While headlines celebrate the power of generative AI, a quieter crisis is unfolding inside marketing organizations: teams aren’t ready. Leaders are pouring millions into tools and scaling back headcount. But many are overlooking the people and processes that turn AI into ROI. To close this gap, marketing teams need a structured readiness framework centered on Roles, Routines, and Relationships.
Table of Contents
- 1 | 2025 has been a year of AI‑driven layoffs
- 2| Automation isn’t the only layoff driver
- 3| The human readiness gap is widening, but not uniformly
- 4| Will AI replace marketing teams? Introducing the Adaptive Org Stack
- 5| A Step‑by‑step blueprint for marketing AI‑ready teams
- Step 6: Develop new metrics for marketing AI success
- 6| A final word and call to action
- Frequently Asked Questions
1 | 2025 has been a year of AI‑driven layoffs
In January 2025, IBM announced a plan to pause hiring for nearly 7,800 roles that were automated through generative AI or robotic process automation. A month later, The Financial Times reported that global media conglomerate WPP cut one in seven back‑office positions after adopting AI for content localization.
Across industries, AI‑related layoffs now dominate the news. A February 2024 PwC survey revealed that one in four global CEOs planned to reduce their workforce by at least 5 percent within the year due to the impact of generative AI.
Yet those same surveys reveal a paradox: most brands still struggle to realize meaningful ROI. In other words, companies are cutting headcount before they have figured out how to make the technology pay off.
2| Automation isn’t the only layoff driver
Many recent job cuts are not just about machines taking over; they are more about shifting budgets. In June 2025, CIO.com reported that nearly half of AI-driven layoffs resulted from strategic workforce adjustments, rather than direct automation.
Executives are cutting jobs to reinvest in AI development, cloud infrastructure, and machine learning. This is despite these systems not consistently performing as well as humans in complex tasks. Marketing leaders face intense pressure to fund AI initiatives, even if that means reducing headcount before the technology is ready to fill the gap.
3| The human readiness gap is widening, but not uniformly
While presenting to CMOs and marketing executives at a June 2025 summit, I conducted a brief survey. I asked how many leaders were actively investing in generative AI and how many had a roadmap to reskill their teams for AI in the next 12 months. Nearly every hand went up for AI adoption. Fewer than 3 percent went up for a reskilling roadmap.
That moment captured a hard truth: while the tools are advancing rapidly, the people using them are often left behind. But the story isn’t black and white.
Recent research from CMSWire, LinkedIn Learning, Gartner, and Deloitte shows that marketing organizations are making progress, especially in launching formal training programs. In fact, AI training saw the largest year-over-year growth across all readiness categories.
Still, the gap remains. As the chart below shows, interest in AI (“mission critical” status among CMOs) continues to outpace confidence in measuring ROI, data fluency, and scalable tool deployment. Teams may be starting to invest in skill-building, but very few have a cohesive strategy to guide it.
4| Will AI replace marketing teams? Introducing the Adaptive Org Stack
AI will not replace marketing teams, but it will reshape every job inside them. Automation will absorb repetitive work, leaving humans to focus on strategy, creativity, and judgment. The real prize is augmentation—the moment when marketers and intelligent systems operate as one high-functioning unit.
Achieving that partnership requires a deliberate redesign of how people work, learn, and collaborate. That redesign is known as the Adaptive Org Stack, a five-layer blueprint that aligns data, technology, structure, culture, and leadership to power human-AI teamwork. The following is an outline of the Adaptive Org stack and its five layers:
Layer 1: Data infrastructure – This is the foundational layer. AI Rises and Falls on data. If data is messy, siloed, or outdated, AI cannot learn or make meaningful predictions. Clean, accessible, real-time data powers the training of generative models, fuels performance insights, and enables personalization at scale.
Layer 2: AI capability – Sitting atop the data layer is AI capability. This refers to tools, models, and AI agents that execute work, including language models for content generation, predictive models for targeting, and automation tools for operational efficiency. This layer receives the most attention, but without the surrounding stack, it often underperforms.
Layer 3: Organizational design – Next comes org design. Team structures must allow AI to flourish. Roles, responsibilities, and workflows must be clearly defined, with cross-functional collaboration as a catalyst.
Layer 4: Culture and mindset – Even the best tools will be resisted by teams that fear experimentation or feel threatened by change. An adaptive culture promotes psychological safety, values learning velocity, and encourages experimentation, failure, and refinement.
Layer 5: Leadership and Governance – At the very top of the stack are vision and trust. Leaders must clearly define the purpose of AI investments and establish strong oversight. Governance should be light yet practical, guiding the ethical use of AI without hindering innovation.
5| A Step‑by‑step blueprint for marketing AI‑ready teams
Complementing the adaptive org chart are the Three Rs: Roles, Routines, and Relationships. These are the day-to-day mechanisms that bring the adaptive org stack to life. Together, they enable human–AI partnerships to thrive.
Roles define who does what in an AI-integrated world. Routines determine how work flows with machines in the loop. Relationships ensure that cross-functional teams move in sync. Here’s what the tactical journey to establishing the Three Rs should look like:
Step 1: Diagnose current skill levels
Before you build anything, assess the baseline. What do your marketers already know about AI? What tools are in use? Which teams are lagging? Use pulse surveys, 360 feedback, and skill assessments to segment your org into learners, adopters, and pioneers. This will help target your efforts and reduce resistance.
Step 2: Build a tiered training ladder
Not everyone needs to become a prompt engineer overnight, but everyone should understand how AI affects their role. From content creators to campaign leads, teams should understand how AI works, its limitations, and when to question its output. Design three levels of training:
- AI Awareness for all employees, covering key terms, risks, and everyday use cases.
- AI Application for marketing managers, covering how to use AI tools for targeting, personalization, and content workflows.
- AI Ownership for strategists and specialists, covering how to evaluate outputs, iterate prompts, and build cross-functional AI pods.
Step 3: Set up cross-functional agile pods
The most successful AI marketing initiatives aren’t housed in a single department. They live at the seams of marketing, data, and tech. Build cross-functional collaboration pods where teams are tethered by shared KPIs, review outputs together, and co-own outcomes.
Finally, bring in HR, Operations, and Legal early. They can help define competencies, redesign org charts, and avoid blind spots related to fairness, hiring, and psychological safety. Legal must also be involved to ensure regulatory compliance across new content and data pipelines.
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Step 4: Re-engineer workflows to match AI’s speed
AI moves fast, so you can’t just bolt AI onto existing slow workflows. Instead, have your cross-functional agile pods build and test new AI workflows, identifying specific “owners” to drive usage and accountability.
A practical way to start is by using the “1‑1‑1” sprint: one AI use case, one cross‑functional pod, one month. The pod members would map out the end-to-end workstreams in their teams, identify where AI can augment, accelerate, or automate, and where human oversight must remain to guide model behavior and output. They then define and document new operating procedures coming out of these exercises.
Step 5: Transition legacy roles and define new ones
As AI automates repeatable tasks, marketing teams must rethink their organizational design by evolving legacy roles and creating entirely new positions focused on human and AI collaboration. The first step is to audit existing roles and group them based on their potential for augmentation, transformation, or automation.
LinkedIn reports that job postings for roles such as Prompt Strategist and AI Compliance Analyst have seen rapid year-over-year growth. These positions combine marketing expertise with AI fluency, signaling a shift in how modern marketing teams are being structured.
Download a cheat sheet of 25 emerging AI Marketing roles below.
Step 6: Develop new metrics for marketing AI success
Finally, assign a cross-functional task force (e.g., marketing, tech, legal, and HR) to monitor emerging risks, track ROI, and evolve AI use policies. The CMO and CDO should chair this council to cement their joint ownership of AI implementation. Examples of new KPIs to track include cycle time reduction, content velocity, and error rates, all of which should be reviewed quarterly.
6| A final word and call to action
AI will transform, not replace marketers’ roles. Technology alone will not guarantee sustainable ROI. CMOs have a rare opportunity to reimagine not just their tech stack but their entire organizational structure. This is an opportunity to transition from overwhelmed teams to AI-powered collaborators guided by empathy, clarity, and a strong sense of purpose. The leaders who succeed will prioritize people, processes, then platforms. In that order.
Ready to build an AI-ready marketing team? Contact The Growth Seat to create the roadmap your people deserve and your bottom line demands.
Frequently Asked Questions
The most significant challenge is not technical but human: marketing teams often lack the training, structures, and support to integrate AI into their day-to-day workflows. Without a clear roadmap for reskilling, even the most advanced tools deliver limited ROI.
AI will transform, not eliminate, most marketing jobs. While some repetitive tasks will be automated, new hybrid roles are emerging that require strategic thinking, ethical oversight, and human-AI collaboration. The future belongs to teams who evolve alongside the tools.
Some emerging roles include AI Prompt Strategist, AI Content Reviewer, Data Product Owner, AI Ethics Officer, and Personalization Architect. Each role focuses on enabling human oversight, creative alignment, and responsible use of AI across the marketing lifecycle.
Start by assessing current capabilities, then implement a structured plan built on the “Three Rs”: Roles (who does what), Routines (how work flows), and Relationships (how teams collaborate). These changes should align with your data and AI infrastructure strategy.
Many AI initiatives stumble because they’re led by IT or procurement with little marketer involvement. Others fail due to a lack of role clarity, ethical guardrails, or process redesign. A tech-first approach, without people-first planning, rarely delivers sustainable impact.
The Adaptive Org Stack is a framework for building AI-integrated organizations. It includes five layers: data, AI tools, team design, culture, and leadership. These work together to ensure human-AI partnership is sustainable, ethical, and effective.
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