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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

Most teams limit AI to Q&A and content creation. Working with GTM (go-to-market) teams as an AI and executive advisor, I’ve found something more powerful: AI makes your thinking better when you lead with your ideas.

Let me show you how I recently worked with Claude and ChatGPT to uncover fresh insights about trust, attribution, and brand perception in the AI era.

I find it ironic that we say AI should augment human intelligence. Yet we ask AI to think for us. The best AI-enhanced work starts with your perspective, your insights, your frameworks. In a world where everyone has access to the same AI tools, original thinking sets you apart.

Here are some prompts that turn AI into a true thinking partner. Let me show you how it works.

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 AI podcast here. Once you hit play, give it just a few seconds then it will start.

Disclaimer: This podcast was generated by AI based on this written newsletter and reviewed by me to ensure ethical and responsible AI use. It’s designed to provide an efficient, more inclusive way to consume information.

Why Use Multiple AIs as Thinking Partners?

Think of it like the Justice League. Each member brings different superpowers to solve problems together.

Claude approaches challenges step by step, while ChatGPT jumps to creative solutions. As a human, I guide with business experience and keep us focused on practical value.

Together, we catch blind spots and build stronger ideas.

I recently asked Jonathan M Kvarfordt, MBA, Head of GTM Growth at Momentum, how he approaches AI collaboration. His perspective reinforces why AI thought partnership matters:

“Most people use AI like a faster search engine or content creator. That misses its real value. I use AI to push my thinking, challenge assumptions, and refine ideas before execution.

When structuring an AI-powered sales coaching framework, I didn’t just ask for “best practices.” I had AI break down great coaching approaches, critique my structure, and pressure-test my thinking. It flagged gaps in feedback loops, helped define better scoring criteria, and refined how we track skill improvement.

The framework became sharper and immediately actionable. AI didn’t give me a single answer. It strengthened my thinking until I got to the right one.

The moat of the future isn’t just using AI. It’s when YOUR human mind is amplified with AI’s capabilities.”

A Real Example: Rethinking Marketing in the AI Era

It started with a LinkedIn post I read that about 70% of pipeline coming from four channels: SEO, paid search, social, and events. Given that, it seemed completely logical to focus our efforts on these four. Less is more!

However, this got me thinking bigger about how AI will change everything. I challenged this “less is more” view.

In the AI era, we’ll need to be in relevant and reputable places where our audiences talk about us: in communities, social media, publications, review sites, and more. Trust signals will come from all these places. AI will gather these signals before humans even start searching.

This raised bigger questions about measurement. How will we track success when AI forms opinions about brands before humans do? We’ll need to measure how easily people find us, where trust signals come from, and how strong those signals are.

My Collaboration With Two AIs

Round 1: Claude Builds on My Perspectives

I shared my thoughts about AI changing how buyers find and trust brands. Claude understood the vision:

“Your observation about AI forming opinions before humans do is particularly insightful… We may need strategies that work for both AI agents and human decision-makers.”

Together, we evolved from “less is more” to “authentic is more” in marketing. In the AI era:

• AI will find what people say about you everywhere online

• Happy customers matter more than clever marketing

• Real employee stories beat planned content

• You can’t fake real community connections

• Face-to-face meetings become more important

Here’s our exchange.

Then Claude proposed something new: a trust signal portfolio to measure brand health. This sparked a deeper discussion about attribution.

I shared that traditional channel tracking won’t work anymore. We need to measure:

• How well buyers can find us

• Where trust signals come from

• How strong these signals are

• Overall brand perception

• Impact on pipeline

I asked Claude to assess my point of view using the Rose/Thorn/Bud framework. This helped us identify strong points, potential challenges, and opportunities to make it better.

See this part of our conversation:

Claude agreed that good business practices will matter more than ever. AI will reward companies that create real value rather than those trying to game the system.

This shift in thinking isn’t just theoretical. Wendy Werve, CMO of financial services compliance platform COMPLY, shares how they’re adapting:

“In financial services, reputation makes or breaks companies and ensuring regulatory compliance is key to building and maintaining that reputation.  With the constantly evolving regulatory landscape, we’re exploring how AI can accelerate and optimize compliance processes for financial services firms, freeing compliance officers and teams to focus on other priorities, and ultimately strengthening market confidence. 

We’re also looking at how we can use AI internally to help us better understand and serve our customers and the market – from identifying key regulatory trends and building resonant content and positioning, to surfacing customer success stories and data, compiling marketing intelligence, evaluating our marketing campaigns and strategies, and monitoring social sentiment. The goal is to spot trends early and focus our efforts where they matter most.

Success in the AI era comes from consistent performance across every interaction.”

Round 2: ChatGPT Adds New Angles

I shared my Claude conversation with ChatGPT. Its response showed the power of getting multiple AI perspectives.

First, ChatGPT noted what worked well, pointing out our focus on trust signals from many sources and the need to rethink attribution. Then it challenged our “just be a good business” approach:

“You and Claude nailed the big shift—AI changes the way brands are perceived, trust signals matter more than ever, and marketing attribution as we know it is due for a major overhaul. However, I’d challenge and expand a few ideas…”

ChatGPT took our attribution discussion further. While agreeing with the concept, it pointed out practical challenges in implementation. It suggested starting with an AI Trust Score that measures:

• What customers say on review sites

• What employees share on job sites like Glassdoor

• When experts mention or cite you

• Real community discussions vs promotional posts

The conversation sparked more ideas. ChatGPT proposed AI-specific PR strategies and brand perception dashboards. As ideas kept flowing, I started calling us “the three chickens.” Sometimes keeping things light helps unlock better thinking.

Using the Rose/Thorn/Bud framework helped us spot strong points, tackle hard problems, and find new opportunities. This structured approach, mixed with casual collaboration, led to insights none of us would have found alone.

Here’s ChatGPT’s take:

Round 3: Finding the Truth Together

After ChatGPT’s analysis, I came back to a simple truth: Focus on being a good business. Give great service to customers who fit best. Be helpful where your audience is. Build a business people love to work for.

Claude helped reinforce why being authentic matters more in the AI era:

“AI might actually push us back toward business basics. Because AI can better detect patterns, it may be better at distinguishing genuine value from artificial attempts to game the system.”

This collaboration showed how human insight and AI perspectives build stronger thinking together. By the end of our long discussion, I joked about being a “fried chicken” from all the thinking. Claude jumped right in, dubbing us “the chicken trio.”

What We Learned

Here are a few big takeaways from this insightful conversation about the impact of AI on marketing:

► Trust looks different now – We need to build it everywhere our audiences gather, not just in traditional channels. And AI will form opinions about our brands before humans do.

► Attribution needs a complete rethink – Simple channel tracking won’t work when AI and humans take signals from many places. We need new ways to measure findability, trust signals, and real business impact.

► AI will reward good business fundamentals – No shortcuts. No gaming the system. Focus on serving customers well and building a company people love to work for.

What’s Next

For those new to AI collaboration, Jonathan M Kvarfordt, MBA has created an excellent framework that takes the guesswork out of AI thought partnership.

His step-by-step prompts is particularly helpful if you’re just starting out, as it prompts you with the right questions and helps structure your thinking process.

Try using multiple AIs as thinking partners on your next big challenge. Start with your perspective. Share it with one AI, then another. Let them build on each other’s ideas. Keep it real, keep it practical. Here are links to step-by-step guides with examples for your reference:

1. 10 Steps in 10 Minutes: Practical Tips for Strategic AI Collaboration with ChatGPT

2. 5 Steps in 5 Minutes: Practical Tips for Strategic AI Collaboration

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly for go-to-market strategy, product, brand, demand, content, and digital, and growth marketing. 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

In my previous newsletter about how a lean team transformed into a human-AI powerhouse, I covered a marketing team’s 6-month journey from a small group to a 45-member powerhouse (25 humans guiding and managing 20 AI teammates).

I shared the human-AI org transformation playbook showing steps to build a powerful human-ai org, earn trust, create learning spaces, and move forward together. The results speak for themselves: 50-75% faster content creation, 98% accuracy in lead qualification, and 35% better campaign performance.

Now an even bigger transformation is happening. As teams get comfortable with their AI teammates, customers are gaining AI buying assistants. The timing isn’t random. AI tools are evolving from executing tasks to working independently.

Today, AI already helps evaluate options. Makes clear recommendations. Handles routine decisions. This changes everything about how customers find and choose solutions.

The winners in this new era will be those who understand and adapt to this shift in buying behavior. We’ll take a look at what this means for your business and how to prepare.

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 podcast here. Once you hit play, give it just a few seconds then it will start.

Disclaimer: This podcast was generated by AI based on this written newsletter and reviewed by me to ensure ethical and responsible AI use. It’s designed to provide an efficient, more inclusive way to consume information.

How AI is Changing the Customer Buying Journey

First, let’s be clear about what we mean by “AI agents.” Paul Roetzer, CEO of the Marketing AI Institute, breaks it down simply. A true AI agent works independently, on our behalf, across five key areas:

• Set goals

• Make plans

• Execute tasks

• Learn from results

• Analyze outcomes

While many solutions call themselves “AI agents” today, most focus on execution and some planning and analysis. But that’s changing fast. Early agency capabilities in AI tools show where we’re headed:

• Gemini Advanced Deep Research – It shows planning abilities. It maps out research steps, finds sources, and creates reports with minimal guidance. (Here’s a sample research that I conducted with Deep Research and its output.)

• ChatGPT Deep Research – OpenAI recently launched its own research feature with ChatGPT Deep Research (Yes, same name :-)). It apparently does even deeper research than Gemini’s because it reasons.

• ChatGPT with Scheduled Tasks – It works independently, delivering responses at specific times or on a recurring basis.

• ChatGPT Operator – In a demo, OpenAI showed how it can order pizza, make restaurant reservations, and buy basketball game tickets on its own. But it hands control back to humans for sensitive tasks like payments, when it gets stuck, or needs approval for important decisions.

• AI Assistants – They can evaluate vendors before humans see them.

And it’s not just these foundational AI models. Your GTM (go-to-market) tools are getting smarter too:

• Experience platforms now create personalized content and journeys for each visitor

• Marketing tools run and adjust campaigns on their own

• Sales tools sort and route leads without help

• Customer success tools spot and fix issues by themselves

As Etai Beck, CEO of Folloze, shares an interesting perspective on this shift:

“What’s fascinating is how AI is transforming personalization in B2B. It’s no longer just about showing different content to different people. Today’s AI understands each buyer’s full context and adapts the entire experience in real-time.

The magic happens when AI handles the heavy lifting of personalization, freeing up humans to focus on meaningful conversations and strategic decisions.”

While these tools don’t do everything on their own, they handle specific tasks without constant human input.

But agency capability doesn’t necessarily equal control. Think of these capabilities like tools in your toolkit. As the toolkit grows, you’ll most likely still decide:

• Which capabilities to use

• When to use them

• How much autonomy to give them

• Where human judgment is important

Why This Story Matters

Your customers are changing how they research and buy. AI forms an opinion about your company before humans do.

Christopher Penn, Co-founder and Chief Data Scientist of Trust Insights, breaks this down perfectly.

“When it comes to creating content today, your content has to be available for two distinct audiences and serve both of them equally – human and machine. Most marketers tend to do one or the other well; SEO folks have long been practicing great content formatting for machines. Content marketers have long been practicing great content for humans. Relatively few do both well, and doing both well is mandatory now.

Think of it like a restaurant. A restaurant has to have clear, easy to understand menus (thanks, Cheesecake Factory, for your 5,000 word menu) for people to order well. But the food also has to not suck. A great menu with terrible food is not going to win customers. Great food with no menu at all is going to be a bad dining experience.”

Chris offers a practical tip to test if you’re serving both audiences:

“Here’s a simple, easy tip to see how your content stacks up. Use any screen reader intended for people with vision disabilities and browse your website and content.

If it’s a straightforward, positive experience where you can get to the content quickly and easily, and the content sounds good when you listen to the screen reader narrate it, you’ve got a winning site.

If it takes you 30 minutes of painful navigation just to get to your content, your site isn’t ready for AI.

And if your content is so dry that you lose interest in listening to it after 10 seconds, then your content isn’t ready for humans.”

I covered how brand trust is the new premium in the AI era previously. Strong brands already spend 5× less to get customers and keep them 50% longer. With AI as an amplifier, brands able to build trust with humans and AI can potentially achieve even better results.

This affects your whole GTM team. Sales teams now talk to buyers who have already gotten AI’s view of your company. Customer success works with users who bring AI tools to their daily work. Marketing teams need to connect with both AI agents and human decision-makers.

This diagram shows five key truths about business today:

1. Being found isn’t enough anymore – Just showing up in AI search results won’t cut it. You need real proof that shows AI and people they can trust you.

2. AI forms opinions before humans do – AI will evaluate your brand first. What it tells people about you influences every decision after.

3. AI amplifies brand impact – AI multiplies every signal about your brand. With trust, you’ll win faster and land bigger deals before your first call.

4. Trust drives business results – If you have trust, you can charge 20% more, convert twice more, and even reduce hiring costs. (Here are more insights on quantifying the value of brand, including an interactive calculator.)

5. Great products need trust to succeed – Even your best product will fail without trust. AI tells buyers whether to trust you before they ever talk to you.

The Evolution of B2B Buying

Let’s look at how B2B buying is changing from my perspective. Remember, we’re all learning together. There’s no ideal roadmap yet.

This table shows what we can expect, understanding these changes and adapting thoughtfully.

First Signs of Change

Here’s a real example: When I got invited to join a business directory, I didn’t schedule calls or read materials. I asked Perplexity to check the company. It quickly found some concerning practices. Decision made, before any sales pitch. (Here are more details on what happened.)

This happens thousands of times each day. AI looks at:

• Your online presence

• Customer reviews and sentiment

• Market position

• Trust signals

Reimagining GTM in the AI Era

The interactive AI-Human GTM Matrix below shows how your go-to-market activities can evolve to serve both human buyers and their AI assistants. Move the sliders to see how different levels of AI involvement changes each stage of the customer journey.

There are three key approaches:

1. Traditional GTM Evolving – Think of this as upgrading your existing playbook. You’re taking what already works, like your content, campaigns, and customer interactions, and making them work better with AI. It’s like adding smart features to a trusted tool.

2. New Human-Focused GTM – This is about doubling down on what humans do best. While AI handles the data and analysis, your team creates more meaningful experiences through workshops, strategic discussions, and relationship building.

3. New AI-Focused GTM – This is building for AI-first interactions. Just like we optimize websites for search engines, we’re now creating content and tools that AI buying agents can easily understand and use.

Kathie Johnson, Chief Marketing Officer of Sitecore, shares:

“Companies that serve both human buyers and AI assistants, rather than choosing between them, will win in the AI era. The leaders will update traditional programs for AI, create special human experiences, and build AI-first capabilities.

Success comes from knowing exactly what to automate, what to keep human, and what to rebuild for AI.”

Let’s look at an example across key stages:

Awareness (80% AI)

• Traditional – Blog posts now include structured data tables for easy AI analysis

• Human-focused – Expert roundtables that generate both stories and data

• AI-focused – Product specs in machine-readable formats

Consideration (60% AI)

• Traditional – ROI calculators that combine AI insights with human validation

• Human-focused – Interactive solution design workshops

• AI-focused – Automated compatibility checking tools

Purchase (40% AI)

• Traditional – AI-enhanced proposals with human-led negotiations

• Human-focused – Strategic alignment workshops

• AI-focused – API-driven pricing systems

These are just examples. Your mix will depend on your customers, product, and market. It’s important to find the right balance between AI efficiency and human connection at each stage.

Try the interactive Human-AI GTM matrix. Move the sliders to different positions. Notice how the highlighted activities shift. Where do you see opportunities to evolve your current approach?

Learning Together: Humans and AI Working Together

We’re starting to see buyers and AI agents work as partners like in doing research and evaluations. Here’s how it looks:

• Human sets the goal: “Find the best project management platform for us”

• AI does the research, comparisons, creates a shortlist

• Human makes key decisions using AI’s findings

Building Trust Step by Step

Remember how we learned to trust online shopping? We didn’t jump straight to buying expensive items on Amazon. We started small, maybe a book or a movie. Same with Uber and Airbnb. Most people didn’t immediately trust strangers to drive them around or stay in their homes.

These companies won trust through transparency with clear policies, real reviews, secure payments. And most importantly, giving users control over their privacy and information.

Now we’re seeing a similar pattern with AI. Both teams and customers are finding their comfort level with different types of AI help.

Here’s one framework that can help think through trust levels. While every organization will find their own way, this example shows different levels of AI involvement:

• Basic Info – Like webinar sign-ups

• Financial Tasks – Like routine purchases

• Personal Details – Like identity checks

• Sensitive Info – Like health records

• Full Access – Like tax planning

We’re at a crossroads between convenience and personal control. AI agents will get us thinking about our risk/benefit tolerance:

• What tasks are you comfortable delegating to AI?

• How much control are you willing to give up?

• What information are you prepared to share?

• Which AI companies/tools will you trust?

• What trade-offs and risks are you willing to accept?

Trust isn’t one-size-fits-all. Companies we already know and trust for specific tasks have a natural advantage in those areas. For example, we might trust our bank’s AI for financial transactions but prefer a specialized healthcare provider’s AI for medical advice. This existing trust gives established brands a head start in their specific domains.

For teams, this might mean starting with AI helping on basic content tasks before moving to more complex analysis. Or, starting with internal work then gradually using AI for customer-facing interactions. For customers, it could mean using AI for initial research before trusting it with purchase recommendations.

Recognizing that both sides of the relationship is important. Your team and your customers are on this trust journey together.

Understanding your own team’s path helps you better serve customers who are figuring out their own balance between AI efficiency and human control.

Looking Ahead

The lines between AI teammates and AI customers are blurring. We’re seeing this everywhere: AI helps our teams create content, while AI helps our customers evaluate that same content. AI assists our sales teams in qualifying leads, while AI helps buyers qualify us as vendors.

This mirroring isn’t random. As tools like Gemini, ChatGPT, and our GTM platforms gain more autonomous capabilities, we’ll see teams and customers using AI in increasingly similar ways. The winners will be those who understand both sides of this evolution.

Every GTM team is figuring this out. Some focus on making content machine-readable. Others create special experiences just for humans. The smartest ones do both.

What’s working for your team? Share your story below so we can learn from each other.

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly for go-to-market strategy, product, brand, demand, content, and digital, and growth marketing. 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

Picture a marketing team where humans and AI work side by side – not as replacements, but as partners with clear roles and responsibilities.

That’s exactly what one forward-thinking CMO achieved, transforming a lean marketing team into a 45-member powerhouse where 25 humans guide and work alongside 20 AI teammates.

To help teams envision how this human-AI partnership works in practice, here’s a clear example of a marketing team structure:

This type of structure, explored in our previous Marketing Team 2.0 newsletter, shows how AI teammates can support and amplify human expertise. The team adapted this model with remarkable results.

Here’s what the real marketing team structure looks like, showing how AI tools (dotted boxes) work with human team members (solid boxes):

Each AI tool has a specific job and works with specific team members. This keeps things clear and helps everyone work better together.

In six months, as I helped lead and guide this team along the way, we achieved what many think impossible: 50-75% faster content creation with better quality, 98% accuracy in lead qualification, and AI becoming a trusted thought partner for strategy development.

Here’s the playbook that made it happen. It reflects our key learnings.

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 podcast here. Once you hit play, give it just a few seconds then it will start.

Disclaimer: This podcast was generated by AI based on this written newsletter and reviewed by me to ensure ethical and responsible AI use. It’s designed to provide an efficient, more inclusive way to consume information.

Why This Story Matters

While this playbook follows a marketing team’s journey, the approach and learnings apply equally to sales and customer success teams. The principles of human-AI partnership, step-by-step adoption, and focus on people first are valuable for any GTM function.

As we explored in my previous newsletter about The Trailblazer Effect, success starts with team members who spot AI opportunities and take action. In this team, a few people were already using AI tools on their own. These “trailblazers” became natural champions and inspired others to get involved.

There’s more competition. It’s hard to stand out. Customer behaviors are changing fast. The way they find solutions, evaluate vendors, form opinions, and make buying decisions are evolving.

Ensuring good product-market fit and effective go-to-market strategy has never been more important. She needed to improve current operations while building for profitability.

Do more with less!

Her already lean team worked at capacity. People wore multiple hats with no in-house copywriters and minimal agency support. They needed a new approach.

She believed AI could help them adapt. However, adding AI tools alone would fall short. Her goals extended beyond the company’s immediate needs.

She cared deeply about her team’s future and wanted to invest in their growth. Building AI skills would help them succeed in this new era.

The Playbook

Here are the five steps that made this transformation successful. Each step was practical and built on the one before it.

Step 1: Start with Understanding

First, I guided the CMO to understand where everyone stood with AI. Some team members were paralyze because of fear of job loss. Some were already using AI without training or responsible AI guidelines, creating potential risks to the business. We needed to act thoughtfully but quickly.

The CMO set clear expectations early. “This is a learning journey we’ll take together,” she told the team. “While there’s no established playbook for AI adoption, we’ll have guidance from someone who’s helped other teams through this journey. Together, we’ll create our own path forward.”

She addressed concerns head-on. This wasn’t about replacing people. It was about helping them grow. We knew AI skills would be important for their careers, both within the company and beyond. Everyone’s voice needed to be heard for AI to benefit all, not just a few.

This approach paved the way for the team to grow into a 25-human, 20-AI structure, where each has a clear role and purpose.

We created multiple channels for input through open group discussions, 1:1 conversations, regular feedback sessions, and a team survey. Here’s a sample employee survey that you can use and modify as needed.

The survey showed important insights:

This human-first approach resonates with many experienced leaders. As Heidi Melin, a Senior Operating Advisor who guides CMOs of Hellman & Friedman’s portfolio companies go through transformation, notes:

“Working with portfolio company CMOs and collaborating with AI advisors like Liza, I’ve learned that understanding your people must come first.

Once you take time to hear their aspirations and concerns, you can show them a clear vision of a human-AI organization. The breakthrough happens when teams see specific examples of how AI can support each role and function, not replace them. This shifts the entire conversation from ‘what will AI do to us’ to ‘how can we build this together.’

When people feel truly heard and see themselves in this future, they move from hesitation to enthusiasm about the possibilities.“

These results guided our workshop design and overall approach. Being open and clear helped build trust for the next steps.

Step 2: Show What’s Possible

AI has changed how customers search, evaluate, and buy. It amplifies both strengths and weaknesses. Good strategies succeed faster, poor ones fail faster.

Our workshops moved beyond generic AI demos to show how AI could help marketing teams respond to these changes and solve real challenges.

Our initial workshop started with how customer behaviors are changing and ways we need to adapt withn the help of AI. This grounded our AI learning with a purpose–serving our customers the best way we can. It wasn’t simply using AI for the sake of AI.

We inspired what’s possible by covering use cases relevant to specific roles. Doing so jumpstarts people’s learning as they can readily see how AI helps them in their job. I discussed this topic in more detail in my previous newsletter titled Making AI Work for Every Marketing Role with Real Use Cases for Their Jobs.

Here’s just a sampling of AI use cases by function. There are many more.

Here’s our core workshop agenda. Then we conducted function-specific sessions for product marketing, campaign teams, and content creators. This approach helped people see immediate value in their daily work.

We chose our initial AI use cases carefully, focusing on three key criteria that tend to drive the most impact:

1. Tasks that are repetitive, time-consuming, and tactical – especially those that multiple people and functions handle. This immediately lightens the team’s load and frees up time for strategic and creative work.

2. Strategic thinking or processes that benefit from consistency – where AI can help standardize proven approaches and frameworks. It’s democratizing strategic thinking.

3. Use cases aligned with strategic initiatives – strategic initiatives have built-in advantages: clear owners, defined KPIs, allocated resources, and high visibility. With this alignment, we have a better shot at successful adoption. Here’s a template you can use for mapping.

Don’t feel the pressure to do everything at once. Start small and use these guidelines to choose your first AI projects thoughtfully.

Mike Kaput, Chief Content Officer of the Marketing AI Institute captures why this relevance matters:

“Most teams struggle with AI adoption because, a lot of times, people can’t see how it fits into their daily work.

Show a content strategist how AI helps them create better content faster, or a campaign manager how it improves targeting, that’s when the light bulb goes on.

Start with real problems your team faces every day. When they see AI solving those problems, you create the momentum needed for real transformation.”

Step 3: Create Space for Learning

The technology isn’t the hardest part—change management is. We created multiple ways for the team to learn, share, and grow together.

We prioritized responsible AI use from the start, collaborating closely with legal and IT teams to set clear guidelines. Teams learned through practical examples what responsible AI use looks like (see examples of responsible uses here) and potential risks to avoid.

This foundation helped us move quickly while protecting the company’s sensitive information. Here are 10 simple strategies to protect your data when using AI.

We made learning AI part of our job, not an afterthought or extra work. We:

• Protected time for AI exploration

• Created safe spaces for questions

• Set up regular help sessions

Three key breakthroughs showed us this approach was working. The team created custom GPTs (AI tools that users train with specific knowledge and guidelines) to handle various tasks:

• Pitch Deck Evolution – Product marketing managers created a custom GPT that reduced a week-long process to days. Teams now update pitch decks consistently and quickly across all chapters.

• Website Content Project – Eight team members across functions used a custom GPT to create website copy based on wireframes, messaging guidelines, and SEO requirements. We completed months of work in weeks, focusing on editing and consistency.

• Strategic Thought Partner – Teams began using AI to evaluate social media strategies, develop campaign plans, build cross-functional operating models, and plan key 2025 initiatives.

As the team worked more with AI, their roles evolved naturally. AI took on routine tasks. This gave people more time for strategic thinking and creative work.

Team members discovered a new dimension to their roles as builders and guides of their AI teammates, making sure their custom GPTs stayed effective and on track.

The team also came up with innovative ways to use AI that went beyond improving tasks. Here are two examples that stood out:

• Supporting Team Transitions – A content marketer preparing for maternity leave built a custom GPT to support her team while she’s away. This AI assistant gives her teammates easy access to her processes, guidelines, and content library. The goal was smooth handoffs and consistent work quality during her absence.

• Enhancing Team Collaboration – A senior leader created a GPT that captures her unique leadership approach. This AI tool helps her team understand her communication style, decision-making process, and how she handles different challenges. It even offers specific suggestions for working effectively with her in various situations. This helps with improving team collaboration as she takes on expanded responsibilities.

If interested, here’s a sample content creation custom GPT in action and a sneak peek of its instructions.

Step 4: Scale Success Through Systems

With early wins showing value, we created two tracking tools to expand these successes across the organization:

Custom GPT Tracking Template

First, we developed a Custom GPT Tracking System to manage our growing collection of AI tools. Clear tracking helped us scale successful approaches and avoid duplicating efforts.

Our tracking covered:

• Clear ownership and accountability

• GPT objectives and capabilities

• How to access the GPT

• Results and benefits

• GPT use limitations and consideration

Below is the Custom GPT Tracking Template. Feel free to adapt this for your team.

Paige O’Neill, CMO of Seismic, shares why this systematic approach matters:

“Anyone can experiment with AI tools. The teams that really transform are the ones that track what works and bring others along on the journey.

When your AI trailblazers share their wins and help teammates learn, you turn individual experiments into company-wide capabilities. That’s how real learning spreads and creates lasting change.”

AI Tech Stack Template

Building on this foundation, we created an AI Tech Stack Template to guide our technology decisions. We learned that building the right AI stack requires careful planning, not rushing to adopt every new tool.

1. Start with Current Martech AI Capabilities – Many existing platforms have powerful AI features. It’s easier to drive adoption with tools your team already knows. Review your platforms’ product roadmap and connect with account teams to maximize current investments.

2. Add Foundational AI Models – We provided paid versions of ChatGPT and Claude to help teams explore core capabilities. These tools offered a secure environment for teams to experiment and learn. The investment was about $1000/month for the entire team.

3. Fill Specific Gaps – Only after maximizing existing tools did we consider new solutions. The AI vendor landscape will consolidate through M&A. Some vendors may not survive. We evaluated additions based on:

• Clear business need

• Integration with current workflow

• Trial/PoC option and ROI potential

• Security and privacy policies

• Vendor stability indicators (Executive team expertise and track record, quality of investors and funding, defensible moats, customer reviews, market position, and growth trajectory)

This AI Tech Stack Template helps map and manage our AI capabilities across functions.

Our approach to building and tracking our AI capabilities delivered measurable results including:

• Content team creating 2x more high-quality assets

• Campaign performance up 35%

• Lead scoring accuracy at 98%

• 15 hours saved weekly on routine tasks

Check out this carousel for more real and quantifiable results from various marketing teams.

Quick Tip: Need to justify AI investment? A different org I worked with got their entire AI program funded after their custom translation GPT (translated customer-facing documents in 4 different languages) saved tens of thousands in localization costs. Start small, show value, then scale.

Step 5: Expand Your Impact

With strong foundations in place, the marketing team is now starting to expand their influence across the company. As the first team to reach 100% AI adoption in just six months, they’re naturally becoming the company’s AI trailblazers.

Early signs of broader impact are emerging:

• Custom GPTs being adapted for other departments

• Marketing-led AI discussions with other teams

• Cross-functional AI initiatives taking shape

This expansion is already breaking down traditional GTM silos. As Sean McCaffrey, Senior Director of Marketing Strategy and Operations at Cin7, noted in my AI Trailblazer Effect newsletter, the real power of AI emerges when it connects workflows across marketing, sales, and customer success, creating more personalized and cohesive customer experiences.

It’s just the beginning, but their story shows how focusing on people creates lasting success with AI.

Looking Ahead

This journey had clear results. In six months, a lean marketing team built a powerful human-AI organization. They strengthened their foundation by understanding team needs, showing practical applications, creating space for learning, and scaling what worked.

More importantly, they proved something valuable. When teams approach AI adoption thoughtfully and systematically, focusing on people first, the results extend beyond metrics. Teams become more strategic. People develop new skills. Work becomes more meaningful.

They continue to push boundaries, now exploring AI tools with autonomous capabilities like Gemini Advanced’s Deep Research and ChatGPT’s Scheduled Tasks. These experiments with basic AI agents (tools that can work on their own, on our behalf) are opening up new possibilities.

The team will also have to think about the newly launched ChatGPT Operator. This demo shows how this AI agent ordered pizza, made restaurant reservations, and bought groceries. This again changes the buyer behavior and journey.

The team will need to cater to both humans and the AI agents that work on their behalf. They’ll also need to determine how to use agents responsibly in their work.

As they develop and use more AI teammates, they’re proving what’s possible with the right approach.

Where is your team on this journey? How are you building your human-AI organization?

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly for go-to-market strategy, product, brand, demand, content, and digital, and growth marketing. 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.

Or, if audio-visual content is your style, check out recorded sessions on a variety of topics I’ve covered. You’ll also find information about my past and upcoming in-person speaking 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

Picture a marketing team where humans and AI work side by side – not as replacements, but as partners with clear roles and responsibilities.

That’s exactly what one forward-thinking CMO achieved, transforming a lean marketing team into a 45-member powerhouse where 25 humans guide and work alongside 20 AI teammates.

To help teams envision how this human-AI partnership works in practice, here’s a clear example of a marketing team structure:

This type of structure, explored in our previous Marketing Team 2.0 newsletter, shows how AI teammates can support and amplify human expertise. The team adapted this model with remarkable results.

Here’s what the real marketing team structure looks like, showing how AI tools (dotted boxes) work with human team members (solid boxes):

Each AI tool has a specific job and works with specific team members. This keeps things clear and helps everyone work better together.

In six months, as I helped lead and guide this team along the way, we achieved what many think impossible: 50-75% faster content creation with better quality, 98% accuracy in lead qualification, and AI becoming a trusted thought partner for strategy development.

Here’s the playbook that made it happen. It reflects our key learnings.

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 podcast here. Once you hit play, give it just a few seconds then it will start.

Disclaimer: This podcast was generated by AI based on this written newsletter and reviewed by me to ensure ethical and responsible AI use. It’s designed to provide an efficient, more inclusive way to consume information.

Why This Story Matters

While this playbook follows a marketing team’s journey, the approach and learnings apply equally to sales and customer success teams. The principles of human-AI partnership, step-by-step adoption, and focus on people first are valuable for any GTM function.

As we explored in my previous newsletter about The Trailblazer Effect, success starts with team members who spot AI opportunities and take action. In this team, a few people were already using AI tools on their own. These “trailblazers” became natural champions and inspired others to get involved.

There’s more competition. It’s hard to stand out. Customer behaviors are changing fast. The way they find solutions, evaluate vendors, form opinions, and make buying decisions are evolving.

Ensuring good product-market fit and effective go-to-market strategy has never been more important. She needed to improve current operations while building for profitability.

Do more with less, while achieving better results!

Her already lean team worked at capacity. People wore multiple hats with no in-house copywriters and minimal agency support. They needed a new approach.

She believed AI could help them adapt. However, adding AI tools alone would fall short. Her goals extended beyond the company’s immediate needs.

She cared deeply about her team’s future and wanted to invest in their growth. Building AI skills would help them succeed in this new era.

The Playbook

Here are the five steps that made this transformation successful. Each step was practical and built on the one before it.

Step 1: Start with Understanding

First, I guided the CMO to understand where everyone stood with AI. Some team members were paralyze because of fear of job loss. Some were already using AI without training or responsible AI guidelines, creating potential risks to the business. We needed to act thoughtfully but quickly.

The CMO set clear expectations early. “This is a learning journey we’ll take together,” she told the team. “While there’s no established playbook for AI adoption, we’ll have guidance from someone who’s helped other teams through this journey. Together, we’ll create our own path forward.”

She addressed concerns head-on. This wasn’t about replacing people. It was about helping them grow. We knew AI skills would be important for their careers, both within the company and beyond. Everyone’s voice needed to be heard for AI to benefit all, not just a few.

This approach paved the way for the team to grow into a 25-human, 20-AI structure, where each has a clear role and purpose.

We created multiple channels for input through open group discussions, 1:1 conversations, regular feedback sessions, and a team survey. Here’s a sample employee survey that you can use and modify as needed.

The survey showed important insights:

This human-first approach resonates with many experienced leaders. As Heidi Melin, a Senior Operating Advisor who guides CMOs of Hellman & Friedman’s portfolio companies go through transformation, notes:

“Working with portfolio company CMOs and collaborating with AI advisors like Liza, I’ve learned that understanding your people must come first.

Once you take time to hear their aspirations and concerns, you can show them a clear vision of a human-AI organization. The breakthrough happens when teams see specific examples of how AI can support each role and function, not replace them. This shifts the entire conversation from ‘what will AI do to us’ to ‘how can we build this together.’

When people feel truly heard and see themselves in this future, they move from hesitation to enthusiasm about the possibilities.“

These results guided our workshop design and overall approach. Being open and clear helped build trust for the next steps.

Step 2: Show What’s Possible

AI has changed how customers search, evaluate, and buy. It amplifies both strengths and weaknesses. Good strategies succeed faster, poor ones fail faster.

Our workshops moved beyond generic AI demos to show how AI could help marketing teams respond to these changes and solve real challenges.

Our initial workshop started with how customer behaviors are changing and ways we need to adapt withn the help of AI. This grounded our AI learning with a purpose–serving our customers the best way we can. It wasn’t simply using AI for the sake of AI.

We inspired what’s possible by covering use cases relevant to specific roles. Doing so jumpstarts people’s learning as they can readily see how AI helps them in their job. I discussed this topic in more detail in my previous newsletter titled Making AI Work for Every Marketing Role with Real Use Cases for Their Jobs.

Here’s just a sampling of AI use cases by function. There are many more.

Here’s our core workshop agenda. Then we conducted function-specific sessions for product marketing, campaign teams, and content creators. This approach helped people see immediate value in their daily work.

We chose our initial AI use cases carefully, focusing on three key criteria that tend to drive the most impact:

1. Tasks that are repetitive, time-consuming, and tactical – especially those that multiple people and functions handle. This immediately lightens the team’s load and frees up time for strategic and creative work.

2. Strategic thinking or processes that benefit from consistency – where AI can help standardize proven approaches and frameworks. It’s democratizing strategic thinking.

3. Use cases aligned with strategic initiatives – strategic initiatives have built-in advantages: clear owners, defined KPIs, allocated resources, and high visibility. With this alignment, we have a better shot at successful adoption. Here’s a template you can use for mapping.

Don’t feel the pressure to do everything at once. Start small and use these guidelines to choose your first AI projects thoughtfully.

Mike Kaput, Chief Content Officer of the Marketing AI Institute captures why this relevance matters:

“Most teams struggle with AI adoption because, a lot of times, people can’t see how it fits into their daily work.

Show a content strategist how AI helps them create better content faster, or a campaign manager how it improves targeting, that’s when the light bulb goes on.

Start with real problems your team faces every day. When they see AI solving those problems, you create the momentum needed for real transformation.”

Step 3: Create Space for Learning

The technology isn’t the hardest part—change management is. We created multiple ways for the team to learn, share, and grow together.

We prioritized responsible AI use from the start, collaborating closely with legal and IT teams to set clear guidelines. Teams learned through practical examples what responsible AI use looks like (see examples of responsible uses here) and potential risks to avoid.

This foundation helped us move quickly while protecting the company’s sensitive information. Here are 10 simple strategies to protect your data when using AI.

We made learning AI part of our job, not an afterthought or extra work. We:

• Protected time for AI exploration

• Created safe spaces for questions

• Set up regular help sessions

Three key breakthroughs showed us this approach was working. The team created custom GPTs (AI tools that users train with specific knowledge and guidelines) to handle various tasks:

• Pitch Deck Evolution – Product marketing managers created a custom GPT that reduced a week-long process to days. Teams now update pitch decks consistently and quickly across all chapters.

• Website Content Project – Eight team members across functions used a custom GPT to create website copy based on wireframes, messaging guidelines, and SEO requirements. We completed months of work in weeks, focusing on editing and consistency.

• Strategic Thought Partner – Teams began using AI to evaluate social media strategies, develop campaign plans, build cross-functional operating models, and plan key 2025 initiatives.

As the team worked more with AI, their roles evolved naturally. AI took on routine tasks. This gave people more time for strategic thinking and creative work.

Team members discovered a new dimension to their roles as builders and guides of their AI teammates, making sure their custom GPTs stayed effective and on track.

The team also came up with innovative ways to use AI that went beyond improving tasks. Here are two examples that stood out:

• Supporting Team Transitions – A content marketer preparing for maternity leave built a custom GPT to support her team while she’s away. This AI assistant gives her teammates easy access to her processes, guidelines, and content library. The goal was smooth handoffs and consistent work quality during her absence.

• Enhancing Team Collaboration – A senior leader created a GPT that captures her unique leadership approach. This AI tool helps her team understand her communication style, decision-making process, and how she handles different challenges. It even offers specific suggestions for working effectively with her in various situations. This helps with improving team collaboration as she takes on expanded responsibilities.

If interested, here’s a sample content creation custom GPT in action and a sneak peek of its instructions.

Step 4: Scale Success Through Systems

With early wins showing value, we created two tracking tools to expand these successes across the organization:

Custom GPT Tracking Template

First, we developed a Custom GPT Tracking System to manage our growing collection of AI tools. Clear tracking helped us scale successful approaches and avoid duplicating efforts.

Our tracking covered:

• Clear ownership and accountability

• GPT objectives and capabilities

• How to access the GPT

• Results and benefits

• GPT use limitations and consideration

Below is the Custom GPT Tracking Template. Feel free to adapt this for your team.

Paige O’Neill, CMO of Seismic, shares why this systematic approach matters:

“Anyone can experiment with AI tools. The teams that really transform are the ones that track what works and bring others along on the journey.

When your AI trailblazers share their wins and help teammates learn, you turn individual experiments into company-wide capabilities. That’s how real learning spreads and creates lasting change.”

AI Tech Stack Template

Building on this foundation, we created an AI Tech Stack Template to guide our technology decisions. We learned that building the right AI stack requires careful planning, not rushing to adopt every new tool.

1. Start with Current Martech AI Capabilities – Many existing platforms have powerful AI features. It’s easier to drive adoption with tools your team already knows. Review your platforms’ product roadmap and connect with account teams to maximize current investments.

2. Add Foundational AI Models – We provided paid versions of ChatGPT and Claude to help teams explore core capabilities. These tools offered a secure environment for teams to experiment and learn. The investment was about $1000/month for the entire team.

3. Fill Specific Gaps – Only after maximizing existing tools did we consider new solutions. The AI vendor landscape will consolidate through M&A. Some vendors may not survive. We evaluated additions based on:

• Clear business need

• Integration with current workflow

• Trial/PoC option and ROI potential

• Security and privacy policies

• Vendor stability indicators (Executive team expertise and track record, quality of investors and funding, defensible moats, customer reviews, market position, and growth trajectory)

This AI Tech Stack Template helps map and manage our AI capabilities across functions.

Our approach to building and tracking our AI capabilities delivered measurable results including:

• Content team creating 2x more high-quality assets

• Campaign performance up 35%

• Lead scoring accuracy at 98%

• 15 hours saved weekly on routine tasks

Check out this carousel for more real and quantifiable results from various marketing teams.

Quick Tip: Need to justify AI investment? A different org I worked with got their entire AI program funded after their custom translation GPT (translated customer-facing documents in 4 different languages) saved tens of thousands in localization costs. Start small, show value, then scale.

Step 5: Expand Your Impact

With strong foundations in place, the marketing team is now starting to expand their influence across the company. As the first team to reach 100% AI adoption in just six months, they’re naturally becoming the company’s AI trailblazers.

Early signs of broader impact are emerging:

• Custom GPTs being adapted for other departments

• Marketing-led AI discussions with other teams

• Cross-functional AI initiatives taking shape

This expansion is already breaking down traditional GTM silos. As Sean McCaffrey, Senior Director of Marketing Strategy and Operations at Cin7, noted in my AI Trailblazer Effect newsletter, the real power of AI emerges when it connects workflows across marketing, sales, and customer success, creating more personalized and cohesive customer experiences.

It’s just the beginning, but their story shows how focusing on people creates lasting success with AI.

Looking Ahead

This journey had clear results. In six months, a lean marketing team built a powerful human-AI organization. They strengthened their foundation by understanding team needs, showing practical applications, creating space for learning, and scaling what worked.

More importantly, they proved something valuable. When teams approach AI adoption thoughtfully and systematically, focusing on people first, the results extend beyond metrics. Teams become more strategic. People develop new skills. Work becomes more meaningful.

They continue to push boundaries, now exploring AI tools with autonomous capabilities like Gemini Advanced’s Deep Research and ChatGPT’s Scheduled Tasks. These experiments with basic AI agents (tools that can work on their own, on our behalf) are opening up new possibilities.

The team will also have to think about the newly launched ChatGPT Operator. This demo shows how this AI agent ordered pizza, made restaurant reservations, and bought groceries. This again changes the buyer behavior and journey.

The team will need to cater to both humans and the AI agents that work on their behalf. They’ll also need to determine how to use agents responsibly in their work.

As they develop and use more AI teammates, they’re proving what’s possible with the right approach.

Where is your team on this journey? How are you building your human-AI organization?

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly for go-to-market strategy, product, brand, demand, content, and digital, and growth marketing. 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.

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

Every brand wants trust. Now we can measure what it’s worth: 5× lower acquisition costs, 50% higher retention, 20% premium pricing. But a big change is that AI evaluates your brand before humans do.

Let me share a recent experience that shows why this matters. When I received an invitation to be listed in a business directory, I didn’t schedule calls or review materials. I simply asked AI about the organization. Within seconds, the AI response showed concerning patterns in their business practices. Decision made, before they could even make their pitch.

This is B2B buying in 2024. The value of a strong brand is no longer just measurable, it’s immediate.

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 podcast here.

Disclaimer: This podcast was generated by AI based on this written newsletter and reviewed by me to ensure ethical and responsible AI use. It’s designed to provide an efficient, more inclusive way to consume information.

The Real Impact of Brand Trust

“We need more brand awareness.”

“Our brand needs a refresh.”

“Brand building is important, but we can’t measure the ROI.”

Sound familiar? For years, marketing leaders have struggled to quantify the business value of brand investments. Board meetings and budget discussions often end with brand initiatives being viewed as a cost center rather than a value driver.

But what if we could measure it and show, in real numbers, how brand strength drives business value?

Amy Heidersbach, CMO of DHI Group Inc., has spent years proving that we can. Working with data from Harvard Business Review, McKinsey, Bain & Company, and other leading firms, she’s developed a framework that quantifies brand impact.

Our research, combining AI analysis of thousands of industry data points and market studies, validates these metrics across the B2B landscape. We’ve partnered to make this framework accessible through an interactive calculator.

“For too long, brand value has been treated as intangible – important, but impossible to measure. That’s a narrative we need to change.

The data is clear: strong brands drive measurable business outcomes. And in the AI era, these impacts are becoming even more pronounced and measurable.

What used to take months or years to demonstrate can now be seen more quickly through AI’s lens.”

Here’s what the numbers tell us for a sample SaaS company targeting the mid-market segment. See the demo below:

1. Customer Acquisition Becomes Dramatically More Efficient

Strong brands can experience up to 5× lower CAC (Customer Acquisition Cost). If you’re currently spending $1,000 per new customer, that’s an $800 savings each time. With your current user base of 100,000 monthly active users, applying this to just 10% of new acquisitions could mean $800,000 in lower acquisition costs annually.

2. Retention and Loyalty Create Compounding Value

Strong brands see 50% higher retention. With your current 85% retention rate and 10,000 premium users paying $100/month, improving retention could generate over $5.1 million in additional retained revenue over the course of the year. Plus, loyal customers are 4× more likely to refer others, with potential referral revenue from premium users who convert at your 10% rate.

3. Pricing Power Gives You More Options

Strong brands can often charge up to 20% more for their services. With your 10,000 premium users paying $100 per month, that extra 20% can translate to $2.4 million in additional annual revenue through premium pricing power alone.

The revenue impact is just the start. Strong brands also:

• Drive 2x higher conversion rates (with top B2B companies achieving up to 11.7% conversion rates)

• Contribute 20-30% of company market valuation (as seen across S&P 500 companies)

• Reduce talent acquisition costs by 43% while improving employee retention by 28%

The SaaS Multiplier Effect kicks in when customers trust you. They spend 25-40% more on additional features, creating a virtuous cycle where user growth leads to exponential value creation.

Want to calculate these numbers for your business? Try our calculator.

I created this calculator with AI (Claude Pro Sonnet 3.5), using simple instructions and no code (Yes, this engineer, many decades ago, turned GTM leader can’t code 😉).

In using the calculator, note that the results represent potential improvements if each brand impact is realized fully. In practice, results may overlap, and brand gains tend to build over time rather than immediately. Use these results as a guideline for possible outcomes, not a guaranteed one-year forecast.

To Amy’s point, the time to lean in on brand is now. The numbers above show the baseline value of a strong brand. But here’s what’s exciting: AI amplifies and accelerates all these KPIs. Let me show you how.

How AI Changes Everything

Yesterday, companies controlled their story through marketing and sales. They built their brand one meeting at a time. Today, AI evaluates your company before any interaction starts. 

Sure, people should verify what AI tells them. But let’s be honest: how many actually do? It’s like Google search results – we know there’s more beyond page one, but we rarely look. When AI gives an answer about your company, that often becomes the only answer most people see.

Here’s what we’re seeing:

• Speed of Impact. I asked AI about a business directory invitation. Within seconds, it showed poor BBB ratings and concerning business practices. Decision made. No calls needed. This happens thousands of times daily across B2B buyers. See my entire conversation with AI.

• Trust at Scale. While checking board game reviews at Target, I watched AI instantly verify trust signals. If buyers expect this level of verification for $30 games, imagine their expectations for six-figure B2B software contracts. Here’s the AI exchange.

• Earlier Influence. Universities now get evaluated through AI before the first campus visit. The same happens in B2B. Your brand opens or closes doors before your sales team knows about the opportunity. See my full collaboration with AI.

AI is an amplifier. It takes your brand’s existing signals, good or bad, and multiplies their impact. Strong brands see their advantages compound faster. Weak brands find their challenges exposed more quickly.

Godard Abel, CEO of G2, sees how this shift transforms business growth.

“Companies can no longer rely on traditional marketing messages and sales conversations to build brand and trust.

Your prospects now use AI LLMs to synthesize all information about your company in real time: website content, educational resources, customer reviews, social media discussions, employee feedback, and community engagement.

Market leaders aren’t just telling a great story. They’re focusing on creating amazing customer experiences that those customers share with the world across digital channels. That authentic customer feedback loop is what drives growth in today’s market.”

In the end, AI creates a faster path to either success or failure.

The AI Multiplier Effect

Here are three ways AI changes the speed and scale of brand impact:

• Customer Acquisition flips completely. AI recommendations put you on shortlists before competitors know about the opportunity. The typical 5x savings in acquisition costs is just the start. Your prospects now come pre-qualified, asking about solutions instead of credentials.

• Sales Velocity jumps forward. No more long cycles explaining who you are. Buyers arrive having seen your customer success stories and industry validation. The conversation starts at “how can we work together?” instead of “why should we trust you?”

• Revenue Impact compounds naturally. Strong brands command 20% higher prices because buyers see proof before discussing cost. Your customer success stories and validated outcomes show up in every AI search. Customers buy more features because they start with trust, not skepticism.

Where Do You Stand?

This new reality means you need to know exactly where your brand stands. Here’s a simple way to think about it:

The matrix shows four possible positions. Each one means something different in the AI era:

• Market Leaders combine high trust with strong product-market fit. AI shows their credibility and customer success stories automatically. They don’t just stay ahead. They pull further ahead.

• Hidden Gems have great products but low trust signals. Even with strong offerings, they might not show up in AI recommendations. Their focus should be building trust markers that both AI and humans can verify.

• Missed Opportunities have strong brand trust but haven’t aligned their product value. Their good reputation buys them time to improve, but they need to move fast. AI increasingly exposes the gap between brand perception and product reality.

• Danger Zone companies face tough choices. Low trust combined with poor product-market fit creates a compound problem as AI amplifies both weaknesses. They need to rebuild fundamentals before scaling.

Alexandra Gobbi, CMO of Veracode, understands how technology companies move between these positions.

“Getting product-market fit or building trust alone isn’t enough anymore. The most interesting shift I see is how quickly companies can move between these quadrants.

A startup with a great product but low trust can rapidly become a market leader by consistently proving their value. Meanwhile, established brands can’t coast on reputation – AI makes it clear when your product isn’t keeping up with market needs.

Success comes from treating trust and product excellence as one connected goal.”

Want to know where you really stand? Ask AI what the market thinks about your company. You’ll get an unfiltered view of your product-market fit and trust signals. The answers might surprise you, but they’ll show you exactly where you are on this matrix and what to fix first.

Making This Practical

1. First, know your numbers. Use our calculator to set your baseline metrics for price premiums, customer acquisition costs, and retention rates. You can’t improve what you don’t measure.

2. Next, see what AI says. Ask ChatGPT, Claude, Gemini, Perplexity, and other AI assistants about your company. What key insights are shared? What strengths get highlighted? What concerns come up? This is what your prospects see when they research you.

3. Finally, build trust signals that AI can find and verify. Start with customer proof. Share success stories with real metrics, customer reviews that show specific value, and case studies with clear outcomes. These give AI concrete evidence of your impact.

Add expert validation to strengthen your position. This includes recognition from industry analysts, mentions in research reports, and recommendations from respected voices in your field. AI picks up these third-party endorsements and uses them to validate your claims.

Keep your digital presence strong and consistent. Create helpful content, engage with your community, and maintain a clear message across all platforms. This helps AI build a complete picture of your value.

Want to dive deeper into the data behind these insights? I’ve used AI (Gemini Advanced Deep Research) to analyze hundreds of industry reports, academic studies, and market data. Here’s the research report for your reference.

Also check out these practical guides on optimizing your AI search strategy (an important part of your overall brand trust equation):

• Beyond AI-Generated Content: A Guide to Standing Out When 50% of Web Traffic Disappears

• Make Your Brand Sourced and a Top Result in AI Search: Practical Strategies for Marketers

Note that in the AI era, your brand value isn’t just what you say it is. It’s what AI tells everyone else it is.

Looking Ahead

AI isn’t changing what makes a great brand. It’s changing how fast the world notices. Great products, happy customers, and genuine value still matter most. AI just makes sure everyone knows about it sooner.

Think of AI as your brand’s megaphone to the market. Build something worth talking about, and AI will spread the word. Create real value, and AI will help you prove it.

Your brand has always been valuable. AI just makes it worth more.

Instead of just asking how to show up in AI, maybe we should ask: Who do we want to be when AI tells our story?

What did you discover when you asked AI about your brand? What surprised you most? Let me know in the comments.

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly for go-to-market strategy, product, brand, demand, content, and digital, and growth marketing. 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.