Last week, I watched a founder spend $5,000 on AI consulting only to get the same mediocre results from a free ChatGPT account. The problem wasn’t the AI. It was the briefing.
This is the pattern I see over and over: Teams blame AI for failures when the real culprit is sitting in the Slack messages.
The Core Misunderstanding
We treat AI like it’s supposed to be a mind-reader. We feed it vague requirements and expect magic. When it fails, we say “AI isn’t ready.” Wrong. You’re not ready to lead AI projects.
Here’s the uncomfortable truth: AI will execute your intent with shocking accuracy—if you can articulate it clearly.
The Three Layers of Failure
- The Ambiguity Trap (Marketing Campaign Example) Wrong brief: “Create a social media campaign for our SaaS product that drives engagement.”
What AI might generate:
Vague inspirational posts
Unclear product value
Generic tone that blends with 10,000 competitors
Right brief:
“Create 5 LinkedIn posts targeting CTOs at Series B companies. Each post should open with a specific problem they face (e.g., ‘Your infrastructure costs 40% more than competitors’), show one concrete metric or stat, and end with a specific question inviting replies. Tone: Confident, not salesy. Length: 150-180 words. Examples of good posts: [insert 3 links]. Examples to avoid: [insert 3 bad posts].”
Result: Instantly better. Because you did the thinking work.
- The Context Collapse (Email Sequence Example) Wrong: “Write a 5-email sequence to convert free users into paying customers.”
Right:
“Write 5 emails for users who signed up 7 days ago but haven’t used the product. Email 1 (Day 7): Address the main objection: ‘setup takes too long.’ Show that onboarding takes 8 minutes with one screenshot. Email 2 (Day 9): Social proof from similar companies. Email 3 (Day 11): Show the single biggest win users report (save 6 hours/week). Email 4 (Day 13): Price justification (ROI math). Email 5 (Day 15): Last-chance urgency + discount. Tone: Personal, not corporate. Reference these competitor emails as inspiration: [links]. Avoid these tactics: [list].”
The difference? You’ve forced yourself to think through why people don’t convert. AI just needs the thinking. It doesn’t create it.
- The Standards Disconnect (Code Review Example) Wrong: “Refactor this function to be more efficient.”
Right:
“Refactor this function for readability, not speed. The execution time is fine at 200ms. What I need: Variable names that explain intent in 2-3 words. Comments for the ‘why’ of complex logic, not the ‘what’. Break it into smaller functions that each do one thing. Remove nested ternaries—make them explicit if/else. Follow our coding standards from [repo link]. Test against these edge cases: [list].”
Now the AI isn’t guessing. It’s implementing a standard.
The Four Ingredients of Clear Instructions
Every successful AI project I’ve seen follows this pattern:
The Constraint:
What are you not trying to do? “This is for CTOs, not developers.” “Aggressive growth, but don’t compromise brand.” “Fast, not cheap.”The Reference:
Show examples of what you do want. “Like this email from [brand], but with our voice.” “This design direction, applied to our product.”The Success Metric:
How will you know if it’s good? “Users should immediately understand the problem we solve.” “Click-through rate above 8%.” “Takes under 10 minutes to implement.”The Guardrails:
What are the non-negotiables? “Never mention pricing unless asked.” “Keep it under 100 words.” “Must work for mobile-first users.”
Provide all four, and AI becomes a multiplier. Skip even one, and you’re gambling.
The Real Work Is Thinking
Here’s what nobody wants to hear: The time you save with AI comes from thinking harder beforehand, not working less.
Some teams use this thinking time to iterate faster. Most just blame AI when iteration doesn’t work.
The teams winning with AI aren’t the ones using the fanciest models. They’re the ones who treat briefing like strategy.
They write down the hard questions:
Who exactly are we talking to?
What do they currently believe?
What specific change do we want?
How will we measure it?
What have we tried before?
Why didn’t it work?
Then they turn those answers into instructions.
A Practical Audit for Your Next AI Project
Before you say “AI failed,” ask yourself:
✅ Could I explain this project to a new hire in 10 minutes without confusion?
✅ Did I show AI examples of what I want?
✅ Did I define how success looks?
✅ Did I specify what not to do?
✅ Did I think about edge cases before asking AI?
If you answered “no” to three or more, you didn’t fail because AI is dumb. You failed because you didn’t do your job.
The Uncomfortable Opportunity
This is actually great news.
It means you’re not waiting for AI to be better. You can start winning today by being more rigorous about your own thinking.
The AI arms race isn’t about who has access to GPT-5. It’s about who knows how to think clearly enough to brief it.
That’s not a technical problem. That’s a leadership problem. And you can fix that today.
Stop blaming the tool. Start upgrading the thinking that guides it.
Top comments (1)
Yeah, humans are lazy. But that's not all bad—it may drive progress in knowledge and memory tools so AI can meet us halfway?