AI is transforming how software is built and operated. At Amzu, we use AI extensively. But we have also seen organizations deploy AI recklessly, creating systems that nobody understands and nobody controls. Here is our philosophy for using AI responsibly.
The Control Problem
The promise of AI is automation. Let the machine handle it. But automation without understanding is dangerous. When an AI system makes a decision you do not understand, you have lost control even if the decision was correct. When it makes a mistake, you cannot diagnose it, fix it, or prevent it from happening again.
We have seen this pattern repeatedly:
- An AI recommends infrastructure changes. Nobody understands why. They implement them anyway. Costs spike.
- An AI approves code changes. A subtle bug slips through. Nobody knows how it passed review.
- An AI generates documentation. It contains confident-sounding errors. Users trust it because it looks authoritative.
"AI that you cannot understand is AI that you cannot control. And AI you cannot control will eventually surprise you in unpleasant ways."
Our Principles
At Amzu, we have developed principles for AI use that maintain human control while capturing AI's benefits:
AI Recommends, Humans Decide
AI can analyze data, identify patterns, and surface recommendations. But for consequential decisions, a human must make the final call. This is not because AI is always wrong. It is because humans need to understand and own the decisions that affect systems and customers.
Explainability Is Required
We do not use AI that cannot explain its reasoning. If an AI recommends an action, it must be able to articulate why in terms that humans can evaluate. "The model thinks this is best" is not an explanation.
Gradual Trust
We start AI systems with tight human oversight. As they prove reliable in a domain, we gradually extend their autonomy. But we never remove human oversight entirely for consequential decisions.
Monitoring and Override
Every AI system we deploy has monitoring that detects anomalous behavior and an override that lets humans take control instantly. If something goes wrong, we can always revert to manual operation.
Where We Use AI
Given these constraints, here is where AI provides the most value in our work:
- Analysis and insights. AI excels at processing large amounts of data and surfacing patterns. Code analysis, log mining, performance profiling.
- First drafts. AI can generate starting points that humans refine. Documentation, test cases, configuration templates.
- Anomaly detection. AI is better than humans at spotting unusual patterns in metrics, logs, and behavior.
- Tedious tasks. Repetitive, rule-based work that follows clear patterns. Formatting, migration, boilerplate generation.
Where We Do Not Use AI
Equally important is knowing where AI does not belong:
- Final approval of production changes. Humans review and approve all changes that affect customers.
- Security-critical decisions. Access control, authentication, and encryption require human judgment.
- Customer-facing communication. AI can draft, but humans send.
- Architectural decisions. System design requires understanding of business context that AI lacks.
The Future
AI capabilities are advancing rapidly. The line between "AI should help" and "AI should decide" will shift. But the principle remains: humans must understand and control the systems they operate.
Using AI responsibly is not about using less AI. It is about using AI in ways that amplify human capability without replacing human judgment. Get this balance right, and AI becomes a powerful ally. Get it wrong, and you have built a system that nobody controls.
We choose to stay in control.