Common Mistakes & Fixes

Common Mistakes and Fixes

Mistake 1: Starting too big

You try to automate your entire weekly reporting process on day one.

Fix: Build one step. Test it. Then add the next.

Mistake 2: Vague instructions

You say "summarize this" instead of "summarize this into five bullets, each under 20 words, focusing on decisions needed and risks identified."

Fix: Specify format, length, focus areas, and what to ignore.

Mistake 3: Testing with fake data

You make up sample inputs instead of using real ones from your work.

Fix: Always test with actual data. Real inputs expose real edge cases.

Mistake 4: Optimizing style over usefulness

The output reads beautifully but does not help you make a decision.

Fix: Always evaluate against: "Did this make a real decision easier?"

Mistake 5: Trusting without verifying

The agent sounds confident so you assume it is correct.

Fix: Always verify facts for medium and high-impact work. Confidence of tone has no relationship to accuracy.

Mistake 6: Building once and walking away

You set up a workflow in week one and never revisit it.

Fix: Spend 15 minutes each week reviewing what worked and what to tighten.

Mistake 7: Automating the wrong thing

You systematize something that only happens twice a year.

Fix: Start with high-frequency, high-friction workflows. Impact = frequency × time saved × friction reduced.

Mistake 8: Measuring only time saved

You saved two hours but your decision quality dropped because you stopped looking at the raw data.

Fix: Track time saved AND decision quality AND rework needed. All three matter.

Mistake 9: Ignoring data sensitivity

You paste confidential client information into a public AI tool without thinking about it.

Fix: Know your organization's data policies. Classify sensitivity before automating. Use enterprise tools for sensitive data.

Mistake 10: Trying to eliminate human judgment

You design a workflow that goes from raw data to final action with no human checkpoint.

Fix: For anything with real consequences, the human reviews before action is taken. Always.

The recovery rule

When a workflow underperforms, resist the urge to scrap it entirely. Diagnose the specific failure. Tighten the instructions. Test again. Most workflows are one or two refinement cycles away from being useful.