The Hidden Cost of Good Enough AI

Most AI disasters weren’t unpredictable. They were ignored.

A hiring algorithm that penalizes women. A chatbot that generates medical advice without disclaimers. A recommendation engine that optimizes engagement by amplifying outrage. In every case, someone in the room knew, or should have known, that the system was doing exactly what it was designed to do. The failure wasn’t technical. It was a leadership failure.

We have a phrase for this in product development: “good enough.” Ship it, measure it, iterate. The problem is that when AI is involved, “good enough” can cause harm at scale before anyone notices the dashboard.

The Real Risks Leaders Underestimate

Automation Without Accountability

When a human makes a bad decision, there’s a face attached to it. Someone can be asked why. When an algorithm makes the same decision ten thousand times, accountability diffuses. “The model decided” becomes a shield, one that no stakeholder should accept.

Every automated decision should have an owner. Not the data scientist who trained the model, but the product leader who decided where that model’s output would be used and what guardrails would surround it. Automation doesn’t eliminate judgment. It concentrates it upstream: in design, in policy, in the choice of what to automate in the first place.

Hallucinations Framed as “Edge Cases”

Large language models hallucinate. This is a known property, not a bug. Yet organizations continue to deploy AI-generated content in contexts where accuracy is critical (customer support, legal summaries, medical information) while treating hallucinations as edge cases that will be “addressed in the next version.”

Edge cases, by definition, are rare. LLM hallucinations are not rare. They are a baseline behavior that varies by domain, prompt structure, and model version. Treating them as exceptions rather than defaults is a framing error, and framing errors in AI deployment have real consequences for real people.

The responsible approach isn’t to avoid LLMs. It’s to design systems that assume the model will be wrong and build verification layers accordingly. The question isn’t “How accurate is the model?” It’s “What happens when the model is wrong, and who bears the cost?”

Metrics Replacing Moral Judgment

Engagement is up. Conversion is up. Retention is up. But is the system making people’s lives better, or just more dependent?

There’s a seductive clarity to metrics. They give us permission to stop asking harder questions. If the A/B test shows a 12% improvement in click-through rate, we ship it. We don’t ask whether the thing people are clicking on is worth their attention. We don’t ask whether the optimization is aligned with their long-term interests or just their immediate impulses.

This is not an argument against measurement. It’s an argument against measurement as a substitute for values. The two should work together. When they don’t, when metrics become the only lens, organizations optimize their way into reputational crises that no dashboard predicted.

A Framework That Isn’t a Checklist

Compliance checklists are necessary but insufficient. They tell you what regulators require. They don’t tell you what’s right. And in the fast-moving landscape of AI deployment, regulations lag behind capabilities by years.

Over the past several years, I’ve developed a framework for evaluating AI decisions that goes beyond compliance. It’s built on three questions that I ask before any model reaches production:

1. Human Agency: Where Must a Human Remain in the Loop?

Not every AI output needs human review. But some do. The line isn’t “high-risk vs. low-risk.” It’s “reversible vs. irreversible.” If the model’s decision can be undone easily (a product recommendation, a content suggestion), automation is appropriate. If the decision has lasting consequences (a loan denial, a medical triage, a hiring screen), a human must be in the loop. Not as a rubber stamp, but as a genuine decision-maker with the authority and information to override the model.

2. Truthfulness: What Happens When the Model Is Wrong?

Design for failure, not success. If the model is 95% accurate, the question isn’t “How good is 95%?” It’s “What happens to the 5%?” If that 5% represents customers who receive incorrect billing, patients who get wrong dosage suggestions, or job applicants who are unfairly screened out. 95% isn’t good enough. The system needs graceful degradation paths, human escalation triggers, and transparent error communication.

3. Power Asymmetry: Who Bears the Cost of Failure?

This is the question most organizations skip. When an AI system fails, does the cost fall on the company or the user? In almost every case, it falls on the user, and disproportionately on users with the least power to challenge it. A wrongly denied insurance claim costs the company a support ticket. It costs the patient a medical procedure.

Ethical AI deployment requires inverting this assumption. Design as if every failure will happen to someone who can’t easily recover from it. This isn’t idealism. It’s risk management that accounts for the full scope of risk, including reputational, legal, and human risk that doesn’t appear on a quarterly report.

My thinking here is shaped by theology, Jungian psychology, and years of building products that affect real people’s daily lives. That’s not a qualification; it’s a lens. One that makes me allergic to the phrase “move fast and break things” when the things being broken are people’s trust, agency, and dignity.

Why This Is a Leadership Advantage

There’s a common misconception that ethical consideration slows down decision-making. In my experience, the opposite is true.

Teams that have clear ethical principles make faster decisions, not slower ones. When you know where the lines are, you don’t waste cycles debating every edge case. You build the guardrails once, and then you move confidently within them. The organizations I’ve seen struggle the most with AI deployment aren’t the ones with too many principles. They’re the ones with none, where every decision becomes a novel philosophical debate because no one established the foundation.

Ethical clarity is a competitive advantage. It builds trust with customers who are increasingly wary of how their data is used. It attracts talent who want to build things they’re proud of. And it reduces the catastrophic risk of a failure that makes headlines, the kind of failure that no amount of “we take this seriously” PR can undo.

The hidden cost of “good enough” AI isn’t what you ship today. It’s what you’ve committed to defending tomorrow. Build accordingly.