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Hello go-to-market leaders, strategists, and innovators! 👋 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

While most companies chase AI citations and search rankings, they’re missing something bigger. AI systems are already analyzing your brand, judging who you serve best, and recommending you (or not) — whether you guide them or not.

And they can only be as clear as you are. If you’re fuzzy on who you serve and what problems you solve best, AI will be too. This is about clarity, positioning, and knowing your customer deeply, so much more than just prompts or keywords.

Key takeaways:

• AI references websites more than any other source when forming brand opinions

• Vague positioning forces AI to piece together your value from scattered signals

• Result: AI creates its own version of who you serve which may not match your intent

• SEO tricks won’t cut it. Say clearly what you do and who you help.

• Be explicit about your ideal fit instead of expecting AI to figure it out

Curious how your brand shows up to AI?

Take this quick 30-second quiz: Can AI Understand Your Website?

It scores how clearly your website communicates to AI and gives you practical tips to improve.

Prefer to Listen? Try the AI-Generated Podcast

For those who prefer to consume information through audio, I’ve used Google’s NotebookLM to transform this newsletter into a short podcast episode, featuring a natural conversation between two AI hosts. You can listen to the 13-min podcast here while driving, walking the dog, or doing chores. Once you hit play, give it just a few seconds then it will start.

What Happens When AI Evaluates Your Competitors

For example, I asked Google to profile project management platforms for a mid-sized company. Within seconds, Google’s AI-powered search results delivered a detailed analysis: which platform was “best for” specific scenarios, pros and cons for each, and clear recommendations with reasoning.

The AI didn’t just find these companies. It judged them. See Perplexity and Google’s AI Overviews responses below.

• Asana – best for growing teams needing automation

• Trello – simplest for visual planners

• Monday – powerful for custom workflows, but complex

It was exactly the kind of guidance buyers want: clear use cases, honest trade-offs, and specific recommendations. But it had to piece this together from scattered information across websites, reviews, and comparisons.

Most companies never explicitly said “we’re ideal for X situation” or “competitor Y is better for Z use case.” The AI made those calls anyway, using whatever crumbs it could find.

When AI Gets It Wrong, You Still Pay the Price

AI forms impressions based on the signals it sees. If those signals are inconsistent or unclear, it will fill in the blanks. That’s not a malfunction. It’s how these systems work. They pattern-match and summarize. But when the patterns are vague or conflicting, AI produces flawed narratives. It might categorize your brand incorrectly, recommend you for the wrong use case, or overlook you entirely. Buyers won’t know it was an AI error. They’ll just assume your brand isn’t for them.

If you’re curious how to guide those signals and increase your odds of being cited directly, I wrote a deeper piece on how to make your brand sourced and a top result in AI search. It walks through how to identify buyer questions, track what AI says, and tune your content accordingly. Read it here.

Here’s why simply chasing citations backfires.

What Most Teams are Missing

A recent Semrush study found that AI systems reference websites more than any other source when forming brand opinions. But most companies still approach this backward.

We’ve been focused on getting AI to find our content. The real challenge is helping it understand and accurately represent our value.

We got away with vague positioning because humans could fill in the blanks.

“Industry-leading solution.” “Best-in-class features.” “Perfect for teams of any size.”

Humans can interpret those claims. They know when “scalable” means 25 users vs. 2,500. AI doesn’t. So it either makes wrong assumptions — or plays it safe with vague, generic recommendations.

Why This Matters More Than You Think

AI isn’t just finding information. It’s interpreting and compiling it. It’s turning scattered signals into structured buying guidance.

In the project management example, AI created decision frameworks that most of those companies didn’t offer themselves.

And the buyers coming from those frameworks are more ready to act. A SEMrush study found that AI search visitors convert at 4.4x the rate of traditional organic traffic. Why?

• They’ve already compared options

• They’ve learned your value

• They arrive with real intent

That makes clarity even more critical. If you’re not clearly positioned, AI won’t know when to recommend you and high-intent buyers won’t know why to choose you.

That also means it’s getting harder to stand out with surface-level content. In Beyond AI-Generated Content, I share how to rise above the noise by telling stories AI can’t replicate and creating content that buyers (and AI) actually remember. Skim it here.

This creates a fundamental problem: if you don’t clearly communicate your ideal customer profile and use cases, AI will invent its own version.

And it won’t invent that version in a vacuum. It will pull from whatever signals it can find such as customer reviews, community posts, influencer roundups, and outdated buyer guides. If you’re vague, those voices will fill in the gaps for you. And their version of your story might not match your intent.

Your website is the one place you control. You can’t dictate what people say in forums or reviews, but you can shape the narrative on your own site.

The sharpest teams know this and they’ve stopped trying to be “best for everyone.” Instead, they’re clear about who they serve best and even point customers to a competitor that might be a better fit. That’s truly being helpful.

Five Changes That Help AI Understand You

Here are five areas where companies can stop making AI guess and start being direct:

1. Say Who You’re Not For

Be bold enough to draw the line. Instead of trying to appeal to everyone, make it clear who you serve best and who you don’t. AI picks up on patterns. If your case studies all feature 250+ employee companies, but your homepage says “great for small teams,” that’s a mismatch. AI will flag it. So will buyers.

The fastest way to build trust is to help people self-select out.

2. Describe the Outcome, Not the Feature

Buyers don’t care that your product is “AI-powered.” They care that it helps them do something specific like predict customer churn or personalize pricing in real time.

Instead of “AI-powered analytics provides better insights,” try: “Need to predict customer behavior with 90% accuracy? Use our Predictive AI module.”

The more clearly you describe the job your product helps someone do, the more accurately AI can match you to the buyer’s intent.

3. Organize by Buyer Goals, Not Product Tiers

Most websites are structured around how you think about your product. AI and buyers don’t care about your internal categories. They care about achieving a specific outcome.

Instead of “Products > Premium > For Experts,” try: “What are you trying to do? → Analyze Data → In Real Time → Live Dashboard.”

This kind of goal-based structure helps both AI and humans navigate directly from intent to solution — without needing a map.

4. Be Honest About Where You Win (and Don’t)

Buyers don’t just want to know who you serve. They want to know when your product is the right tool for the job.

Say: “Best for mid-sized teams with flexible workflows. Not ideal for large enterprises requiring strict compliance.”

This kind of clarity helps AI route the right buyers your way and helps humans trust you faster. You’re not trying to win every deal. You’re trying to win the right ones.

5. Use Structured Content, Not Just Storytelling

Narrative copy is great for humans but AI needs structure to understand and cite you accurately.

Use tables, FAQs, labeled specifications, comparison grids, and feature lists. These formats make it easy for AI to extract meaning and build summaries.

Bonus: humans love them too. Especially when they’re scanning for answers.

The Business Case for Transparency

This approach might challenge old-school marketing instincts, but it aligns with sound business fundamentals: not all customers are created equal.

Customers you can’t serve well drain resources, churn faster, and rarely become advocates.

In a world shifting toward sustained profitability, your goal isn’t more leads, it’s better-fit ones. The ones you can serve exceptionally well. The ones who stay longer, buy more, and refer others.

Transparency helps AI recommend you to those right-fit prospects and steer others toward better alternatives. You win the customers you can delight. Your competitors win the ones they serve best. Everyone benefits.

What This Looks Like in Practice

This shift is already playing out on forward-thinking websites.

Andy Crestodina, Co-Founder and CMO of Orbit Media Studios, sees it first-hand.

“There is a true story in the life of your visitor. This is the reason they are on your page. The better you know that reason and provide the answers their looking for, the more likely you are to answer their questions and earn their trust.

Train AI on your audience and ask it for a gap analysis. Then fill those gaps and win the lead.”

This alignment matters just as much inside the org as it does on your website.

Megan Cabrera, VP of Marketing Operations at Sophos who is leading and driving human-AI org transformation, puts it this way:

“The same challenge we face inside — teaching AI to make smart decisions — applies externally too. When AI research tools evaluate us, they need the same kind of structure and clarity we give our internal models.

If we don’t provide it, they’ll guess. And those guesses influence what buyers see.”

Your Next Steps

Start by seeing your brand the way AI does.

Prompt to try: “I’m looking for [your category] solutions for [your target market]. Please profile the top competitors and give me recommendations with pros, cons, and best-fit scenarios.”

Then assess the results:

• How does AI describe your value?

• Which scenarios does it say you’re best for?

• When does it recommend a competitor?

Now audit what AI is learning from.

1. Pick one piece of content your team uses often in sales or marketing.

2. Ask: “If an AI had to recommend us based on this, would it have enough clarity to get it right?”

3. If not, fix it. Make the connections obvious. Don’t make AI or buyers figure it out.

None of this works without clarity. If you don’t understand your customer or your value, you can’t guide AI or anyone else. The teams that get this right are clear on who they serve, what they solve, and why it matters. Their content works because their strategy is solid.

A confused buyer buys nothing.

And in today’s crowded markets, confusion is everywhere. When buyers feel overwhelmed, they don’t turn to you. They turn to community threads, peer reviews, buyer guides, and increasingly, AI to make sense of the madness. That’s why clarity is a competitive advantage.

The good news is that you don’t need to win the click to win the mind. If your site is clear, consistent, and grounded in truth, AI will carry your message forward in search results, summaries, and beyond. Your website becomes your most powerful amplifier, not just for buyers, but for the AI that increasingly guides them.

You can’t control the algorithms. But you can control the signals you send. So be the clearest signal in the noise.

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey!

For those who prefer more interactive learning, explore our applied AI workshops, designed to inspire teams with real-life use cases tailored to specific go-to-market functions.

Also check out this team transformation case study and step-by playbook of how we helped transform a lean GTM team into a human-AI powerhouse with human and AI teammates.

Or, if audio-visual content is your style, here are virtual and in-person speaking events where I’ve covered a variety of AI topics. I’ve also keynoted at many organization and corporate-wide events. Whether through the newsletter, multimedia content, or in-person events, I hope to connect with you soon.

Hello go-to-market leaders, strategists, and innovators! 👋 Thank you for dropping by to learn practical AI applications and gain strategic insights that help you grow your business, elevate your team’s strategic value and now, bring your best ideas to life.

Quick Take

Most teams still ask AI to help them write faster or summarize better. The best teams use AI to turn ideas they’ve been sitting on into working solutions — fast, interactive, and real.

The AI skills you’ve developed for building AI teammates are now also turning marketing concepts into working solutions. Here are some key takeaways:

• While competitors describe competitive analysis, you build interactive comparisons where stakeholders can filter and gain real-time insights

• Instead of explaining ROI calculations in presentations, you create working calculators that generate personalized projections

• Rather than outlining buyer journeys in documents, you prototype actual interactive experiences that qualify and educate prospects

• The same strategic prompting skills used for AI teammates also help you build AI-powered apps with AI platforms (e.g., ChatGPT, Claude, and Gemini) you already have

• These AI-powered apps serve both internal and external uses

Prefer to Listen? Try the AI-Generated Podcast

For those who prefer to consume information through audio, I’ve used Google’s NotebookLM to transform this newsletter into a short podcast episode, featuring a natural conversation between two AI hosts. You can listen to the 14-min podcast here while driving, walking the dog, or doing chores. Once you hit play, give it just a few seconds then it will start.

Why This Matters Now

A lot of AI talk still circles around speed. Faster content. Faster emails. Faster decks.

But speed isn’t the biggest gain. It’s finally having a way to get the ideas out of your head and into something others can explore. We all have brilliant concepts rattling around, but translating them into something others can understand and act on is hard.

AI democratizes software development. What used to require developers and weeks of back-and-forth now happens in one conversation. You can show your thinking instead of struggling to explain it.

We’re in a new go-to-market era where:

• Buyers want to try, not just hear.

• Execs need proof, not more pitch decks.

• Teams move faster when they can align around something real.

Building with AI lets you skip the long explanation. You can show the thing. Test it. Iterate. Share it in the room, or let others explore it on their own time.

And the tools are ready but good enough to build something meaningful in an afternoon. That’s what this newsletter is about.

My Observations

I’m seeing a quiet shift.

Some teams still present slides packed with bullets and static comparisons. Others show up with interactive dashboards that let stakeholders filter by company size, explore use cases, and see real-time insights.

It’s the same research but one buries the findings and the other brings them to life.

From Concepts to Clickable Reality

You might have heard the new AI term: vibe coding.

It’s the ability to build software by describing what you want in plain language. No code and no waiting. Just an idea that becomes a working tool.

This lets go-to-market teams quickly launch things like quizzes, assessments, demos, or lead forms, without relying on other teams to get started.

The idea took off thanks to Andrej Karpathy, founding member of OpenAI and former Director of AI at Tesla, who called it a way to “just go with the flow.” You let the AI handle the technical work so you can focus on your ideas and keep moving forward.

You can start with AI platforms like ChatGPT, Claude, or Gemini. Or you can try specialized no-code builders like Lovable, Replit, or Bolt.

For most GTM professionals, the AI platforms are all you need. They handle interactive tools, calculators, assessments, and lead qualification well.

Specialized tools offer extras like drag-and-drop editors, automation, and connections to other business systems. But those features are typically needed by GTM ops teams working on complex integrations – where IT and legal usually get involved anyway.

You don’t need to find the perfect tool. Use what you already have. Focus on being clear about what you want to build. Let the AI do the heavy lifting.

You don’t need to become a developer. You just need to know how to describe what you want clearly. AI handles the rest.

What to Expect on Your First Build:

• Plan for 2-3 iterations to get your vision right – AI rarely nails it on the first try

• Start with simple functionality, then add complexity

• Expect some back-and-forth as you refine your requirements

• Your second and third projects will go much faster as you get the hang of it

Isar Meitis, CEO of Multiplai.ai and Host of Leveraging AI Podcast, puts it well:

“The #1 problem I hear from professionals isn’t that AI doesn’t work. It’s that they’re paralyzed by choice.

In our recent Ultimate AI Showdown podcast, Liza showed how to build apps with AI platforms like ChatGPT and Claude, while Marwan Kashef, MMAI showed what’s possible with specialized tools like Bolt, Lovable, and Replit.

It’s clear that you don’t need to solve the ‘perfect tool’ puzzle before you start. Begin with the AI platforms you likely already use. Learn the fundamentals of turning ideas into working solutions. Then, if you need more sophisticated features, you’ll know exactly what to look for in specialized platforms.”

Check out The Ultimate AI Showdown recording here.

Three Ideas That Became Reality

To inspire what’s possible, here are three project management challenges that transformed from concepts into working solutions.

Each one took roughly 30-90 minutes to build, depending on complexity (that includes research time). Pulling insights from a blog post to create an interactive FAQ might take 30 minutes or less, while sophisticated calculators can take 60-90 minutes. Your first few builds will likely be on the longer end as you learn the flow.

1. Dynamic Competitive Infographic

I used Gemini’s Deep Research to analyze Asana, Monday.com, and Smartsheet across vendor websites, analyst reports, customer reviews, and industry publications.

Note my Deep Research prompt and the research output from Gemini below. Here’s the full output.

Notice on the top-right corner that you can select what type of app or experience you want to create.

After the research, I selected “web page” as the output format. Gemini turned the report into an interactive infographic. You can now look at detailed profiles, compare ratings by metric, and see which solution fits different scenarios.

This is a new feature in Gemini. Currently, the interactive output isn’t shareable externally. Only the creator can access it. I expect that to change soon as other models already allow sharing.

Watch the process and see the infographic in action in this demo video.

What took weeks of manual research and design now happens in one conversation. The result transforms information into an experience that helps stakeholders make informed decisions.

2. Business Impact Calculator

I used Claude to research project management metrics and business value benchmarks, then had it build a working calculator. Claude’s research feature is still in beta, but it’s strong at building functional interfaces.

Users enter their company details and see projected productivity gains, cost savings, and payback periods based on industry data.

Below are my research prompts and here’s Claude’s output. Notice that Claude asked clarifying questions prior to doing the research. You’ll also see my responses.

I then asked Claude to create a simple interactive calculator using the benchmark data.

Try the interactive business impact calculator. I also walk through the full build process, how to use it, and how to remix it in this demo video.

3. Choose-Your-Own Adventure Web Experience

I used ChatGPT to research mid-market buying behaviors and best practices for web engagement. It handled the complexity well, pulling insights from multiple perspectives.

Here’s ChatGPT’s Deep Research output based on the prompt below. Similar to Claude in the previous use case, ChatGPT asked a few questions before starting the research.

Based on the research, I built a choose-your-own-adventure experience with AI. Visitors select their role and challenges, then follow personalized paths to relevant demos.

I created two versions to compare AI platform (ChatGPT vs Claude) strengths.

ChatGPT Version:

ChatGPT handled the research and built the web experience. It did a great job with the research.

However, rendering an interactive web experience is new for ChatGPT. So it’s not as good as Claude yet but the outputs are usable and shareable.

Try the ChatGPT choose-your-own-adventure web experience here.

Claude Version:

I used the same research from ChatGPT but built the experience with Claude. The design and interactivity are stronger, and the app is easier to share.

Try the Claude choose-your-own-adventure web experience here.

Both turn static buyer research into a dynamic experience that qualifies prospects and educates along the way. 

If you’re interested, here are the ChatGPT demo and Claude demo videos.

More Ideas Made Real

These three are just the beginning. Here are more working solutions that I built with Claude in the same way:

• AI Working Style Assessment – Understand how you work with AI today and get personalized strategies to improve.

• Brand Impact ROI Calculator – Quantify the value of brand investment with industry-backed data.

• Interactive Sales Workflows Simulator– Models lead qualification, sales discovery & call prep, objection handling, proposal generation, and deal velocity analysis.

• Interactive Starter Kits – Get ideas for custom GPTs by marketing function.

• AI Jargon Translator – Test how well you understand AI jargon.

• Mindful Moments – Take a refreshing break to reset and recharge during your day with this app.

• Interactive Games – Pictionary, Battleship, Blackjack, and more. Built to help my kids learn AI. Still surprisingly useful for work for onboarding and offsite ice breakers.

• Emoji Jeopardy – Holiday game where teams decode clues like 😱🏠1️⃣📌🦶. (What is Home Alone?)

Each one started as a simple idea, described clearly and built quickly.

Internal and External Impact

The competitive analysis becomes both a team alignment tool and a customer-facing resource. The ROI calculator supports internal planning and external sales conversations. The interactive journey helps product teams make better decisions and helps prospects self-qualify.

Some of these tools are final. Others are just version one. But all of them make your thinking tangible. They help others react, improve, and buy in.

The Skills That Transfer

The same prompting skills you’ve used to build AI teammates also apply here.

Clear context, specific requirements, and step-by-step refinement create functional apps/tools instead of conversations.

The shift is simple. Instead of asking, “Help me think through this,” you ask, “Help me build something others can use.”

Jessica Lanier, Vice President Marketing & Communications at Cox Automotive Inc., experienced this shift firsthand:

“Liza really got our strategic brains buzzing on how AI can revolutionize our marketing efforts. We moved well beyond tactical thinking into deep, strategic use cases that could be game changers for our organization.

The team left inspired with new knowledge about what’s possible when you stop just explaining concepts and start building them.

We’re still pondering the learnings — we just couldn’t stop talking about the possibilities days after the workshop. We look forward to continuing our AI journey with Liza. The passion this team brings to everything they do makes me excited to see what they’ll create with all this new inspiration.”

Where Others See Limitations, You See Opportunities

While some teams wait for designers or developers, you’re already building. While others describe strategy, you create something people can use. While competitors talk about value, you build tools that prove it.

The competitive advantage comes from making ideas tangible instead of keeping them abstract.

Your Next Steps

Start Simple: Try this exact prompt and URL to see the transformation in action.

Use any of these AI platforms (paid versions recommended and I got the best results using these models):

• Claude: Sonnet 4

• ChatGPT: o4-Mini-High

• Gemini: 2.5 Flash or 2.5 Pro

“Transform this article into an interactive ‘Pick Your Challenge, Get Your Solution’ tool. Ask users to select their main problem, then show them which solution from the article helps solve it, plus one action step to get started. Output the interactive app please. https://www.marketingprofs.com/articles/2023/50442/ai-use-cases-cmos“

See my results: Claude version and ChatGPT version. Compare the outputs and pick your preferred style. Gemini output apps are currently not shareable.

Once you see how it works, use your own blog post and adapt the prompt to fit your content. Each blog will need a slightly different approach depending on the topic and structure.

Congratulations, you’re officially a vibe coder! 🤪

Go Bigger: Pick one concept your team keeps talking about. Instead of explaining it again, build something interactive that brings it to life. Start with the AI platforms you already use. Keep it simple. Make it explorable.

What to Expect: Your first build might take 60-90 minutes as you learn the flow. Expect 2-3 iterations to get your vision right. Your second and third projects will go much faster.

The goal isn’t perfection. It’s progress. Getting from idea to first version. From abstract to actionable.

Your ideas don’t belong in just your head, slides, or long meetings. AI gives you the tools to make them real now.

Share Your Success: Let us know what you plan to build or share what you’ve already built to inspire others!

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey!

For those who prefer more interactive learning, explore our applied AI workshops, designed to inspire teams with real-life use cases tailored to specific go-to-market functions.

Also check out this team transformation case study and step-by playbook of how we helped transform a lean GTM team into a human-AI powerhouse with human and AI teammates.

Or, if audio-visual content is your style, here are virtual and in-person speaking events where I’ve covered a variety of AI topics. I’ve also keynoted at many organization and corporate-wide events. Whether through the newsletter, multimedia content, or in-person events, I hope to connect with you soon.

Hello go-to-market leaders, strategists, and innovators! 👋 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 AI teams are optimizing their prompts. The best are redesigning how they think.

If your AI work feels decent but underwhelming, you’re not alone. Most teams are spending time perfecting AI conversation mechanics while competitors gain real advantages by asking fundamentally different questions.

What you’ll discover:

• Why teams with solid prompting skills still hit walls

• The one shift that separates AI tools from AI thinking partners

• Seven thinking moves that uncover insights your competitors miss

• Why mastering strategic questioning now determines who wins with AI agents later

• Real examples of teams turning AI limitations into breakthrough opportunities

The difference isn’t better prompts. It’s the courage to question what everyone else takes for granted.

How are you really using AI right now? Take 60 seconds for personalized insights and guidance on how to get more out of AI.

Take your AI Working Style Assessment here.

Prefer to Listen? Try the AI-Generated Podcast

For those who prefer to consume information through audio, I’ve used Google’s NotebookLM to transform this newsletter into a short podcast episode, featuring a natural conversation between two AI hosts. You can listen to the 15-min podcast here while driving, walking the dog, or doing chores. Once you hit play, give it just a few seconds then it will start.

What I Keep Seeing

A few months ago, I worked with two SaaS companies.

Company A had given up on AI for strategy work. “We tried it for competitive analysis and market research. The outputs were generic. AI just isn’t there yet for complex thinking.”

Company B was satisfied with their AI usage. “We use it for email drafts, call summaries, and content outlines. Saves us 5 hours a week. We’ve got this figured out.”

Both teams were using solid prompting techniques. Good structure, clear context, examples. But both were treating AI like a better search engine instead of a thinking partner.

But the problem isn’t AI’s capabilities. It’s the depth of your questions.

The Missing Piece in Every Prompting Framework

OpenAI, Anthropic, and Google have published excellent prompting guides covering structure, examples, and parameter tuning. I use a framework called GRACE – inspired by Christopher Penn‘s RACE framework, adding G for Goal because stating the objective upfront keeps both you and the AI focused.

Here’s what this looks like in practice:

Same mechanics. Completely different results.

Prompting techniques are getting easier. As AI advances with better memory, reasoning, and context understanding, the technical mechanics will become simpler.

The thinking layer – how deep your questions go, what assumptions you challenge – that’s the human advantage that matters more as AI advances. AI can’t push you to think deeper. It can only work within the cognitive framework you provide.

From AI Tool to AI Thinking Partner

Most teams ask AI: “Help me write a sales email.”

Strategic teams ask: “Challenge my assumptions about why prospects aren’t responding.”

This shift changes everything. Jason Cormier, Founder of AI Marketing Forum, sees this pattern across the marketing community.

“I see teams master the mechanics of prompting but still hit walls. They’re missing what I call “directive intelligence.” It’s the ability to guide AI toward what you don’t already know.

Most people use AI to confirm what they think. The best use it to discover what they’re missing. If you’re working through this, you’re not alone. We have hundreds of marketing leaders sharing what’s working in the AI Marketing Forum.”

More teams are starting to build AI teammates (using custom GPTs, Claude Projects, Gemini Gems, etc.) that work alongside them to do specific work. You can read more about a human-AI powerhouse team case study and step-by-step playbook here.

The quality of your AI teammate depends on how you guide their thinking. Give them tasks, get an assistant. Ask better questions, get a thinking partner.

7 Ways to Think Deeper

These seven thinking moves help you reframe problems before you even write a prompt. Each one helps you see the problem differently, often in ways your competitors haven’t considered.

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.”

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 competitive advantage may be hiding in a question you haven’t asked yet.

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.

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey!

For those who prefer more interactive learning, explore our applied AI workshops, designed to inspire teams with real-life use cases tailored to specific go-to-market functions.

Also check out this team transformation case study and step-by playbook of how we helped transform a lean GTM team into a human-AI powerhouse with human and AI teammates.

Or, if audio-visual content is your style, here are virtual and in-person speaking events where I’ve covered a variety of AI topics. I’ve also keynoted at many organization and corporate-wide events. Whether through the newsletter, multimedia content, or in-person events, I hope to connect with you soon.

Hello go-to-market leaders, strategists, and innovators! 👋 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

When resources are tight, leaders stop asking “How can AI help us work faster?” and start asking “What could we become?”

That shift changes everything.

This happens whether you’re a startup with one marketer or an enterprise team navigating approval processes and budget cycles.

Frank Nardi leads Cloud Coach with a highly efficient team, focused on maximizing resources as they scale. Rather than getting stuck on constraints, Frank leveraged AI to turn static buyer personas into dynamic, evolving systems. Now, messaging is tested before launch, campaigns align with real buyer needs, enabling his GTM team to compete effectively with companies 10x their size.

Megan Ratcliff and the Dice team faced the challenge of a new product that broke all the traditional go-to-market rules at the company. Instead of months of alignment meetings, she created a GTM strategist that coordinates across departments.

Companies with the fewest resources are building some of the most advanced uses of AI today because they have no choice but to reimagine everything. Whether you’re constrained by budget, approvals, or old systems, those limitations push you toward the breakthroughs that actually matter.

Your biggest constraint might be your secret weapon.

Prefer to Listen? Try the AI-Generated Podcast

For those who prefer to consume information through audio, I’ve used Google’s NotebookLM to transform this newsletter into a short podcast episode, featuring a natural conversation between two AI hosts. You can listen to the 10-min AI podcast here while driving, walking the dog, or doing chores. Once you hit play, give it just a few seconds then it will start.

When Constraints Force Breakthrough Thinking

In my previous newsletter “AI is Breaking Department Silos: Moving from Org Charts to Work Charts,” I explored how AI enables teams to organize around workflows instead of departments. Today’s examples show this transformation in action.

The leaders making the biggest leaps share something unexpected. They started with significant constraints.

Limited resources force better questions:

• What if we can’t hire more people?

• What if we must achieve bigger goals with less?

• What if we can’t get approval for new platforms?

• What if we’re stuck with legacy systems?

These questions lead to breakthrough thinking.

These constraints exist everywhere, from startups to Fortune 500s.

Frank Nardi: CEO as AI Strategy Partner

Frank Nardi leads Cloud Coach, a project management/PSA platform for professional services teams. As CEO and former CRO with deep go-to-market experience, he faces a classic startup reality: scaling smart with a lean, focused team, finite budgets, and ambitious goals.

Rather than letting those limits define the approach, Frank reimagined how work gets done.

The Challenge – Frank and team needed to quickly and efficiently target ideal buyers and stay aligned with a moving market. Traditional personas were static, slow to produce, and often outdated by launch time. This led to misaligned messaging, poor targeting, and inefficient ad spend. For a fast-growing company, wasted spend adds up fast.

AI as a Teammate – Frank built adaptive buyer personas using four AI tools, but this approach works with any combination—some teams get 80% of the benefit using just ChatGPT and one other AI. The key is matching AI capabilities to your biggest business needs rather than making one AI do everything. Frank chose his tools strategically:

• ChatGPT – Foundational strategic messaging and persona development

• Claude – Ad strategies and campaigns

• NotebookLM – Sales knowledge base (plus Frank likes the AI podcast feature for sales)

• Jasper – Brand voice consistency

Using CRM data, call transcripts, customer insights, market research and psychological drivers as inputs, Frank created adaptive personas that evolve continuously. Every message, ad, and email gets pressure-tested against these personas before hitting the market, providing instant feedback on relevance and helping the team move faster with better precision.

But even the best AI outputs can miss the mark. That’s why every message is still pressure-tested by humans. Responsible AI means pairing fast automation with real-world judgment.

The Bigger Transformation – Frank is designing an AI-enabled workplace where every team member has personalized tools that eliminates busywork and boosts performance allowing them to focus on high-impact, strategic work.

Frank’s next step is to automate this into live agents connected to real-time prospect data, latest emails, transcripts, and win/loss signals to keep the feedback loop fast and actionable.

Frank shared his vision:

“At Cloud Coach, we’re designing an AI strategy that empowers every team member by automating repetitive tasks so they can focus on high-impact work. The goal is to accelerate execution, continuously adapt to customer needs, and create a seamless flow of knowledge that breaks down silos and enables smarter, faster decisions.”

The Early Results – Personas are now fully embedded in workflows, guiding messaging decisions early and often instead of sitting idle in decks. Teams now make more informed decisions, run focused campaigns, and validate messaging before going to market. Creative aligns with what buyers actually care about.

Frank’s Pro Tip – “Treat personas and targeting as living systems that evolve alongside your customers.”

Frank represents an early look at Stage 4 of the AI Work Chart Maturity Model with work organized by what needs to be done, not departmental silos. Smaller companies can compete with enterprise resources by reimagining how work flows.

The data backs this up from the 2025 Microsoft Work Trend Index report. Companies making this transformation see workers who are:

• More optimistic about future work opportunities (93% vs 77% globally)

• Less worried about AI taking their jobs (21% vs 38% globally)

• Better able to take on meaningful work (90% vs 73% globally)

• More capable of handling additional responsibilities (55% vs 20% globally)

These aren’t just productivity gains. They’re fundamental shifts in how people experience work.

Megan Ratcliff: Cross-Functional AI Orchestrator

Megan Ratcliff at Dice, the career marketplace that connects tech professionals with opportunities, is part of a team that grew from a small marketing group to a 45-member powerhouse with 25 humans and 20 AI teammates in just 6 months. Now, they have 63 AI teammates and expect having more than 100 this summer.

She and her team faced a unique challenge: launching a new product that didn’t fit inside their traditional GTM box. Everything was different – the audience, sales process, invoicing, and messaging.

The challenge required cross-functional collaboration, but no one person could own such a complex shift across departments.

The Challenge – Standard GTM approaches failed because this product required different invoicing, sales motions, and messaging. The kind of cross-functional coordination that usually takes months through endless alignment meetings.

AI as a Teammate – So Megan built a GTM strategist (a custom GPT) to serve as their “connective tissue and voice of reason.” This AI teammate helps define the ideal customer profile, determines next steps, and guides each department on their specific contributions.

For example, when they needed to define the ICP for this relatively new product that had some data but required additional research, the GTM strategist helped determine how to source that additional information. Then it told the team what the next task was and which department should handle it.

The Transformation – Megan’s role is changing. She’s now working across the entire GTM motion and becoming a much more valuable coworker in the process. AI gave her the tools to answer questions and guide action at a strategic level.

As Megan puts it:

“AI has unleashed my superpowers. I’ve always been a strategic thinker, but having tools to answer questions and guide action has been transformational. I’m working across the entire GTM motion now.”

The Results – The GTM strategist is getting them started and organizing the work, speeding up time to action. Megan envisions they’ll iterate on it over time and eventually allow it to work on their behalf, but for now it’s getting them started on a major business transformation. Instead of departmental silos, there’s unified movement toward shared goals.

Megan’s Pro Tip – “The first few things you make will probably be mediocre. That’s okay. Keep going because you’ll get better and better.”

The Pattern: Three Critical Questions

Both Frank and Megan answered three questions that separate AI optimizers from AI transformers:

1. What do you finally have time to focus on?

• Frank – Strategic thinking across the business instead of tactical execution

• Megan – Cross-functional orchestration instead of departmental tasks

2. What parts of your role become more strategic, creative, or cross-functional?

• Frank – Building adaptive systems instead of static processes

• Megan – Breaking down silos instead of working within them

3. What does your job become when AI becomes a teammate, not just a tool?

• Frank – CEO as AI strategy architect designing human-AI workflows

• Megan – Marketing professional as cross-functional orchestrator

A Simple Framework to Begin

Here’s a simple framework to get started:

Understanding AI isn’t something you can delegate. It’s not a report you can read or a task you can hand off.

Unlike past innovations, AI changes how work happens. That means everyone, from individual contributors to CEOs, needs to put hands on keyboard.

Try tools. Break things. See what AI can and can’t do. Only by doing will you start to see where the real opportunities lie.

Exploration isn’t optional. It’s the first step in reimagining your role, your team, and your business.

Your Next Steps

The leaders getting ahead aren’t waiting for perfect conditions or unlimited resources. They start with what they have and build AI teammates that transform how work happens.

Here’s how to begin:

1. Identify your biggest constraint

2. Map what you’d do despite the constraint

3. Design one AI teammate to bridge the gap

4. Test for 30 days and measure results

5. Share learnings and expand what works

Small steps lead to significant gains in productivity, innovation, and competitive positioning. While these examples focus on go-to-market, these principles apply across all functions.

Your scarcity might be your secret weapon. The question is: Will you keep treating AI as a tool or turn it into your strategic advantage?

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey!

For those who prefer more interactive learning, explore our applied AI workshops, designed to inspire teams with real-life use cases tailored to specific go-to-market functions.

Also check out this team transformation case study and step-by playbook of how we helped transform a lean GTM team into a human-AI powerhouse with human and AI teammates.

Or, if audio-visual content is your style, here are virtual and in-person speaking events where I’ve covered a variety of AI topics. I’ve also keynoted at many organization and corporate-wide events. Whether through the newsletter, multimedia content, or in-person events, I hope to connect with you soon.

Hello go-to-market leaders, strategists, and innovators! 👋 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

AI is changing how teams work together. It’s moving us from strict department lines to simple, flexible ways to get work done.

Enter work charts.

What’s different?

• Org charts = who knows what (departments + roles)

• Work charts = what needs doing (jobs-to-be-done + workflows)

In this edition, I’ll share real-world examples and proven results from companies already starting to work this way and how you can take the first steps.

The shift:

• AI breaks down traditional silos by making expertise available on demand.

• Forward-thinking companies organize around workflows, not functions.

• Humans + AI systems work side by side.

• Rising customer expectations + economic pressure accelerate this.

Start with one cross-functional workflow. Prove value. Expand.

While my work often focuses on go-to-market teams, these concepts apply across all functions and can scale throughout the entire company.

The shift:

• AI breaks down traditional silos by making expertise available on demand.

• Forward-thinking companies organize around workflows, not functions.

• Humans + AI systems work side by side.

• Rising customer expectations + economic pressure accelerate this.

Start with one cross-functional workflow. Prove value. Expand.

While my work often focuses on go-to-market teams, these concepts apply across all functions and can scale throughout the entire company.

Prefer to Listen? Try the AI-Generated Podcast

For those who prefer to consume information through audio, I’ve used Google’s NotebookLM to transform this newsletter into a short podcast episode, featuring a natural conversation between two AI hosts. You can listen to the 12-min podcast here while driving, walking the dog, or doing chores. Once you hit play, give it just a few seconds then it will start.

The Shift Already Happening

If you’ve read my previous newsletters, you’ve seen how AI is changing go-to-market teams. In “AI Is Redefining GTM Jobs,” we explored how teams move through three phases: using AI as tools, guiding AI as teammates, and orchestrating AI systems.

The walls between departments are blurring as:

• AI teammates make expertise available across traditional boundaries

• Customer workflows run with fewer handoffs and stops

• The gap between “who knows what” and “what needs to be done” shrinks

This marks the beginning of a new way to organize teams.

In “Human-AI Org Transformation Playbook,” we examined how a lean marketing team grew to include 25 humans and 20 AI teammates working side by side.

From Org Charts to Work Charts: The Next Step

We can see a practical shift in how organizations will structure themselves:

This approach aligns with trends we’re seeing across industries. The Microsoft 2025 Work Trend Index Report offers a similar model. They note that “teams form around goals, not functions, with AI helping employees do more and work faster.”

Ethan Mollick, Associate Professor at the Wharton School, recently shared insights from a Harvard study with P&G professionals. Cross-functional teams working with AI experienced an interesting finding:

“You stop caring as much about the normal boundaries of your job.”

When specialists from different functions used AI, the lines between expertise areas nearly disappeared. Traditional silos broke down as AI helped people think beyond their specialized training.

Leaders are experiencing this firsthand. Scott Braun, CMO of SimpliSafe, shares:

“When we started with AI at SimpliSafe, honestly, we weren’t quite sure how it could help us at scale. With the help of Liza Adams during a strategic AI workshop where she inspired us with what’s possible with various applied AI use cases, we quickly realized the real power wasn’t in just making our old processes more efficient.

We can get the most out of it when we use it as a teammate, as a thought partner, and to connect dots across functions in our org. It was a wake-up call.”

Where We Are Today: Reality Check

While work charts show where we’re heading, most companies are at earlier stages:

The AI Work Chart Maturity Model presents a staged and simplified view of how organizations are evolving in the AI era. Your company’s path may look different based on industry, maturity, and structure.

Stage 1: Traditional Org + AI Tools

Most organizations currently work here, with individual AI usage within functional silos. Teams use AI separately with limited sharing across departments.

Stage 2: Traditional Org + AI Teammates

Forward-thinking companies work at this level, with defined AI teammates working alongside humans in departments. AI teammates have clear roles and help improve team output within traditional structures. While organizational silos still exist, AI augments individual and team productivity dramatically.

In working with the Dice marketing team, we successfully transitioned to Stage 2 by embedding structured AI teammates into their existing organization. What started as a lean team evolved into a 45-member powerhouse with 25 humans and 20 AI teammates working side by side.

That was in January 2025. Today (May 2025), they have 63 AI teammates. And they anticipate having more than 100 by this summer.

The results:

• 50–75% faster content creation with higher quality

• 98% accuracy in lead qualification

• 35% improvement in campaign performance

Dice built their AI teammates using ChatGPT Custom GPTs and Claude Projects to support specific tasks. But you can also use Gemini Gems, Copilot GPTs, or Glean Apps.

While each company’s structure varies, this diagram shows a conceptual example of how marketing teams can pair human expertise with assigned AI teammates to enhance productivity and reduce repetitive tasks.

AI teammates take on well-defined tasks (like data analysis, content generation, compliance checking), guided and maintained responsibly by human team members.

The Dice marketing team, led by Carol-Lyn Jardine, has since become a trailblazer for the company, conducting AI workshops and mentoring other departments like Sales and Customer Success. They are now beginning to explore cross-functional AI workflows—an early move toward Stage 3 of the AI Work Chart Maturity Model.

Stage 3: Connected Workflows

Pioneers explore this stage, creating workflows where AI connects previously separate processes. Cross-functional workflows form with AI serving as the link between departments. AI enables smoother handoffs, faster information flow, and more consistent customer experiences across teams.

Cin7 offers a real-world example of a company with some workflows operating at Stage 3 of the AI Work Chart Maturity Model.

Sean McCaffrey, Senior Director of Marketing Strategy and Operations at Cin7, shares:

“When we first used AI to identify our most engaged accounts, it opened the door to smarter marketing. Now, we’re going further.

We’re using AI-powered SDR outreach to follow up on demo requests and post-event engagement—personalized at scale on behalf of our human sales reps. Each message adapts based on CRM data, industry, and what we know from their website visits. We’re sharing relevant customer stories and speaking directly to pain points we’ve seen in similar businesses.

We’re also sharing what we learn from sales conversations with customer success for better onboarding.

What began as one focused solution has become a connected workflow.

This level of scale and precision simply wasn’t possible with manual email sends. AI isn’t just making our process more efficient—it’s helping us meet prospects where they are, with what they care about.”

Their results speak for themselves:

• 78% open rate + 20% booked meetings for demo requests

• 92% open rate + 3.5% booked meetings for event follow-up

AI has moved from simply enhancing tasks to orchestrating cross-functional work at Cin7.

Stage 4: Work Chart Organization

The full work chart model takes shape when companies organize primarily around jobs to be done rather than departments. This creates a new way to structure teams.

Most companies are in Stage 1 or early Stage 2. The full work chart model (Stage 4) exists in pieces but not yet fully in most organizations. However, this change moves faster than many expect.

The Stage 4 work chart structure aligns with my automation vs. augmentation framework above that I sketched out over a year ago. I originally drew this for marketing but this can be applied across organizations. It was a hypothesis back then. Now we’re seeing evidence that this is becoming reality.

In this model, experts (both human and AI) come together to support specific workflows—some joining for short periods (ephemeral) while others stay involved longer (everlasting).

Three Forces Speeding Up This Change

Why is this organizational shift happening now? Three key forces come together:

1. Economic Pressure

Microsoft found that 53% of leaders say productivity must increase, while 80% of workers report lacking time and energy. Their data shows employees face interruptions every 2 minutes—275 times daily. Traditional structures can’t solve this productivity gap.

2. AI Advancement

AI is growing from simple tools to systems that can coordinate work. OpenAI describes five levels of AI growth:

1. Chatbot – Answers questions with information.

2. Reasoner – Asks questions and adapts to context.

3. Agent – Takes action, plans, and completes tasks autonomously.

4. Innovator – Uncovers insights and proposes new ideas independently.

5. Organization – Works and learns across many functions with minimal human input.

We’re currently between levels 2 and 3, with levels 4-5 on the horizon. As AI reaches these higher levels, work charts become a more natural way to organize.

3. Higher Customer Expectations

Customers don’t care about internal departments. They want seamless experiences. 

Jacob Warwick, CEO of ThinkWarwick Global and an executive coach explains:

“Top leaders no longer hide behind department lines. The best are business leaders first, functional leaders second. They see problems and collaborate to fix them—even if it means funding another team’s solution.

AI exposes exactly where customers get frustrated and leave. Customers don’t care about your org chart. They leave when they hit walls between teams. Period. Top leaders must do the same or quickly become irrelevant or replaced. “

A Simple Template to Begin the Change

Here’s a practical approach to move toward work chart organization:

This approach lets you start the change without disrupting your entire organization. Begin small, show value, and grow what works.

Your Next Steps

The move from org charts to work charts happens step by step but has already begun. Companies that see this coming can make choices today that support this change.

Here’s how to get started:

1. Map your key workflows across department lines

2. Find your worst handoff points

3. Create one cross-team test with shared AI support

Small steps in this direction lead to significant gains in customer experience, team effectiveness, and company speed. While I’ve focused on go-to-market teams, these concepts apply across HR, finance, product, engineering, and other functions.

This shift aligns perfectly with what I covered in my “AI is Raising Customer Expectations” newsletter. As buyers increasingly demand seamless experiences, companies need unified workflows that connect insights across the entire customer journey—exactly what work charts enable.

As the Microsoft 2025 Work Trend Index notes: “We are entering a new reality—one in which AI can reason and solve problems in remarkable ways… A new organizational blueprint is emerging, one that blends machine intelligence with human judgment, building systems that are AI-operated but human-led.”

The move from org charts to work charts has already begun. The companies that embrace this shift today will have a massive competitive edge when AI-driven organizational design becomes the standard, not the exception. Where is your organization on this journey?

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey!

For those who prefer more interactive learning, explore our applied AI workshops, designed to inspire teams with real-life use cases tailored to specific go-to-market functions.

Also check out this team transformation case study and step-by playbook of how we helped transform a lean GTM team into a human-AI powerhouse with human and AI teammates.

Or, if audio-visual content is your style, here are virtual and in-person speaking events where I’ve covered a variety of AI topics. I’ve also keynoted at many organization and corporate-wide events. Whether through the newsletter, multimedia content, or in-person events, I hope to connect with you soon.