An MRI for organizations.
Not a mirror.
Surveys hold a mirror up and ask people to describe themselves. AI4EBITDA reads what people already wrote, across 410 million leadership profiles, fifteen years of validated data, and a 200-year-old science of language.
analysed to date
performance data
vs. financial outcomes
or self-reports required
No surveys. No interviews. No private data.
We use publicly available data to decode the real conditions shaping an organization. From those signals we generate operational specifications that let leadership teams define goals, benchmark progress, and execute their mandates with confidence. Nothing about the method requires access to private systems, individual consent at the subject level, or anyone's time.
The same public footprint a journalist, analyst, or competitor could read. We just read it at scale, against a validated framework, and report what it says about the organization's capacity to deliver.
The average person uses about 45,000 words. We focus on 1,500.
You speak and write with a working vocabulary of roughly forty-five thousand words. Most of those are interchangeable. Synonyms, fillers, technical terms specific to your job. The pattern that reveals how you think sits inside a much smaller set: about fifteen hundred high-signal words you reach for again and again, and the strongest signal in that set is not the content words you would expect.
It is the boring little words. Pronouns, articles, prepositions, conjunctions. They are invisible to the speaker, invisible to anyone trying to perform a different self, and they predict personality, decision style, and risk posture better than any noun or verb. Content words tell you what someone is thinking. Function words tell you who they are.
We are not reading what you said. We are reading how often you reach for I versus we, for the versus a, for but, because, although. Function-word density is involuntary. Content is performed.
Psycholinguistic profiling. Older than the personality test.
Reading psychology from language patterns is not a new field. It is older than Myers-Briggs, older than DISC, older than every survey-based assessment in use today. The peer-reviewed literature contains thousands of citations across linguistics, psychology, political science, and forensic analysis.
What is new is the data and the compute. Until recently, you could only score a handful of subjects: political leaders, criminal defendants, public figures whose writing was already collected. AI4EBITDA reads four hundred and ten million profiles continuously, from the open record alone.
One pipeline. Six steps.
Every score we publish, for an individual, a team, or a company, is the product of the same six-step path. Words become signals. Signals become mindsets. Mindsets become work-styles. Work-styles roll up into a company score that predicts financial performance.
For engaged companies, AI4EBITDA writes a per-leader development program tied directly to their lowest-scoring mindsets. We monitor whether the practices are followed, and we re-score continuously. Every point gained increases the probability of delivering the mandate.
Six mindsets. One composite score.
Each mindset is named for the thinker who first formalized the underlying body of work. The science is rigorous; the idea behind each one is simple. We score each mindset on a continuous scale, never binary, and roll the six into one composite for every person.
| Mindset | What it measures | Why it matters |
|---|---|---|
| IRC. Iterative Reasoning Think in steps | The ability to reason in steps, hold uncertainty, and course-correct when the first answer does not hold up. |
The single strongest predictor of productive AI use. High-IRC people treat AI output as a starting hypothesis, not a final answer. |
| LeCun-Skinner. Adaptive Intelligence See downstream | Systems-level thinking, downstream-consequence awareness, and the capacity to integrate new information into existing models. |
Essential for directing AI on complex problems. Low scorers accept outputs at face value without probing assumptions. |
| Clayton. Strategy & Innovation Recognise real change | Whether someone recognises when a new method represents a genuine shift, not just a faster version of the old way. |
Predicts willingness to build AI-native workflows rather than layering AI onto legacy processes. |
| Taguchi. Execution Discipline Learn from failure | Whether structured improvement and learning from failure are built into how work gets done. |
High Taguchi scorers naturally run the verify-adjust loop that makes AI use safe and effective. |
| Deming. Quality Orientation Quality before speed | Orientation toward process quality over speed and output volume. |
High Deming scorers ask "is this right?" before "is this fast?" A critical check on AI overreliance. |
| Ackoff. Systems Thinking See the whole | The ability to see the whole system and design for it, not just optimise individual parts. |
AI produces narrow answers to narrow questions. Ackoff-oriented thinkers know to ask bigger ones. |
The frameworks behind these mindsets, Deming on quality, Taguchi on disciplined improvement, Ackoff on systems, are well-established business theory. We use them as scientific anchors, not as the headline. What matters in practice is the simple version on the badge: think in steps, see downstream, learn from failure, quality before speed.
Four work-styles. How the mindsets show up at work.
The six mindsets roll up, drawing on Torbert's developmental action-logic framework, into four work-styles. This is a useful shorthand for distribution, planning, and program design. It is not the whole story; the underlying mindset score is what AI4EBITDA tracks and develops.
Dysfunctions and expert opportunities. Read in the same data.
The same word-density signal that scores mindset also surfaces patterns that block, or amplify, execution. We do not diagnose individuals. We flag patterns at the population level so leadership can act on them before they show up in the financials.
Blockers · Dysfunction patterns
Amplifiers · Expert opportunity patterns
Human and AI intelligence, scored on the same scale.
The cognitive framework that measures how a person reasons, decides, and learns also measures the AI systems your team is deploying. Traits. Mindset. Dysfunction. Computable for both. That makes one of the hardest questions in mid-market AI overwhelm answerable: are the leaders we have, and the AI systems we are buying, cognitively compatible?
The cost of an AI rollout is not the licence. It is the dozens of leaders who have to direct, trust, and verify it. If their cognitive architecture and the system's are mismatched, the rollout stalls and the EBITDA hit lands twelve to eighteen months later. We score the match before you sign the contract.
An MRI. Not a mirror.
Survey-based assessments (Myers-Briggs, DISC, Hogan) ask people to describe themselves. The output is filtered by self-image, by how the subject wants to be seen, and by what they know about the test. AI4EBITDA reads what people already produced, before they knew anyone was looking.
A mirror tells you what someone wants to see. An MRI tells you what is actually there. AI4EBITDA is the second one.
The numbers, with their source tags.
Two distinct datasets sit behind the AI4EBITDA framework. Mixing them weakens the proof. Here they are kept separate, with the relationship between them shown last.
scored across the database
against the framework
across six named dimensions
in the validation cohort
since 2006, vs. 646% S&P 500
over 15 years (ACSI top percentile)
AI4EBITDA scores vs. ACSI outcomes
the financial outcome they predict
required. Public language only.
The 1.4M number is our methodology dataset. The 350 number is ACSI's independent validation cohort. They are not the same thing and they are not interchangeable. The proof of the framework comes from how cleanly our scores predict ACSI's outcomes (p=0.019), not from the size of either number on its own.
Language today. Financial outcome later.
The causal chain runs in one direction: decision-quality language predicts mandate credibility, mandate credibility predicts execution, execution predicts customer satisfaction, and customer satisfaction predicts long-run shareholder return. Each layer takes twelve to thirty-six months to manifest. AI4EBITDA scores the leading layer.
By the time a board sees a financial problem, the leadership decision that caused it was made two to three years earlier. AI4EBITDA scores the layer where that decision is still being made, and where intervention still moves the outcome.
What executives actually ask about the science.
1. People lie. Usually to themselves.
When you fill out a Myers-Briggs or DISC survey, you answer based on who you think you are, or who you want your boss to think you are. Psychologists call this social desirability bias. It is not dishonesty. It is human nature. People answer in ways that make them look good, capable, and fit for the role. The result? The assessment tells you what the person believes about themselves. Not what they actually do under pressure, at scale, in complexity.
2. Self-awareness is the thing we are trying to measure, not a tool we can rely on.
Here is the irony of every survey-based leadership tool: the people who need assessment the most have the lowest self-awareness. A narcissist does not know they are a narcissist. An obstructionist does not score themselves as difficult. A leader who is cognitively overloaded will rate themselves as highly adaptable. You cannot measure a blind spot by asking the person where they cannot see.
3. They produce personality types, not performance predictions.
Myers-Briggs gives you INTJ or ENFP. DISC gives you D, I, S, or C. What does that tell you about whether an engineer will produce 30% more barrel-equivalent output than the person next to them? Nothing. These tools describe personality style. We measure cognitive capacity, decision architecture, burnout trajectory, and execution readiness. Those are the things that actually connect to financial outcomes.
The proof is not a testimonial. It is mathematics.
We have validated our methodology against 350 companies using the American Customer Satisfaction Index, an independent third-party measure of actual business performance.
The result: Pearson p = 0.019. In plain English, there is less than a 2% chance the relationship between our scores and real business outcomes is random.
Cohen's d = 0.83 on rich data profiles. In academic research, anything above 0.8 is classified as a large effect.
This is not a sales claim. It is measured against the same statistical standard used in pharmaceutical trials.
We use only publicly available material that the subject already chose to publish. Profiles, websites, filings, press releases, public talks, published writing. The same footprint a journalist, analyst, or competitor could read.
No internal email. No Slack. No surveys. No interviews. No IT access. No private documents.
We report patterns at the team or company level rather than portraits of named individuals, with the exception of the executives explicitly scoped into the engagement. The methodology is published. The validation against ACSI at p = 0.019 is independent. Customers see exactly what we measured and how.
If a question on data ethics has not been answered for you here, raise it in the discovery call. We will walk you through what gets collected, what does not, and how it is governed.
Yes, on the cognitive variables the framework measures. We score outputs, decision patterns, and interaction language for an AI system the same way we score them for a human leader. What that gives you is comparability: a single scale where you can see whether a given leader and a given AI system are cognitively compatible for the workflow you want to run.
It does not mean AI is conscious or has personality. It means cognition is measurable, and we measure it consistently across both.
For mid-market companies under board pressure on AI investment, this is the difference between buying a tool and knowing whether the people you have can actually run it.
Ready to see what your language already says?
See a scrubbed sample report. Or get a benchmark on your own organisation. No surveys, no IT access, no disruption.