Liza Adams

You hear it all the time: AI makes work faster. But in my experience, strategic and creative work with AI takes longer now. And that’s exactly why the results are better.

Before AI, I’d have an idea, run a few Google searches, maybe brainstorm with a friend, and go. Now my conversations are longer, richer, and go in directions I never expected.

Here’s what my process looks like these days.

I use multiple AIs like Claude, ChatGPT, and Gemini. I brainstorm with each of them. I copy what one says into another to get a different take, then bring that response back. We go back and forth until the ideas start to come together or until I’ve heard enough to make a call.

Each turn teaches me something new:

• Paths I hadn’t considered

• Gaps in my thinking

• Assumptions worth testing

• “What if?” questions to explore

It’s like being a curious kid who keeps asking “why?” and actually gets real answers every time.

For quick tactical decisions, moving fast often makes sense. But for strategic work, the journey matters as much as where you end up. The detours, the surprises, the perspectives I pick up along the way. All of that makes the final thinking stronger.

Of course there’s a risk. You can over-think, go down too many rabbit holes, or never feel ready to decide. That’s where the human comes in. I have to know when it’s time to stop exploring and start choosing.

This is also why I’m excited about how AI tools are getting better at handling long conversations. Think of it like packing for a trip. Your AI can only carry so much in its suitcase (context window) before it runs out of room. The old solution was to stop and start a new conversation, basically throwing out everything and beginning again. But now these tools are learning to pack smarter.

Claude and ChatGPT are using the equivalent of compression bags (conversation compacting), squeezing down earlier parts of the conversation to make room for more. Gemini’s approach? Just bring a bigger suitcase. Either way, I can keep a rich conversation going instead of losing all that context.

Most people ask one question and move on. But the ones who keep asking “why?”, challenging assumptions, and pressure-testing ideas from different angles are doing the most interesting work.

If you want to go deeper on using AI as a thinking partner, I wrote about a three-level approach to critical thinking. See link in the comments.

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7 Ways to Think Deeper

1. Challenge Your Assumptions

Instead of: “How do we reduce churn?”

Try: “What if churn isn’t the problem? What if it’s showing us a product gap?”

Why it works: You pause before fixing and ask if you’re solving the right thing.

Potential outcome: A SaaS team discovers churned customers outgrew their product. Churn becomes upsell opportunities.

2. Borrow from Other Industries

Instead of: “How do we improve trial conversion?”

Try: “How do language learning apps keep people engaged daily?”

Why it works: You find new ideas by studying how others solve similar problems in different contexts.

Potential Outcome: A product team adds streaks and milestones to help users reach activation faster.

3. Try the Opposite

Instead of: “How do we shorten the sales cycle?”

Try: “What if making it longer helped us close bigger deals?”

Why it works: Sometimes the thing you’re trying to optimize is the thing getting in your way.

Potential Outcome: A B2B company adds business audit step, helping them close higher-ACV customers.

4. Find Hidden Connections

Instead of: “How do we improve pricing?”

Try: “What patterns show up when we compare churn reasons to our competitors’ ads?”

Why it works: Some of your best insights live in unlinked data.

Potential Outcome: A team repositions after discovering churned users match competitor’s target audience.

5. Find the Simplest Change

Instead of: “How do we drive more revenue?”

Try: “What’s one sentence in our demo that changes how people see the product?”

Why it works: Small shifts often create the biggest results.

Potential Outcome: A team moves their outcome statement to demo opening for better conversion.

6. Find the Excluded

Instead of: “How do we raise prices?”

Try: “Who are we unintentionally leaving out?”

Why it works: You expand opportunity by seeing who’s missing.

Potential Outcome: An analytics platform creates startup tier, opening new market segment.

7. Use Old + New

Instead of: “How do we improve email performance?”

Try: “What if we brought back personal touches using today’s tools?”

Why it works: Some tactics work no matter the decade.

Potential Outcome: A team adds timely check-ins and thank-yous based on user behavior.

Rhiannon Naslund, Chief Marketing Officer at Origami Risk is driving this shift in thinking and evolution in her team.

“This kind of shift in thinking doesn’t come naturally to everyone. That’s why we’re focused on giving people the space to learn and build confidence.

We’re showing what it looks like to guide AI with deeper thinking, using real examples that connect to their role. When someone sees how the way they structure a question changes what AI gives back, like refining messaging for a healthcare risk manager or pressure-testing a new idea, it clicks. They start to see how much impact they can have by pushing AI to think differently.”

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The Bigger Picture

Whether you think you’ve mastered AI or you’re still struggling with it, you’re probably operating at 20% of what’s possible.

The biggest AI advantage doesn’t come from better tools or prompts. It comes from questioning what everyone else takes for granted.

This becomes critical as we move toward AI agents that work autonomously. Teams that can’t think strategically with AI now won’t be able to build agents that think strategically later.

Erin Mills, CMO of Quorum and co-host of FutureCraft GTM Podcast, sees this connection clearly as someone building both AI strategies and autonomous systems.

“Organizations with strong fundamentals in strategic AI use are better positioned for what comes next. If your team struggles to frame the right problems or identify blind spots in their current approach, those same gaps will show up when you try to build AI agents that operate independently.

In our FutureCraft GTM conversations, we’re seeing a shift toward systems that make decisions on their own. What matters most now is having the judgment to guide them well. The teams that master strategic questioning now will be the ones successfully deploying autonomous AI later.”

Your Next Steps

Pick one challenge your team is working on this week. Before jumping into solutions, ask: “What assumptions are we making that might not be true?”

Then reframe it using one of the seven thinking moves above.

Want your team to think deeper? Forward this newsletter. The shift from good prompting to strategic thinking separates the winners from the optimizers.

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