Practical AI in Go-to-Market Get practical insights in using AI for go-to-market strategy, initiatives, workflows, and roles.
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:
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.
Strategic thinking or processes that benefit from consistency – where AI can help standardize proven approaches and frameworks. It’s democratizing strategic thinking.
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.
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.
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.
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.