Why AI Is Like Working With Rain Man
Generative AI isn’t magic. It’s not sentient. (It’ll actually tell you that itself.) And definitely can’t mimic human judgment. But when you know what it does well — and where it stubs its toe — it can be a superhero.
In other words, treat it like a talented human. We all have weaknesses, but effective leaders don’t ignore them. They build systems that support employee weak spots, and lean on strengths and skills. The same goes for AI. If you give it too much rope, it’ll spin out generic, repetitive, or flat-out wrong results. Instead, learn what it can and can’t do. Then build systems around it to refine the gold and drain the dross.
What Gen AI Nails
When you use generative AI the right way, it becomes a force multiplier.
Writes well with the right input: Feed it a clear angle, a target tone, and real-world examples. It’ll produce strong, clean, on-brand copy.
Integrates SEO efficiently: Point it to priority keywords and preferred structures. It can work them in without sounding robotic or spammy.
Researches fast inside guardrails: Define the goal, scope, and constraints. It’ll serve up links, data points, and structured takeaways.
Builds structure-rich content: Want a table, bullet list, or cleanly sectioned piece? Tell it what you’re building and who it’s for, and it’ll do it beautifully.
Iterates at light speed: Ask for five versions of a weak section. It’ll give you options to refine and remix.
Where it Lands in Slop
Hand AI a vague prompt and you’ll get at best a stuttering mess.
Writes vanilla when unguided: Generic in, generic out. If you don’t tell it the style, persona, and point of view you’re looking for, it’ll write the literary equivalent of shampoo instructions.
Repeats itself without planning: Without a content strategy, it’ll loop back to the same words, phrases, and sentence structures.
Invents or misrepresents data: Leave it to do its own “research,” and it’ll hallucinate wrong answers, then try to convince you they’re true.
Muddles outputs without a goal: Ask for a table without defining the audience or use case, and you’ll get a beautifully formatted brick.
Wastes your time during revisions: If you don’t have a system in place to track changes or preserve strong sections, each iteration is a new spin of the wheel.
So What’s the Fix? Systems.
Think less “magic machine” and more “fast assistant with no instincts.” It’s kind of like working with Rain Man. If you give it the right support, you’ll never have to wonder where your fish sticks are.
Use templates. Tell AI the goal and voice, and show it a sample of what success looks like. Don’t let it guess. (It will. Badly)
Keep a “what works” folder. Save snippets, formatting styles, tone notes, and high-performing outputs. Reuse and refine instead of starting cold.
Ask for multiple versions, then splice. Don’t edit a weak section. Get five rewrites, then cut and combine the best.
Audit outputs critically. Verify links, data, and claims. If a stat looks too good to be true, it probably came out of the LLM hat.
Treat AI like a specialist. It’s great at executing, but it can’t strategize. The idea, structure, and quality control are up to you.
Bottom Line
The worst way to use generative AI is to over-trust it. The second worst way is to avoid it. The best is to know its strengths and weaknesses. Build workflows that harness the first and protect against the second, just like you would with a human hire.
