The pressure on marketing leaders right now is palpable, and it is different from anything that came before. AI has arrived inside the organization, and the gap between what it promises and what it is delivering is creating genuine anxiety at every level.
According to McKinsey, 78% of companies have deployed AI in at least one business function, and more than 80% report no tangible impact on their bottom line as a result.1 Organizations are moving fast and getting stuck in the same place: layering AI onto existing structures that were never designed to support it. The work itself, how decisions get made, who owns what, remains largely unchanged.
In our Human + Machine: Redefining Culture Insights, gathered from senior marketing and enterprise leaders across our community, a clear pattern emerged. The real struggle is the organizational work: building the conditions that allow AI's potential to take hold. Inside most organizations right now, roles are in flux, decision rights are murky, and the people closest to the work are figuring it out largely on their own.
Here is what we believe: the leaders gaining ground are starting with a harder question: what does our organization need to become, and are we built for it? The answer starts with understanding who does what, and why. This article offers a framework for thinking through that question and moving forward with more clarity.
As AI reshapes execution, traditional roles, hierarchies, and training models are starting to break down. In response, organizations are developing frameworks to better understand how work is evolving. Holding companies, like Publicis Groupe, are already moving in this direction – reframing operating models around emerging role types and developing new learning and development tracks so talent is empowered by AI.
The goal is to give leaders a way to look at their teams and see clearly what kind of work each person is doing, where the gaps are, and where the transitions are already underway. Four interdependent areas of focus:
1.Relationships, Accountability, & Context
2. Strategy & Creative Direction
3. System, Data, & Workflow Orchestration
4. Execution, Optimization, & Output Refinement
The talent market is one of the clearest signals we have that this shift is real and already underway. Assemble’s Q1 2026 report2 on the state of enterprise marketing tracks where companies are spending money and what skills they are struggling to find. The picture lines up closely with the framework above.
AI-skilled marketing talent commands a salary premium of 28% overall, and up to 43% in marketing specifically. The organizations moving on this now are building an advantage that is hard to close later.
Shakir Moin, President of Marketing at Coca-Cola North America, shared how his team has approached this kind of redesign at a recent Virtuosi League Executive Roundtable in Atlanta. The story is worth understanding because it shows the framework in motion inside a real organization working through real constraints.
The Coke team began with a strategic thesis. Coming out of COVID, they studied 140 years of Black Swan events and the ways each one fundamentally changed human behavior. That exercise led them to a conviction: the old playbook, marketing one idea to 330 million people, had run its course.
The idea they landed on was what Shakir called “de-averaging at scale”: more targeted, more human, more responsive to how people discover and choose brands today. Once they had that conviction, they took it to their CEO with both the strategy and the structure question on the table. If this is where we are going, the existing organization would not get them there.
The numbers tell part of the story. By May 2025, roughly 80% of Coke’s marketers were in a different role in some form. What made the difference was how they got there.
Shakir and his team went through every single role and every single process — not from a role perspective, but from a wiring perspective. Who has decision rights? What is this process for? Does it still make sense given where we are going? A packaging graphic change that had taken nine months was traced back through legal requirements, bottler approvals, and artwork sign-offs that had been standard practice for fifty years. When they looked at it honestly, the timeline came down to 37 days.
They doubled the creative team at the same time, bringing in a new profile they call Creative Technologists: young graduates who understand AI tools deeply, paired with experienced creatives. The internal signal was intentional. Shakir was clear that there would be job displacement in this transition, but equally clear that new roles were being created alongside it. Helping people see where they could fit in the new direction was as important as the structural decisions themselves.
The data architecture decision came from the same logic. Data at Coke was scattered across customer teams, consumer teams, and strategy functions. When you are leading a change that requires real-time intelligence, you cannot call fifteen people or send a chain of emails. So for the first time, everything related to business decisioning around consumers and customers is moving into marketing. A data architect is joining the team this summer to make that infrastructure real.
The mandate from leadership throughout was clear: figure out what you genuinely need and build toward that. Shakir noted that his bosses were likely expecting the answer to be fifty more people. The actual answer was a different kind of team, with different capabilities, oriented around a different way of working.
What is early but already visible: North America, long seen inside Coke as a developed market, is now being approached as an emerging growth opportunity. That is what happens when an organization genuinely reorganizes around where growth can come from and builds the capability to pursue it.
The four areas of focus describe where work needs to go. Getting there requires something else: the human conditions that make any reorganization hold.
Rebecca Messina, a senior advisor at McKinsey and founder of Marcaps, a firm dedicated to studying operating models, shared recent findings at the Virtuosi League Executive Roundtable in Atlanta. Across 1,000 companies, only 14% are seeing genuinely outsized impact from AI. That means 86% are investing, experimenting, and pushing forward without seeing the results they need. The research examined what separates them.
Across the sample, technology choices, org structure, and insource versus outsource decisions were all table stakes. Two things separated the top performers, working together.
The first is what the research calls AI Stewardship. Four elements tend to be present in the companies pulling ahead: a clear strategy built around specific problems AI is meant to solve, strong data and governance practices, meaningful measurement, and Decision Authority clarity — people know when AI is making a decision and when a human is. Companies with all four have a 41% better chance of outperforming their peers.
The second factor is Operational Clarity. People understand why the change is happening, what the vision is, and who decides what. Layer this in alongside AI Stewardship and the odds of outperforming peers jump to 84%. This is a clear example of ways we see enterprise momentum stalling in some organizations and accelerating in others, illustrated in the Enterprise Momentum Model.
The implication for how organizations manage relationships, accountability, and context is significant. When people do not feel safe, do not understand why things are changing, or do not know who is responsible for what, the reorganization will not hold. Getting those conditions right is a leadership job, and it sits in this area of the framework more than any other.
The data on this is consistent. According to research cited by Epsilon, 51% of marketers name fear of change as a key barrier to AI adoption.3 Gartner projects that 60% of enterprise AI initiatives will fail by 2027, with gaps in change management and workforce engagement as the primary culprits.4 Most leaders reading this are somewhere inside those statistics, whether they have named it yet or not.
For most marketing teams, that fear takes recognizable shapes: anxiety about displacement, pressure from uneven readiness across the team, and the ambiguity that comes when no one has clearly named what is changing or why. Leaders who are making genuine progress tend to address that last one first. They name what is happening before asking people to change.
Melody Lee, CMO of Mercedes-Benz USA, has described what happened when her team shifted from positioning AI as an efficiency tool to positioning it as a growth engine. The energy around it changed. People changed their perspectives, rather than focusing on what AI might do to their roles they started working to identify pain points and create solutions. That shift came from how leadership chose to frame the conversation, and it preceded any technology decision.
At Coke, they drew on the industrialization analogy when navigating the transition with their teams: when automation changed manufacturing, new industries and new roles followed. The creative technologist hires were a tangible version of that message — people who joined the team because the redesign made space for them.
Here are a few questions worth sitting with as you think about your own team.
Look at your team through the lens of the four areas of focus. Where are most of your people sitting right now? Where are the gaps? Which areas have grown organically, and which ones have you deliberately built toward?
Have you acknowledged the shift happening in execution roles? Most people responsible for production, optimization, and output are already living it — spending more time directing and refining AI outputs than producing work themselves. The question is whether you have named that shift, supported it, and updated how you measure it.
Ask where AI amplifies what your people are already great at. Adoption works when it feels personal. The leaders making progress were helping individuals see what AI could do for their own work and strengths. That conversation moves faster than a mandate.
Have you named the fear directly? The teams making progress are led by people who acknowledged the uncertainty honestly before asking others to move through it. Transparency about what is changing and why builds trust faster than polished messaging will.
Every team navigating this moment hits the same wall: the external pressure to move is outpacing the internal readiness to do so, and the people caught in the middle feel it most. The leaders who break through are the ones who name that honestly and keep moving anyway. The four areas of focus give you a language for those conversations. The clearer your team is on where they fit, the faster everything else follows.
Review the Insights for Theme 2: Human + Machine: Redefining Culture
Sources
¹ McKinsey & Company – The state of AI: How organizations are rewiring to capture value (Q1 2025)
² Assemble – The Assemble Signal: The State of Marketing Organization (Q1 2026)
³ Epsilon – The state of AI in marketing (Q1 2025)
⁴ Gartner – Forecast Analysis: AI Spending (Q4 2025)