drifts back toward "don't understand" and every alignment back toward "can't align." The companies that win aren't the ones that removed their bottlenecks once — they remove them faster than the market recreates them.
Avoiding a loss is the defensive case; the offensive case is larger, and just as documented.
Netflix's recommender — an accurate customer model with the whole company aligned to it — was described by its own executives, in a peer-reviewed paper, as saving more than $1B a year and driving ~80% of hours streamed (the documented order of magnitude for that era's base). McKinsey puts top-quartile personalization above $1 trillion of value across US industries.
MIT CISR found future-ready firms — business and technology integrated — report revenue growth ~17 points and net margin ~14 points above their industry average, and boards with three or more digitally fluent directors post materially higher margins and growth. Translation is a line item the market rewards.
Microsoft went from ~$300B in 2014 to over $3T in early 2024 on a "One Microsoft" alignment agenda. Amazon institutionalized alignment as a written mechanism — Working Backwards — and AWS came out of it. Coordination is worth trillions, not basis points.
At the far end sits Eli Lilly's TuneLab, launched in September 2025: a federated-learning platform giving selected biotechs access to drug-discovery models trained on proprietary data Lilly values at over $1 billion — roughly eighteen models built on more than 500,000 data points accumulated over two decades. Partners fine-tune on their own infrastructure and share only model updates; within two months, hundreds of companies had applied. Modeling removed so completely that the shared model itself became the thing to monetize.
MAIT's Model step needs a target. The taxonomy locates the one bottleneck that's actually binding.
the diagnostic the flywheel's outputModel–Apply–Improve, run in a loop, is what produces it: every group deciding from the same picture.
read the framework the full cycle's outputRun continuously against decay, MAIT removes bottlenecks faster than the market recreates them.
read the frameworkAddress the root cause the taxonomy names, not the symptom. Model, Apply, and Improve for Decision Dominance; the full cycle, continuously, for Transformation Dominance.