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Your Team Will Only Build What They Believe AI Can Do: Four Mindset Shifts to Move Them Forward

Liza Adams · March 19, 2026 ·

Your team is using AI. They prompt it for blog topic ideas. They ask it to do competitive research. Someone built an AI teammate to write nurture emails. They are all individual productivity plays.

That’s not a knock on the team. It’s what happens when people build based on what they’ve seen so far.

When the iPhone launched, most people treated it like a fancy way to make phone calls. It took time before anyone saw the wallet, the GPS, the camera, the way it would reorganize daily life. The technology didn’t change. What people believed it could do changed, and then everything they built on it changed too.

AI is in that same moment for most teams.

Quick Take

The hardest part of this transformation isn’t AI, it’s us: how we think about AI, how we grow with it, and how we move from scattered experiments to something that actually changes how we work.

This is Part 1 of two. This one covers where to start and how to measure where your team is. Part 2 covers how to move them forward.

Key takeaways:

Your customers already use AI to research, compare, and decide. That’s the starting point.
Four dimensions show where your team is and where they’re headed: Q&A Machine to Thought Partner, Productivity to Innovation, Tool to Teammate to Workflow, Task to Outcome.
The progression starts with understanding how your customers changed, then shifting how your team thinks about AI, then building. You can start building now, but teams that shift their mindset first build for impact instead of just speed.

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. I used Google’s NotebookLM to create these and personally reviewed them for accuracy and responsible AI use.

It Starts with the Customer

I covered four buyer behavior changes in a recent newsletter. AI builds shortlists before your sales rep talks to the buyer, AI shows buyers everything about you (good or bad), peers and communities carry more weight than your pitch, and buyers expect connected experiences across the full lifecycle.

Your Buyer Changed. Your GTM Playbook Didn't.

Every AI transformation conversation should start with the customer. Your customers changed. Your team needs to understand how.

When I work with teams, I first show how buyers are using AI to find, evaluate, and make decisions about solutions and vendors. We open ChatGPT, Perplexity, Claude, Gemini, and Google AI Mode as their target buyer. We ask AI to compare them to competitors. We analyze each AI’s response.

Bernd Leger is CMO of Showpad, a sales enablement platform used by marketing and sales teams worldwide. His team ran this exercise recently.

Bernd Leger, CMO of Showpad

“We talk about buyer engagement every day. So I figured we’d score well when we asked AI to evaluate us as a buyer would. We did, mostly. But the AI surfaced gaps between what we say on our site and what customers say in reviews. One of my team leads said, ‘If I were a buyer and this is what I saw, I’d want to dig deeper.’ Our positioning was strong. The signals across sources just weren’t telling the same story yet. That’s a gap you don’t find in a traditional competitive analysis.”

That’s what moves AI from “productivity tool” to “how we compete.”

Transformation starts with your team seeing how their customers already use AI to make decisions which makes this urgent. The four mindset shifts that follow change what they believe AI can do, and that belief determines what they build.

Four Dimensions Worth Measuring

How your team thinks about AI determines what they build with it. A team that sees AI as a search engine will use it for search. A team that sees AI as a thought partner, a teammate, a way to reimagine work will build differently. The progression is in the mindset, and the four dimensions below show where that mindset actually sits.

The gap between people is real, and it’s nobody’s fault. When differences are visible, like physical limitations, we give grace. We just help. When they’re invisible, like how someone’s brain processes information, we’re quick to judge.

AI right now rewards a specific way of thinking. If you naturally break problems into steps and see how a process connects end-to-end, it feels like a superpower. If your brain works differently, the same tools feel confusing and frustrating. That’s a difference in thinking style, not a lack of effort.

(I wrote more about this here, featuring a 6’11” former NBA player and me at 4’10”. The visual says it all.)

Liza Adams with Zaza Pachulia, former Golden State Warriors center

Your job as a leader is to see where each person actually is and help them progress.

I use four dimensions to measure where teams are and where they’re headed. Each one has three stages: Developing, Contributing, and Leading. Where you start is less important than whether you’re progressing.

Four dimensions of AI growth: developing, contributing and leading

Dimension 1: From Q&A Machine to Thought Partner

Most people start using AI as a search engine with better answers. That’s Developing. Contributing looks like using AI to pressure-test ideas, offer options, and challenge your thinking. Leading is when AI advises and you judge. You’re still making the decisions, but AI is helping you see what you might be missing.

A content lead at Developing asks AI to draft a blog post. At Contributing, she asks AI to identify gaps in her content strategy based on what buyers are searching for. At Leading, she uses AI to challenge her assumptions about what the audience actually needs before she commits to a content calendar.

How to Think Critically with AI walks through this progression with examples at each stage.

Critical thinking framework: challenge the assumption with AI

Dimension 2: From Tool to Teammate to Workflow

A tool does a task when you ask. A teammate has context about your work and collaborates with you over time. A workflow connects multiple teammates across a process with defined handoffs.

Most teams are still at tool. They’re using ChatGPT or Copilot for one-off tasks. The progression is building AI teammates trained on your knowledge, then orchestrating them into workflows that span your team or across teams.

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, some served one person’s needs just fine. But the 57 that stuck had something in common: their builders made five design decisions intentionally before they started. I covered those decisions in 211 AI Teammates, Only 57 Stuck.

Debbie Umbach is CMO of M-Files, a leader in AI-native document management. She ran this assessment with her marketing team.

Debbie Umbach, CMO of M-Files

“At M-Files, I found a team that was solid at using AI for everyday tasks. The next steps were building AI teammates and connecting them into workflows where the output of one feeds the next. Most people haven’t been shown what that looks like. Once we mapped the team across the four dimensions, I could see exactly where to focus.

Within a couple of workshops, light bulbs came on. People got excited once they put hands on keyboard and saw what was possible. We went from scattered AI use to people building teammates and thinking about how to connect them.”

Debbie’s team saw the progression firsthand. Once you have teammates that work, connecting them is where the real scale happens. I covered that process in Your AI Teammates Aren’t a Team Yet.

The AI progression: from tools to teammates to workflows

Dimension 3: From Productivity to Innovation and Growth

Most teams start with speed: faster reports, faster drafts, faster data formatting. Based on my work with GTM teams, 80-85% of AI use is speed: faster reports, faster drafts, faster data formatting. Speed is a good starting point, but it can’t be the ceiling.

The next step is quality. AI that pressure-tests your messaging against buyer personas before you launch. AI that runs A/B variations and identifies which positioning resonates by segment. AI that assesses your competitive gaps instead of just summarizing competitor websites.

The biggest opportunity is reimagined work, new capabilities that change what your team can do for customers and grow the business in ways that weren’t possible before.

Frank Nardi at Cloud Coach lived through this. His team started where most do, using AI to speed up content and messaging. Then Frank used AI to turn static buyer personas into dynamic, evolving systems that test messaging against real buyer needs before anything launches. Now his lean GTM team competes with companies 10x their size because AI gave them capabilities they couldn’t have staffed for. Same team, same budget. The belief about what AI could do changed, and so did the work. His full approach is in The Boldest AI Moves Are Coming From the Smallest Budgets.

The Boldest AI Moves Are Coming From the Smallest Budgets

Dimension 4: From Task to Outcome (Org Charts to Work Charts)

Completing tasks is table stakes. The progression is toward outcomes: team outcomes first, then business outcomes, then customer impact.

A demand gen manager at Developing uses AI to generate campaign assets. At Contributing, she connects her AI work to team KPIs and helps teammates adopt what’s working. At Leading, there’s a clear line between what she builds with AI and business results or customer impact.

Megan Ratcliff, formerly of Dice, started by building AI teammates to handle individual campaign tasks (Developing). Then she connected those teammates across workflows, shared what worked with her team, and started influencing results beyond her own function (Contributing). Eventually she built a cross- functional GTM strategist that coordinated across marketing, sales, and customer success for a campaign launch that broke every traditional playbook at the company. Her role fundamentally changed from integrated campaigns to GTM architect. AI gave her the tools to work at that level (Leading).

This is where the shift from org charts to work charts becomes real. When people connect their AI work to outcomes that span functions, they stop working inside their role and start working across the customer journey. I wrote about this broader shift in No Daylight: How AI is Reorganizing GTM Teams Around Customers, Not Departments. Megan’s story and the full team playbook are in The Boldest AI Moves Are Coming From the Smallest Budgets and A Leader’s Playbook: How a Lean Team Transformed Into a Human-AI Powerhouse.

See Where You Are

I built an AI Growth Self-Assessment that walks you through questions across all four dimensions and shows where you’re Developing, Contributing, and Leading. Start with yourself before you assess your team.

Sample AI Growth Self-Assessment Result

Seeing Where Your Team Actually Is

I built a sample interactive AI growth dashboard to help teams see this clearly. It tracks each person across the four dimensions, shows where gaps are, and identifies next steps. It also connects to recognition because progress is expected, not optional.

AI growth dashboard: heatmap by team member

The dashboard includes metrics at each stage: queries per week, AI teammates built, workflows connected, and business outcomes tied to AI work.

Three factors influence how contribution is recognized:

Impact – Not all contributions are equal. Quality over quantity. Some AI teammates improve internal efficiency. Others directly affect customer experience or revenue. The question to ask is: what does this enable?
Multiplier – Doing the work is expected. Helping others do the work is how we scale. The people who share what they’ve built, document what works, and coach teammates through their first AI teammate create more value than someone building alone.
Responsibility – Using AI within guidelines and protecting data. This is mandatory. Teams that skip governance create risk that slows everyone else down.

Where you start is less important than whether you’re progressing. The message to your team: we commit to providing tools, time, and support to help you get there.

What Comes Next

This framework shows you where each person is across the four dimensions, where the gaps are, and who’s ready to move. That clarity is the first step. Part 2 covers what to do with it.

Part 2 covers what applied work actually looks like, including the workshops that shift how people think, the phases teams move through, and the infrastructure that keeps transformation going after any initiative ends.

If you want to get ahead, start with the customer exercise. Open ChatGPT, Perplexity, Claude, Copilot, Gemini, and Google AI Mode as your target buyer. Ask them to compare you to competitors. What comes back will tell you more than any internal assessment.

I’d love to hear where your team lands across these four dimensions. Reply to this email. Your input directly shapes what I write next and what I cover in Part 2.

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) or 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.

AI Moved Fast: What Changed and What to Do Next

Liza Adams · March 5, 2026 ·

Scale AI: democratized building, centralized enablement

You’ve seen the LinkedIn posts. Someone built an app in 20 minutes. Someone else automated a full content workflow over the weekend. What those posts don’t show: the enterprise security policies, the legal reviews, the IT constraints, the fact that half your team doesn’t have access to the same tools. AI capabilities jumped in the last eight weeks. Your team’s security policies, legal reviews, and IT constraints stayed right where they were.

Quick Take

AI platforms shipped more capability in the last eight weeks than in the previous six months. Autonomous agents, desktop AI assistants, enterprise workflow studios, all went live in a short timeframe. If you feel like you missed a month and everyone else already caught up, you didn’t and they haven’t.

The progression I’ve been writing about still holds: Tool to Teammate to Workflow. But what’s possible inside each stage changed fast enough that your mental model from December needs an update.

Key takeaways:

AI teammates now have agency. They plan, execute, and check in at decision points. The shift from “I ask, it answers” to “I delegate, it delivers” is real, and most teams haven’t caught up to it yet.
Self-contained workflows stay inside your AI platform. Anyone can build them today. System-connected workflows touch your CRM, HRIS, or marketing automation. Those need IT and Legal. That determines whether you can start now or need to loop in others first.
The gap between “what’s possible” and “what’s possible for your team” depends on constraints like security policies, legal reviews, tool access, and organizational complexity.
Not everyone needs to be orchestrating human+AI workflows now. Knowing where you are is more important than racing to the next stage.
There’s an interactive AI Evolution Framework guide at the end that helps you find your starting point, the right platforms, and whether you need IT involved.

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 19-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.

Here’s What Actually Happened

Eight weeks that changed AI: platform launches from Anthropic, OpenAI, Google and others

That’s eight weeks. For context, Custom GPTs launched two years ago and most teams are still figuring those out.

If the people you work with seem confused, stressed, or frozen, this is why. The capabilities moved but the constraints didn’t. IT policies, security reviews, legal approvals, tool access, organizational complexity, these are all right where you left them. And nobody is mapping the difference between “what’s technically possible” and “what’s actually possible for your team, in your org, with your constraints.”

The Stages Haven’t Changed. What’s Inside Them Has.

If you’ve been reading this newsletter, you know the progression: Tool to Teammate to Workflow.

Stage 1 got better. When AI gives you a ‘good enough’ answer, you stop pushing. You stay at Stage 1 longer than you need to.

Stage 2 is where the real change happened. AI teammates now have agency. They plan, break work into steps, execute, and check in with you at decision points. The shift from “I ask, it answers” to “I delegate, it delivers” is real. They work inside your computer, navigating files, apps, and browsers to get the job done while you focus on something else.

Anthropic’s head of product described Cowork as enabling AI to “deliver polished, near-final work.” OpenAI’s CEO said you can go “from a clean sheet of paper to sophisticated software in a few hours.”

But the biggest change is at Stage 3. Workflows split into two distinct modes, and the line between them changes who needs to be involved, what you can build on your own, and where governance actually matters.

The interactive AI Evolution Framework guide maps each stage in detail, including platforms and who needs to be involved.

Self-Contained vs System-Connected Workflows

The most important distinction in this whole framework is one you won’t find in any AI product announcement. It’s the line between self-contained and system- connected workflows.

1) Self-contained workflows keep everything inside your conversation or uploaded files within your AI platform. You chain multiple AI teammates together. You feed the output of one into the next. You produce a complete sales toolkit, a campaign brief with supporting assets, a market analysis with recommendations, all from a single starting prompt.

No external system connections. No sensitive data flowing through APIs. You can build these today, by yourself, without permission from anyone.

2) System-connected workflows reach into your CRM, your HRIS, your marketing automation platform. They read and/or write sensitive data. They need connectors, security reviews, and often legal sign-off.

You’ll hear the term MCP (Model Context Protocol) more and more. It’s how AI connects to business systems, and it already has over 100 million monthly downloads. Anthropic and Microsoft both launched enterprise connector ecosystems. This infrastructure is moving fast.

These workflows need an enablement team, working with IT and legal, to build the connections. You bring the knowledge and expertise of the jobs to be done. They provide the infrastructure.

This helps answer the common question: “Can I build this myself, or do I need to loop in others first?”

For self-contained work, start. For system-connected work, partner with your enablement team.

Who Makes This Work at Scale?

When I say “enablement team,” I don’t mean a new department. I mean a function. It might live in marketing ops, rev ops, IT, or a small cross-functional group. Someone who understands how work flows across teams, knows how to build automations and integrations, and can work with IT and Legal on security. People with this combination are hard to find right now.

They’re responsible for:

Training people to build well from the start
Supporting them when they get stuck
Prioritizing which system connections to build now and which to hold
Setting up governance so the organization isn’t flying blind
Watching the costs, because usage-based pricing on automation tools, API calls, and connectors adds up fast once workflows go live

That last one catches people off guard. Most AI subscriptions are flat-rate but most workflow infrastructure is not. Few teams are budgeting for this yet. The training, support, governance, and cost management are what keep it from breaking.

Here’s what it looks like when you find these people. Some may be in your org already.

A cybersecurity company's human+AI marketing org with AI teammates — Human+ AI Transformation Case Study with Builders and Enablers

At a global cybersecurity company I work with, Marketing Ops stepped into this role. Everyone across marketing built their own AI teammates freely. Over 200 were created during experimentation. 57 are now in daily use. But when those teammates needed to connect to CRM, marketing automation, or internal repositories, Marketing Ops handled the integration and governance. They worked with Legal and IT to make sure the access was responsible and secure. Full case study here.

The model is democratized building, centralized enablement. Everyone builds. A smaller team makes it safe and scalable. This works because the people closest to the work know what to build. A product marketer knows what a positioning teammate should do. A field marketer knows what an event planning teammate needs. You don’t want to bottleneck that. But when those teammates need to connect to business systems, you need someone who knows how to do that without creating risk.

Dominic Abou-Jaoude is the Director of Marketing Data & Analytics at Sophos, sits on his company’s AI Committee, and built an internal triage system that helps people figure out when to bring in his team.

Dominic Abou-Jaoude, Director of Marketing Data & Analytics at Sophos

“The way I know when to step in is pretty straightforward. If someone is building something with their own files and their own thinking, they should just go do it. I want them to. That’s how they figure out what works.

The second a workflow needs to push or pull from a core system, like Salesforce, then I step in to help. Because one bad connection to a live system could create a stability or a security issue. My job is to work with our engineering and security teams to make sure the plumbing is secure and reliable so people can keep building on top of it.”

Kimber Spradlin, Chief Marketing Officer at Graylog, is coming at this from the other direction. Her team already has custom GPTs. Now she’s moving them toward orchestrated workflows using Claude.

Kimber Spradlin, Chief Marketing Officer at Graylog

“I built a drag-and-drop workflow designer for our team offsite inspired by Liza’s dynamic ‘AI teammate and workflow Ideator’ app. Vibe-coded the whole thing.

The reason I could design the system is because I’d already built the individual piece using custom GPTs. I knew what each step needed because I’d spent time using them one at a time first. Now we’re moving it to a production server and building the actual agents behind each step. We learned a lot from building and using custom GPTs . It was the reason I could see how the pieces fit together.”

Here’s the dynamic AI workflow app that sparked Kimber’s approach. It offers sample AI workflows and AI teammates for each one.

Dominic and Kimber are at different starting points in the same progression. Both are building.

Find Your Starting Point

Your situation is different from the person sitting next to you. Different tools, different constraints, different starting point. A CMO at a 5,000-person company and a VP of Sales at a 50-person startup are both reading this newsletter right now, and their next move isn’t the same.

I built an interactive AI Evolution Framework guide that helps you figure out yours. It has three tabs.

Start with the decision guide on the first tab. Pick what you’re trying to do, and it maps you to the right stage, the platforms that fit, and whether you need to involve your enablement team. Takes about 30 seconds.

The other two tabs go deeper. One breaks down what’s changed inside each stage, including the platforms available now and how to know where you are. The other shows the operating model: what you build, what your enablement team builds, and how the roles work together. The short video below walks through both.

Interactive AI Evolution Framework Guide

See the 90-sec video preview of the interactive guide below.

If you want the quick-reference version, the one-pager maps every platform to every stage across all four ecosystems (Google, Microsoft, Claude, OpenAI) on a single page. Keep it open when you’re deciding what to build next. Here’s the one-pager.

Platform guide: which AI tool to open for each kind of work

What Comes Next

The framework gives you a starting point. These are the decisions that come after.

How fast should teams move from Stage 2 to Stage 3? Some teams I work with are still building their first AI teammates. Others are ready to orchestrate. The temptation is to push everyone toward workflows, but rushing past the teammate stage means you miss the part where people learn to evaluate AI critically. That skill is what makes Stage 3 work.
Where does the enablement team role land in smaller organizations? At a large company, you have an ops team, IT resources, and legal review capacity. At a 50-person company, the person building AI teammates might also be the person managing governance. The roles still apply. The question is how they map when one person is wearing three hats.
Are Custom GPTs a dead end? Ethan Mollick said they are. I get why. AI is moving toward systems that orchestrate themselves, picking tools and skills without you directing every step. But the thinking behind the GPTs is important. The knowledge you put in, the way you learned what AI is good at and where it falls short, the judgment about when to trust the output, all of that carries forward. You can’t set up AI-orchestrated workflows well if you’ve never orchestrated them yourself.

Go Deeper

Your Turn

This is my best thinking right now. It will keep evolving as the platforms advance and as I learn with teams putting this into practice. I want to hear from you.

Where is your team in this progression?
Where are you stuck?
What would be most helpful to go deeper on?

I read every response, and your input directly shapes what I write next. The conversations that feed this newsletter go both ways. Always have.

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) or hands-on building (create AI teammates 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.

Your Buyer Changed: Four AI-Driven Shifts Breaking the GTM Playbook

Liza Adams · February 19, 2026 ·

Your buyer now uses AI to research, compare, and build a shortlist before they ever talk to your team. They trust peers over your pitch. And AI shows them everything about your brand, good or bad. Most GTM playbooks were built before any of this was true.

Quick Take

I’ve spent the past couple of years writing about AI search, brand trust, team structure, workflow design, and leadership. Each felt like a separate topic. It took a room full of CMOs and one word (attribution) to help me see they’re all connected. They all trace back to the same root cause: how your buyer changed.

This newsletter connects the dots. Four shifts in buyer behavior are breaking how you reach customers, how you measure results, and how your teams work together. Most teams are feeling the symptoms but haven’t seen the full picture yet.

Key takeaways:

Your buyer’s AI builds the shortlist now. If AI can’t find your content or doesn’t understand what you’re best at, you’re not in the conversation.
AI shows your buyer whatever it finds about you, from every source, all at once. Consistent signals build trust. Mixed signals create confusion. If it’s unclear, AI may fill in the gaps on its own.
Influence is moving to peers, communities, and real human connections. AI reinforces those voices over yours.
Connected, personalized experience across the full customer lifecycle is now the minimum to compete. Delivering it requires breaking down silos between marketing, sales, and CS.
Replace attribution and per-department KPIs with three metrics your customers actually feel: time to value, customer satisfaction, and net revenue retention. These are numbers your CFO already watches.
These four shifts don’t each break one thing. They ripple across your content strategy, your metrics, and your team structure at the same time. That’s why everything feels like it’s in motion.

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 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. (There are some typos in the AI explainer but they are minor.)

How This Started

A couple weeks ago, I was co-presenting on a human+AI transformation journey during CMO Coffee Talk with Megan Cabrera, VP of Marketing Operations at Sophos. We had 200+ CMOs and marketing leaders on Zoom. The conversation was flowing. Then the topic of attribution came up, and I had a reaction I wasn’t expecting.

I said what I’ve long been thinking. The chat on Slack lit up with comments like “spot on” and “preach” to “Liza for president.”

Matt Heinz, Founder of Heinz Marketing and CMO Coffee Talk Host, shared his thoughts on the topic.

Matt Heinz, Founder of Heinz Marketing and CMO Coffee Talk Host

“We’ve been debating attribution in CMO Coffee Talk for years. It always gets heated because every CMO is stuck between a CEO that wants credit assigned and a buying process that makes clean credit impossible.

Liza nailed something most of us have been feeling but hadn’t said out loud: we keep trying to fix attribution when the buying process it was built to track doesn’t exist anymore.

When the buyer’s AI has already built a shortlist before your rep picks up the phone, arguing about which touch gets credit is the wrong conversation. We’ve argued about who gets credit for the deal. Meanwhile nobody’s asking whether the customer felt like we actually helped them buy.”

I wrote about it in a LinkedIn post that struck a nerve. The comments and DMs kept coming. But the more I sat with the response, the more I realized attribution was just one symptom. I started listing other GTM practices that feel like they’re running on borrowed time. The list got long. Then I noticed they all trace back to how your buyer changed.

Here are the four buyer shifts driving the change, and what they mean for your GTM playbook.

Four Ways Your Buyer Changed

1. You might not make the consideration set.

Your buyer’s AI builds the shortlist now. It’s not your SDR’s email or your Google ad. AI.

I ran this test myself. I opened ChatGPT as a VP of Sales Ops looking for a sales engagement platform. I gave it my company size, my pain points, the outcomes I cared about, and asked it to rate the top platforms. I ran the same prompt in Google AI Mode, Claude, and Perplexity.

I didnt specify the vendor names in my prompt. But each one built comparison tables, rated Outreach, Salesloft, Gong, Clari, and HubSpot Sales Hub on my criteria, and made a recommendation in seconds.

When I looked at what AI actually cited:

Review sites like G2. Cited.
Reddit and community forums. Cited.
Vendor blogs. Cited.
Generic listicles. Cited.
Constellation ShortList for Sales Engagement. Not cited.
Forrester Wave for B2B Sales Tech. Not cited.
Gartner MQ for CRM Customer Engagement. Not cited.
Anything behind a form. Nowhere to be found.

The buyer never visited a vendor website. AI formed its opinion from open sources. Content behind a gate didn’t exist.

If AI can’t find you, can’t access your content, or doesn’t understand what you’re best at, you’re not on the list. The buyer never knows you were an option.

I shared more on the gated content piece in a recent LinkedIn post. Here’s the AI search prompt and the full response from Google AI Mode.

Comparison Table in Google AI Mode’s Response

2. Whatever AI finds about you, it shows your buyer. Good or bad.

You don’t get to choose what your buyer sees first anymore. AI pulls from everywhere: your website, support documentation, G2 reviews, Glassdoor, Reddit threads, LinkedIn posts from your employees, case studies (if they’re accessible), news coverage.

It reads all of it at once and looks for patterns. Consistent signals build trust and mixed signals create confusion. AI may show both or make up a response. And you may not like what it serves up.

I experienced this myself when I received an invitation to be listed in a business directory. I didn’t schedule a call or review their materials. I asked AI. Within seconds, it showed me red flags in their business practices that sealed the decision before they could make their pitch. Here are my prompt and the AI response.

Your buyers are doing the same thing when they evaluate you.

This means your brand needs to hold up across every channel AI reads. Here’s a simple framework I use for thinking about this:

Visibility – Does AI know you exist as a credible option?
Sentiment – Is what AI says about you accurate, helpful, and consistent?
Recommendation – Does AI suggest you for the right situations?

If you haven’t checked how AI describes your company and your category, that’s a good place to start this week.

AI reads all of these info at once and looks for patterns to determine signal

3. Influence and decisions are moving to peers, communities, and real human connections.

Recent research from Forrester (2025) and TrustRadius (2025) shows that B2B buyers rely most on other buyers, such as peers, customers, and online reviews, as their most trusted sources, well ahead of vendor sales reps and marketing content. AI works the same way; when it answers questions, it tends to pull from those same places, including review sites, communities, and real customer stories, not your latest pitch.

I saw this play out in real time. A GTM VP in one of my communities asked a simple question: “We’re evaluating AI SDRs. Anyone using one generating good results?” Within an hour, four peers jumped in with honest takes, including one who named a vendor the VP hadn’t considered and another who said prospects kept dropping off because the AI talked in circles instead of giving straight answers. The VP told me she narrowed her shortlist that afternoon based entirely on those responses. No demo, no sales call, no content download. The vendors on her original list had no idea they’d already been cut.

I belong to several GTM and exec communities like Pavilion, Marketing AI Institute, Women in Revenue, AI Marketing Forum, CMO Huddles, and CMO Coffee Talk. That’s where real conversations happen, people being completely transparent, opening their networks and helping each other. People share their vendor experiences, offer recommendations, foster introductions, and make decisions. Relationships and trust are built with professional reputations put on the line.

The places where real influence happens are getting harder to measure and impossible to control. This could be a conversation at an industry event, a recommendation in a Slack community, an honest review from a customer who actually uses your product every day. In many cases, these go beyond supporting your sales funnel; they are the funnel.

AI handles the research. Humans handle the relationships. And they’re reinforcing each other.

4. Connected, personalized experience is now table stakes.

Your buyers have always wanted a smooth experience across every interaction with your company across marketing, sales, onboarding, and support. For years, this was a differentiator. If you delivered it, you stood out. Most companies couldn’t, so the bar stayed low.

That bar’s now higher.

AI makes it possible to deliver connected experiences at scale. Which means buyers now expect it. Not having it is no longer an acceptable gap. It’s a reason to choose someone else.

And here’s why this shift has the biggest internal implications. You can’t deliver a connected experience if your teams run separate systems, measure separate KPIs, and hand off customers like a relay baton. When marketing says one thing, sales delivers another, and customer success solves different problems entirely, AI makes those gaps visible to buyers faster than ever.

This is the shift that forces the internal rethink: how your teams are structured, how they share data, and how they measure success. Darcy Kurtz, CMO of WP Engine, reinforces this:

Darcy Kurtz, Chief Marketing Officer of WP Engine

“I’ve sat in both chairs. CMO building demand, CRO accountable for revenue. What you see from the revenue side is how much context disappears between teams. Marketing builds one story, sales tells another, onboarding starts from scratch. The customer feels every seam. AI makes it worse because the whole journey happens faster now. There’s no time to cover the gaps.

You don’t fix this with better handoffs. You fix it by making marketing, sales, and CS stop operating like three different companies.”

WP Engine’s Marketing Leadership Team During an AI Workshop

What This Means for Your GTM Playbook

If these were four separate shifts, you could address them one at a time. Update your SEO strategy here, fix a handoff there. But they’re not separate. They’re happening together, and they’re feeding each other. That’s why so many GTM leaders I talk to feel like everything is in motion at once. It is and it shares a root cause.

These four shifts ripple across how you reach your buyer, how you measure results, and how your teams work together, all at the same time.

How you reach your buyer

Shift from SEO to GEO – The goal is getting cited by AI, not just ranking on a page. Gartner estimates brands will lose 25% of their web traffic as AI answers buyer questions directly. Your content now serves two audiences: humans who want story and connection, and AI that needs structure, clarity, and access. Anything behind a form is invisible to AI. That doesn’t mean every form disappears. A webinar registration or a personalized assessment is worth the form. But the thought leadership and expertise that builds your AI visibility needs to be findable.
Audit every channel AI reads – Your website and G2 profile aren’t enough. Run the Visibility, Sentiment, Recommendation framework from Shift 2 across support docs, Glassdoor, Reddit, employee LinkedIn posts, and news coverage.
Earn the voices AI amplifies – Make it easy for real customers to share real experiences. Show up where your buyers spend time. This is harder to scale and impossible to fake, which is exactly why it works.

Deb Wolf, CMO at Dataminr, shares sage advice.

Deb Wolf, Chief Marketing Officer at Dataminr

“Here’s my advice to every CMO right now: stop what you’re doing and go be your own buyer. Open ChatGPT, Perplexity, Claude, Gemini, Google AI Mode. Search for your company the way a prospect would. Ask it to compare you to your competitors. What comes back will tell you more about your go- to-market gaps than any dashboard.

Most of us are still optimizing a playbook we built for a buyer journey we haven’t personally experienced as a buyer. The shift is real, but you won’t feel the urgency until you see what AI actually says about you.”

How you measure what’s working

This is the one that triggers the strongest reactions when I bring it up with CMOs.

Everyone knows attribution is getting harder. Fewer people have said out loud that the real problem is your metrics were built for a buyer journey that no longer exists.

MQLs measure marketing activity, not buyer intent. Per-department KPIs (leads for marketing, close rates for sales, renewals for CS) optimize each team’s scorecard while the customer falls through the gaps between them.

An analysis of 478 B2B companies found that GTM effectiveness dropped from 78% in 2018 to 47% in 2025 (Martech.org, 2025). Less than half of every GTM dollar is producing results. That’s a measurement problem built on assumptions about buyer behavior that no longer hold.

Replace it with metrics your customers experience and care about:

Time to value – how many days from signed contract to first live campaign, first threat detected, or first workflow created? This tells you whether your customer is getting results.
Customer satisfaction – do they show up to your QBRs, respond to your CSM, and agree to be a reference? This tells you whether your customer trusts you.
Net revenue retention – are existing customers expanding their contracts, adding more features, or increasing usage? This tells you whether your customer grows with you.

When customers get results, trust you, and grow with you, that’s a GTM engine that’s working.

I know what you’re thinking. “My CEO wants to know which campaign sourced the deal.”

Try this in your next leadership meeting:

“We’ve been asking which touch gets credit. But our buyer built their shortlist with AI before we knew they existed. Instead of tracking what we can’t see, what if we measured what our customer actually feels? How fast are new customers getting to value? Are they satisfied enough to expand? Is our net revenue going up or down?”

These are numbers your CFO already watches because they predict the revenue outcomes the board cares about.

You can keep tracking what you track today. But shift the weight toward outcomes your customers experience and away from activity metrics that no longer reflect how buyers actually buy.

How your teams work together

This is where it all comes together. And it’s the hardest part to change.

When buyers expect a connected experience across the full lifecycle, teams can’t operate as separate functions that hand off to each other. Every handoff loses context. Every silo creates a gap the buyer feels.

Most companies aren’t there yet, but the direction is clear. The Microsoft 2025 Work Trend Index found that teams are forming around goals, not functions, with AI helping people work across traditional boundaries. The emerging model points to journey teams that own customer outcomes end to end, with AI carrying context so the buyer’s story doesn’t restart every time they talk to someone new.

G2 just created their first-ever President of Go-to-Market role, consolidating marketing and revenue under one leader. When the company that runs one of the largest B2B review platforms restructures because buyer behavior changed, pay attention.

Sean McCaffrey at Cin7 is building this at the operational level. His team connected marketing, sales, and CS into one AI-native growth engine designed around how buyers actually buy. I wrote about his full approach in a recent newsletter.

This also changes how teams adopt AI. The value comes when AI connects workflows across teams, not when each department gets its own tools.

Go Deeper

Each of these areas has a full newsletter behind it. Here’s where to go next based on what’s most relevant to you:

AI search and content visibility – Make Your Brand Sourced and a Top Result in AI Search | Standing Out When Web Traffic Declines
Brand trust and AI perception – Brand Economics in the Age of AI | When AI Judges Your Brand Before Humans Do
Team structure and customer outcomes – No Daylight: How AI is Reorganizing GTM Teams | How a Leading Cybersecurity Company Redesigned Marketing Workflows | 80% Meeting Rates: A Real-Life AI Growth Engine Built Around the Customer
Metrics and leadership – The GTM Leadership Criteria That Worked for Decades Aren’t Enough Anymore
Workflow transformation – Your AI Teammates Aren’t a Team Yet | The End of Handoffs

What’s Breaking Down and What’s Replacing It

Here’s the full picture in one view. Each of these connects back to one or more of the four buyer shifts above.

What’s NOT Dead

This is a big shift. But some things are getting stronger, not weaker. Brand matters more than it has in years because AI reads everything and looks for consistency. Human relationships are more differentiated than ever because everything else can be scaled. Strategic thinking is more important because AI exposes weak strategy faster. And content still matters but the format, distribution, and audience have changed.

Where to Start

You don’t need to rethink everything at once. But you do need to start seeing the full picture. Here are three things you can do this month.

Run the AI search exercise from shift #1 for your own company – Open ChatGPT, Perplexity, Claude, Gemini, Copilot, or Google AI Mode as your target buyer. Search for solutions in your category the way a prospect would. Ask it to compare top vendors on a set of eval criteria and offer a recommendation with reasons why. The gaps will be obvious.
Check your metrics against customer outcomes – In your next leadership meeting, ask each function lead to name one metric that measures the customer’s success, not the department’s. When marketing says MQLs, sales says close rate, and CS says churn, you’re looking at the misalignment.
Find your worst customer handoff – Map what the customer actually experiences at the transition between two teams versus what each team thinks they deliver. That gap is your first redesign opportunity. Start there.

None of this requires starting from scratch. It requires seeing the full picture and deciding where to start.

If you run the AI search exercise, I want to hear what you found. DM me or drop it in the comments. The CMOs who’ve done this tell me it’s the most eye- opening 10 minutes they’ve spent this quarter.

Want practical AI insights like this delivered every two weeks? Subscribe to the Practical AI in Go-to-Market newsletter.

For applied learning: Our applied AI workshops offer both strategic sessions (use cases and roadmaps) and hands-on building (create AI teammates 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’d love to connect.

80% Meeting Rates, 3x Reply Rates: A Real-Life AI Growth Engine Built Around the Customer

Liza Adams · February 5, 2026 ·

AI-native growth engine infographic: the five-stage AI growth flywheel and revenue results — Inspired by Sean McCaffrey, Sr. Dir of Strategy & Operations at Cin7

Around 98% of your website visitors leave without taking action. If that many people walked into a physical store, looked at products, checked out price tags, and walked out, you’d fix it tomorrow. Sean McCaffrey at Cin7 decided to fix it online. He built a five-stage AI growth engine designed around how customers actually buy, not around internal workflows. Today, Sean and his team are seeing 80% meeting booking rates, 3x higher email replies, 10% lower acquisition costs, and more human conversations.

Quick Take

This newsletter features how one strategic marketing operations leader built a connected AI system that captures demand in the moment, recovers interest that would otherwise disappear, learns from what closes deals, and feeds those insights back into every customer touchpoint.

Key takeaways:

AI creates growth when it’s wired into your revenue system around customer needs, not bolted on as isolated tools to do old work faster.
The right tech stack depends on your situation. This story shows what’s possible, not what to copy.
If we simply train AI to do old work faster, we unintentionally create a case for automating humans out. But when we use AI to reimagine work that wasn’t possible before, work that drives growth, humans become more essential. AI handles the repetitive work so humans can have better conversations.

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 15-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.

The Shift

Your buyers are researching in communities, asking peers, and using ChatGPT, Gemini, Perplexity, and Google AI Mode to evaluate vendors before they ever reach your site. They avoid forms. They expect relevant responses the moment they do engage, and if you don’t deliver, they leave without telling you why.

Sean and his team decided to build around the customer instead.

One Team’s Response

Cin7 is an inventory management company serving product sellers. Sean McCaffrey, Senior Director of Marketing Strategy and Operations, joined three and a half years ago. I worked with the team early in their AI journey in 2024 and have watched them build this over time.

His starting point wasn’t “what AI tools should we buy?” It was “how do we actually meet customers where they are?”

The answer became what Sean calls an AI-native growth engine. It captures demand when buyers are paying attention, recovers the ones who leave before converting, learns from what actually closes deals, and feeds those insights back into every customer touchpoint.

The flywheel has five stages. Each one exists because of a specific customer behavior. Sean and his team didn’t build all five at once. They started with one, proved value, then expanded. You can do the same.

AI-Native Growth Engine video explainer thumbnail

Stage 1: Engage in the Moment

Buyers don’t fill out forms like they used to, but they do respond when something feels relevant.

Sean’s team uses Qualified, an AI SDR that talks to website visitors in real-time. More on the specific tools later. This story is about the approach, not a vendor recommendation. It pulls firmographic data through a Clearbit integration, knows what pages they’ve seen, and adapts the conversation based on industry.

A visitor from a food and beverage company doesn’t get a generic “How can I help you?” They get asked about expiration date tracking and inventory spoilage. The conversation starts with their likely problem, not a blank slate.

Of visitors who engage and meet their criteria, 80% book meetings compared to 50% through traditional forms.

The AI isn’t replacing human conversations. It’s making sure qualified buyers actually get to a human instead of bouncing.

Sean McCaffrey, Senior Director of Marketing Strategy & Operations

“Before, reps spent 30 minutes asking ‘what do you guys do?’ Now they walk in with full context.”

Stage 2: Recover What Would Otherwise Disappear

Most of those visitors aren’t gone forever. Sean’s team built a way to find them. Vector identifies who visited from the US. Clay adds context and confirms they’re a good fit. Together, they flag the people who looked seriously but didn’t reach out.

These aren’t cold prospects. They’re people who showed interest and match the profile. They just weren’t ready yet.

Stage 3: Re-engage with Context

The follow-up isn’t generic. Those contacts flow back into Qualified, which sends emails on behalf of the human SDRs. The emails are informed by what the system knows: which pages they visited, what their company does, what problems they’re likely facing.

Sean’s team draws a clear line here: lead with helpfulness, not stalking. The email doesn’t say “I saw you on our pricing page.” It offers a point of view on a problem worth solving. That’s the line between creepy and valuable.

The AI handles the repetitive work. Humans step in for the conversations worth having.

Reply rates are 3x higher than before.

Stage 4: Learn from What Closes

Closed-won calls recorded in Gong feed into AirOps, where the team extracts what actually worked: objections that came up, language buyers used, triggers that created urgency, competitive positioning that won.

The team uses those insights to update ad copy, landing pages, email sequences, and the AI SDR’s conversation flows. When “fear of stockouts during peak season” keeps showing up in winning deals, the team puts that same language into ads and outreach. The system learns from every win.

Stage 5: Multiply Without Burnout

Producing one piece of content and moving on leaves value on the table.

The team built a hub-and-spoke workflow in AirOps. One webinar becomes blog posts, LinkedIn content, email sequences, ad options, and sales talk tracks. A human reviews and approves the outline before full generation. Brand guidelines keep everything on voice.

The content is grounded in themes and pain points the team knows resonate because they’ve heard them in customer conversations.

Hub-and-spoke workflow turning one webinar into blog posts, LinkedIn content, emails, ads and sales talk tracks

What It Actually Took

This didn’t happen overnight.

Foundation first – The first year was making sure HubSpot and Salesforce were properly integrated and data was clean. You can’t build AI systems on messy data. Nobody wants to hear that, but skipping it breaks everything that comes after.
Content Cleanup – The AI SDR gave wrong answers in the first month. Sean traced it back to outdated help center content. The AI was doing its job. The content was the problem. They spent 30-45 days reading chat transcripts and cleaning up the knowledge base before expanding.
Controlled testing – Every tool ran in a sandbox before going live. Sean and his team earned leadership trust by bringing results, not just ideas. He added his CRO to the Slack channel showing real-time visitor alerts. Within a week, the CRO was on board.

Enterprise teams will face longer timelines with additional compliance, security, and procurement reviews. The principles still apply. Start with foundation and controlled testing, then expand.

The Results

Year over year, the team saw 10% lower customer acquisition cost, 20% lower cost per lead, 80% meeting rates through the AI SDR versus 50% through forms, and 3x higher reply rates.

The numbers tell part of the story. Here’s what changed day to day in the reps’ work according to Sean:

“They know the prospect’s business, their pain points, what content they engaged with. Discovery calls actually discover something. The AI handles repetitive admin work and analysis so humans can have better conversations.”

Before You Build Your Version

Sean’s specific tools solved his specific problems. Yours will differ. The underlying principles hold across industries, even when the tools don’t.

What I’ve seen across the teams I advise:

Max out what you’re already paying for. Sean’s team uses HubSpot, Salesforce, Clearbit, and Gong as core infrastructure before reaching for anything specialized. Most CRM, marketing automation, and analytics platforms already have AI built in. Your existing platforms are already connected to your data and known by your team. Getting full value from that investment is faster to adopt and often takes teams further than expected.
For the gaps, decide: build, buy, or keep it human-led. Sean’s system uses Qualified, Vector, Clay, and AirOps. These solved specific problems his existing stack and AI platforms alone couldn’t.

If you’re evaluating a build: start with a platform like ChatGPT, Claude, Gemini, or Copilot. Cin7 uses ChatGPT for everything from research and analytics to strategy. Build only for what’s truly unique to how you work. The demo is quick. Shipping and maintaining it is the real cost. Know what it costs to run at scale.

If you’re evaluating a buy: vet the vendor carefully. Many of these tools won’t be around in 18 months. Look at data trust, workflow fit, and whether they can keep up. Do trials. Stay flexible.

And some decisions are best kept human-led for now.

Your stack will look different. It depends on your goals, team, skills, and budget. Know which decision you’re making before you make it. Sometimes the answer is what you already have. Sometimes it’s a specialized platform. Sometimes it’s custom. Sometimes it’s a person.

Note: This isn’t a vendor recommendation. It’s a look at what becomes possible when you design AI around customer needs instead of internal workflows.

Framework: start with what you have, then decide whether to build, buy or keep it human-led for the gaps

The Bigger Point

Using AI well means more than using ChatGPT, Copilot, Gemini, or Claude. AI can be wired into your entire revenue system, but only if you design it that way.

Sean and his team aren’t doing old work faster. They’re doing work that wasn’t possible before: engaging every visitor in real-time, recovering demand that would have disappeared, learning from closed deals at scale, creating content informed by what actually resonates.

If we simply train AI to do old work faster, we unintentionally create a case for automating humans out. But when we use AI to reimagine work that wasn’t possible before, work that drives growth, humans become more essential.

Customers expect you to know who they are, meet them where they are, and pick up the conversation where it left off. In the AI era, company size matters less. The advantage goes to teams that learn how to listen, respond, and improve continuously.

The money follows when you earn trust. We now have the tools to do that better than ever.

Your Next Steps

Sean’s flywheel is one version of what’s possible. You don’t have to build all five stages to start seeing value.

Where is demand leaking? Where do high-intent visitors go when they don’t convert? If you don’t know, that’s the first problem to solve.
Are you building for growth or productivity? Are you using AI to reimagine work around customers, or only to do old work faster? The answer determines whether humans become more essential or more optional.

I’d love to hear what you’re building. Reply to this email or share on LinkedIn. The trailblazers in this community are figuring this out together, and what you learn helps others move forward.

Want practical AI insights like this delivered every two weeks? Subscribe to the Practical AI in Go-to-Market newsletter.

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’d love to connect.

Your AI Teammates Aren’t a Team (Yet): From Building Teammates to Orchestrating Workflows

Liza Adams · January 22, 2026 ·

Infographic: the AI progression from tools to teammates to workflows

You’ve built the AI teammates. Custom GPTs, Copilot Agents, Gemini Gems for content, research, email, social. Now they’re sitting in separate chats, doing separate things, with no idea the others exist.

Connecting them is where the real value lives. McKinsey’s 2025 State of AI report found 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.

This is Part 2 of a two-part series. Part 1 covered designing individual AI teammates. Part 2 covers connecting them into workflows. New here? Start with this one or go back to Part 1. Either works.

AI Video Explainer and AI Podcast Versions of This Newsletter

To support different learning styles, this newsletter is available as a 7-min AI video explainer and a 14-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.

Quick Take

There’s a progression: Stage 1 is using AI as a tool. Stage 2 is building AI teammates. Stage 3 is connecting them into workflows. Not everyone needs Stage 3. But knowing it exists changes how you build.

How do you know if you’re ready? You have AI teammates that work well on their own but require you to copy outputs between them. Multiple people touch the same workflow with no shared context. Handoffs are where things break down. If that sounds familiar, keep reading.

One team’s SDR open rates jumped from 15% to 40%. Another saw 35% improved campaign performance. Not from building more AI teammates, but from connecting the ones they had.

Here are a few key insights:

Not everything needs to be a workflow. If a tool or standalone teammate solves the problem, use it.
The best workflow opportunities hide in the gaps between teams, where handoffs break and context gets lost.
Let people across the org build AI teammates freely. When those teammates need to connect to business systems, a centralized team steps in.

Four questions to answer before connecting AI teammates into workflows:

Do I need to build this at all?
Teammate or team?
Where does this workflow add value?
Who builds and who integrates?

Bryan Bowles, VP Marketing at GHX, watched his team make this progression during a hands-on workshop.

“Our teams moved from ‘using AI as a tool’ to ‘designing AI teammates and workflows’ in just a few hours. Real use cases, hands-on building, open sharing. That combination sparked a level of creativity and collaboration that’s going to change how we work going forward. The hardest part of AI isn’t the technology. It’s helping humans rethink what’s possible.”
Bryan Bowles, VP of Marketing at GHX

Question 1: Do I Need to Build This at All?

The fastest way to fail at AI is to build something that already exists or automate something that shouldn’t exist at all.

Before you build, know where you are in the AI tech stack:

Start with what you already have – Your CRM, marketing automation, CMS, and analytics tools already have AI built in or coming soon. Use those first.
Use foundational models to innovate – Pick one or two platforms like ChatGPT, Claude, Gemini, or Copilot. Go deep. This is where you build custom teammates and workflows. This newsletter covers this.
Consider specialized AI tools where they add clear value – If layers 1 and 2 don’t solve it, evaluate niche tools. They can deliver fast ROI. Just stay flexible — don’t sign long-term contracts for tools that might not exist in their current form in 18 months.

I discussed this AI GTM tech stack strategy in more depth in a previous post, including how to evaluate specialized tools.

Know your options before you build. Sometimes the answer is a tool you already have. Sometimes it’s a specialized platform. Sometimes it’s custom. The wrong move is assuming you need to build everything from scratch.

Question 2: Teammate or Team?

Part 1 covered how to build one AI teammate well. Before you build more, ask: am I adding a specialist to a system, or creating a generalist that handles multiple jobs?

Think LEGOs. One big brick looks impressive but limits what you can build. Smaller pieces take more thought to put together, but they combine into anything.

A generalist works well when:

Your process is predictable and repeatable
One person runs the full workflow
You want quick answers in a single thread
Simplicity beats depth for your use case

Specialists work well when:

Campaigns vary in type, steps, or audience
Multiple people own different pieces
Deep domain expertise adds value
The playbook changes depending on the project

Neither is wrong. Just make the choice on purpose.

One practical constraint: some AI platforms limit how many knowledge documents you can attach to a single teammate. A generalist trained on everything may hit that ceiling. Specialists go deep in a narrower domain, so they’re less likely to run into this.

What Chaining Means

Chaining connects AI teammates so they work in sequence in a workflow. Each one sees the full conversation and builds on what came before. Instead of copying outputs between chats and re-explaining context, information flows through.

How chaining works varies by platform. ChatGPT supports @mentions to invoke multiple teammates in the same conversation. Others require switching between chats or configuring orchestration behind the scenes. The mechanics are evolving fast.

We covered the mechanics of chaining in The End of Handoffs: How AI Teammates Work Together. The core principle: design teammates for connection from the start. If they can’t see each other’s work, you’re back to copying and pasting.

This only works when each AI teammate is well-trained. Weak links in the chain make problems worse, not better. It’s also essential that there’s a human checking and guiding the AI teammates’ work along the way.

One team chained GPTs to automate their product launch process. On paper, it looked efficient. In practice, the output was a mess. The VP of Marketing realized they were automating a broken process.

The original workflow had content briefs created before positioning was finalized, assets built in silos and “aligned” at the end, and three review cycles that kept catching the same issues. The AI just made those problems faster and more visible.

They scrapped the chain and reimagined the workflow: positioning locked → content + assets in parallel → compliance review → launch. The AI teammates stayed the same. The workflow around them changed completely.

Joe Sturgess, Director of Performance Marketing at Katalon, has been building AI teammates with his team.

“We’ve been building AI teammates that slot right into our daily workflows, from campaign planning to content optimization to performance analysis. We treat AI like a colleague you’re still getting to know. It needs context, feedback, and a bit of patience. But once it clicks, it becomes a force multiplier. The goal isn’t just automation. It’s freeing up space for higher-value thinking and testing bold ideas faster.”

Question 3: Where Does This Workflow Add Value?

The best workflow opportunities hide in the gaps between teams, where handoffs break and context gets lost.

Two Types of Workflows

Internal workflows connect teams within your organization. The handoffs between product marketing and demand gen, between sales and customer success, between brand and field marketing. The friction lives in the gaps between functions.
External workflows face the market. Campaign execution, lead nurture sequences, customer onboarding, sales enablement. The friction shows up in the customer experience.

Both are worth pursuing. Start where you see the biggest gap between what teams think they deliver and what actually happens. Map what the customer (internal or external) actually experiences versus what each team thinks they deliver. That gap is your workflow opportunity.

Two Approaches to Workflow Design

Once you’ve identified the gap, you have two approaches.

Friction-First: Start with your current workflow and find what’s broken.

Where does work get stuck or delayed?
Where do handoffs create rework?
Where does the customer experience break down?
Where can AI accelerate steps, smooth handoffs, or improve consistency?

This approach works when the workflow is fundamentally sound but has friction worth removing.

Outcome-First: Start with the outcome. Design backward.

What’s the primary outcome? What does good look like?
What are the constraints (compliance, brand, legal, trust)?
What steps actually drive that outcome? Who should own each: human, AI, or hybrid?
What’s the simplest version of this workflow?

This approach prevents “faster horse” thinking. Don’t automate a broken process. Reimagine it.

Check out a global cybersecurity company’s outbound account research workflow and enterprise content creation workflow built using chained AI teammates here.

Hackathons as a Discovery Tool

Some teams use hackathons to surface workflow opportunities. People showcase what they’ve built. The strongest entries are reimagined workflows with connected human and AI teammates working across functions.

Hackathons help you see what teammates already exist, evaluate which ones deliver real value, identify gaps, and inspire others to think in workflows rather than tools. The winning entries often become templates for how work gets done.

Rhiannon Naslund, CMO at Origami Risk, used a leadership applied AI strategic workshop to prepare her team for a companywide hackathon.

“We started with a leadership session to align on how AI teammates could work together across our marketing workflows. When the company held a hackathon a few weeks later, our team was ready. They’d been building and refining their AI teammates since the workshop. The hackathon gave them a stage to show what connected workflows could do — and inspired other teams to think the same way.”

Question 4: Who Builds and Who Enables?

Organizations handle this differently based on size, risk tolerance, and technical capacity. Here’s an approach that works well for teams that want to scale quickly, upskill their people, and maintain security controls.

Democratized Building, Centralized Enablement

Let people across the organization build AI teammates. That’s where experimentation happens. A product marketer knows what a positioning teammate should do. A field marketer knows what an event planning teammate needs. Don’t bottleneck this creativity.

But building is just the start. Training, support, governance, and integration are centralized functions, whether that’s one team (like Marketing Ops, Rev Ops, or an AI enablement function) or several working together.

Enablement includes:

Training – helping people build well from the start
Support – unblocking teams when they get stuck
Governance – policies, security reviews, and working with Legal and IT
Integration – connecting AI teammates to business systems

Some organizations centralize all four in one team. Others split them across Ops, IT, and L&D. The key is that these functions are coordinated, not scattered.

Chaining vs. Connecting to Systems

Chaining (covered in Question 2) is something anyone can do. You control what gets shared.

Connecting to systems is different. When AI starts reading from or writing to CRM, marketing automation, or customer data, the rules change. Some organizations allow direct connections. Others put a buffer layer in between using tools like Zapier, Make, n8n, or Glean. Others route everything through a centralized team.

Think of it like building security. Direct connection is like giving visitors a key. A buffer layer is like having a front desk that makes sure the right people get to the right places.

Know your company’s approach before you build something that needs system access.

When You’re Ready to Scale

As your workflows grow, you’ll need infrastructure: tracking what teammates exist, helping people find the right ones, governing who can connect to business systems.

We covered that scaling playbook in the case study about a leading cybersecurity company, including how they organized teammates into layers and built systems for tracking, navigation, and governance (see infographic below). If scaling is your challenge, start there.

For now, focus on connecting your first workflow.

The Bottom Line

Building AI teammates is Stage 2. Connecting them into workflows is Stage 3. That’s where AI moves from productivity tool to growth driver.

Not everything needs to be Stage 3. But sitting in meetings debating handoffs that AI could handle isn’t a great alternative.

Your next step: Pick one workflow that crosses teams or involves handoffs. Map what the customer (internal or external) actually experiences versus what each team thinks they deliver. That gap is your starting point.

New to AI teammates? Start with Part 1: Designing AI Teammates That Get Used.

If you’re experimenting with workflows, I’d love to hear what’s working. Reply to this email or share on LinkedIn.

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