The Three Questions Leaders Must Ask Their AI Teams
Escape the AI performance trap. A leader's framework to demand architectural proof of safety, security, and control for trustworthy systems.
As a leader, you are caught in a difficult position. You face immense pressure to deploy Artificial Intelligence. Your board, your investors, and the market itself demand that you use this transformative technology to stay competitive. At the same time, you harbor a deep and entirely rational concern: a loss of control.
You see the headlines about AI failures, the warnings from pioneers about existential risks, and you sense the fragility in the systems your own teams are building. You are being asked to sign off on technology that even its creators admit they do not fully understand. How can you lead your organization into this new era with confidence?
The answer is not to become an AI expert yourself, but to learn to ask the right questions.
For decades, leadership in technical domains has been about managing risk through rigorous inquiry. When building a bridge, we do not just ask, Will it stand? When designing a nuclear reactor, we do not just ask, Does it generate power? We ask second and third-order questions about failure tolerances, material fatigue, containment protocols, and verifiable safety mechanisms.
We must now bring this same level of rigor to AI. The problem is that most leaders are still asking first-order questions. They are trapped in a cycle of inquiry focused on performance, not assurance. This is the single greatest strategic mistake an organization can make when deploying AI in any system where failure has consequences.
This article will provide you with three strategic questions that cut through the hype and force a conversation grounded in the physics of reality. These are not gotcha questions. They are the starting point for a new kind of dialogue between you and your technical teams. This is a dialogue that shifts the focus from what an AI can do to what it will not do and moves you from a position of hope to a position of proof.
The Performance Trap: Why Your Current Questions Are Failing You
Before we get to the right questions, we must diagnose the wrong ones. Right now, the conversations in most boardrooms and strategy sessions revolve around a set of performance-based metrics. These questions include:
What is the model's accuracy?
How much faster will this make our process?
Can you show me a demo of it working?
What is our ROI on this AI initiative?
These questions are not irrelevant. They are essential for measuring business value, but they are dangerously insufficient for measuring risk. They create what I call The Performance Trap.
The Performance Trap is a cognitive bias where a system's impressive capabilities in a controlled environment are mistaken for reliability in the chaotic real world. A demo is a performance, not proof. An accuracy score of 99.9% is a measure of success in a curated dataset, not a guarantee of safety against the 0.1% of scenarios that could bankrupt your company or cause a catastrophic failure.
As seen in the design of autonomous navigation systems for missions at NASA or ESA, a 99.9% success rate would be a laughable and terrifying metric. The entire discipline of engineering for critical systems is pathologically obsessed with the 0.1%. It is about designing, from first principles, systems that are not just high-performance, but fundamentally stable.
To escape The Performance Trap, you must ask questions that probe the stability, boundaries, and integrity of the system, not just its capabilities.
Question 1: How do we know its limits, and what happens when it reaches them?
This is the foundational question of operational safety. It is a two-part inquiry that forces your team to define the system's boundaries and its fail-safe mechanisms.
The Wrong Question It Replaces: How accurate is it? or How well does it perform?
Why This Question Matters:
An AI model is not a magical oracle. It is a sophisticated mathematical tool that is only valid within a specific set of conditions and data distributions. This is known as its Operational Design Domain (ODD). Outside of that domain, its behavior is undefined and potentially chaotic. A facial recognition system trained on daytime photos may fail catastrophically at dusk. A trading algorithm tested in a bull market may implode during a sudden crash.
True safety is not achieved by hoping your system never leaves its comfort zone. True safety is achieved by:
Rigorously defining the boundaries of that comfort zone.
Building a system that knows when it is approaching the edge.
Architecting a guaranteed, predictable, and safe response for when it crosses that edge.
Your concern as a leader is not the model's average performance, but its behavior at the margins. The greatest risks are always found at the edge cases. This question forces that conversation into the open.
What a Good Answer Looks Like:
A strong answer will be grounded in humility and architectural thinking. Your team should be able to present a formal document that defines the ODD. They should use language like:
The system is designed to operate under these specific conditions, and here is our monitoring system for detecting when those conditions are no longer met.
We have defined a formal 'safety case' that explicitly states the assumptions under which the system is reliable.
When the system's confidence score drops below a predetermined threshold, or if it encounters a novel input it cannot classify, it is designed to trigger a 'graceful degradation' protocol.
The fail-safe is not another AI; it is a simple, deterministic system. For example, it might hand control back to a human operator, shut down a process, or revert to a simple, rules-based logic that is proven to be stable.
They should be showing you architectural diagrams, not just performance charts. They should be talking about constraints and fail-safes, not just capabilities.
What a Bad Answer (Red Flag) Looks Like:
A red flag is any answer that dismisses the question or reveals a lack of architectural foresight.
The model is so powerful, it can handle almost anything. (This is hubris and a sign they have not seriously considered the boundaries.)
We achieved 99.8% accuracy on our test set, so edge cases are extremely rare. (This confuses performance with safety and ignores the fact that rare events have the highest impact.)
If it fails, we have a team on standby to fix it. (This is a reactive, not a proactive, safety posture. For a critical system, this is unacceptable.)
We cannot know all the edge cases, so we will just keep training the model with more data as we find them. (This is a recipe for discovering your system's failure modes in production, with your customers and your capital.)
Question 2: How can we prove it will always follow our most critical rules?
This question addresses the black box problem, but in a way that is far more productive than simply asking for an explanation. It shifts the focus from interpretability to verifiability.
The Wrong Question It Replaces: Can you explain how the AI works?
Why This Question Matters:
For many modern AI systems, particularly deep learning models, a full human-understandable explanation of their internal decision-making process is not possible. Asking for one can lead to vague, unsatisfying answers or simplified post-hoc rationalizations that may not reflect the model's true reasoning.
But as a leader, you do not necessarily need an explanation. You need an assurance. You need to know that the AI, whatever its internal process, is incapable of violating your organization's most fundamental, non-negotiable rules. These could be safety constraints (e.g., never operate the robotic arm when a human is present), ethical red lines (e.g., never make an automated decision based on protected demographic data), or financial controls (e.g., never execute a trade that exceeds this risk limit).
This question forces the conversation beyond the probabilistic nature of the AI model and into the deterministic world of system architecture and formal verification.
What a Good Answer Looks Like:
A mature technical team will talk about building a scaffold or governor around the AI model. They will describe a hybrid system where the AI provides the sophisticated recommendations, but a separate, simpler, and verifiable system enforces the hard constraints. This can take the form of dedicated runtime monitors that check every output or be guided by principles seen in emerging concepts like constitutional AI.
The neural network suggests a course of action, but we have a formal logic layer that verifies the suggestion against our three core safety rules before it can be executed. We can mathematically prove that this layer will always reject a rule-violating action.
We use a technique called 'shielding.' The AI operates freely within a proven-safe envelope, but if it ever tries to issue a command that would leave that envelope, the shield intervenes.
For our most critical ethical constraints, we do not rely on the model to learn them. We have hard-coded them as immutable rules in the system's control logic. The AI's job is to optimize within those rules, not to define them.
We can provide you with an audit trail that shows not just the AI's decision, but also the verification check from the safety layer that confirmed the decision was compliant.
What a Bad Answer (Red Flag) Looks Like:
A dangerous answer is one that relies on hope or training as a substitute for architectural guarantees.
We trained the model on a dataset that was carefully curated to reflect our ethical values. (This is necessary but insufficient. Training influences behavior; it does not guarantee it.)
The model is a black box, so we cannot provide a 100% guarantee, but its performance shows it has learned the rules. (This is an admission that the system is not architected for high-stakes environments.)
We cannot formally prove it, but the probability of it violating that rule is infinitesimally small. (In critical systems, infinitesimally small probabilities occur with surprising regularity. This is not a basis for trust.)
Question 3: How do we protect the system's integrity from its data pipeline to its final decision?
This question expands the concept of security from a simple IT problem to a challenge of complete system integrity. It forces your team to think like an adversary.
The Wrong Question It Replaces: Is the AI system secure?
Why This Question Matters:
When leaders ask if a system is secure, IT teams often think about firewalls, access control, and encryption. These are vital, but for AI systems, the attack surface is vastly larger and more insidious. Adversaries do not just need to breach your network; they can manipulate your AI by poisoning the data it learns from or by feeding it specially crafted inputs to trick it into making a disastrous decision.
The integrity of an AI system is a chain. It is only as strong as its weakest link, which includes:
The Data Pipeline. Is the data you use for training and operation authentic and untampered with?
The Model Itself. Can an adversary steal your model or, worse, subtly modify it?
The Input Layer. Is the system resilient to adversarial inputs designed to deceive it?
The Decision Output. Can the final action be intercepted or altered?
This question forces a full-spectrum view of security, appropriate for a world where attacks are becoming increasingly sophisticated.
What a Good Answer Looks Like:
A security-conscious team will talk about a DevSecOps or Secure-by-Design philosophy. They will describe security as a feature built-in from day one, not a patch applied at the end.
We have a rigorous data provenance and validation process. We can trace our training data back to its source and have automated checks for anomalies that could indicate poisoning.
We treat our trained models as critical assets, with strict version control and access management. Any update to the model goes through the same security review as a major software release.
We conduct adversarial testing, where we actively try to fool our own models with deceptive inputs. This helps us understand their vulnerabilities and build defenses, like input sanitization and anomaly detection.
The system architecture employs a Zero Trust model. The AI component is isolated and cannot directly access critical systems. Its recommendations are passed through a secure API to a separate, hardened execution controller.
What a Bad Answer (Red Flag) Looks Like:
A weak answer will focus narrowly on traditional IT security or betray a naive understanding of AI-specific threats.
The system runs on our secure cloud infrastructure, so it is protected by their firewalls. (This completely ignores the data and model integrity issues.)
We have not focused on adversarial attacks yet; our priority has been improving performance. (This means they have a massive, unexamined vulnerability.)
Data poisoning is mostly a theoretical academic concern. (This is demonstrably false and a sign of a team that is not up-to-date on the current threat landscape.)
Security is handled by the IT department. (This indicates a siloed approach where the unique vulnerabilities of the AI system itself are likely being overlooked.)
Conclusion: From Inquiry to Action
The purpose of these three questions on limits, rules, and integrity goes beyond a simple checklist. They are a tool to transform your relationship with your technical teams. They elevate the conversation from a superficial review of performance metrics to a deep, strategic partnership focused on building resilient, trustworthy, and defensible systems.
The pressure to deploy AI will not subside. The only way to succeed is to lead with disciplined inquiry. Start with these three questions. Insist on clear, architectural answers. This approach protects your organization from catastrophic risk while building a foundational, competitive advantage: the ability to use the power of AI with the wisdom of proven engineering.
Actionable Takeaways
For Policymakers
Mandate assurance, not just performance, in technology procurement for public and critical systems. Your requirements should specify the need for verifiable safety cases and resilient architectures. Champion investment in national digital twin environments for the rigorous testing and validation of autonomous systems.
For Leaders and Founders
Demand architectural proof from your teams. Ask them how they are verifying the system's limits, how they are enforcing your non-negotiable rules, and how they are protecting the integrity of the entire system. Prioritize building a culture where safety and assurance are seen as a foundational competitive advantage, not a compliance burden. If answers are inadequate, mandate a formal review, engage a third-party audit, and make AI assurance a governance priority.
For Researchers and Builders
Focus your talents on the hard problems of assurance. The next great breakthroughs will be in the areas of formal verification for complex systems, resilient multi-agent coordination, robust defenses against adversarial manipulation, and exploring ways to instill AI with instincts for humane alignment over unchecked power.
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— Sylvester Kaczmarek