Newsletters

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

Quick Take

• AI is changing jobs faster than ever. Some roles will disappear, and while new roles will emerge, the bigger challenge is whether we can adapt quickly enough.

• GTM teams will move from using AI as just a tool to closely working with AI teammates. This significantly changes daily tasks.

• Teams that move through all three phases will have an advantage over their competitors, doing more complex work faster and with fewer people.

• Companies are already hiring for new roles that blend GTM expertise with daily AI collaboration.

• AI will make many roles more efficient, meaning fewer people may eventually be needed. Professionals who learn how to collaborate closely with AI will be better positioned to compete for these valuable roles.

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.

The 100-Year View: Jobs Change, Humans Adapt

Jobs have always changed over time.

When electricity arrived, blacksmiths became auto mechanics. When computers came along, file clerks moved into system administration. When the internet took off, retail workers shifted to e-commerce. Some jobs disappeared, but new careers took their place.

(See the timeline chart below, created by AI/Claude Sonnet 3.7, based on research from AI/ChatGPT 4.5. If interested, here’s the full research report, the infographic and my prompts.)

The big difference today is speed. Earlier changes took decades. AI is happening in months or weeks.

Ethan Mollick, Associate Professor at The Wharton School, puts it best:

“It is time to stop pretending that the world isn’t changing, and time to start taking control to get the future we want. We can’t predict which future we get, but we can try to steer towards a better one.”

AI is already changing how we work. We can either actively guide the change or let it happen to us.

Paul Roetzer, CEO of Marketing AI Institute and SmarterX, as well as the creator of JobsGPT, offers practical insight:

“AI isn’t just changing jobs; it is changing how we think about work itself. The real challenge isn’t whether AI will replace jobs, but how fast professionals and organizations can adapt.

With JobsGPT, we’re helping people understand how their roles are evolving, assess AI’s impact on specific tasks, and identify the skills needed to stay ahead. Those who thrive won’t just use AI. They will rethink workflows, develop new skills, and build AI-forward careers.”

Curious about how AI might impact your job?

Try JobsGPT. It shows you which tasks AI can support and the new skills you’ll need.

Can we reskill fast enough? Can companies create new roles quickly enough?

History shows humans adapt. With every new technology, we’ve always found new ways to contribute and add value. We’re built to adapt as long as we prepare now.

How AI Is Changing GTM Roles and Creating New Jobs

Go-to-market roles are already shifting significantly as AI adoption grows. This evolution follows a clear progression:

• Today – Doing tasks and coordinating projects

• Tomorrow – Guiding AI strategy and daily use

• Future – Designing and overseeing entire AI systems

This shift is affecting marketing, sales, and customer success teams equally. Forward-thinking companies are already hiring for new roles that blend GTM expertise with AI skills:

These new roles require daily collaboration with AI and often command six-figure salaries. Job titles like AI Agent Engineer, Gen AI Marketing Lead, and Experience Transformation AI Sales Specialist show how quickly the job market is changing.

Real-World Example of a New Job: Head of GTM Innovation

Even AI leaders like OpenAI are creating new roles that bridge technology and go-to-market functions. They’re currently hiring a “Head of GTM Innovation.” This is a perfect example of how orgs are rethinking traditional boundaries.

This person will:

• Lead engineers who improve how customers experience OpenAI

• Work with Sales, Technical Success, and Revenue teams

• Build AI tools that make GTM teams more effective

• Speak up for what users really need

What makes this interesting isn’t just the job title, it’s where it sits in the organization. This role lives in the Technical Success department, not in marketing or sales. OpenAI is building bridges between their AI experts and customer teams.

This is exactly the kind of silo-breaking we’ve been talking about. Remember what Jacob Warwick said: “The best executives are rebuilding how work gets done.”

Smart companies like OpenAI aren’t simply adding “AI” to old job titles. They’re creating whole new roles built for a world where humans and AI work as partners.

Here’s how this evolution might look specifically for Product Marketing as an example.

Check out this evolution yourself using the interactive models below:

• Interactive Marketing Role Evolution Model – Models Brand Marketing, Content Marketing, Digital Marketing,Product Marketing, Field Marketing, Marketing Operations, Demand Generation, Growth Marketing, and Partner Marketing

• Interactive Sales Role Evolution Model – Models Account Management, Sales Development, Solutions Sales, Sales Enablement, Sales Operations, Channel Sales, Inside Sales, Enterprise Sales, and Sales Engineering

• Interactive Customer Success Role Evolution Model – Models Customer Success Management, Customer Onboarding, Technical Customer Success, CS Operations, Customer Enablement, Customer Support, Digital Customer Success, Voice of Customer, and Renewal Management

Jason Warwick, CEO of ThinkWarwick Global, who coaches top executives on career growth and compensation, puts it simply:

“AI is changing how we lead, what we earn, and where our careers go—and it’s happening faster than any tech we’ve seen before. The best leaders aren’t just using AI tools. They’re completely rethinking how work happens. They tear down walls between teams, clear roadblocks, and build environments where people and AI succeed together.

The strongest leaders today are building their superpowers at the intersection of AI, leadership, and psychology. They understand technology, but also what makes people tick and how to bring out their best. As AI handles more routine tasks, your ability to influence, inspire, and connect on a human level becomes your competitive edge. Being authentic, showing integrity, and understanding what motivates people creates more value as AI clears away busywork and opens space for meaningful impact.

Leaders who can get their teams excited about AI instead of fearing it will build an unfair advantage. Those who wait or resist will be left behind. Your paycheck will reflect your ability to combine AI with strong teams. If you want to stay relevant, don’t passively wait for AI to define your job. Decide how AI fits into your vision, lean into your human leadership strengths, and take action now.”

Jacob has more great insights at www.execsandthecity.com.

Real-World Success: Human-AI Marketing Team in Action

This isn’t just theory. In my previous newsletter titled “A Leader’s Playbook: How a Lean Team Transformed Into a Human-AI powerhouse”, one forward-thinking CMO transformed a lean marketing team into a 45-member powerhouse where 25 humans built and guide 20 AI teammates responsibly.

In just six months, they saw clear results:

• 50-75% faster content creation

• 98% accuracy in lead qualification

• 35% better campaign performance

The team created human-AI workflows. Each person has specific AI teammates trained to support them.

See the full case study and step-by-step playbook.

Inside Human-AI Workflows: How the Collaboration Actually Works

In successful workflows, humans provide strategic direction, creativity, and final decision-making. AI rapidly analyzes data, surfaces insights, and handles routine tasks.

This goes beyond casually using tools like ChatGPT. It’s an intentional, structured partnership between GTM experts and AI trained for their specific roles.

Below is a detailed example of a Sales workflow:

Try the interactive workflow simulators for each GTM team:

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

• Interactive Marketing Workflows Simulator – Models product launch campaign, content creation, event planning, competitive analysis, and campaign performance.

• Interactive Customer Success Workflows Simulator – Models customer onboarding, issue resolution, product adoption, expansion planning, and renewal processes.

While each workflow shows collaboration within a single team, the strongest results happen when workflows connect across GTM teams. This breaks down silos, creating smoother experiences for your customers.

Alina Vandenberghe 🌶️ he Co-Founder and Co-CEO of Chili Piper, puts it this way:

“AI is the ultimate unfair advantage for GTM teams if you use it right. The strongest teams don’t simply automate tasks. They train AI to think like their best reps and eliminate tasks that slow them down.

If your team still qualifies leads manually, spends hours on research, or does repetitive tasks, you’re behind. Map your slowest processes first. Then let AI clear those bottlenecks—whether it’s data entry, customer insights, or sales follow-ups.

The future won’t wait. Teams that work with AI as a teammate will drive the best results.”

Three Ways to Pick Your First Human-AI Workflow

To choose your first AI teammate, here are three approaches to consider:

• Focus on key strategic projects. These efforts usually get quicker buy-in and support. Here’s a template you can use to map key AI use cases to strategic initiatives.

• Start with your biggest pain points. Quick results build confidence in AI.

• Empower your trailblazer. Begin with team members already excited about AI. Learn more about the important role and impact of trailblazers in scaling AI adoption here.

Once you pick your first use case, clearly map each step of the workflow. Decide exactly which tasks need human judgment and which ones AI can handle. Define clear points where humans and AI pass tasks to each other.

Looking Ahead: The Next Evolution in Human-AI Teams

As teams become comfortable with AI, systems will handle more complex work and require less oversight.

This means human roles will become even more important. People who guide AI will focus on strategic tasks. Teams will be able to do more without adding people.

But there’s also another side. AI will make many roles more efficient. Over time, fewer people may be needed to do the same work. People who know how to collaborate closely with AI will become more valuable and better positioned to compete for these roles.

Preparing now by reskilling and upskilling is the smartest way to remain competitive as these changes happen.

Skills you’ll need will also evolve over time, following the same progression from using AI tools to managing AI teammates to overseeing autonomous systems.

Where Are You in This Evolution?

Where does your team stand? Are you focused on AI tool usage, or building AI teammates?

Here are three ways to take the next step:

1. Share this newsletter with colleagues and discuss which workflows you could enhance with AI teammates.

2. Map one critical process in your GTM function using the human-AI workflow framework.

3. Create a simple AI teammate for one specific task and track the results.

I’d love to hear about your experiences. What workflows are you enhancing with AI teammates? What challenges are you facing? What benefits have you discovered?

Let’s continue learning together as we build the future of GTM functions in the AI era.

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

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

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

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

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

Quick Take

Companies miss revenue targets for many reasons. Market shifts. Economic headwinds. Changing customer needs.

But look between these external factors and you’ll find missed opportunities because of the gap between marketing and sales.

Marketing counts leads. Sales tracks closed deals. Customer success measures satisfaction scores. Everyone focuses on their own numbers instead of shared goals. As a result, the company could be losing money through inefficiency.

Here’s how: Marketing delivers 5,000 leads, hitting their target. Sales closes 500 deals, reaching quota. But what about the 4,500 leads that went nowhere? Some were poor fits, some got slow follow-up, some received inconsistent messaging. If just 10% of those could have closed with better handoffs, that’s 450 more deals and millions in revenue.

To be fair, misalignment isn’t just a sales follow-up problem. If marketing generates leads that aren’t truly qualified, or if sales has insights on ideal customers that marketing isn’t factoring in, both sides miss the mark. Alignment is a two-way street.

Note: All interactive models referenced in this newsletter were created with AI (Claude Artifacts) using natural language prompts.

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 This Story Matters

Customers don’t care about your org chart. They see your company as one entity, not separate departments. They expect seamless experiences regardless of which team they’re talking to.

The irony here is that AI tools can make our organizations more human-centered. As data analytics expose the gaps between teams and calculate their revenue impact, companies naturally rethink how they work together.

When the cost of slow handoffs appears in dollars on a dashboard, no one can ignore it. When the impact of misaligned goals becomes visible, change follows.

These alignment principles work across all business functions. AI makes alignment visible by converting patterns into insights and insights into action.

But AI isn’t a magic fix. It’s an amplifier. It highlights inefficiencies, but true alignment comes from leadership, culture, and execution. The companies that succeed won’t just adopt AI tools, they’ll include alignment into their strategy and incentives.

The Hidden Cost of Misalignment

When marketing and sales measure success differently, problems happen naturally. Marketing aims for more leads. Sales works on closing deals. Both teams can hit their targets while the company loses money.

This interactive calculator shows exactly what misalignment costs your business. There are two views you can explore:

Response Time Impact shows how lead follow-up speed affects revenue:

• Monthly Leads – Enter your total leads (example: 500)

• Average Deal Size – Your typical deal value (example: $25,000)

• Quick Response Win Rate – Conversion rate when leads get fast follow-up (example: 45%)

• Slow Response Win Rate – Conversion rate when follow-up is delayed (example: 20%)

• % of Leads with Quick Response – Adjust this slider to see impact (example: 50%)

The calculator shows your current monthly revenue ($4,062,500), potential revenue with 100% quick response ($5,625,000), and the monthly revenue you’re losing due to slow follow-up ($1,562,500). Note that these figures are illustrative examples. Plug in your own numbers to see your specific revenue impact.

Goal Alignment Impact shows how marketing-sales alignment affects revenue:

• Marketing Metrics – Monthly leads (1000), lead quality score (60%), cost per lead ($200)

• Sales Metrics – Lead follow-up rate (40%), conversion rate (25%), average deal size ($25,000)

This view shows your current revenue ($2,500,000), potential aligned revenue ($3,750,000), and monthly revenue lost from misalignment ($1,250,000).

Of course, these numbers aren’t one-size-fits-all. Conversion rates and revenue impact depend on factors like industry, sales cycle, and competitive positioning. Use this as a directional guide, not a predictor.

The key insight is that time matters, but so does cross-team alignment. When marketing generates leads that sales doesn’t follow up on, or when sales ignores certain lead types, the company leaves serious money on the table.

👉 Try the interactive calculator using your own data and scenarios. (Better experience on desktop vs mobile)

Latané Conant (she/her), Chief Revenue Officer at 6sense and author of “No Forms. No Spam. No Cold Calls,” notes:

“B2B buying isn’t a straight line — it’s an average 11-month maze with 640 interactions, and 81% of buyers have already picked a winner before they ever talk to sales. That means revenue teams need show up early, often, and in sync.

AI ensures alignment by making every touchpoint count, turning fragmented signals into a connected, high-impact buying experience. When teams have a shared understanding of the buying journey, they can engage the full buying team with the right actions at the right time, without stepping on each other’s toes along the way.”

The Power of Strategic Focus: Aligning Teams on the Right Targets

When marketing and sales align, they start saying “no” together.

While the interactive calculator shows the revenue cost of misalignment, there’s another hidden expense around wasted effort going after prospects that aren’t right for your business. The most resilient companies today have shifted from “growth at all costs” to “sustained profitability.”

This simple but powerful targeting framework can transform how teams collaborate:

• Green (center) – Your ideal customers. Focus most resources here where ROI is highest

• Yellow (middle) – Opportunistic prospects requiring careful qualification

• Red (outer) – Poor-fit prospects to avoid unless there’s a compelling strategic reason

See how the targeting framework works below:

👉 Play around with the interactive B2B target framework. (Better experience on desktop vs mobile)

When marketing and sales share this classification framework, they naturally start working as one unit. Marketing stops generating leads that sales won’t pursue. Sales stops complaining about lead quality. Both teams become accountable for pursuing the right opportunities, not just more opportunities.

What makes this approach powerful is how it creates natural alignment. The framework shows exactly which segments deserve focus and which should be deprioritized. This clarity eliminates the blame game and builds mutual accountability around customer fit.

The most aligned teams don’t just measure handoffs – they agree on who they’re targeting in the first place.

The Alignment Framework That Changes Everything

Response time is just one piece. The bigger challenge is aligning teams on shared outcomes.

This interactive framework helps teams align on what matters. Each market segment (Top Accounts, Key Verticals, Volume Business) has specific goals. Every function contributes to these shared outcomes.

For example, in the “Key Verticals” segment, marketing might own “achieving 30% market awareness in healthcare.” Sales commits to “closing 20 healthcare deals this quarter.” Product builds “healthcare-specific features by June.” When one area falls behind, everyone sees it and can help.

The matrix shows:

• Who owns which goals for each segment

• What teams need from each other

• Where handoffs work well or break down

• Which functions need support

The color coding serves as a signal, not a judgment. Green areas need maintenance. Red areas need attention. This visual approach helps teams support each other toward common goals.

👉 See the interactive framework in action. (Better experience on desktop vs mobile)

Kimberly O’Neil, who served as COO of Encompass Technologies and developed multiple cross-functional alignment frameworks, shares:

“The best teams I’ve led focus on shared goals, not just their own tasks. Using frameworks like this cut down friction between departments.

What makes it powerful is how it forces teams to map their workflows together. It helps with understanding exactly what they need to get from each other and what they must give or deliver to others. When teams make clear commitments about their inputs and outputs, work gets done, deals close faster and sales grow.

AI could track these connections daily instead of waiting months to see results. Companies that work this way will beat those where each team only cares about their own numbers.”

I saw first-hand how Kim put these frameworks to work and aligned our executive team at Encompass. We failed, learned, solved problems, and succeeded together!

From Framework to Reality

A GTM team recently implemented this approach with a simple principle that success belongs to everyone.

Each function mapped their goals to segment targets. Marketing didn’t just set lead goals. Sales didn’t just forecast deals. They focused on shared success:

• What each segment needs to win

• How teams support each other

• Clear deliverables between functions

• Regular progress checks

Consider this example: For enterprise accounts, marketing committed to delivering 50 qualified meetings per quarter. Sales promised detailed feedback on every meeting within 48 hours. Product agreed to join key calls to answer technical questions. This clarity eliminated the blame game (“these leads are bad” or “sales isn’t following up”) and built mutual accountability.

AI can make this even more effective by:

• Alerting when high-value leads haven’t been contacted within an hour

• Flagging when content for a specific industry isn’t being used by sales

• Highlighting which types of leads convert best across segments

• Predicting which deals need executive support to close

Early signs show promise. Teams catch handoff issues faster. They jump in to help before deals stall. Most importantly, they’re having better conversations about what customers really need.

But for these frameworks to stick, teams need more than dashboards. They need clear ownership and incentives. Alignment thrives when leaders tie success metrics to shared outcomes, not just individual KPIs. Change happens faster when compensation, feedback loops, and enablement reinforce cross-team collaboration.

Jonathan Moss, EVP of Growth, GTM Strategy, and Operations at Experity, shares:

“Most business ideas stay trapped in static slides and spreadsheets. The companies pulling ahead are those turning thinking into doing with AI-built models anyone can use. We’ve seen teams create interactive calculators in minutes that would have taken weeks to code.

The real power comes from giving people tools to test scenarios with their own numbers and see results instantly. These models solve the last-mile problem in GTM strategy: turning insights into actions that drive revenue. Interactive tools lead to faster, smarter decisions across the organization.”

Build Your Own Interactive Models

Creating models like these is one of AI’s most useful yet overlooked powers. Most teams share static slides that no one uses. AI helps turn ideas into tools people actually use.

Building models like the Revenue Calculator or GTM Alignment Matrix is easier than you think. No coding needed. Just describe in plain language:

• Your key inputs (leads, close rates, deal size)

• The math you want done

• How to show the results

• What people should be able to change

For example, I recently asked AI: “Create an interactive model that shows revenue impact of lead response time. Let users input monthly leads, average deal size, and conversion rates for fast and slow response. Calculate the difference in revenue.” Within minutes, I had a working calculator without writing a single line of code.

The video below show how I prompted AI in plain English to create an interactive model.

AI does the technical work. Your ideas become tools people use. Teams work with numbers instead of just reading them.

This changes how insights spread. Teams see money impact with their own numbers. They spot gaps where they happen. Data drives action.

Looking Ahead

AI gives us new ways to see how team alignment affects money. Companies that build shared goals will win. Those with strict silos risk missing growth.

As we explored in my previous newsletter on AI Raising Customer Expectations: How to Unify Go-to-Market Workflows, customers don’t see your org silos, they expect seamless experiences. When marketing and sales align on targeting the right prospects from the beginning and sharing insights throughout the journey, they naturally deliver these connected experiences.

When teams see the numbers, something clicks. Data makes the invisible visible. Sales understands marketing’s challenges. Marketing sees what sales needs. People start working together because they can see how their work connects. They focus on the same goals instead of competing priorities.

The result is better experiences for customers and stronger work places where success belongs to everyone.

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

Companies miss revenue targets for many reasons. Market shifts. Economic headwinds. Changing customer needs.

But look between these external factors and you’ll find missed opportunities because of the gap between marketing and sales.

Marketing counts leads. Sales tracks closed deals. Customer success measures satisfaction scores. Everyone focuses on their own numbers instead of shared goals. As a result, the company could be losing money through inefficiency.

Here’s how: Marketing delivers 5,000 leads, hitting their target. Sales closes 500 deals, reaching quota. But what about the 4,500 leads that went nowhere? Some were poor fits, some got slow follow-up, some received inconsistent messaging. If just 10% of those could have closed with better handoffs, that’s 450 more deals and millions in revenue.

To be fair, misalignment isn’t just a sales follow-up problem. If marketing generates leads that aren’t truly qualified, or if sales has insights on ideal customers that marketing isn’t factoring in, both sides miss the mark. Alignment is a two-way street.

Note: All interactive models referenced in this newsletter were created with AI (Claude Artifacts) using natural language prompts.

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 This Story Matters

Customers don’t care about your org chart. They see your company as one entity, not separate departments. They expect seamless experiences regardless of which team they’re talking to.

The irony here is that AI tools can make our organizations more human-centered. As data analytics expose the gaps between teams and calculate their revenue impact, companies naturally rethink how they work together.

When the cost of slow handoffs appears in dollars on a dashboard, no one can ignore it. When the impact of misaligned goals becomes visible, change follows.

These alignment principles work across all business functions. AI makes alignment visible by converting patterns into insights and insights into action.

But AI isn’t a magic fix. It’s an amplifier. It highlights inefficiencies, but true alignment comes from leadership, culture, and execution. The companies that succeed won’t just adopt AI tools, they’ll include alignment into their strategy and incentives.

The Hidden Cost of Misalignment

When marketing and sales measure success differently, problems happen naturally. Marketing aims for more leads. Sales works on closing deals. Both teams can hit their targets while the company loses money.

This interactive calculator shows exactly what misalignment costs your business. There are two views you can explore:

Response Time Impact shows how lead follow-up speed affects revenue:

• Monthly Leads – Enter your total leads (example: 500)

• Average Deal Size – Your typical deal value (example: $25,000)

• Quick Response Win Rate – Conversion rate when leads get fast follow-up (example: 45%)

• Slow Response Win Rate – Conversion rate when follow-up is delayed (example: 20%)

• % of Leads with Quick Response – Adjust this slider to see impact (example: 50%)

The calculator shows your current monthly revenue ($4,062,500), potential revenue with 100% quick response ($5,625,000), and the monthly revenue you’re losing due to slow follow-up ($1,562,500). Note that these figures are illustrative examples. Plug in your own numbers to see your specific revenue impact.

Goal Alignment Impact shows how marketing-sales alignment affects revenue:

• Marketing Metrics – Monthly leads (1000), lead quality score (60%), cost per lead ($200)

• Sales Metrics – Lead follow-up rate (40%), conversion rate (25%), average deal size ($25,000)

This view shows your current revenue ($2,500,000), potential aligned revenue ($3,750,000), and monthly revenue lost from misalignment ($1,250,000).

Of course, these numbers aren’t one-size-fits-all. Conversion rates and revenue impact depend on factors like industry, sales cycle, and competitive positioning. Use this as a directional guide, not a predictor.

The key insight is that time matters, but so does cross-team alignment. When marketing generates leads that sales doesn’t follow up on, or when sales ignores certain lead types, the company leaves serious money on the table.

👉 Try the interactive calculator using your own data and scenarios. (Better experience on desktop vs mobile)

Latané Conant (she/her), Chief Revenue Officer at 6sense and author of “No Forms. No Spam. No Cold Calls,” notes:

“B2B buying isn’t a straight line — it’s an average 11-month maze with 640 interactions, and 81% of buyers have already picked a winner before they ever talk to sales. That means revenue teams need show up early, often, and in sync.

AI ensures alignment by making every touchpoint count, turning fragmented signals into a connected, high-impact buying experience. When teams have a shared understanding of the buying journey, they can engage the full buying team with the right actions at the right time, without stepping on each other’s toes along the way.”

The Power of Strategic Focus: Aligning Teams on the Right Targets

When marketing and sales align, they start saying “no” together.

While the interactive calculator shows the revenue cost of misalignment, there’s another hidden expense around wasted effort going after prospects that aren’t right for your business. The most resilient companies today have shifted from “growth at all costs” to “sustained profitability.”

This simple but powerful targeting framework can transform how teams collaborate:

• Green (center) – Your ideal customers. Focus most resources here where ROI is highest

• Yellow (middle) – Opportunistic prospects requiring careful qualification

• Red (outer) – Poor-fit prospects to avoid unless there’s a compelling strategic reason

See how the targeting framework works below:

👉 Play around with the interactive B2B target framework. (Better experience on desktop vs mobile)

When marketing and sales share this classification framework, they naturally start working as one unit. Marketing stops generating leads that sales won’t pursue. Sales stops complaining about lead quality. Both teams become accountable for pursuing the right opportunities, not just more opportunities.

What makes this approach powerful is how it creates natural alignment. The framework shows exactly which segments deserve focus and which should be deprioritized. This clarity eliminates the blame game and builds mutual accountability around customer fit.

The most aligned teams don’t just measure handoffs – they agree on who they’re targeting in the first place.

The Alignment Framework That Changes Everything

Response time is just one piece. The bigger challenge is aligning teams on shared outcomes.

This interactive framework helps teams align on what matters. Each market segment (Top Accounts, Key Verticals, Volume Business) has specific goals. Every function contributes to these shared outcomes.

For example, in the “Key Verticals” segment, marketing might own “achieving 30% market awareness in healthcare.” Sales commits to “closing 20 healthcare deals this quarter.” Product builds “healthcare-specific features by June.” When one area falls behind, everyone sees it and can help.

The matrix shows:

• Who owns which goals for each segment

• What teams need from each other

• Where handoffs work well or break down

• Which functions need support

The color coding serves as a signal, not a judgment. Green areas need maintenance. Red areas need attention. This visual approach helps teams support each other toward common goals.

👉 See the interactive framework in action. (Better experience on desktop vs mobile)

Kimberly O’Neil, who served as COO of Encompass Technologies and developed multiple cross-functional alignment frameworks, shares:

“The best teams I’ve led focus on shared goals, not just their own tasks. Using frameworks like this cut down friction between departments.

What makes it powerful is how it forces teams to map their workflows together. It helps with understanding exactly what they need to get from each other and what they must give or deliver to others. When teams make clear commitments about their inputs and outputs, work gets done, deals close faster and sales grow.

AI could track these connections daily instead of waiting months to see results. Companies that work this way will beat those where each team only cares about their own numbers.”

I saw first-hand how Kim put these frameworks to work and aligned our executive team at Encompass. We failed, learned, solved problems, and succeeded together!

From Framework to Reality

A GTM team recently implemented this approach with a simple principle that success belongs to everyone.

Each function mapped their goals to segment targets. Marketing didn’t just set lead goals. Sales didn’t just forecast deals. They focused on shared success:

• What each segment needs to win

• How teams support each other

• Clear deliverables between functions

• Regular progress checks

Consider this example: For enterprise accounts, marketing committed to delivering 50 qualified meetings per quarter. Sales promised detailed feedback on every meeting within 48 hours. Product agreed to join key calls to answer technical questions. This clarity eliminated the blame game (“these leads are bad” or “sales isn’t following up”) and built mutual accountability.

AI can make this even more effective by:

• Alerting when high-value leads haven’t been contacted within an hour

• Flagging when content for a specific industry isn’t being used by sales

• Highlighting which types of leads convert best across segments

• Predicting which deals need executive support to close

Early signs show promise. Teams catch handoff issues faster. They jump in to help before deals stall. Most importantly, they’re having better conversations about what customers really need.

But for these frameworks to stick, teams need more than dashboards. They need clear ownership and incentives. Alignment thrives when leaders tie success metrics to shared outcomes, not just individual KPIs. Change happens faster when compensation, feedback loops, and enablement reinforce cross-team collaboration.

Jonathan Moss, EVP of Growth, GTM Strategy, and Operations at Experity, shares:

“Most business ideas stay trapped in static slides and spreadsheets. The companies pulling ahead are those turning thinking into doing with AI-built models anyone can use. We’ve seen teams create interactive calculators in minutes that would have taken weeks to code.

The real power comes from giving people tools to test scenarios with their own numbers and see results instantly. These models solve the last-mile problem in GTM strategy: turning insights into actions that drive revenue. Interactive tools lead to faster, smarter decisions across the organization.”

Build Your Own Interactive Models

Creating models like these is one of AI’s most useful yet overlooked powers. Most teams share static slides that no one uses. AI helps turn ideas into tools people actually use.

Building models like the Revenue Calculator or GTM Alignment Matrix is easier than you think. No coding needed. Just describe in plain language:

• Your key inputs (leads, close rates, deal size)

• The math you want done

• How to show the results

• What people should be able to change

For example, I recently asked AI: “Create an interactive model that shows revenue impact of lead response time. Let users input monthly leads, average deal size, and conversion rates for fast and slow response. Calculate the difference in revenue.” Within minutes, I had a working calculator without writing a single line of code.

The video below show how I prompted AI in plain English to create an interactive model.

AI does the technical work. Your ideas become tools people use. Teams work with numbers instead of just reading them.

This changes how insights spread. Teams see money impact with their own numbers. They spot gaps where they happen. Data drives action.

Looking Ahead

AI gives us new ways to see how team alignment affects money. Companies that build shared goals will win. Those with strict silos risk missing growth.

As we explored in my previous newsletter on AI Raising Customer Expectations: How to Unify Go-to-Market Workflows, customers don’t see your org silos, they expect seamless experiences. When marketing and sales align on targeting the right prospects from the beginning and sharing insights throughout the journey, they naturally deliver these connected experiences.

When teams see the numbers, something clicks. Data makes the invisible visible. Sales understands marketing’s challenges. Marketing sees what sales needs. People start working together because they can see how their work connects. They focus on the same goals instead of competing priorities.

The result is better experiences for customers and stronger work places where success belongs to everyone.

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

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.