There's a spectrum of tasks you can hand to an AI agent. On one end: harmless. On the other end: career-ending mistakes.
Here's how to think about where the line is.
The risk framework
Every task falls into one of four quadrants:
| Low Impact | High Impact | |
|---|---|---|
| Reversible | ✅ Safe to automate | ⚠️ Review before commit |
| Irreversible | ⚠️ Document heavily | ❌ Never automate |
Safe to automate (green)
- Research: Summarizing articles, gathering competitive intel, compiling lists
- Drafting: First drafts of emails, blog posts, documentation
- Formatting: Converting between formats, cleaning data, organizing files
- Scheduling: Finding meeting times, sending calendar invites (with human approval)
Review before commit (yellow)
- Outbound communication: Emails to customers, social posts, press releases
- Code changes: AI can write code, but you should review before deploying
- Financial calculations: AI can help analyze, but verify before acting
- Hiring decisions: AI can screen, but humans should decide
Document heavily (orange)
- Access to sensitive systems: AI with API keys to payment systems, cloud infrastructure
- Legal documents: Contracts, NDAs, compliance-related work
- anything with PII: Customer data, employee records, medical information
Never automate (red)
- Firing someone: No AI should ever deliver this
- Legal representation: Court filings, legal strategy, patent applications
- Financial transactions: Moving money, approving wire transfers
- Medical decisions: Anything touching health, safety, or life
The real-world risks
Risk #1: Hallucinations
AI makes things up. This is well-known but easy to forget when you're tired.
What happens: AI writes a blog post citing a study that doesn't exist. You publish it. Readers call you out.
Mitigation: Always fact-check citations. Use tools that cite sources (like Perplexity).
Risk #2: Data leaks
AI agents can remember what you tell them. Some of that data trains models. Some of it might be exposed.
What happens: You paste customer data into an AI to "analyze it." That data becomes part of the model's training data.
Mitigation: Use AI tools with enterprise privacy options. Don't paste sensitive data into public AI tools.
Risk #3: Overconfidence
AI is very persuasive. It will tell you something with total confidence even when it's wrong.
What happens: AI gives you bad advice on a contract. It sounds confident, so you act on it. The contract has a loophole.
Mitigation: Verify anything important with a human expert.
Risk #4: Dependency
The more you use AI, the less you develop your own skills.
What happens: You stop writing because AI "does it better." Eventually, you can't function without it.
Mitigation: Use AI to amplify your skills, not replace them. Keep practicing the core skills yourself.
A practical rule
If you wouldn't do it while drunk, don't do it with AI.
That means:
- Don't send AI-written emails when emotional
- don't let AI make decisions when tired
- Don't trust AI more than you'd trust an intern
What to tell your team
If you're working with others:
- Never give AI access to credentials they shouldn't have
- Always review before sending external communications
- Document what AI did so there's an audit trail
- Escalate anything involving money, legal, or people to a human
The bottom line
AI agents are powerful. But power without judgment is dangerous.
Use AI for the cognitive load — research, drafting, analysis. Keep the judgment for yourself.
The line is clear: AI handles the thinking. You handle the deciding.