AI Amplifies How You Think. For Better Or Worse
by Tyler Kelley
You’re using AI wrong.
Not because you haven’t adopted it. You have. Not because you’re not seeing results. You are.
You’re using it wrong because you think the win is speed.
Faster emails. Quicker proposals. More output in less time. You celebrate every hour saved like it’s proof you’re ahead of the curve.
But speed stopped being the advantage months ago. Everyone has access to the same tools now. Everyone can work faster.
The real divide is between people using AI to amplify good thinking and people using it to amplify weak thinking.
And most of you are in the second group without realizing it.
AI Doesn’t Think for You
AI is not a thinking tool. It’s an amplification tool.
You bring the thinking. AI makes more of it. Faster. Bigger. More articulate. More comprehensive. More confident sounding.
Clear thinking gets amplified into better strategies, deeper insights, more thorough analysis.
Weak thinking gets amplified into articulate confusion, polished mediocrity, confident wrongness.
The tool amplifies both equally well. It has no preference.
And that’s the problem most leaders miss. They assume that because the output looks good, the thinking behind it must be sound.
It’s not.
What Actually Changed When I Started Using AI
Before AI, I never responded to RFPs. Small agency, can’t pull people off client work for three days to chase a lottery ticket. The economics didn’t make sense.
ChatGPT made RFPs faster. I thought that was the win.
It wasn’t.
The win was that I could finally apply the same strategic thinking to RFPs that I brought to my best client work. The kind of thinking that requires understanding the client’s customers, their competitive landscape, their internal dynamics, their actual problems.
I already knew how to think this way. Twenty years of practice gave me that. What I didn’t have was time to apply it to RFP work.
AI didn’t give me better thinking. It gave me the capacity to use the thinking I already had.
When I look at an RFP now, I don’t just answer their questions. I research their competitive set. I explore what their customers are saying. I identify strategic gaps they haven’t named. I map their positioning challenges.
Then I write responses that speak to their actual situation, not generic best practices that could apply to anyone.
AI helps me do the research and exploration that used to take days. But I’m the one deciding what questions to ask, what patterns matter, what insights are actually relevant.
If I didn’t have two decades of strategic thinking to bring to this work, AI wouldn’t help. I’d get comprehensive, well-formatted proposals that look professional but demonstrate no real understanding of their business.
Generic recommendations. Surface-level analysis. The kind of response that checks boxes without proving you understand anything.
The tool would amplify nothing because I’d be bringing nothing worth amplifying.
Where Most People Get This Wrong
I see people excited about using AI for customer research. They can “analyze feedback in minutes instead of hours.”
Great. What are they actually doing?
They’re feeding AI raw feedback and asking it to “find themes.”
The output looks sophisticated. Charts, categories, trend analysis. But it’s missing the layer that matters. The interpretation that comes from understanding business context, customer journey, competitive alternatives, strategic implications.
They automated pattern recognition. Pattern recognition without context is just patterns. Not insight.
Compare that to someone who actually understands their customers. They’re not asking AI to find themes. They’re testing hypotheses about why certain feedback exists. They’re exploring how different segments experience the same problem differently. They’re connecting patterns to business outcomes they’ve seen before.
AI helps them go deeper into questions they already knew to ask. It doesn’t replace their judgment about which patterns matter.
Or positioning work. People ask AI to “generate positioning statements” and get back elegant language that sounds professional.
Elegant language built on unclear thinking is still confusion. Just better written.
The people using AI effectively for positioning already understand their differentiation, their ideal customer, their competitive alternatives. They use AI to test ways of articulating what they already understand. To refine and pressure test their thinking.
One group uses AI to do their thinking. The other uses AI to extend thinking they’ve already done.
The Test You’re Probably Failing
Is AI helping you think differently, or just helping you write faster?
Thinking differently means your understanding changes.
You had a hypothesis about your customer’s problem. You used AI to explore whether it holds across segments. You discovered what you thought was one problem is actually three different problems that look similar.
Your thinking changed. You understand your customer more clearly now.
Or you’re deciding which market to enter. You have intuition but use AI to pressure test it against data you couldn’t gather manually. You discover your intuition was right about the opportunity but wrong about the timing.
Your thinking improved. Your decision is better informed.
Or you’re working on positioning. You think you understand your differentiation. But when you use AI to explore competitor positioning, you realize you were focused on differences that don’t matter to customers. You adjust your entire approach.
Your thinking shifted. You’re solving a different problem than you thought.
That’s amplifying good thinking. You start with clarity and leave with better clarity. You start with questions worth asking and discover better questions. You start with judgment and sharpen it.
Writing faster is different.
You need a content strategy. You ask AI to create one. You get back a comprehensive document. Goals, personas, content pillars, distribution channels, metrics. All the right sections.
It looks complete. Sounds professional. You could implement it tomorrow.
But your thinking didn’t change. You gained no new insight into your audience. You discovered nothing about your competitive position. You developed no clearer point of view about what makes your content worth attention.
You documented a generic strategy faster than you could write it yourself. That’s it.
Or you analyze sales data. Feed it to AI, ask for insights. Get back charts showing trends. Conversion rates by channel. Customer acquisition costs over time. Revenue by segment.
Looks thorough. But you learned nothing about why those patterns exist. You developed no hypotheses about what drives the trends. You didn’t connect data to strategic decisions you need to make.
You reformatted existing data into different views.
Output increased. Thinking didn’t improve.
One approach uses AI to deepen understanding. The other uses AI to avoid developing understanding.
One Question
When you use AI, does your thinking improve or does your output just increase? If you can’t answer that honestly, you’re probably in the group that’s building on a weak foundation.
And six months from now, you’ll understand why that matters.
Tyler Kelley is the Co-founder and Chief Strategist of SLAM Agency. He writes about how AI is changing the way companies work and grow. His focus is on helping organizations use AI to build visibility, strengthen relationships, and equip teams to deliver results that matter in an AI-driven future.