I originally published this post in June. I’m sharing it again because I feel that it has become even more relevant in recent months.
For the past decade, the enterprise AI narrative has been dominated by a single, powerful paradigm: deep learning. By feeding massive neural networks unprecedented volumes of data, we have achieved remarkable breakthroughs in computer vision, natural language processing, and predictive modeling.
These models are extraordinary pattern-matching engines. They can identify subtle correlations in high-dimensional data that would be impossible for a human to detect.
But as organizations attempt to deploy these models into core business workflows, a fundamental limitation is becoming increasingly apparent.
Deep learning models are exceptionally good at answering the question, “What is happening?” They are fundamentally incapable of answering the question, “Why is it happening?”
This is the correlation trap. And in the complex, high-stakes environment of the modern enterprise, correlation is no longer enough.
The Limits of Pattern Matching
Consider a predictive maintenance model deployed in a manufacturing plant. The model analyzes sensor data and predicts that a specific machine is likely to fail within the next 48 hours. This is valuable information.
But what should the plant manager do about it?
Should they shut down the machine immediately? Should they replace a specific part? Should they adjust the operating temperature? The predictive model cannot answer these questions because it does not understand the causal mechanisms driving the failure. It only knows that certain patterns of sensor readings are correlated with historical failures.
If the plant manager takes an action that changes the underlying system dynamics—for example, by adjusting the temperature—the model’s predictions may become entirely invalid, because the correlations it learned from historical data no longer hold true.
This is the fundamental problem with relying solely on correlative models for decision-making. They describe the world as it was, but they cannot reliably predict how the world will respond to interventions.
The Causal Revolution
Causal AI represents a paradigm shift from pattern matching to systemic understanding.
Unlike traditional machine learning models, which learn statistical associations from data, causal models explicitly represent the cause-and-effect relationships between variables. They incorporate domain knowledge and structural assumptions to build a mathematical representation of the underlying system.
This allows causal models to answer “what if” questions.
Returning to the manufacturing example, a causal model would not just predict the machine failure; it would identify the specific root causes driving the prediction. It could simulate the impact of different interventions—”What if we reduce the operating speed by 10%?”—and provide the plant manager with actionable recommendations.
As Scott Cunningham emphasizes in his work on causal inference, the design stage—where researchers explicitly map out the structural relationships between variables—is the most critical part of the process. Without this structural understanding, any statistical analysis is merely describing correlations, not uncovering truths.
The Subjectivity of Causality
One of the most challenging aspects of causal inference is that it requires making assumptions. Unlike pure machine learning, where the algorithm “learns” everything from the data, causal models require human experts to define the causal graph.
This introduces an element of subjectivity. As some researchers argue, the best that applied causal inference can ever do is take a subjective but reproducible set of variables and state precise structural assumptions about them.
This subjectivity is often uncomfortable for data scientists trained in the objective certainty of mathematics. But in the enterprise, this subjectivity is actually a feature, not a bug. It forces organizations to explicitly encode their domain expertise and business logic into the AI system. It transforms the model from a black box into a transparent representation of the organization’s understanding of the world.
Causal AI in the Enterprise
The imperative for causal AI extends far beyond manufacturing. It is critical for any enterprise application where decisions have significant consequences and where the environment is subject to change.
In financial services, causal models are essential for stress-testing portfolios and understanding the true drivers of market risk. In healthcare, they are necessary for personalizing treatment plans and evaluating the efficacy of new drugs. In marketing, they are required for optimizing campaign spend and understanding the true incremental impact of advertising.
Furthermore, causal AI is a prerequisite for robust governance and auditability. When a correlative model makes a prediction, it is often a “black box.” When a causal model makes a recommendation, it provides a transparent chain of reasoning that can be audited and explained to regulators.
Causal AI and Regulatory Compliance
Perhaps the most compelling use case for causal AI in the enterprise is regulatory compliance. In financial services, regulators are increasingly demanding that organizations not only report their risk exposures, but explain the causal drivers behind them.
A traditional correlative model can tell you that a portfolio has a 5% probability of losing more than 10% of its value in the next year. But a regulator will ask: what are the specific risk factors driving that probability? How would the probability change if interest rates rose by 200 basis points? What is the causal mechanism linking a specific geopolitical event to the portfolio’s performance?
These are causal questions. They require causal models to answer. And as regulatory frameworks like Solvency II and IFRS 17 become increasingly sophisticated, the demand for causal reasoning in financial AI will only grow.
At Wangari, we believe that causal AI is not just a competitive advantage for financial institutions; it is a regulatory necessity.
The Wangari Approach
At Wangari, we believe that the future of enterprise AI is causal.
We are building infrastructure that combines the pattern-matching power of deep learning with the rigorous reasoning capabilities of causal inference. Our goal is to provide organizations with AI systems that not only predict the future, but empower them to actively shape it.
The era of “black box” correlation is coming to an end. The causal imperative is here.
Meanwhile, at Wangari
In mid-November, I’ll be speaking at the Machine Learning Week Europe. I’ll be running two sessions:
A workshop on how to create reliable, high-quality sports predictions — applied to this season’s Bundesliga. I co-authored the book Soccer Analytics with Machine Learning (O’Reilly, 2026); this is the practical version of it.
A table discussion titled How to get AI projects production-ready. I’d run a course on that topic last summer; it’s just getting timelier now.
If you’re still in for a stay in Munich from Nov 16-18 — tickets are still on sale and you can get 15% off with the code AriMLW on checkout. See you there!
Reads of the Week
Jean Piaget Walks Into an AI Lab... by Jennifer Harris Forrester, PhD: An entertaining read on how AI might be learning. Does AI learn like humans? (Just on steroids, because of all the computing speed?) Or does it have a unique way of perceiving the world? Don’t let the author’s humility fool you; this piece has some meat.
Your Causal Variables Are Irreducibly Subjective by David Reber: A thought-provoking piece on why applied causal inference must embrace subjective but reproducible structural assumptions. The author challenges the illusion of pure objectivity in data science, arguing that explicitly stating our assumptions is the only path to true scientific rigor.
Alex Carrega has an intriguing question for us. Causal AI for Cybersecurity: Can Neural Sentinels Understand Why an Attack Happens? In a rigorous and high-quality article, he details why, in cybersecurity, learning how an attack happened and not knowing why it happened is not enough. He also goes into the key mechanics of how it works and how to implement it. A wonderful reference, especially if you’re technically inclined.