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CEOs Are Looking for a Different Kind of CMO. Are You It?

Liza Adams · September 3, 2026 ·

CEOs Are Looking for a Different Kind of CMO. Are You It? title graphic

One of the CEOs I spoke with wanted a CMO who could walk into a board meeting and make a credible case for where the market was heading: what customers were telling them, what was changing around those customers, what it could mean for the business, and what the company should consider doing next.

A couple of CEOs asked me recently what to look for in a CMO now, given what AI is making possible.

I spent years leading marketing. My work since then with CMOs and GTM teams, founders, leadership teams, and PE and VC partners has given me a view of what AI is changing in the role and what companies increasingly expect.

Three areas consistently come up: knowing the customer and market at a depth that wasn’t practical before, leading brand and trust across the company, and carrying customer context across the GTM journey.

Quick Take

What connects these three areas for me is an outside-in view of the business, starting with the customer and market rather than the marketing function.

  • Know the customer and market at a depth that wasn’t practical before – AI can connect signals teams couldn’t reasonably track at the same scale or timing.
  • Lead brand and trust across the company – AI brings customer, employee, leadership, community, and company signals into the same view.
  • Carry customer context across the GTM journey – AI can connect context across Marketing, Sales, and CS and help close the gaps customers experience.
  • Bring people through the change – The CMO still has to build trust, influence across functions, and help teams change how they work.
  • Connect all of this to business and financial outcomes – A broader view of the customer, market, company, and customer journey gives the CMO more to bring to business decisions.

These are observations from current work and conversations, rather than a best- practice model.

Three capabilities, one broader business view: customer and market, company and reputation, entire customer journey

Watch Or Listen Instead

This newsletter is also available in two additional formats for different learning styles. The 6-min cinematic AI video explainer is a broad overview of the argument. The 18-min AI podcast goes deeper. Both were made with Gemini Notebook and personally reviewed for accuracy and responsible AI use.

What Experience Gives Us

Experience builds judgment. You’ve seen enough situations to recognize patterns, ask better questions, and identify problems earlier. That experience is useful, but it can also make us rely too heavily on what worked before.

CMOs have always been expected to understand customers, earn trust, know the business, and work across the leadership team. AI is making more of that practical at a scale and cadence that were hard before: working across far more customer and market information, helping teams rethink workflows, roles, handoffs, and the customer journey.

Katy Keim has lived the CMO-to-CEO path more than once. She was CMO at Lithium Technologies and EVP of Marketing at ServiceSource before becoming CEO of LQ Digital and most recently LeanData.

Headshot of Katy Keim, CEO of LeanData
Katy Keim, CEO of LeanData

“I’ve been in the CMO seat, and I know how easy it is to default to what worked before. The leaders who stand out aren’t running a playbook. They’re curious. They’re learning from their peers, picking up new signals from the market, and connecting it all back to the customer. It’s about pattern recognition from their experience, not stale tactics. This market demands a new growth mindset from marketing leaders.”

In my work, three areas show how AI can make this broader CMO role more practical:

1. Know the Customer and Market at a Depth that Wasn’t Practical Before

AI lets us bring together more customer and market information, more often, then connect signals that would otherwise stay separate.

A few examples I’m seeing:

  • Bring more customer signals together – Usage, support tickets, call notes, engagement, renewal history, and other internal data can be analyzed alongside public information such as funding, acquisitions, leadership changes, or industry news. Declining engagement and usage mean something different when they happen alongside a leadership change. An acquisition may signal an expansion opportunity.
  • Keep market and competitive intelligence current – AI can watch competitor positioning, pricing, reviews, category conversations, analyst commentary, and the language buyers use. Teams can move from periodic competitive updates to a much more current view. I’ve found this to be one of the clearest examples of work an all-human team couldn’t keep up with at the same cadence.
  • Pressure-test decisions with simulated buyers – Before a pricing change, repositioning, or campaign, teams can build AI advisors trained on what they know about buyer priorities, challenges, roles, objections, communication styles, and sources they trust. They use them to challenge the thinking before it lands in market.

Human judgment stays central. AI handles collection, synthesis, analysis, and scale. People decide what applies, where the AI may be wrong, and what the business should do.

That gives the CMO a richer view of customers and the market to bring into product, GTM, strategy, and board conversations. I went deeper on several of these workflows in 5 Reimagined Workflows: Work We Skipped That AI Now Does.

2. Lead Brand and Trust Across the Company

Brand is what people experience and say about a company.

Marketing contributes to that experience. So do Product, Sales, Customer Success, HR, leadership, partners, and employees.

AI makes those connections easier for people outside the company to see. Ask an AI to search about a company and it can pull from the website, customer reviews, analyst coverage, community conversations, press, employee experiences, and other public information. Buyers increasingly get a full view assembled from many sources, not just the story marketing publishes.

That gives the CMO a broader leadership opportunity: help the company understand what those experiences add up to, where they reinforce the brand promise, and where they don’t.

Anna Griffin has led brands across complex enterprise environments including CA Technologies, Smartsheet, Intercom, and now Commvault. Her experience spans the parts of the company that create the brand and the people outside it who ultimately decide what to believe.

Headshot of Anna Griffin, Chief Marketing Officer of Commvault
Chief Market Officer of Commvault

“Brand has never been what you say about yourself. It’s what every customer, employee, partner, and prospect experiences and says about you. That’s always been true. What AI has done is make it visible all at once, at scale, in ways that are very hard to ignore. You either build it intentionally across the whole company, or the market and AI build it for you.”

A few ways I’m seeing this show up in the work with AI:

  • Look at the company from the outside in – Search as a buyer, customer, candidate, or partner. Look at the situations they’re in, the problems they’re trying to solve, what they hear about you, and where the story feels unclear or inconsistent.
  • Be useful and honest about where you fit – Create content around customer situations, questions, and outcomes rather than leading with features and capabilities. Be clear about who you’re ideal for, where you’re not, and what customers actually achieved. That gives people and AI clearer evidence about when to recommend you.
  • Find gaps marketing can’t close alone – If the brand promises “easy to work with” but onboarding is painful, that requires work with the team responsible for onboarding. If the company talks about transparency while candidates experience something different, changing the careers page only fixes one part of the problem.

AI search is part of this too: make owned properties clear, create useful content, structure information so machines can understand it, and monitor how AI represents the company. I went deeper on this outside-in view in When AI Judges Your Brand Before Humans Do.

But AI is also picking up experiences marketing doesn’t control. I saw a post recently where a job candidate asked for the salary range before an interview. The CEO responded that if money was their main driver, they weren’t the right fit. I wondered how that same CEO would respond if a buyer asked for a price range before agreeing to a sales call.

Marketing has been trying to explain what brand is for years. AI search may have just shown it for us.

Anna also uses a phrase I like: brand is rented, not owned. You keep earning it through the experiences people have with you and the evidence those experiences leave behind.

3. Carry Customer Context Across the GTM Journey

Customers experience one company, even though we organize ourselves into Marketing, Sales, and Customer Success. They experience the marketing, sales conversation, handoff, onboarding, product, support, renewal, and everything between.

AI is particularly useful here because it can carry context from one part of that journey to another.

There’s also evidence that AI can help people work across those boundaries. A Harvard-led field study with P&G professionals found that AI helped commercial and R&D specialists produce more balanced solutions that drew on both areas of expertise. Wharton professor and AI researcher Ethan Mollick described the result this way: “The distinction between specialists virtually disappeared in AI-aided conditions.”

Customers experience one company: six journey stages owned by different teams, connected by AI

The harder part is getting people to trust new inputs, change workflows, and reconsider practices that have worked for years. A sales leader may trust instinct built over decades. A CS leader may trust the data their team already uses. The CMO has to bring people along through real work, shared wins, and clear human judgment.

  • Carry context across the customer journey – AI can look across calls, CRM notes, onboarding feedback, support tickets, usage, and renewal data so the customer’s story doesn’t keep restarting as they move between teams.
  • Find where the experience breaks – Something showing up as a CS problem may have started with expectations set during the sales process. An adoption problem may trace back to positioning or onboarding. AI makes those connections easier to see.
  • Work toward shared customer outcomes – When I ask Marketing, Sales, and CS what success looks like for the same customer, the answers come back as functional metrics: qualified leads, sales-cycle length, churn. Then we ask what result the customer needs from all three, such as time to first value, adoption, satisfaction, retention, or expansion. The conversation changes.

I explored what this could look like organizationally in No Daylight: How AI is Reorganizing GTM Teams Around Customers, Not Departments.

Changing shared goals, workflows, and long-standing ways of working still requires trust between leaders and compassion for the people doing the work. Some people move quickly with AI. Others need more time, support, practice, or evidence.

AI can help connect the work. Leaders still have to bring people along.

What This Adds Up To

CMOs are also being held more accountable for the results of the business. In the Spring 2025 CMO Survey, 63% reported increased pressure from the CFO and 61% from the CEO to prove marketing’s value. IBM’s 2025 CMO Study found that 64% of CMOs globally are accountable for profitability and 53% for revenue growth.

That requires connecting what customers experience and what GTM does to the economics of the business.

I often start with time to value, customer satisfaction, and net revenue retention because they begin with outcomes the customer experiences. From there, we use the company’s data to connect those outcomes to adoption, retention, expansion, referrals, payback, efficiency, and growth. That’s a more useful board conversation than trying to force every dollar of revenue back to a marketing touch.

Kate Bullis, Global Marketing Practice Leader at ZRG, runs 20 to 25 executive marketing searches a year and sees firsthand how the hiring conversation is changing.

Headshot of Kate Bullis, Global Marketing Practice Leader at ZRG Partners
Kate Bullis, Global Marketing Practice Leader at ZRG Partners

“The most successful GTM leaders aren’t just functional experts; they’re business leaders who think like GMs. CEOs are expecting GTM to show up this way. Great GTM leaders optimize entire customer and market experiences, not just their department, because they understand customers don’t care about your org chart. The importance of this is not new, but AI has made it increasingly important.”

Much of this broader role depends on influence. Product, Sales, CS, HR, Finance, and other leaders bring expertise and accountability of their own. The CMO earns influence by bringing something useful to the table: customer intelligence that changes a decision, a clearer view of the market, a gap in the customer experience, or evidence that connects the work to the business.

The company has a role too. Some leadership teams want the CMO operating this broadly; others have a different mandate. Company stage, business model, goals, and leadership relationships will affect what makes sense.

Some CMOs will want to stay focused on leading the marketing function, and some companies will continue to want that. A more functional role brings clearer boundaries and deeper focus within marketing, but potentially less influence over the customer experience, market decisions, and business outcomes beyond the function.

The broader role comes with tradeoffs too: more shared ownership, more influence without direct authority, and more accountability for outcomes the CMO may not fully control. If CEOs want that kind of business leader, the mandate, measures, decision rights, and relationships around the role need to support it.

Both sides have to want the broader version. It takes two to tango.

A Few Questions Worth Asking

If I were advising those CEOs today, these are some of the questions I’d suggest they ask:

  1. What’s a playbook you believed in that you no longer use? What evidence changed your mind?
  2. Tell me something AI helped you see about customers or the market that changed a business decision.
  3. Describe a cross-functional change that required people to work differently. How did you bring them along?
  4. Walk me through a time AI helped you connect a customer or GTM issue to a financial outcome. What did you do with that insight?

I’d pay as much attention to the follow-ups as the first answer. Can they get into the actual work, explain what AI helped them see or do, where it fell short, what they changed, and what they learned?

Nobody has all the right answers yet. I’d look for intellectual humility, curiosity, ability to bring people through change cross-functionally, and evidence that they’re learning from real work. That can be hard at this level because we’ve spent our careers being rewarded for what we know and what has worked before. AI keeps asking us to question both.

I’m still learning with the leaders and teams working through this. What I see so far is an opportunity for a broader business role, with deeper accountability for customers, the market, the company experience, and business outcomes.

For CMOs, the question is whether that’s the role you want to grow into, and whether you understand what comes with it.


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 to shift mindsets through practical AI uses and hands-on sessions to build human+AI workflows. You’ll leave with either a clear plan or working solutions. Office hours provide follow-on support to keep momentum going.

For ongoing advisory: I work with leaders as a strategic advisor through their AI transformation, providing guidance, frameworks, and a sounding board as they build. If that’s what you need, let’s talk.

For team transformation: I partner with GTM leaders to move from scattered AI adoption to an operating model where human and AI teammates work together across 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 conferences and corporate events.

If any of this resonated, I’d love to hear from you. Reply to this email, find me on LinkedIn, or come say hi at an event.

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.

The AI progression: from tools to teammates to workflows

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.

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

Headshot of Kimber Spradlin, Chief Marketing Officer at Graylog
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.

Interactive AI Evolution Framework guide: from tools to teammates to workflows
Interactive AI Evolution Framework Guide (90-sec Video Preview)

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

  • Watch three human+AI workflows built live on Leveraging AI with Isar Meitis
  • How to connect individual AI teammates into workflows that produce better results
  • How chained AI teammates move expertise across teams without losing context
  • Applied AI workshops: shift your team’s mindset through applied use cases, then build AI

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 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.

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