Hello go-to-market leaders, strategists, and innovators! đź‘‹ Thank you for dropping by to learn practical AI applications and gain strategic insights to help you grow your business and elevate your team’s strategic value.
Quick Take
Most people use AI to get tasks done faster and take the first response. They’re training themselves to accept answers without question. Every day, teams walk away from high value insights because they never learned to think systematically about what AI tells them.
This approach changes any AI interaction into deeper insight. Here’s what happens when you shift your approach:
- Ask “why” behind every recommendation – You see how AI thinks, can check its work, and train yourself to think more systematically about your own decisions
- Challenge assumptions in any context – Whether you’re questioning “educational content performs best” for social media or “Europe is our best market” for expansion, the same principles apply
- Get multiple views and confidence levels – Turn any AI response into a thinking exercise that shows blind spots and options
- Critical thinking works regardless of scope – The same line of question that improves email subject lines also makes sense for strategic planning
- Work in a judgment-free space – AI doesn’t care about your ego or timeline, making it easier to question assumptions you’d defend in front of colleagues
The difference isn’t the technology or the complexity of your work. It’s how you think about thinking. When you approach AI as a thinking partner rather than a task doer, every interaction becomes an opportunity to make your decision-making better.
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From Execution to Insights
AI can help with any type of work across your entire GTM org and beyond. Whether you’re writing email subject lines or planning market expansion, creating battle cards or setting sales territories, the same tech supports both daily tasks and big decisions.
But here’s what separates good AI use from transformational AI use: applying this approach to any of it, tactical or strategic work as shown in the table below.
Most teams automate what they already know how to do. Teams that understand AI’s real potential use it to uncover what they don’t yet know.
The Power of Asking “Why”
The key is asking AI to explain its reasoning.
Most people take AI’s first answer and run with it. But when you ask “Why do you recommend this?” or “What’s your reasoning behind this ranking?”, we learn something new.
You see how AI thinks. When you ask for reasons behind every response, you’re not just getting answers. You’re learning to think more clearly yourself.
You can check its work. This is how you catch AI confidently recommending terrible strategies that look brilliant at first glance. You catch gaps in logic. And you train yourself to think more clearly about your own decisions.
We’ve been rewarded our entire lives for having the right answers with good grades, promotions, recognition. But watch any great meeting: the most valuable person isn’t the one with all the answers. It’s the one asking the insightful questions that shift how everyone thinks about the problem. AI doesn’t change this dynamic, it amplifies it.
Getting better AI outputs is just the beginning. The real value is building better thinking habits that stick with you in every conversation and decision. Whether you’re planning enterprise strategy or choosing social media topics, asking “why” transforms any work into deeper analysis.
Mandy Dhaliwal, CMO at Nutanix, has experienced this firsthand.
“It’s important not to use AI like a Q&A machine. We guide its thinking, we brainstorm together, but we still make the final call.
How many breakthrough ideas get killed because someone had to wait for the next team meeting to bounce them around? We can test messaging ideas off hours or work through event plans during our morning walk.
That immediate access to a thinking partner completely changes how we make decisions.”
The Psychology Behind Better Questions
When you shift from asking AI to execute tasks to asking it to challenge your thinking, the psychological barriers that normally keep us from questioning our own work disappear.
This works because:
- AI doesn’t judge – No ego, politics, or timelines. This makes honest evaluation possible.
- Private testing leads to public confidence – Challenge ideas with AI first, then show up to meetings prepared.
- Silos vanish – A Harvard study with P&G professionals found teams working with AI “stop caring as much about the normal boundaries of your job.” AI focuses on problems, not politics.
- Real example: strategic confidence builds fast – During a recent workshop, an ABM marketer used AI to challenge her key account marketing plan. Despite her excitement about the possibilities, she admitted, “It hurt because I’d worked so hard on this. This is my baby and AI was calling it ugly. But the questions were so good.” She realized she could strengthen her strategy by validating key assumptions.
This shift happens faster than you might expect. This week in Atlanta, I conducted function-specific AI workshops with 60+ marketers at Cox Automotive Inc., the company behind Kelley Blue Book and Autotrader that helps dealers and partners buy and sell millions of vehicles each year. Ramon L. Cortes, AVP of Marketing Operations, saw these mindset changes happening in real-time.
“I watched lightbulbs come on and mindset shifts happen right before my eyes. Once people started asking AI deeper questions and working as a group, the discussions became richer, more analytical, and focused on business outcomes.
These kinds of conversations typically don’t happen until later in our process – sometimes not until we’re already presenting to executives. Now it will happen sooner, which means we’ll identify gaps faster, use resources more efficiently, and increase our strategic value.”
The Critical Thinking Framework
This approach turns every AI conversation into critical thinking practice. I’ve been using this with teams for a couple years and my own thinking has gotten sharper because of it.
Here’s a three-level approach behind this thinking.
Level 1: Basic Evaluation
- Offer some alternatives to this approach.
- Give me the pros and cons of each option.
- Rank these ideas based on [specific criteria].
- Rate these from highest to lowest confidence.
- Sort these options into must-have, preferred, and nice-to-have.
- Break this down into smaller steps and show the timeline.
Level 2: Different Views
- How might this be seen by [specific stakeholders]?
- Put these options in a 2×2 chart using [X and Y criteria].
- What would [competitor/customer/exec team] think about this approach?
- Show me the decision tree for checking these choices.
- What are the key factors and how does weighing them change the outcome?
- Compare how this problem is solved in [different industry/company size/region].
Level 3: Assumption Challenging
- What assumptions am I making that might not be true?
- What would make this idea 10x stronger?
- Where might this approach fail or backfire?
- Run 3 what-if scenarios and show how each changes the outcome.
- Challenge my assumption that [specific belief]. What situations would make our current approach actually work best?
- Point to relevant data sources and provide reasons for your recommendations?
Yes, this takes more time upfront, but it saves you from spending weeks executing the wrong plan.
The important step is to always ask “What’s your reasoning?” or “Why do you recommend this?”
These techniques work whether you’re allocating marketing budgets or brainstorming content ideas. The scope changes, but the thinking discipline stays the same.
Critical Thinking in Action
Here are examples of what you can expect when you ask basic questions vs insightful ones. These assume some basic context was provided, but the real difference is how better questioning turns any conversation into deeper insights. With basic questions, we get basic answers. Insightful ones result in thoughtful responses that help us make better decisions.
The final judgment and answer are always on us, not AI.
Case 1: Social Media Topics (Level 1 Critical Thinking)
Instead of “Please give me social media topic ideas” that outputs this fairly generic response below:
Try: “Please suggest 3 social media topics for our demand gen audience. I’ve been doing mostly educational content but engagement feels flat. What different approaches should I try? Please rank them by likely impact vs effort and tell me why.”
Sample Response:
Case 2: Pricing Model Analysis (Level 2 Critical Thinking)
Instead of asking “Please compare these pricing options” which gives you this basic answer:
Try: “We’re considering three pricing models for our project management platform. Please put them in a 2×2 matrix using customer acquisition vs revenue predictability. Please show how sales, finance, and product would view each differently.”
Sample Response:
Case 3: Account-based Marketing Strategy (Level 3 Critical Thinking)
Instead of “Please create an ABM strategy for our cybersecurity platform targeting mid-market companies” that gives you this answer:
Try: “We’re considering ABM for our cybersecurity platform. What if our assumption that “bigger accounts mean better ROI” is wrong? Challenge this approach. What alternative targeting strategies might work better? What could make traditional ABM backfire for us?”
Sample Response:
In the AI era, the most dangerous decisions aren’t the ones you get wrong. They’re the ones you make quickly, confidently, and unquestioned because the answer sounded right.
Note: In any of the examples above and in your work, context and what we share with AI so that it can better help us are key. Reminder to share your goal, role you want it to play (e.g., competitive analyst, skeptical buyer, etc.), actions it should do or not do, context (e.g., current situation, relevant files, accurate data, etc.), and examples (i.e., what good looks like).
Where Do You Stand?
Want to see how you currently approach AI? Take this quick assessment to discover whether you’re using AI as Task Executor, Analytical Collaborator, Developing Critical Thinker, and Critical Thinking Partner. You’ll get personalized next steps based on your results.
The quiz takes 3 minutes and shows you specific ways to level up your AI thinking, regardless of whether you’re working on major strategic initiatives or daily tactical tasks.
The Breakthrough
Critics worry AI will make us intellectually lazy. The opposite is happening with teams that take this approach.
When you systematically challenge AI outputs and ask for reasons behind every recommendation, you develop stronger evaluation skills than most traditional education provides. You’re getting hands-on practice in logic, understanding perspectives, and systematic analysis.
These same analytical habits show up in your team meetings, strategic reviews, and decision-making conversations.
I’ve noticed this with my own thinking. The questions I ask now, of AI and in regular work conversations, are richer. I automatically look for alternatives, ask for confidence levels, and pressure-test assumptions in ways I didn’t before.
Your Critical AI Thinking Starter Kit
Want to start using AI as thinking partner? Try this prompt with your next project:
“I’m working on [PROJECT DESCRIPTION] with the goal of [SPECIFIC OUTCOME]. Here’s some context: [SITUATION/CONSTRAINTS]. You’re a [strategist/devil’s advocate/customer advocate].
Instead of solving this for me, suggest 5-7 strategic questions I should ask you that will help me think more critically, evaluate options thoroughly, consider different perspectives, and understand implications I might be missing. Focus on questions that challenge assumptions and identify blind spots.”
Then use those questions. Ask for rationale. Push back on the reasoning. Build on the ideas. That’s where the real value lives.
Sydney Sloan, CMO of G2, applies this thinking to customer feedback analysis.
“When I look at customer feedback, I don’t just ask AI to tell me what people are saying. I ask it to spot what we might be getting wrong. Like ‘Based on these reviews, what customer problems are we not solving that we think we are?’ or ‘What are customers actually using our product for that’s different from what we built it for?’ Those questions reveal gaps between what we assume and what’s really happening.”
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 comes from questioning what everyone else takes for granted, not from better tools or prompts.
When teammates say this is overthinking, show them the difference between your basic and thoughtful responses, the value becomes obvious quickly.
This also becomes essential as we build AI teammates. Teams that can’t think critically with these tools now won’t be able to build teammates that think critically later.
Pick one decision your team made in the last month. Ask AI to play devil’s advocate and identify risks you missed. Share those insights with your team. That’s how you demonstrate AI’s potential for critical thinking and get the most out of it.
AI makes it easier than ever to act fast. But it also makes it easier to be confidently wrong. Clear thinking still sets you apart.
Remember: you’re building critical thinking habits that improve every meeting and decision.
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