There’s a peculiar tension that surfaces in almost every AI team I’ve worked with or observed. It usually starts with a seemingly innocuous question: “Is this model working correctly?” What follows is rarely a simple technical check. Instead, it triggers a cascade of ownership disputes that span code, data, business logic, and ultimately, the definition of truth itself. In traditional software engineering, we have established patterns for responsibility. A frontend engineer owns the UI rendering; a backend engineer owns the API logic; a DevOps engineer owns the deployment pipeline. But when you introduce a probabilistic model into the stack—something that learns patterns from data rather than executing explicit instructions—the lines of accountability blur into a fog of statistical uncertainty.
This ambiguity isn’t just an organizational annoyance; it is the single greatest risk factor in production AI systems. When a deterministic function fails, the traceback is usually clear. When a neural network degrades, the failure mode is often silent, insidious, and distributed across multiple domains of expertise. Who owns the truth of a prediction when the model is mathematically sound but the training data is sociologically biased? Who is responsible when a decision logic is correct, but the underlying data pipeline introduced a subtle drift? These are not questions of code; they are questions of structure, governance, and the philosophical alignment of a team.
The Illusion of the Monolithic Model
Many organizations attempt to solve the ownership problem by hiring a “Data Scientist” and handing them a dataset, expecting a model to emerge fully formed, like a statue from marble. This approach treats the AI system as a monolithic entity owned by a single role. It is a dangerous oversimplification. Modern AI systems are complex sociotechnical constructs. They consist of data ingestion, preprocessing, feature engineering, model architecture, training infrastructure, evaluation metrics, and inference serving. Each of these components has different technical requirements and, crucially, different definitions of “correctness.”
Consider the training data. To a Data Engineer, correctness means the pipeline is robust, the schema is enforced, and the data is delivered on time. To a Data Scientist, correctness means the data distribution matches the problem space and contains enough signal to learn from. To a Domain Expert, correctness means the labels reflect semantic reality. When a model fails because of “bad data,” these three perspectives often point fingers at one another. The Data Engineer blames the Scientist for vague requirements; the Scientist blames the Engineer for dirty pipelines; the Domain Expert blames both for missing the nuance of the real world.
Ownership of the model architecture itself introduces another layer of complexity. In the early days of deep learning, researchers often built bespoke architectures for every problem. Today, the trend has shifted toward leveraging pre-trained foundation models and fine-tuning them. This shifts the ownership of “intelligence” from the team’s internal innovation to the external provider of the base model. Suddenly, the team owns the adaptation, the alignment, and the safety tuning, but not the core knowledge representation. This creates a dependency that requires its own form of ownership—someone must track the versioning, capabilities, and limitations of the external model, treating it as a volatile external dependency rather than a static library.
The Data Pipeline as a Source of Truth
In my experience, the most critical—and often neglected—ownership domain is the data pipeline. We tend to romanticize the model architecture, focusing on the elegance of transformers or the novelty of a loss function. Yet, the model is only as good as the data it sees. There is a prevailing myth that data is a static resource, something you mine like coal. In reality, data is a flowing river, constantly changing in composition and velocity.
Who owns the river? If you ask a software engineer, they might say the database administrator. If you ask a machine learning engineer, they might say the data scientist. The reality is that data ownership requires a dedicated steward—a role that sits at the intersection of engineering and analytics. This person (or team) must own the “ground truth” of the system. They are responsible for ensuring that the labels used for training are consistent, that the features are calculated correctly, and that the sampling strategies do not introduce unintended biases.
Let’s take a practical example: a fraud detection system. The model learns to distinguish between legitimate and fraudulent transactions. The “truth” here is defined by historical labels. But who owns the validation of those labels? Often, they come from human investigators who marked transactions as fraudulent after the fact. If the investigation team changes their criteria for flagging fraud (perhaps to reduce workload), the statistical properties of the training data shift, even though the underlying reality of the world hasn’t changed. This is a data drift problem, but it is fundamentally a human process problem. Without a clear owner of the data generation process—the human-in-the-loop feedback mechanism—the model will silently degrade, predicting based on outdated definitions of truth.
Furthermore, the ownership of data privacy and compliance adds a legal dimension to this technical role. With regulations like GDPR and CCPA, the “right to be forgotten” extends to the training data. If a user requests deletion, does that mean removing their row from the database? Or does it require retraining the model to unlearn that data? This is a frontier of AI engineering that requires tight collaboration between legal, data engineering, and ML researchers. Ownership of data truth is not just about accuracy; it is about ethics and legality.
Correctness: Statistical vs. Semantic
One of the most profound disconnects in AI teams is the gap between statistical correctness and semantic correctness. A model can be statistically perfect—achieving 99% accuracy on a validation set—while being semantically useless or dangerous. This is where the role of the Subject Matter Expert (SME) becomes critical, yet often underutilized.
Consider a medical imaging AI designed to detect pneumonia. A computer vision engineer optimizes for pixel-level accuracy. They might achieve a state-of-the-art F1 score. However, a radiologist (the SME) looks at the model’s output and notices that the model is latching onto artifacts in the X-ray machine’s hardware rather than lung opacity. The model is statistically correlated with the target variable (because machines used on sicker patients might have different calibration artifacts), but it is semantically wrong. It hasn’t learned medicine; it has learned machine calibration.
Who owns the diagnosis of this error? The engineer sees high metrics and assumes the model is working. The doctor sees a flawed reasoning process. In a well-structured team, the SME owns the “semantic validation” of the model. They are not there just to label data; they are there to interrogate the model’s logic. They ask: “Does this prediction make sense in the context of the physical world?”
This ownership of semantic truth requires a feedback loop that is often too slow in standard CI/CD pipelines. We can automate the calculation of precision and recall, but we cannot easily automate the evaluation of causal reasoning. Integrating SMEs into the core development loop—rather than treating them as external stakeholders—is essential. They must have veto power over metrics that look good on paper but fail in practice.
The Decision Layer: Where Logic Meets Probability
Once a model is trained, it outputs a probability. A score between 0 and 1. But the world operates on decisions: approve the loan, reject the loan; flag the tumor, ignore the tumor. The translation of probability into decision is a distinct engineering problem that requires its own owner.
Machine learning models typically output a likelihood, not a certainty. It is the responsibility of the “decision engineer” or the product manager to set the threshold. Where do we draw the line? A 50% probability of fraud might be acceptable for a low-value transaction but catastrophic for a high-value one. This decision boundary is a business logic layer that sits on top of the model.
If the model’s performance degrades (e.g., the probability calibration shifts due to data drift), the decision logic must adapt. Who monitors this? If the model owner is purely a researcher, they might not care about the business impact of the threshold. If the product owner sets the threshold without understanding model calibration, they might set impossible expectations.
Ownership of decisions requires a hybrid role—often found in MLOps or specialized ML Product Management. This role owns the “utility function” of the AI. They map the statistical output of the model to the business value of the organization. They are the translators between the probabilistic world of the model and the binary world of action. They must answer the question: “Given the model’s current uncertainty, what is the optimal action to take?”
Furthermore, in high-stakes environments, we must consider the ownership of the “default action.” When the model is unsure (low confidence), what happens? Does the system default to a human review, or does it default to a rejection? This decision must be made upfront, documented, and owned. It is a safety mechanism that prevents the automation of uncertainty.
The Rise of the ML Engineer and the Ops Gap
As AI moves from research labs to production, a new role has solidified: the Machine Learning Engineer (MLE). This role is distinct from the Data Scientist. The Data Scientist focuses on the “what” (what architecture to use, what features matter). The MLE focuses on the “how” (how to train it efficiently, how to serve it with low latency, how to keep it running).
The MLE is often the owner of the infrastructure that supports truth. They build the feature stores that ensure training and inference data are consistent. They implement the model registries that track lineage and versioning. Without this role, the “truth” of the model is ephemeral. A Data Scientist might train a great model on their laptop, but if the MLE cannot replicate the environment in production, the model’s behavior changes. The truth becomes relative to the hardware.
In my experience, the friction between Data Scientists and ML Engineers is a common source of failure. Scientists want to iterate quickly and experiment with new architectures; Engineers want stability, scalability, and maintainability. Ownership of the production environment belongs to the MLE, which means they must enforce standards on the Scientists. Conversely, the Scientist must own the clarity of the requirements so the MLE can build the right infrastructure.
Consider the concept of “training-serving skew.” This occurs when the code used to transform data during training is different from the code used during serving. It is a silent killer of AI systems. Who owns the prevention of skew? It is a shared responsibility, but it requires a unified ownership of the feature engineering codebase. This code should not live in a Jupyter Notebook owned by a Scientist; it should live in a versioned repository owned by the engineering team.
Truth Ownership in the Age of Generative AI
The emergence of Large Language Models (LLMs) has complicated ownership structures further. In traditional supervised learning, we had ground truth labels. In generative AI, the “truth” is often a matter of retrieval accuracy or hallucination minimization. The ownership of correctness in an LLM-based application is distributed across several new components.
First, there is the ownership of the context (RAG – Retrieval Augmented Generation). If the model generates an incorrect answer, is it because the model is incapable of reasoning, or because the retrieved documents were irrelevant? The owner of the retrieval system—the vector database, the chunking strategy, the embedding model—owns a significant portion of the factual accuracy. A perfect LLM will still lie if given bad context.
Second, there is the ownership of the prompt engineering. The prompt is the interface to the model’s reasoning capabilities. It is a form of programming, but it is non-deterministic. Who owns the prompt library? In many teams, it’s ad-hoc. A developer writes a prompt, tests it once, and ships it. But prompts need versioning, A/B testing, and evaluation just like code. The “truth” of the output is highly sensitive to subtle changes in prompt wording.
Finally, there is the ownership of safety and alignment. Who ensures the model doesn’t generate harmful content? This is often pushed to the “Safety Team” or “Trust & Safety” within an organization. However, safety cannot be bolted on at the end. It must be integrated into the fine-tuning and reinforcement learning from human feedback (RLHF) processes. The ownership of “harmlessness” is a distinct vector of truth that competes with factual accuracy and helpfulness.
Structuring for Success: The Cross-Functional Pod
So, how should a team be structured to manage these disparate ownership domains? The traditional silos of “Data,” “Engineering,” and “Product” fail to capture the interdependencies of AI. The most effective structure I have seen is the cross-functional pod model, centered around specific use cases or capabilities.
A typical AI pod might consist of:
- A Product Manager: Owns the business objective and the decision logic. They define what “success” looks like in business terms.
- A Data Scientist/Researcher: Owns the model architecture and the statistical validity of the approach. They experiment and find the best mathematical solution.
- An ML Engineer: Owns the productionization, the feature pipeline, and the serving infrastructure. They ensure the model runs reliably at scale.
- A Domain Expert: Owns the semantic truth and the quality of the labels. They validate that the model’s logic aligns with reality.
- A Data Engineer: Owns the raw data ingestion and storage. They ensure the river of data flows cleanly.
Crucially, in this structure, ownership is shared. The pod shares the goal of the specific use case. The Data Scientist does not throw a model “over the wall” to the ML Engineer; they collaborate on the deployment. The Product Manager does not set arbitrary deadlines; they work with the Domain Expert to understand the feasibility of the data.
This structure also facilitates the concept of “Algorithmic Auditing.” In a siloed organization, auditing is an external check that happens late in the cycle. In a pod, auditing is continuous. The Domain Expert is constantly auditing the outputs; the ML Engineer is constantly auditing the latency and drift; the Product Manager is constantly auditing the business impact.
Documentation as a Contract of Truth
In the absence of rigid role definitions, documentation becomes the contract that binds the team’s understanding of truth. In AI, standard software documentation is insufficient. We need specific artifacts that capture the nuances of the system.
Model Cards and Dataset Cards (popularized by Google) are essential tools for ownership. A Model Card documents the intended use, limitations, and performance metrics of a model across different demographics. It forces the team to agree on the boundaries of the model’s truth before it is deployed. Who fills this out? It requires input from the Scientist (performance), the Domain Expert (limitations), and the Product Manager (intended use).
Another critical artifact is the Feature Registry. This documents the lineage of every input variable used by the model. If a feature is defined as “average transaction value over 30 days,” the registry must specify exactly how that is calculated, who owns the calculation logic, and what the data freshness SLA is. Without this, “truth” becomes a moving target.
I recall a project where a model performance suddenly dropped. The investigation took weeks because no one could remember how a specific feature was engineered. It turned out a junior engineer had changed a database query, altering the definition of the feature. The model was still training correctly according to the new data, but the semantic meaning of the feature had shifted. A simple entry in a feature registry, owned by the Lead ML Engineer, would have prevented this.
Metrics: The Compass of the Team
What gets measured gets owned. In AI teams, we must be careful about the metrics we choose to optimize. If we only measure model accuracy, we incentivize the Data Scientist to overfit to the validation set. If we only measure system latency, we incentivize the ML Engineer to use simpler, less accurate models.
The team needs a hierarchy of metrics that reflects the multi-faceted nature of AI truth.
- Business Metrics: Revenue saved, user retention, click-through rates. Owned by the Product Manager.
- Model Metrics: Precision, Recall, AUC, Calibration error. Owned by the Data Scientist.
- System Metrics: Latency, throughput, uptime, cost per inference. Owned by the ML Engineer.
- Quality Metrics: Label accuracy, inter-annotator agreement, semantic consistency. Owned by the Domain Expert.
When these metrics conflict, the team must negotiate. For example, increasing the complexity of a model might improve business metrics (better predictions) but degrade system metrics (higher latency) and increase costs. Ownership of the trade-off belongs to the Product Manager, but they must be informed by the technical constraints provided by the Engineers.
Furthermore, we must monitor for concept drift. The world changes, and the model’s understanding of it must change too. Ownership of drift detection is usually an MLE responsibility, but the response to drift is a team effort. When drift is detected, does the model need retraining? Does the feature engineering need updating? Or has the business objective itself shifted? These questions require the whole pod to convene.
The Human Element: Psychological Safety and Ownership
Finally, we cannot discuss ownership without addressing the human psychology behind it. AI development is fraught with uncertainty. Models fail silently. Experiments yield diminishing returns. The pressure to deliver “magic” often leads to cutting corners.
When a model fails in production, the blame game can be toxic. If the culture punishes the Data Scientist for a bad prediction, they will stop taking risks and stick to safe, boring models. If the culture punishes the ML Engineer for downtime, they will refuse to deploy updates, leading to stagnation.
True ownership requires psychological safety. Team members must feel safe to say, “I don’t know why the model made that decision,” or “This data looks suspicious.” In traditional software, “I don’t know” is often unacceptable because code is deterministic. In AI, “I don’t know” is a valid starting point for investigation.
Leadership must foster an environment where the “truth” is viewed as a puzzle to be solved collectively, rather than a standard to be met individually. Post-mortems should focus on systemic improvements—how do we change our data validation to catch this next time? How do we improve our monitoring?—rather than individual blame.
Consider the phenomenon of “automation bias.” When humans interact with AI systems, they tend to over-trust the output. If the AI owner (the team) does not communicate the uncertainty of the model to the end-user, the user will assume the AI is always right. The ownership of communication is vital. The team must own the narrative of the model’s capabilities. They must educate the users on when to trust the AI and when to apply human judgment.
Emerging Roles: The AI Product Manager
As AI matures, a specialized role is emerging: the AI Product Manager (AI PM). Unlike a traditional PM who manages a backlog of features, an AI PM manages a pipeline of data and hypotheses. They understand that shipping an AI feature is not a linear process of “design, build, ship.” It is an iterative cycle of “collect data, train, evaluate, deploy, monitor, collect feedback.”
The AI PM owns the feedback loop. They are the bridge between the end-user’s experience and the model’s training data. They ensure that the data generated by user interactions is captured and utilized for future retraining. This is the concept of “Data Flywheel” ownership. The product gets better because it learns from usage, and the AI PM is the custodian of that learning process.
They also own the “cold start” problem. How do you launch a model that needs data to learn but needs to be live to generate data? They orchestrate strategies like human-in-the-loop bootstrapping or using proxy data, making critical decisions about the initial truth of the system before it has enough data to define truth for itself.
Legal and Ethical Ownership
We must address the elephant in the room: legal liability. As AI regulations evolve (e.g., the EU AI Act), the concept of “owner” takes on a legal dimension. There must be a designated person or entity responsible for the compliance of the AI system. This often falls to the CTO or a dedicated Head of AI Governance.
However, legal ownership cannot exist without technical ownership. You cannot certify a system you do not understand. This forces a convergence of legal and engineering teams. The “truth” of an AI system must be documentable in a way that satisfies regulators. This includes proving where the training data came from, how the model was validated, and how decisions were audited.
In the future, I predict we will see “AI Auditors” as a standard external role, similar to financial auditors. They will inspect the team’s structure, the documentation, the code, and the data to certify that the “truth” claimed by the company is accurate. The internal team must structure itself to be auditable.
Practical Steps to Define Ownership Today
If you are building or leading an AI team, how do you start untangling this web? You do not need to wait for a crisis. You can take concrete steps now.
1. Map the AI Supply Chain: Draw a diagram of your AI system from raw data to final decision. For every box in that diagram, assign a primary owner. It is okay for ownership to be shared, but there must be a single point of contact for questions.
2. Create a “Truth Charter”: Gather the team and write a document that defines what “truth” means for your specific application. Is truth statistical accuracy? Is it user satisfaction? Is it regulatory compliance? Write it down. This sounds fluffy, but it aligns the team on the north star.
3. Implement Model and Data Cards: Force the discipline of documentation. No model goes to production without a card that details its intended use, limitations, and metrics. No dataset is used without a card describing its composition and potential biases.
4. Establish Cross-Functional Reviews: Move away from code reviews that only look at syntax. Implement “Model Reviews” where the Domain Expert reviews the logic, the Engineer reviews the scalability, and the Product Manager reviews the business fit.
5. Monitor the Feedback Loop: Ensure there is a clear path for user feedback to influence the model. Who owns the pipeline that takes a user complaint and turns it into a retraining signal? If this path is broken, the model will never learn.
The Future of AI Teams
Looking forward, the structure of AI teams will likely become even more fluid. As AI tools become more powerful, the barrier to entry for building models lowers. We might see “Citizen Data Scientists”—domain experts who build their own models using AutoML tools. This democratization shifts ownership closer to the domain, which is good for semantic accuracy but risky for technical robustness.
How do we govern a thousand models built by a thousand domain experts? We will need centralized platforms—Internal AI Platforms (IAP)—that provide standardized tooling for data validation, model training, and monitoring. The platform team owns the infrastructure of truth, while the domain teams own the specific application of that truth.
Another trend is the rise of “Synthetic Data.” As privacy concerns grow and real data becomes scarce, teams will increasingly generate their own training data. Who owns the truth of synthetic data? The generator? The validator? This introduces a meta-layer of ownership that we are only beginning to explore. If we train a model on synthetic data, and that model fails in the real world, is the failure in the simulation or the reality?
In the end, the ownership of AI is the ownership of a complex adaptive system. It requires humility. It requires acknowledging that no single person understands the entire system. The “truth” of an AI model is not a static artifact that can be held by one person; it is a dynamic consensus achieved by a team of diverse experts.
The most successful AI teams I have seen are those that embrace this complexity. They do not try to simplify the problem into a single role. Instead, they build a structure where the Data Scientist, the Engineer, the Domain Expert, and the Product Manager can all point to the system and say, “I own my part of the truth, and I trust my colleagues to own theirs.” This trust is the bedrock upon which reliable AI is built.
We are still in the early days of engineering AI systems. The patterns we establish today—the way we assign responsibility, the way we document decisions, the way we handle failure—will define the reliability of the technology for decades to come. It is a heavy burden, but it is also an exciting challenge. It is the work of building not just software, but a new kind of institutional knowledge.
When you look at your own team, ask yourself: If the model makes a wrong prediction tomorrow, who will we call first? If the answer is “everyone and no one,” you have work to do. If the answer is a specific name, backed by a clear process and a supportive team, you are on the right path. The ownership of truth is not a title; it is a culture.
And in that culture, we find the resilience needed to navigate the uncertain waters of artificial intelligence. We find the patience to debug not just the code, but the data. We find the courage to question not just the metrics, but the meaning. And ultimately, we build systems that are not only smart but wise.
The journey is long, but the destination—a future where AI is reliable, accountable, and beneficial—is worth every step. Let us build teams that are worthy of that future.
So, the next time you sit down with your team to discuss a model’s performance, look around the table. See the Engineer, the Scientist, the Expert, the Manager. Recognize that the “truth” you seek is distributed among them. Your job is not to find the single owner, but to weave their perspectives into a cohesive whole. That is the art and science of AI team structure.
And it is a pursuit that demands our best efforts.
