Newsletters

211 AI Teammates. Only 57 Stuck. Here’s the Difference.

Liza Adams · January 8, 2026 ·

In 2025, AI teammates proved they work. Custom GPT usage increased 19x. Moderna went from 750 to over 3,000 GPTs. BCG has built 36,000, calling itself the top creator of custom GPTs globally.

Companies everywhere started building. But building is the easy part. The harder question is why some AI teammates get used every day while others get forgotten within a week.

Quick Take

Why do some AI teammates become part of how teams actually work while others get forgotten in a week? It comes down to five design decisions made before anyone starts building.

This is Part 1 of a two-part series. Part 1 covers the design decisions that determine whether your AI teammate thrives or dies. Part 2 covers how to connect teammates into workflows. These principles apply whether you’re building custom GPTs, Gemini Gems, Copilot Agents, Claude Projects, or Glean Apps.

One team I worked with created 211 AI teammates during their experimentation phase. They kept 57 in active workflows. The rest weren’t all failures: some were duplicates, some too narrow for broad use, some served one person’s productivity needs just fine. But the 57 that became part of how the team actually works had something in common. Their builders made five design decisions intentionally before they started. (For a deeper dive, check out this leading cybersecurity company’s case study.)

Whether you’re building your first AI teammate or auditing your fiftieth, these decisions separate AI teammates that stick from ones that get forgotten.

Key Takeaways:

The name you choose signals the relationship you’re creating. Mismatched expectations kill adoption.
You don’t need perfect internal documentation to start, but you do need to be intentional about what your AI teammate knowsInstructions determine whether your AI teammate thinks with you or for you, and that difference compounds over time.
The best AI teammates are designed for the least expert user, not the person who built them.
For AI teammates that inform strategy or decisions, design them to enhance human judgment rather than bypass it.

AI Video Explainer and AI Podcast Versions of This Newsletter

To support different learning styles, this newsletter is available as an 7-min AI video explainer (see below) and a 12-min AI podcast with two AI hosts. If you haven’t seen these AIs in action, they’re worth a view. The tech is advancing in amazing ways. I used Google’s NotebookLM to create these and personally reviewed them for accuracy and responsible AI use.

Captions are auto generatedPlayAI Video Explainer Version of This Newsletter

The Five Design Decisions

Based on my work with GTM teams, 80-85% of AI use focuses on speed (do this task faster), 10-15% focuses on quality (do this task better), and only 3-5% focuses on innovation (do it differently). The risk is stopping at speed.

One team that pushed through all three saw 50-75% faster content creation, 98% lead qualification accuracy, and 35% improved campaign performance. (See my previous newsletter on Human + AI Org Transformation Case Study)

The teams that get those results design AI teammates with intention. Here are the five key decision points.

Decision 1: What relationship are you creating?

Too many AI teammates get built and barely used. Often the problem is the name, not the tech. A Sales Assistant that’s really a lead scorer confuses people. A Helper with no clear purpose gets ignored. An Assistant that tries too hard to sound human feels awkward.

Every AI teammate falls somewhere on a spectrum.

Tools are pure function with no personality, like a Lead Scorer, Data Categorizer, or Testimonial Finder. People use them once, get what they need, and move on. The clarity is the feature.
Sidekicks adapt and collaborate, like a Draft Helper, Campaign Partner, or Strategy Assistant. They work alongside you without pretending to be someone specific. Fun names work here. Robin or Chewy signal helpful collaborator.
Personas extend someone’s thinking, like LizaGPT (my digital twin), CEO Jordan bot, or an Enterprise Buyer Persona. Less about tasks, more about testing ideas, challenging logic, and finding blind spots. The name tells you whose perspective you’re getting.

Match the name to the relationship you’re creating. Clearer expectations mean your AI teammates actually get used.

Decision 2: What does it need to know?

This is where teams get stuck. They won’t build because they think they need perfect internal documentation first.

Some have nothing written down. Others have scattered or outdated playbooks. And those with established best practices assume what worked before still works.

You don’t need perfect internal knowledge to build effective AI teammates. You can use external research to fill gaps, validate what you have, or challenge assumptions you didn’t know you were making.

Deep research features in ChatGPT, Claude, Gemini, and Perplexity can gather industry benchmarks, best practices, and frameworks in minutes. Use that as your AI teammates starting knowledge, then refine based on your team’s situation.

I used this approach for LizaGPT. I asked the AI to research publicly available information about me, my work, and my frameworks. That research report became part of the knowledge base, giving my digital twin context about how others see my work. (See previous newsletter on how I built and use LizaGPT)

Start with external research. Test it in practice. Let your team improve it over time. That becomes knowledge worth keeping. See some examples by function in marketing.

Decision 3: How should it engage?

This is where good AI teammates become great ones, or quietly make your team weaker.

I wrote about this in a previous newsletter on critical thinking with AI. Your AI teammate can follow brand guidelines better than most people on your team. It can write a persona-based email in seconds. Maybe too convenient.

Go-to-market teams are doing the hard work of building AI teammates trained on brand guidelines, messaging frameworks, customer personas, and strategic templates. This is a solid foundation.

But many are unintentionally outsourcing the thinking along with the execution.

For routine, high-volume tasks like summarizing call notes or categorizing support tickets, full automation makes sense. The problem is when we do the same for strategic work that defines our value.

The difference is in the instructions.

Your AI teammate knows your brand, personas, and frameworks. Make sure your instructions use that knowledge to think with you, not for you. Ask for analysis before recommendations. Request trade-offs instead of just answers. Keep humans in control of key decisions.

Jim Kruger, CMO of Informatica, had this insight during a strategic applied AI workshop I facilitated with his team:

“The best marketers I’ve worked with can tell you why something works, not just what works. AI can give you ‘the what’ all day long. If you’re not careful, you end up with a team that can execute but can’t explain the strategy behind any of it to apply to future initiatives.”

Decision 4: How easy is it to use?

When you build an AI teammate, you quietly become a product designer.

Most builders skip that part. They focus on what the AI knows, not how easy it is to use. Then they wonder why nobody else uses it.

Not every AI teammate needs great UX. Personal productivity tools can be scrappy. But the more it’s shared across a team, the more design matters. You can’t assume everyone knows how to get the best out of what you built. Good design shows them.

Today’s AI performs best with structured input. Not everyone thinks that way. And they shouldn’t have to.

If adoption depends on knowing how to prompt well, adoption will stay low. The best AI teammates are designed for the least expert user on the team. The goal isn’t to dumb things down. It’s to meet people where they are.

Every element of friction is a reason for someone to give up and go back to the old way of working.

I built one for myself called the GPT Instruction Architect, based on the GRACE framework (Goal, Role, Actions, Context, Examples). It’s a little meta: a custom GPT that helps you create instructions for a custom GPT you’re trying to build.

But you don’t need my version. Whether you build your own, use mine, or just keep a checklist, the value is having a consistent approach your whole team follows. When everyone uses the same framework, you get better quality, clearer structure, and AI teammates that reflect your team’s standards instead of whoever happened to build them.

Here’s a simple prompt template to ask AI to write instructions for you. There’s no shame in asking AI for help. In fact, it’s smart.

Renée Gapen, SVP of Marketing at PointClickCare noted:

Renee Gapen, SVP of Marketing at PointClickCare

“Ideas, insight, and imagination, are human superpowers, but not everyone thinks in frameworks! The tool we built handles the structure by asking the right questions, so people can stop thinking about ‘how’ to instruct AI and start thinking about ‘what’ they want to create. This makes it easier for our team to focus on the possible rather than getting mired in the process.”

Decision 5: Does the human still own the decision?

You can write perfect instructions and still lose your strategic edge.

Decision 3 was about setting up the right ask. This is about what happens after you get the answer. One controls the input, the other determines what you do with the output. Both protect human judgment, but at different points in the process.

This applies mainly to AI teammates that inform strategy or decisions, not simple task-based tools.

Even if you’re building a Sidekick and not a Persona, you can design it to enhance judgment rather than bypass it.

An answer machine gives you one answer. You accept or reject it.

A thinking partner gives you options, explains the rationale, surfaces trade-offs, and lets you evaluate and decide.

The difference looks like this. “Here’s your email” is an answer machine. “Here are three approaches, here’s why each might work, and here’s what to watch for with each. Which direction fits your situation?” is a thinking partner.

Each conversation should teach you something. You learn how to evaluate ideas, understand trade-offs, and make better decisions over time. That’s how you move past using AI for speed and start using it for quality and innovation.

If you’re not sure whether your AI teammate is helping you think or thinking for you, try asking it: What assumptions are you making? or What would a different persona think?

If the response surprises you, you’ve got thinking work to reclaim.

Alexandra Gobbi is CMO at Unanet. After an AI workshop with her marketing team, here’s what she observed.

“We came in thinking we were AI forward. We were using AI daily. We left with a new lens. So did the team. It’s not about using AI more. It’s about thinking with it more critically. Challenging it to go deeper. Not accepting the first answer. One team member told me she’ll carry that perspective forward. AI generates options. Humans make the final call.”

The Bottom Line

AI teammates fail for predictable reasons. None of them are about the technology.

The 57 that stuck aren’t lucky. They’re intentional. Their builders thought through the relationship they were creating, what the AI teammate needed to know, how it should engage, how easy it would be to use, and whether humans would stay in control of decisions that matter.

Before you build your next AI teammate, make these five decisions on purpose:

Get these right, and you’ll build AI teammates worth keeping.

What’s Next

Building great AI teammates is step one. But McKinsey’s 2025 State of AI report found that workflow redesign drives the biggest impact from gen AI, yet only 21% of organizations have done it. Most are still bolting AI onto existing processes.

Part 2 (coming January 22) covers how to move from teammates to workflows: where workflows add value, how to design teammates for connection, and who should build versus integrate. (See also: What We Learned in 2025 and Where Were Headed in 2026)

If you’re building AI teammates, I’d love to hear which of these five decisions has been hardest to get right. Drop a comment and share if you found this helpful.

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

For applied learning: Explore our applied AI workshops, offering both strategic sessions (use cases and roadmaps) and hands-on building (create AI teammates and workflows during the workshop). You’ll leave with either a clear plan or working solutions.

For team transformation:See real examples—a lean GTM team’s step-by-step playbook and a global cybersecurity leader scaling to 150+ marketers with 57 AI teammates integrated into daily workflows.

For speaking: Here are virtual and in-person 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.

People First, AI Forward: What We Learned in 2025 and Where We’re Headed in 2026

Liza Adams · December 16, 2025 ·

We spent 2025 testing whether humans and AI could actually work together. They can. Now the question is how we scale it.

Quick Take

2025 was the year AI moved from experiment to reality. Teams built AI teammates, buyer behavior shifted with AI search, and the teams that pushed past “faster” into “different” proved what’s possible when you put people first and move forward with AI.

Most companies are still stuck on “faster.” And focusing on productivity alone is a trap.

Now we’re heading into 2026. More companies will pile in. AI capabilities will keep advancing. The messy middle is coming. But we have proof now. We proved it together in 2025. Now we scale it together in 2026.

What 2025 proved:

People built AI teammates – Custom GPT usage increased 19x this year
AI search changed buyer behavior – AI now forms opinions about your brand before humans do
Results moved from faster to better to different – Speed was the start, but reimagined work is where humans become essential

Where 2026 is headed:

Reimagine workflows around the customer, not your org chart
Prepare for AI that acts, not just answers
Scale the human + AI model, from pockets of success to how the whole org operates

We don’t have to figure this out alone. The trailblazers are reaching back to help. Rising tide lifts all boats.

AI Video Explainer and AI Podcast Versions of This Newsletter

To support different learning styles, this newsletter is available as an 7-min AI video explainer (see below) and a 12-min AI podcast with two AI hosts. If you haven’t seen these AIs in action, they’re worth a view. The tech is advancing in amazing ways. I used Google’s NotebookLM to create these and personally reviewed them for accuracy and responsible AI use.

Captions are auto generatedPlay7-min AI Video Explainer

What 2025 Proved

1. People started building AI teammates.

People moved from chatting with AI to building AI teammates trained on their own knowledge, guidelines, and how they work.

The data confirms what I’ve been seeing. OpenAI’s State of Enterprise AI report shows custom GPT usage increased 19x this year. Top-performing companies send 7x more messages to custom GPTs than average companies. Moderna went from 750 custom GPTs in April 2024 to over 3,000 a year later.

Here’s what a human + AI team could look like:

Across teams I worked with, the pattern was the same. Digital twins of their thinking and voice. Specialized AI teammates for positioning, content, competitive intel, sales enablement.

Some went further, building “thinking systems” where AI teammates work together while you observe and learn. Learn how to build thinking systems here. And others “vibe coded” AI apps in minutes by simply using plain English. Here’s how with some examples, including ROI calculators, choose-you-own-adventure dashboards, and interactive enablement games.

For a deeper dive, check out “A Leader’s Playbook: How a Lean Team Transformed Into a Human + AI Powerhouse” and “First, You Built a Digital Twin. Now, It’s Time to Build a Team”.

Where to start: Build one AI teammate for a task you repeat often. Give it your context, your examples, your standards for good work. Use it for two weeks. Then notice whether it feels like a tool you use or a teammate you work with.

2. AI search changed buyer behavior.

AI now answers buyer questions directly, often without sending them to your website. Buyers get recommendations, comparisons, and opinions about your brand before they ever talk to your sales rep.

And these buyers convert. A SEMrush study found AI search visitors convert at 4.4x the rate of regular organic traffic. They’ve already compared options. They arrive ready to act.

The insight that changed how I think about this: AI forms opinions about your brand before humans do.

GTM teams need to think about:

Visibility (does AI know you exist?)
Sentiment (how does AI describe you?)
Recommendation (does AI choose you for the right situations?)

As Wil Reynolds, VP of Innovation at Seer Interactive, simply put it at this past October’s Marketing AI Conference in Cleveland:

“We need to be seen, believed, and chosen in AI search.”

If AI struggles to understand what you do and who you’re best for, human buyers face the same problem. Worse yet, AI can draw its own conclusions.

For strategies on adapting, see “When AI Judges Your Brand Before Humans Do” and “Make Your Brand Sourced and a Top Result in AI Search: Practical Strategies for Marketers.”

Where to start: Ask AI about your category. See how you’re described, whether you’re mentioned, and what AI recommends for different use cases. The gaps will be obvious.

3. Results moved from faster to better to different.

Speed came first. Then quality. But the third wave is where the real opportunity lives: doing work that wasn’t possible before.

Based on my work with GTM teams, here’s roughly how AI use breaks down:

80-85% focuses on speed: “Do this task faster”
10-15% focuses on quality: “Do this task better”
3-5% focuses on innovation: “Do it differently”

The risk is stopping at speed. One team that pushed through all three saw 75% faster content creation, 98% lead qualification accuracy, and 35% improved campaign performance.

If we only teach AI to do our jobs faster, we risk becoming unnecessary. AI is cheaper, doesn’t sleep, doesn’t make typos. But when we push into quality and innovation, we become essential. Reimagined work requires human judgment, creativity, relationships, strategic thinking. AI enables it. Humans drive it.

For examples of “different” in practice, see “The End of Handoffs: How AI Teammates Work Together,” “Your Strategy Doc Just Became a Living AI Sales Coach ,” “Your Competitors Can Copy Your AI. Here’s How to Build a Lasting Moat.“

The question for 2026: different for what purpose? The answer is the customer.

Where to start: Look at your team’s biggest recurring workflow. Ask: How could AI make this faster? Then: If we designed this from scratch today, what would we build instead? The gap between those answers shows your opportunity.

Where to Focus in 2026

The teams that succeeded in 2025 proved the model works. Now the question is how do we scale it, and toward what end?

Three priorities.

1. Reimagine workflows around the customer.

Your customers don’t care about your org chart. They see your company as one entity. They expect the same experience no matter which team they’re talking to.

AI is forcing this into the open. When data flows across systems and AI can see the entire customer journey, the gaps between product, marketing, sales, and customer success become visible. The cost of slow handoffs shows up in dollars. Misaligned goals become impossible to ignore.

McKinsey’s 2025 State of AI report confirms this: workflow redesign drives the biggest impact from gen AI. Yet only 21% of organizations have redesigned their workflows. Most are still bolting AI onto existing processes.

G2 just made a structural move that signals where this is heading. They created their first-ever President of Go-to-Market role, consolidating teams under one leader. Their CEO, Godard Abel, explained why:

“The way software buyers make decisions is transforming fast, with AI reshaping how they research and purchase. To meet this new norm and help our customers succeed, we’re leaning into tighter alignment between marketing and revenue.”

Here’s a real-life workflow example of what this looks like. One team redesigned their SDR workflow with AI teammates at each step. Instead of generic sequences, each prospect gets messages tailored to their role, company, and industry.

Early results from this workflow:

1-3 hours saved per day per rep
2-3x improvement in open rates (from 15% to 40%)
100+ hours saved per week across the team

For more insights, check out “No Daylight: How AI is Reorganizing GTM Teams Around Customers, Not Departments,” “AI is Breaking Department Silos: Moving from Org Charts to Work Charts,” and “How a Leading Cybersecurity Company Redesigned Marketing Workflows to Scale Business Impact with AI.”

Avoid the trap of automating old processes, especially broken ones. When we automate broken workflows, AI amplifies what’s broken. The goal is reimagining work, not just speeding it up.

Where to start: Find your biggest customer experience gap that sits between two teams. Map what the customer actually experiences versus what each team thinks they deliver. That gap is your 2026 opportunity.

2. Prepare for AI that acts, not just answers.

In 2025, AI search changed how buyers research. In 2026, AI will start acting on their behalf.

“Agent” is everywhere right now, which makes it mean almost nothing. Here’s what actually matters: AI is moving from answering to acting autonomously.

A simple way to think about it:

Automation: You design the steps. AI runs them.
Agents: You describe what you want. AI figures out how.

Today’s AI can plan, execute, and analyze on its own. Think deep research, computer use, ChatGPT’s agent mode. You don’t tell it which websites to visit or how to pull findings together. It figures that out. It doesn’t yet set its own goals or learn across tasks. But we can see that’s where we’re headed.

I tested this using ChatGPT’s Agent Mode as a mystery shopper. I asked it to research and compare project management tools. The AI didn’t just gather information. It browsed websites, formed preferences, created ratings, made recommendations, and tried to start the webinar sign-up process. See results below.

Additionally, it evaluated the experience with the various websites: how easy it was to find information, where it was getting stuck, and what actions it wasn’t able to complete.

For the full breakdown, see “AI Agent Mystery Shopping: Walk in Your Customer’s Buying Process.“

This matters for two reasons:

Your customers will use agents. AI will research, compare, shortlist, and start buying on their behalf. Your website needs to cater to both humans and the agents that help the humans. We need to make sure that both can get to what they need and do what they need to do easily.
Your team will use agents. AI teammates will coordinate work across platforms and systems. The workflows you design today need to account for AI that acts, not just helps.

When AI acts on its own, human judgment matters more, not less. Your ability to guide AI, set guardrails, ask the right questions, and know when to step in becomes essential. This is why critical thinking and responsible AI practices aren’t nice-to-haves. They’re how you stay in control. Learn more here: “AI Is Only as Sharp as Your Questions: How Critical Thinking Turns Any Work into Better Decisions.”

Where to start: Run your own AI agent mystery shopping test. See how AI researches your category, what it recommends, where it tries to take action, and where it gets stuck. Then ask: are we ready for buyers who let AI do the work?

3. Scale the hybrid human + AI model.

The trailblazers proved humans and AI work well together. Now the challenge is scaling across the organization.

The trailblazers were self-motivated, curious, willing to push through friction. You can’t expect the whole org to have that same drive.

And let’s be honest about the human side. The fear is real. “If AI can do parts of my job, what happens to me?” That question deserves an honest answer.

Compassionate leadership means this: You have a responsibility to upskill and reskill your employees. Not because it’s nice, but because it’s your job. You hired them when they had the skills your business needed. The needs changed. Your job is to give them skills to compete now.

This makes business sense. Upskilling people who know your culture costs less than hiring new talent. And AI skills are an investment in their careers, not just your company.

Real investment is people first, AI forward. Understand their concerns. Inspire them with what’s possible. Give them hands-on training and space to learn with others. And use AI yourself. You can’t just hand out licenses and send a Slack announcement.

Anna Griffin, Chief Market Officer of Commvault, on what comes next:

“We’re past experimentation. Now it’s about making this real for the whole org. Clear decision rights, connected processes, less friction. And we have to hold ourselves accountable as leaders. Not just expecting our teams to change, but creating the conditions for them to succeed.”

Scaling also requires infrastructure:

Governance – Who decides what AI teammates and workflows get built? What guardrails ensure responsible use? How do you maintain quality as more people create more?
Quality – One team created 211 AI teammates during experimentation and kept 57 in regular use. Not every AI teammate earns its place. That’s the messy middle. And it’s a sign you’re doing it right.
Enablement – Everyone can build AI teammates. But cross-platform workflows need specialized skills. Often it’s Ops, working with IT and legal, to make it happen.

Megan Cabrera, VP of Marketing Operations, shares:

“We let everyone build their own AI teammates. That’s where the creativity and experimentation happens. But when workflows need to connect to CRM, marketing automation, or other core systems, that’s where my team steps in. We work with IT and legal to make sure it’s done right. Democratized creation, centralized integration. That’s how you scale without creating chaos.”

The Rule of Thirds applies. In most major changes, roughly:

One third will lead the change
One third will follow with support
One third won’t embrace it

Your responsibility looks different for each group. Leaders need room to run. Followers need guidance and support. Those who won’t embrace it need help finding where they’ll thrive.

Before you sort your team, ask yourself: Have I truly invested? Do I use AI myself? Or did I just expect people to figure it out while I watched from the sidelines?

Nice leadership keeps everyone comfortable and sets them up to fail when the world changes. Compassionate leadership invests in people, sets clear expectations, and makes tough decisions when needed.

Where to start: Find your trailblazers. Give them a way to share what they’ve learned. Their real-world knowledge is more valuable than any training program you could buy.

To learn more on scaling, see “How a Leading Cybersecurity Company Redesigned Marketing Workflows to Scale Business Impact with AI” and “The GTM Leadership Criteria That Worked for Decades Aren’t Enough Anymore.”

The Year Ahead

2026 will be messier than 2025. More noise, more hype, more pressure. AI capabilities will keep advancing. The path won’t be straight.

We’re navigating new waters together. None of us have all the answers. Give yourself grace. Give your team grace. The fact that you’re engaged puts you ahead of most.

But we have proof now. Proof that people can build AI teammates that actually help. Proof that humans and AI work better together than either does alone. Proof that reimagined work creates value that faster work alone never could.

The teams that went first in 2025 are reaching back to help. The frameworks exist. The playbooks are being written. The trailblazers are willing to share what they’ve learned.

This is happening. The transformation is real.

This is doable. We proved it.

And we don’t have to figure it out alone. Rising tide lifts all boats.

Thank you for being part of this community. I’m grateful for every conversation, every question, every story you’ve shared with me this year. This work is better because of you.

From our family to yours, wishing you rest, joy, and time with the people who matter most. Merry Christmas and happy holidays!

See you in 2026.

The Practical AI in Go-to-Market newsletter shares learnings and insights in using AI responsibly. Subscribet oday and let’s learn together on this AI journey.

For team transformation: See real examples—a lean GTM team’s step-by-step playbook and a global cybersecurity leader scaling to 150+ marketers with 57 AI teammates integrated into daily workflows.

For speaking: Here are virtual and in-person 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.

Your Strategy Doc Just Became a Living AI Sales Coach

Liza Adams · December 4, 2025 ·

Hello go-to-market (GTM) 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

I uploaded a marketing strategy document to AI and asked it to build me a sales training tool. A few minutes later, I had a working simulator where two AI personas debate each other in real time. One plays the skeptical buyer using real pain points. The other plays the sales rep using actual messaging. Then it coaches you on what the rep did well and what they missed.

This is what I’m calling a thinking system. It’s AI using AI. You set up the scenario, and AI-powered personas call AI to generate each response in real-time. You’re not driving the conversation. You’re watching AI talk to AI while you learn.

This kind of work wasn’t possible before because the economics didn’t make sense. Now you can build it in minutes without developers.

Key Takeaways:

AI can now build applications that have AI reasoning inside them. Describe what you want, upload a document, and get an interactive system where AI powers the experience.
There’s a meaningful difference between knowledge tools (you drive the conversation) and thinking systems (AI invokes AI while you observe and learn).
Strategy docs, playbooks, and research reports can become thinking systems that train, coach, and challenge your team.
Start with documents your team created but rarely uses. They’re full of expertise waiting to be activated.

AI Video Explainer and AI Podcast Versions of This Newsletter

To support different learning styles, this newsletter is available as an 6-min AI video explainer (see below) and a 12-min AI podcast with two AI hosts. If you haven’t seen these AIs in action, they’re worth a view. The tech is advancing in amazing ways. I used Google’s NotebookLM to create these and personally reviewed them for accuracy and responsible AI use.

Captions are auto generatedPlayAI Video Explainer

Watch This

Two AI personas. One strategy document. A live debate that’s different every time you run it.

I used Claude for this example. ChatGPT and Gemini have similar capabilities but ways to share the output may vary. The specific tool matters less than understanding what’s now possible.

See the 3-min video of the Sales Debate Simulator in action below.Play

Prefer to scan? Here are the screenshots.

Look at the specificity. Alex opens with classic buyer skepticism: thirty minutes before another meeting, current setup works “reasonably well.” Jordan doesn’t pitch features. The rep probes for pain points and learns Alex is balancing cost control with traveler satisfaction.

The conversation evolves naturally. Pushback on ROI claims, concerns about IT strain, questions about implementation. Jordan responds with metrics, case studies, and a 4-6 week timeline with minimal IT involvement.

Run it twice and you’ll get a completely different conversation. That’s because each response is a live AI call. The personas aren’t following a script. They’re invoking AI to generate every line in real-time.

All of that came from the strategy document. The AI improvised a realistic discovery call on the fly.

And then it coaches you. The simulator produces a scorecard: what the rep did well (mirrored communication style, asked strategic questions, offered credible proof points), what they missed (no urgency, didn’t qualify budget, weak transition to pricing), and suggested next steps (reference call, ROI analysis, pilot proposal).

This is coaching you can run a hundred times.

Try the Sales Debate Simulator yourself.

What’s Actually Happening Here

A few things worth naming.

The document came alive. This wasn’t a chatbot that answers questions about the strategy. This was two AI personas using that strategy to have a realistic conversation with each other while I watched.

The AI built a thinking system. I didn’t write code. I didn’t hire a developer. I described what I wanted, uploaded a document, and got an application where AI invokes AI. Each persona’s response is a live AI call. That’s why every run is different. It’s not scripted.

This is new work, not faster work. Nobody was running persona-based debate simulations before. The economics didn’t work — hire actors, schedule workshops, fly people to training. Now it takes minutes and runs as many times as you want.

About the Strategy Document

Acme Company is fictional, but everything behind it is real.

The personas, pain points, and objections come from actual B2B buyer research. The messaging, positioning, and competitive differentiation are pulled from a real marketing strategy document that I anonymized for this demo.

Here’s what was in it.

What Else You Could Build

The debate simulator is one example of a thinking system. Before we explore more possibilities, it helps to understand the distinction between two types of tools you can create from your documents.

Knowledge Tools (Chat-Based)

Knowledge tools like custom GPTs are powerful on their own—assessing, creating, advising, chaining together. Here’s what your existing documents could become:

Want to explore this further? Check out A Leader’s Playbook newsletter on custom GPTs. The End of Handoffs introduced connecting or chaining AI teammates together.

Thinking Systems (AI-Powered)

Thinking systems take it further. It’s AI using AI. You set up the scenario, and AI-powered personas interact with each other, each response generated by a live AI call. You’re not driving. You’re observing, learning, and getting coached.

With knowledge tools, you drive. With thinking systems, you step back and watch AI talk to AI.

Jennifer Pockell Dimas, Chief Marketing & Experience Officer at Telarus, put it this way:

Jennifer Dimas, Chief Marketing and Experience Officer of Telarus

“Our teams spend so much energy creating sales playbooks, selling assets and competitive battlecards. Then they live in a folder somewhere. I love the idea that those same materials could become practice environments where reps build real skill is a fundamentally different return on that investment.”

Why This Matters for Your Team

The Sales Debate Simulator wasn’t about making an existing task more efficient. There was no existing task. This is new work that creates new value.

That’s the shift worth paying attention to. We’ve spent the past two years learning how AI can accelerate what we already do. Write faster. Research faster. Summarize faster.

But the more interesting question is what work becomes possible that we couldn’t do before. Decisions improve when you can simulate them first. Skills develop when practice is free.

Mary Gilbert (Kerford), Founder of InfiniteEdge, has seen several waves of marketing transformation. This one feels different to her:

“Every transformation wave I’ve seen changed what we could measure or automate. This one changes what we can build. When your intellectual property becomes infrastructure that develops your team, you’re playing a different game.”

The companies that figure this out won’t just be more efficient. They’ll be doing work their competitors haven’t imagined yet.

Try It Yourself

Start simple. The key is having a solid foundational document to feed AI. Garbage in, garbage out.

Here’s the prompt for the Sales Debate Simulator:

"Please build a simulator where two AI personas have a live conversation with each other based on the attached marketing strategy document. I want to watch them talk, not participate. Each conversation should be unique. At the end, give me coaching notes on what worked and what didn't."

Here’s another idea. A Buying Committee Simulator. It simulate a group demo with 3-4 stakeholders at once (eg., IT Manager worried about integration, Finance Manager focused on ROI, Travel Manager wanting ease of use). They interrupt each other, have side conversations, and you watch the sales rep try to satisfy everyone.

Then it gives an assessment of who’s bought in and who’s not after the meeting and why.

"Please build a simulator where an AI sales rep presents to a buying committee of 3-4 AI stakeholders (use the personas from the doc). They should react, interrupt, ask tough questions, and debate each other. I want to watch, not participate. At the end, show me which stakeholders are sold, which aren't, and why."

Here’s Claude’s rendition of a Buying Committee Simulator. Try it yourself.

Below are screenshots of Buying Committee Simulators from ChatGPT and Gemini:

The first version won’t be perfect. AI is a starting point, not an ending point. Review what it builds, test it against your needs, and refine from there.

A note on what you upload: be thoughtful about the documents you share with AI tools. Avoid sensitive customer data, confidential financials, or anything under NDA unless you’re in an enterprise environment with appropriate protections. When in doubt, anonymize first.

And remember, these tools are guides for practice and reflection, not replacements for real customer conversations. Use them to sharpen your thinking, then go have the actual conversations.

Where This Is Heading

The people building this muscle memory now will have a significant advantage as AI capabilities expand. Learning to think in terms of “what thinking systems could I design?” rather than “what tasks can AI do for me?” is the skill that compounds.

The document you wrote last quarter doesn’t have to stay static. It can become a system that trains, coaches, and challenges your team every day.

That’s not a small upgrade. That’s a different category of value.

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

For team transformation: See real examples—a lean GTM team’s step-by-step playbook and a global cybersecurity leader scaling to 150+ marketers with 57 AI teammates integrated into daily workflows.

For speaking: Here are virtual and in-person 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.

No Daylight: How AI is Reorganizing GTM Teams Around Customers, Not Departments

Liza Adams · November 13, 2025 ·

Quick Take

Most common question after the work charts newsletter: “How do I actually organize my team this way?”

Here’s what you need to know:

The structure is Journey Teams plus a Shared Expertise Pool. Journey Teams own Net New Revenue or Net Revenue Retention end-to-end. The Expertise Pool provides unified data, AI systems, and brand narrative that both teams build from.

You’re hiring and developing for capabilities, not titles. Journey Teams need people who can generate pipeline, build messaging, drive adoption, enable sales, and orchestrate AI systems. The Expertise Pool needs people who can build unified infrastructure and own core narrative.

Three capabilities separate who can make this transition from who can’t. Deep expertise in something that drives outcomes (with willingness to evolve), thinking in customer results instead of functional metrics, and ability to orchestrate humans and AI toward value.

AI enables coordination that manual processes couldn’t sustain. Your teams can stay aligned without constant meetings because AI teammates catch inconsistencies before they reach customers.

You don’t need to reorganize everything at once. Start by connecting one workflow across teams. Prove the model works. Let the results build the case for broader change.

AI Video Explainer and AI Podcast Versions of This Newsletter

To support different learning styles, this newsletter is available as an 8-min AI video explainer and a 12-min AI podcast with two AI hosts. If you’ve not seen these AIs in action, they’re worth a view/listen. The tech is advancing in amazing ways. I used Google’s NotebookLM to create these and personally reviewed them for accuracy and responsible AI use.

Captions are auto generatedPlay8-Minute AI Video Explainer

Why This Matters Now

McKinsey’s Mar 2025 State of AI report analyzed 25 organizational attributes and found that workflow redesign drives the biggest impact from gen AI.

As Alex Singla, McKinsey’s global leader of QuantumBlack AI, notes: “The organizations building genuine competitive advantage are thinking in terms of wholesale transformative change that stands to alter their business models, cost structures, and revenue streams, rather than proceeding incrementally.”

Yet only 21% of organizations have fundamentally redesigned workflows.

And the pressure is accelerating. AI isn’t just exposing internal gaps. It’s surfacing them to your customers.

Sarah Gavin, Chief Communications Officer and Acting CMO at Zendesk

“AI doesn’t just read what we say in our marketing, it surfaces customer reviews, employee feedback, and every other signal about our brand. Internal inconsistencies that used to be hidden are now visible to buyers.

To be recommended by AI, you have to be genuinely helpful and trustworthy across every touchpoint. You can’t polish your way out of broken experiences anymore.”

When your Marketing team says one thing, Sales delivers another, and CS solves different problems, AI surfaces that inconsistency to buyers. The gap between the job to be done and where the expertise resides becomes visible. AI doesn’t care about your silos. Neither do your customers. They only care about outcomes.

And your peers see this shift coming.

Heidi Melin, Senior Operating Advisor at Hellman & Friedman, 6X CMO and Board Member

“I spent years focused on how work actually flows across teams and where it breaks down.

Now, advising CMOs at Hellman & Friedman portfolio companies, the conversation is shifting. Leaders aren’t just asking ‘how do we make our marketing function more efficient?’ They’re also asking ‘if we could redesign how work flows from marketing through sales to customers, what business impact becomes possible?’ That’s a fundamentally different question.”

The companies that eliminate these gaps will win. The question is how you organize your team to capture that advantage.

The Structure: Journey Teams + Expertise Pool

Work reorganizes around customer outcomes, not departments.

Journey Teams own complete customer outcomes end-to-end. No departmental handoffs. Each team unifies the capabilities needed to deliver their mission:

New Customer Journey Team is accountable for Net New Revenue AND time-to-value (e.g., getting customers to see value within 90 days). This team includes pipeline generation, messaging, sales development, enablement, and early adoption specialists.
Growth Customer Journey Team is accountable for Net Revenue Retention AND customer satisfaction (e.g., maintaining 90%+ CSAT while expanding revenue). This team includes account management, customer success, expansion enablement, and retention specialists.

Notice what changed. Marketing isn’t measured on MQLs. Sales isn’t measured on closed deals alone. CS isn’t measured just on renewals. The entire Journey Team is accountable for the customer outcome end-to-end. When everyone shares the same metric, the handoffs that used to break customer experience disappear.

A Shared Expertise Pool provides the infrastructure both Journey Teams build from:

GTM Infrastructure delivers unified data, AI systems, and governance. The handoff from new to growth customer becomes a status change in one system, not a handoff between departments.
Brand & Core Narrative provides the single source of truth for who you are and what you stand for. The foundation both Journey Teams translate for their specific journey.

AI makes this coordination sustainable. Your teams build AI teammates trained on the core brand narrative. These AI teammates flag inconsistencies before messaging goes live (“this promise conflicts with the onboarding experience”). They surface patterns from customer signals across both journeys (“sales is promising X, but CS reports X causes support tickets”).

Instead of constant alignment meetings, your teams focus on strategic decisions and the exceptions AI surfaces. The technology catches the gaps humans used to miss.

This is “No Daylight” in practice. Everyone builds from the same data foundation, the same brand truth, with AI ensuring consistency across execution.

What This Looks Like in Practice

Two real transformations show how this works.

Marketing Ops Evolves to GTM Infrastructure

A global cybersecurity leader transformed their marketing ops function into the enablement layer for the entire GTM team. Picture this as the GTM Infrastructure lead in the Shared Expertise Pool.

The 75 trailblazers across marketing built their own AI teammates using ChatGPT Enterprise. Everyone could create, experiment, and connect workflows within the AI platform.

But when workflows needed to cross platforms (connecting to CRM, marketing automation, internal repositories), marketing ops provided the technical expertise and governance. They created a governance framework called the “AI Network” to organize and track AI teammates. Not everyone should have read/write access to core business systems. Marketing ops prioritized these complex integrations, built them properly, and worked with Legal and IT to ensure responsible access and security.

The model: democratized AI teammate creation, centralized technical integration and governance. This scales without creating risk.

The work now supports 150+ people across marketing and beyond with 57 AI teammates in regular use. The team achieved significant time savings and improved campaign engagement by 2-3X through these innovative, connected workflows.

Read the full case study.

Integrated Campaigns Reimagines to Journey Orchestration

Megan Ratcliff worked herself out of a traditional job and reimagined her work into something that wasn’t possible without AI. She evolved from integrated campaigns to GTM Architect, orchestrating work across marketing, sales, and customer success.

Her work always spanned boundaries. AI gave her the tools to orchestrate at that level. “AI has unleashed my superpowers. I’m working across the entire GTM motion now.”

Picture Megan as part of a Journey Team, coordinating the capabilities needed to drive customer outcomes end-to-end.

Her team (25 humans and 63 AI teammates) achieved results that show what’s possible when you organize around outcomes instead of functions. Up to 75% faster content creation, 98% lead qualification accuracy, and 35% improved campaign performance.

Read about Megan’s journey here.

People who can orchestrate across boundaries and reimagine their work around customer outcomes are creating roles that didn’t exist before. Here’s an example:

Three Capabilities That Separate Who Can Evolve From Who Can’t

You have people on your team right now who can make this transition. Here’s what to look for.

1. Deep Expertise With Willingness to Evolve It

Deep capability in something that drives outcomes. This could be sales methodology, strategic positioning, customer psychology, data infrastructure, or AI orchestration itself. The key is willingness to evolve what “deep expertise” means as AI changes the work.

Deep expertise in competitive positioning translates to leading messaging across Journey Teams. Strong data systems capabilities become the foundation for unified infrastructure in your Expertise Pool. Understanding audiences at a strategic level evolves into orchestrating AI systems that generate pipeline at scale.

The capability that matters is understanding what drives results. The tactics change. The strategic thinking gets amplified.

2. Think in Customer Outcomes, Not Functional Metrics

A Harvard study with P&G professionals found that when specialists from different functions used AI, they experienced an interesting shift: “You stop caring as much about the normal boundaries of your job.” When AI helps people think beyond their specialized training, traditional silos break down naturally.

The people on your team who already think this way are ready to lead. Others can develop this capability with the right support and space to learn.

This shift changes how work gets measured. No more departmental KPIs where Marketing optimizes for leads, Sales optimizes for close rates, and CS optimizes for retention, while the customer experiences the gaps between them. Journey Teams own customer outcomes. Everyone succeeds or fails together.

3. Orchestrate Humans and AI Toward Value

Who on your team is already building and guiding AI teammates? Who coordinates across teams naturally? Who focuses on what drives value instead of what’s always been done?

These capabilities can be developed. The people who start learning now will have options. The people who wait won’t.

Your Job as a Leader

Invest in upskilling everyone. Shift mindset and behavior by showing them what’s possible with AI in use cases relevant to their roles. Provide hands-on training in collaborating with AI and building AI teammates. Give them space to experiment and learn. Create pathways for people to reimagine work and evolve into new roles. Make AI fluency a core capability, not optional.

Not everyone will move at the same pace. Some will lead the change, some will follow once they see it working, and some will need more time or different support. Have compassion for people navigating this transition and help them find paths where they can succeed.

Here’s the strategic choice you’re making:

By late 2025, the data confirmed it. While 80% of companies set efficiency as an AI objective, the highest performers set innovation and growth as objectives too.

They refused to choose between efficiency and growth.

Your Transformation Path

You don’t need to reorganize everything at once.

Start by connecting one painful workflow across teams. Maybe it’s deals that CS wasn’t prepared for. Maybe it’s messaging that doesn’t match the customer experience. Maybe it’s prospect insights that never reach the sales team.

Fix that one workflow. Build one AI teammate that connects it. Prove that eliminating the gap improves customer outcomes.

Beware of the trap of simply automating old processes, especially broken ones. When we automate broken workflows, AI amplifies what’s broken. It becomes a faster path to failure. And we can’t reimagine the future by simply automating the past.

Here’s how this might work.

A PMM team was frustrated that Sales never used their 5-page competitive battlecards. They realized Sales didn’t need all that detail. They needed a 1-page summary with the top 3 competitor gotchas and 2 must-win value props.

They built one AI teammate trained on all their battlecards with a single job to generate that 1-page summary on demand. The PMM and one SDR tested it together, refined the outputs, built trust in the system.

Once it worked, they shared the approach across the team and identified the next workflow to connect. Competitive messaging improved, handoff friction disappeared, and both teams moved on to fix the next gap.

The journey from here:

First milestone – Fix individual handoffs. Learn to connect workflows
Second milestone – Connect multiple handoffs. Build toward unified journey coverage
Third milestone – Evolve team structure around journeys. Implement the full model

Each step moves you closer. The key is momentum without breaking your team.

The companies that eliminate the daylight between what customers need and what teams deliver won’t just be more efficient. They’ll do work that wasn’t possible before.

This is a journey, not a weekend project. Some companies will move through these stages in months. Others will take years. The timeline matters less than the direction.

What important is starting.

The companies making small moves today will have structural advantages when market pressure accelerates this shift.

Think big. Start small. Move fast with ongoing momentum.

What’s Next

Build the change infrastructure. Invest in upskilling and put hands on keyboard. Model the behavior you want to see. Make AI fluency a leadership competency. Give people space to experiment, fail, and learn.

Not every company will transform at the same pace. Some might stay at Stage 2 or 3 and that might be exactly right for their business. But if eliminating customer friction creates competitive advantage, and AI makes it visible to buyers, the market will decide how fast this matters.

The technology is ready and the patterns are becoming more clear. Control what you can. Start where you are. Put your team in the best position to compete.

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

For team transformation: See real examples—a lean GTM team’s step-by-step playbook and a global cybersecurity leader scaling to 150+ marketers with 57 AI teammates integrated into daily workflows.

For speaking: Here are virtual and in-person 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.

How a Leading Cybersecurity Company Redesigned Marketing Workflows to Scale Business Impact with AI

Liza Adams · October 30, 2025 ·

Quick Take

Last year, companies invested an estimated $35-40 billion in AI. Budget and tools arent the barrier to AI-driven results. The difference lies in approach: some buy tools and hope for transformation; others design connected systems and measure impact from day one.

A global leader in cybersecurity is a case in point. For years, this company has applied AI to stay ahead of fast-moving threats, from advanced threat intelligence to innovation across its security portfolio. Now, that same mindset is redefining how they market, communicate, and operate.

Rather than bolting AI onto existing processes and technology, this company redesigned its marketing workflows from the ground up, building systems where people and AI collaborate side by side. The result: fast execution, higher quality output, and measurable business impact across every stage of the go-to-market engine.

In less than six months, the marketing organization trained 75 trailblazers and embedded 57 custom GPTs into daily work—all under a model grounded in responsible AI principles, with human review and validation at every step.

Security was foundational to their AI deployment. The team partnered closely with its CISO and security teams to ensure every system was deployed safely and aligned with company security standards. They jointly reviewed proposed workflows and partnered together during onboarding.

The company selected ChatGPT Enterprise to ensure company data remains private (never shared for model training or external use), allowing employees to experiment confidently within a secure environment.

Key insights that separate successful companies from those that struggle:

Shift from tool adoption to work redesign – Treat AI as teammates that change how work gets done, not productivity software you bolt onto existing processes
Create enablement systems, not just tools – Design workshops, office hours, and spaces for teams to experiment and share what works
Design connected workflows, not isolated productivity hacks – Link AI teammates and platforms together in systematic processes
Partner with security from day one – Work closely with CISO and security teams to review workflows, ensure safe deployment, and select platforms that protect company data
Measure business impact from day one – Track efficiency gains, quality improvements, and new capabilities (not just tool usage)

This shift created a disciplined framework for scaling responsible AI use, strengthening human creativity instead of automating it away.

AI Video Explainer and AI Podcast Versions of This Newsletter

To support different learning styles, this newsletter is available as an 7-min AI video explainer (see below) and a 16-min AI podcast with two AI hosts. If you haven’t seen these AIs in action, they’re worth a view. The tech is advancing in amazing ways. I used Google’s NotebookLM to create these and personally reviewed them for accuracy and responsible AI use.

Captions are auto generatedPlay7-Min Video Explainer Version of This Newsletter

Their Human + AI-Assisted Marketing Org

Here’s what this transformation looks like in practice. They redesigned their marketing org chart to include AI teammates that work alongside humans:

New to building human + AI teams? Check out A Leaders Playbook: How a Lean Team Transformed Into a Human + AI Powerhouse for step-by-step guidance for getting started.

This approach goes beyond layering AI onto existing workflows. The redesigned operating model blends human creativity with AI efficiency. The company architected a new model collaboration where human creativity and AI capabilities are interwoven by design:

75 trailblazers across marketing functions driving adoption
211 AI teammates (i.e., custom GPTs) created during experimentation, refined to 57 now embedded into regular daily workflow
Marketings proven results helped support the decision to provide enterprise AI access to employees across the organization

The difference compared with companies that see zero return? They built a repeatable framework for human + AI teaming, from experimentation to scale. Each AI teammate has a clear role, owner, and oversight process, and every AI workflow is transparent and auditable, ensuring quality and compliance across global markets.

Stop Random Acts of AI

Their journey began with a single insight: transformation doesn’t happen by adding AI licenses. It happens by re-architecting how people learn and collaborate.

This began with strategic experimentation. The 75 trailblazers, or early adopters, across marketing drove the use of AI, focusing on high-value use cases that showed clear, measurable impact.

But identifying the right people was only the first step. Through my work with them, we created space for teams to learn, experiment, and build confidence. Deploying AI tools without equipping people to use them is where most companies waste money. We focused on inspiring responsible use, teaching practical skills, and embedding AI into real work. They didn’t just adopt AI. They built a human + AI system that works.

Our enablement approach gave people permission to try things and places to share, including:

Foundational AI workshops to understand how customer journeys and behavior are changing because of AI and how to better serve them with AI teammates working alongside humans
Applied AI sessions where people saw how to use AI in key use cases relevant to their function plus hands-on exercises where they built their first AI teammates using custom GPTs
AI automation and agent workshops to help build AI-infused workflows for Marketing Ops
A dedicated Teams channel for sharing what works and what doesn’t
Weekly office hours to get help to move from from simple questions to building real solutions that scale
Show-and-tell sessions where teams shared successes and inspired each other

Within 6 months, we saw widespread adoption across marketing and compelling business results (see “Measure What Matters”). The team continues to transform and scale across the organization.

When early duplication emerged (teams building similar GPTs in isolation), the team responded with better coordination.

The VP of Marketing Operations, built a three-pillar measurement framework to track their progress. We measured business focus (efficiency gains), people focus (satisfaction and skill development), and differentiation focus (market positioning through AI search optimization).

She shared her key insight:

This approach reflects the company’s people-first, AI-forward philosophy. The training and enablement focused on investing in their teams future capabilities. By teaching employees to work alongside AI, They positioned their workforce to add more value, stay competitive, and thrive in an AI-driven workplace.

Build Systems, Not Tools

Most companies get stuck at Phase 1 where they primarily use AI as tools for individual tasks. They never progress to Phase 2 (guiding AI as teammates) or Phase 3 (orchestrating AI workflows). I covered this topic in detail in one of my previous newsletters titled: AI Is Redefining GTM Jobs: From Tool Users to Teammates to Orchestrators

The team built what they call an AI Network. Its a layered architecture where different types of AI teammates have clear roles and can work together alongside humans.

Without a system to organize AI teammates, chaos happens fast. Imagine three different teams each building their own content optimizer GPT because nobody knows the others exist. Or someone spending an hour trying to figure out which of 50 GPTs to use for a simple task. This is where most companies hit a wall.

Below is the AI Network with sample elements at each layer: AI teammates/GPTs (circles), specialized AI platforms (diamonds), human work (pentagons, trapezoids, and squares).

Foundation Layer: Core AI teammates that establish organizational knowledge and brand voice

Brand GPT (maintains consistent messaging across all content)
Audience GPT (ensures content targets the right buyer personas)
Compliance GPT (keeps content aligned with legal and regulatory requirements)

Story/Campaign Layer: Specialized GPTs for strategic content and campaign development

Campaign GPT (develops integrated campaign strategies)

Domain Layer: Function-specific tools that understand product nuances

Product GPT (specialized for product content)

Asset Layer: Content creation specialists for specific formats

Blog Post GPT, Email GPT, Social GPT, Event GPT (each optimized for their formats unique requirements)

QA Layer: Quality assurance and optimization specialists

SEO GPT (optimizes content for search visibility)
Content Relevance GPT (ensures accuracy and message alignment)
Localization GPT (adapts content for global markets)

Human in the Loop: Strategic oversight and approval processes

Reviews, approvals, and strategic direction at key decision points

Automation Layer: Specialized AI tools and automation/integration tools needed to support end-to-end workflows

Platform integrations that create seamless handoffs between AI teammates and AI business systems like CRM, marketing automation, AI SDR, intent and enrichment platforms.

Most companies focus on individual AI tools when they should be designing AI ecosystems. But this company understood that sustainable AI advantage comes from connected workflows, not isolated productivity hacks. This is why their approach scales while others plateau.

This is also an example of governance in action. The architecture determines whether a task needs a specialized GPT, can be handled by existing tools, or requires a combination with human oversight and support.

Measure What Matters

The company chose a hybrid approach. They use ChatGPT Enterprise as their foundation but build custom workflows that integrate with their existing tech stack. The key is measurement at every level.

Three workflows show how this plays out:

1. SDR Transformation – The SDR team built custom GPT chains that turn company profiles into personalized Outreach campaigns. Instead of generic sequences, each prospect gets messages tailored to their role, company context, and industry challenges. The system even helps SDRs prepare for calls by analyzing tech used and suggesting conversation angles.

Here are some early results using an innovative workflow:

Time saved: 1-3 hours per day per rep
Quality improvement: 2-3x better open rates (15% to 40%)
Scale impact: 100+ hours saved per week across the team

Note in the workflow below that the team applies critical thinking throughout, ensuring that the humans still evaluate tradeoffs and make the final decisions. If interested, here’s a guide on critical thinking with AI including real-life examples.

The SDR operations leader in the Americas region, not only made his team more productive, he also helped them improve the quality of the work and reimagined how work is done. He said:

2. Enterprise Content Creation – The Americas Marketing team automated deep research for enterprise content creation. What used to take 10+ hours of manual research now takes minutes. The AI pulls company information, analyzes competitors, and suggests positioning angles specific to enterprise buyer concerns. The end-to-end workflow is shown below:

While the full automated workflow is still being built, the Enterprise Audience GPT is already in wide use. The Head of Americas Marketing, shares the impact:

3. AI Search Strategy – While other companies optimize for Google rankings, the company optimizes for AI-powered search results. They create content specifically designed to be cited by ChatGPT, Perplexity, and other AI systems. As a result, their Managed Detection and Response (MDR) solution continues to climb in share of mentions across major AI search platforms.

The company used the approach I described in my newsletter titled Make Your Brand Sourced and a Top Result in AI Search: Practical Strategies for Marketers. They are also evaluating AI search tools from Profound and Scrunch AI.

Beyond efficiency wins, these are business model changes. The decision to provide enterprise AI access to all employees wasnt based on marketings enthusiasm. It was based on measurable results that proved systematic AI adoption works.

The team is betting that success in one function will create demand in others. Marketing is a key member of the companys AI Committee, working to become an internal catalyst for AI transformation. They’re sharing their success and best practices with other departments like Sales, Support, and Legal.

The Infrastructure Advantage

Here’s what makes them different: they built infrastructure, not just use cases. Think of it as an AI Supermarket.

A supermarket organizes everything so you can see what exists and where to find it. Produce here, dairy there, pasta down aisle five. But the store alone doesn’t make dinner. A recipe tells you what to grab and what order to combine it. The human acts as the chef, calling each ingredient at the right time.

The company built the same thing for AI:

The Map (GPT Tracker) – A master list of every AI teammate in the org. It tracks each GPT’s purpose, owner, capabilities, and limitations. Think of it as inventory management for AI teammates. Without it, people build duplicates, waste time hunting, or default to the two GPTs they know instead of the ten that could help.
The Directory (AI Navigator) – A custom GPT that knows all the other custom GPTs and can recommend which ones to use for specific tasks. Tell it what you’re making and it points you to exactly what you need. It can suggest a single GPT for simple needs, or recommend a sequence of GPTs that work together for complex workflows.
The Recipes (Workflows) – Defined sequences for common tasks. Brand GPT lays the foundation, Campaign GPT builds strategy, Asset GPTs create content, QA GPTs polish before publish. The order is just as important as the ingredients.
AI Triage System – Simple rules for deciding what to do with AI requests. If you need basic help (writing emails, simple research), handle it yourself with standard AI tools and custom GPTs. If you need complex automation or integrations, it goes to Marketing Ops for prioritization. They have the technical skills and tools to build it properly.
Governance Structure – Marketing Ops is part of the companywide AI Committee where the company makes decisions about AI tools, policies, and budgets. This means their proven approach influences company-wide AI decisions instead of every department starting from scratch.

The Director of Marketing Data and Analytics, reflected on the importance of the AI triage system.

The Real Choice Every Company Faces

AI tools give you short-term help. Systems give you long-term advantage.

Companies that orchestrate human-in-the-loop AI systems will outpace those that just buy tools. Your competitors will figure this out eventually. The question is whether you’ll beat them to it.

Most companies ask what can AI do for us? The winners flip the question: how do we redesign work so humans and AI collaborate systematically? Heres what that looks like:

Start with measurement, not tools. Know what success looks like before you buy anything. Track both time saved and new work that wasn’t possible before. Without ways to measure results, you wont be able to prove AI’s business value.
Think systems, not solutions. Single AI tools help with productivity. Connected workflows create real change. Focus on building processes you can repeat rather than collecting random productivity tricks.
Build structure for growth. Create simple rules that help teams know when to build, buy, or skip AI solutions rather than expecting everyone to become AI experts.
Build real support systems for your teams. Run workshops on specific use cases, hold regular office hours for questions, and create spaces where people can share whats working. Intentional support drives faster adoption than leaving teams to figure it out themselves.

This systematic approach is more accessible than most expect. Companies dont need massive budgets, technical teams, or years of planning – just engaged people and clear frameworks. With the right systems, oversight, and culture, AI becomes a trusted teammate that helps people focus on strategy, creativity, and growth.

In a world where many organizations race to automate, this leading cybersecurity company stands out for building AI that elevates human work. It’s not just about faster output. It’s about smarter marketing, stronger governance, and a sustainable advantage for the business.

They chose transformation. The results speak for themselves.

The Practical AI in Go-to-Market newsletter shares learnings and insights in using AI responsibly. Subscribetoday and let’s learn together on this AI journey.

For applied learning :Explore our applied AI workshops, offering both strategic sessions (use cases and roadmaps) and hands-on building (create AI teammates and workflows during the workshop). You’ll leave with either a clear plan or working solutions.

For speaking: Here are virtual and in-person 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.