The CMO factory problem: How AI can free marketing leaders to actually lead

David C. Edelman is a digital transformation pioneer, executive advisor, and Harvard Business School professor with over 30 years of experience driving growth at the intersection of technology and consumer experience.

Across my clients, including a CMO at a major consumer brand, a chief experience officer at a financial services company, and others in pharmaceuticals, industrial goods, and tech, I am hearing an escalating chorus of the same thing in different words: “I can’t believe how much of this job, and my time, is managing sheer production.” They aren’t complaining. They are really just diagnosing. And the diagnosis, I’m realizing, is nearly universal.

Alarm bells are ringing. Every thought leader insists that marketing leaders need to be at the C-suite table driving AI strategy, championing customer experience, and shaping enterprise direction. Most CMOs agree, in principle. But then Monday arrives, and with it a calendar filled with creative reviews, agency alignment calls, campaign performance readouts, and approval queues that never empty. The job description says visionary. The calendar says factory foreman.

This is not a time-management problem. It is a structural one. And it can separate the higher performing leaders from the heavily distracted others. As Georgetown University professor Cal Newport cites in his book “Deep Work: Strategies for Success in an Unfocused World,” “To produce at your peak level you need to work for extended periods with full concentration on a single task free from distraction.” Stepping up to drive a C-suite agenda requires that “peak level” of thinking to determine what to bring to the leadership agenda, how to position it, and how to drive its mobilization. That will never happen in a quality way when you need to stay on top of all aspects of a factory. Every day.

But I’ve also seen that AI, applied correctly, offers a genuine path to liberate CMOs’ time for more deep thinking, but only if leaders are willing to redesign how their organizations work, not just what tools they use.

How the factory gets built

The modern marketing organization was designed for a world of scarce content, sequential workflows, and expensive iteration. In that world, multilayered review made sense. Every asset was costly to produce, errors were costly to correct, and the CMO’s judgment was the last line of defense against brand risk. The org. chart reflected that logic. Everything flowed upward.

The tools have changed. The workflows largely have not. Which means CMOs are still being pulled into decisions that, in an AI-enabled organization, don’t need to travel that far up the chain. Time for relationship-building conversations and for forward-looking thinking are the first things sacrificed when a fire drill arrives, precisely because they feel more movable than the weekly production meeting. Over time, the calendar becomes a portrait of the factory, not the strategy.

Norm de Greve, CMO of General Motors, laid out the danger in a recent McKinsey article: “When they become CMOs, ones requirements for success fundamentally change. They change from being an expert in marketing to becoming a general manager who needs to speak the language of the CEO.” Yet, the factory trap is precisely what prevents that transition from happening.

What AI actually makes possible

The mistake most organizations make with AI is treating it as an accelerant for the existing workflow. Generating creative variants more cheaply and turning around performance reports in hours instead of days is real value. But it is not transformation. It is a faster factory.

Genuine transformation happens when AI changes who needs to be involved in a decision, and how far up the organization that decision needs to travel. ANA research across companies including Sephora, Mondelēz, and Target found that organizations treating AI as purely a task-execution tool report significantly lower business gains than those who restructure roles and workflows around AI capabilities.¹ As Gartner’s Ewan McIntyre warned heading into 2025, “CMOs cannot risk incremental change when the enterprise expects transformative results.”² Using AI to do the same things faster is incremental change. Redesigning how the organization works is transformation.

The most architecturally sophisticated CMOs aren’t just deploying AI for efficiency. They’re embedding it at the point of creation. In prestige retail, where new products launch weekly against competition from Amazon and TikTok Shop, brand governance has historically required multiple layers of human review. The emerging model inverts that. Guardrails live inside the tools themselves, so a junior designer gets real-time feedback the moment something violates brand standards — without escalation, without delay, and without senior involvement. The hierarchy doesn’t slow down. It disappears. As Walmart’s SVP of transformation put it, when you embed AI directly into how people work, “the impact isn’t incremental. It’s transformational.”

Mondelēz built something similar at a strategic level. Rather than reviewing individual content, Michael Lambert’s team built a proprietary platform grounded in four knowledge bases — brand guidelines, historical creative assets, performance data, and channel intelligence. Every output is already informed by what has worked before. The CMO’s judgment isn’t absent; it’s encoded into the system. That is a fundamentally different relationship between leadership and production.

As I have seen in my advisory work, three organizational moves consistently separate CMOs who escape the factory from those who remain trapped in it.

1. Encoding your judgment into the system, not your calendar. Every hour spent reviewing work to apply brand standards is an hour that could go toward something only the CMO can do. Brand guardrails, tone-of-voice standards, and creative principles should live in AI tools, not approval queues. At Target, a centralized creative technology function lets hundreds of marketing professionals produce AI-assisted content without individual approvals, because the standards are built into the environment. This isn’t abdication; it is leadership operating at the right level of abstraction.
2. Shifting from approving outputs to designing the system. Marketing leaders can take a high-leverage step to define the parameters that allow teams to operate with real autonomy. Central to this is building a brand knowledge platform with machine-readable guidelines, structured historical performance data, and tagged asset libraries so that AI systems generate outputs grounded in the organization’s own accumulated intelligence. It also means clarifying what teams own outright, what requires input, and what requires a decision. Ambiguity about decision authority is a hidden driver of meeting overload. When teams don’t know what they’re empowered to decide, everything escalates.
3. Redistributing accountability through agile pods. Instead of 13 people needing 13 weeks to get an email out the door, have three to four small, cross-functional teams of five or six people, each focused on a specific objective, working in short sprints with full end-to-end ownership. AI makes this possible by empowering each person to have a wider range of capabilities and not be trapped in narrow specialization. Custom generative pretrained transformers store all data and content from every past test and program, rapidly highlighting customer segments not addressed and showing what’s been learned from those that have. Creative tools allow quick generation of prototypes. Data query tools enable immediate insights that can combine multiple data sets without any need for routing a request through a data science expert.

The model works not because the teams are smaller, but because the delays that plague most marketing operations are slashed. Immediate access to insights, empowerment to create mock-ups, and accountability is explicitly pushed down to the team. At a major pharma client, agile pods spanning analytics, media, creative, strategy, and operations launched a complex geographic test in six weeks that would previously have taken six months. The head of marketing for the brand wasn’t in every room. The system was designed so they didn’t need to be.

The diagnostic question

When I start working with a marketing leader, I ask a simple question: When did you last spend several hours on your own or with your leadership team thinking about where your category, your customers, and your competitors will be in two years? Not through an off-site with a packed agenda, but a genuine day of forward thinking.

Most can’t remember. That’s the signal.

A recent study by BCG of what they labeled “AI-first CMOs” makes the opportunity clear: By using AI to gain real-time intelligence and break down organizational silos, CMOs can evolve from tactical marketers to strategic growth architects.³ But that evolution doesn’t happen by adding tools to a factory that was already running too hot. It happens when marketing leaders make the deliberate choice to redesign how their organizations work, by encoding judgment into systems, building teams with real autonomy, and reclaiming the time that only they can spend on strategy, customer truth, and what’s coming next.

The leaders who make the choice, today, to step back and rethink their model will be in a very different position in two years than those who use AI to run the same factory faster. The question is whether you’re willing to step out, make the case, and commit.