Muses of a Reticent Techie

We live in an era where Artificial Intelligence (AI) is no longer just a futuristic promise—it’s part of everyday business. From chatbots that handle customer queries to predictive systems that detect fraud, AI is everywhere. Yet, beneath the excitement lies a hidden challenge: companies are hitting “coding plateaus.”

A coding plateau happens when adding more algorithms or code no longer produces better business outcomes. Why? Because business problems aren’t just technical; they’re contextual. AI can identify patterns, but it doesn’t understand why a decision matters in a particular moment. This is where people step in, bringing their deep-seated knowledge, industry context, and the ability to navigate ambiguity. Enter the concept of Chaos Engineering for People in the Age of AI.

Let’s get to it

Traditionally, chaos engineering meant deliberately breaking parts of your technical system to see how resilient it is. For example, Netflix would shut down parts of its servers randomly to ensure the rest of its infrastructure kept working. Now, in the AI era, the same principle applies not only to systems but also to people and processes.

Imagine your fraud detection AI flags 1,000 suspicious transactions in a single day. If left entirely to automation, genuine customers may get blocked. If left entirely to humans, the workload becomes overwhelming. The resilience test is: how well can people and AI handle stress together? How do humans intervene when AI breaks down, and how does AI support humans when scale is too high? That’s the real-world meaning of chaos engineering for people in the AI age.

Think of it like stress-testing your team’s ability to add context when AI reaches its limits. That context is your hidden weapon in an AI-heavy world.

How it helps

So why focus on people when AI seems to be the silver bullet? Because resilience isn’t about perfection—it’s about adaptability. Let’s break it down.

Traditional focus (AI-first, code-first)

• Pros: Scalable, fast, data-driven, less prone to fatigue.
• Cons: Hits plateaus when context is missing, struggles with ambiguity, risks blind spots.

Chaos engineering for people (AI + human context)

• Pros: Humans can handle exceptions, apply intuition, challenge assumptions, and bring empathy to decision-making.
• Cons: Slower than machines, requires constant training, can be inconsistent without proper culture and process.

By embracing chaos engineering for people, organizations can simulate stressful or failure scenarios—like an AI model drifting off accuracy, or a recommendation engine showing biased results—and check how teams respond. Do they blindly trust the machine? Or do they bring their expertise to challenge the output?

Real-world case studies

Netflix

Netflix popularized chaos engineering by deliberately breaking infrastructure to test resilience. As its recommendation AI grew, Netflix discovered that when AI-driven personalization faltered (for example, recommending irrelevant shows), human product managers quickly shifted priorities—ensuring core streaming performance was maintained. This showed that people + AI resilience was far more valuable than AI perfection.

Financial Services

Banks using AI for fraud detection learned through chaos exercises that AI often produced false positives. During one such test, thousands of legitimate transactions were flagged. Human fraud analysts stepped in, applying their knowledge of customer behavior and regulatory context to restore balance. The result: a blended approach where AI handles volume, and people handle nuance.

Healthcare

Hospitals adopting AI diagnostics ran chaos drills to see what would happen if the system misdiagnosed rare diseases. Doctors acted as the fail-safes, bringing years of expertise and empathy. Instead of replacing humans, the AI became an assistant—speeding up analysis but never overruling human judgment. Patient outcomes improved when both worked together.

Practical steps for leaders

Managers and product leaders often ask, “Okay, but what can I do tomorrow to apply this?” Here’s a checklist to get started:

• Run tabletop chaos scenarios: Simulate what happens if your AI produces biased or wrong results. Who catches it? How fast?
• Identify human-in-the-loop points: Map out where human judgment must override AI (e.g., healthcare, financial approvals).
• Cross-train teams: Ensure product, business, and tech teams understand each other’s roles so context is shared, not siloed.
• Measure resilience, not just accuracy: Track how quickly and effectively humans can step in when AI falters.
• Reward context-driven decisions: Encourage teams to challenge AI outputs instead of blindly accepting them.
• Embed empathy into workflows: Remember that customer trust often depends on how humans handle AI mistakes.

Why managers and product leaders should care

If you’re leading a team, chaos engineering for people isn’t just a tech experiment—it’s a leadership strategy. Here’s why:

• Prepares teams for surprises: AI models will drift, data will be biased, regulations will change. Teams trained in chaos scenarios adapt faster.
• Protects customer trust: When systems fail, it’s often people who save the customer experience, not code.
• Builds confidence in AI adoption: Teams won’t fear AI if they know their expertise still matters in the loop.
• Informs product design: Stress-testing human-AI interactions reveals where workflows break and where product improvements are needed.

In essence

• Companies are hitting AI plateaus where coding alone doesn’t solve business challenges.
• Human context and expertise are critical for bridging gaps AI cannot address.
• Chaos engineering for people ensures resilience: testing how humans + AI work together under pressure.
• Pros: better handling of ambiguity, empathy, and context.
• Cons: slower pace, needs deliberate investment in culture and training.
• Leaders should use chaos engineering as a strategic tool—not just a technical one—to prepare teams for real-world disruptions.
• Practical steps include running simulations, rewarding contextual decisions, and cross-training teams.

“AI may predict the storm, but only people can decide whether to dance in the rain.” 🌧️💃

Bibliography

• Gremlin. What is Chaos Engineering? https://gremlin.com/chaos-engineering
• Harvard Business Review. Why AI Alone Won’t Solve All Your Problems.
• IEEE Spectrum. The Human Factor in AI Systems.
• Netflix Tech Blog. The Birth of Chaos Engineering.