CMO Shakeups in 2025: What They Mean for AI Marketing

Marketing leadership churn isn’t new—but 2025 made it hard to ignore. The year saw a long list of high-profile CMO changes across household brands like Airbnb, Hinge, Nike, McDonald’s, Mattel, and Tylenol, as reported in Rebecca Stewart’s roundup of the biggest CMO shakeups. When that many brand leaders move at once, it’s not just “people news.” It’s a signal.

Here’s the signal I think most teams miss: a new CMO often resets the company’s appetite for AI—what gets funded, what gets paused, what gets measured, and who gets hired. If you work in media and entertainment (or you sell into it), this matters because marketing is where AI becomes customer-visible first: recommendations, personalization, lifecycle messaging, creative testing, social content, and measurement.

This post is part of our AI in Human Resources & Workforce Management series, so we’ll treat CMO shakeups as a workforce planning problem too: which skills suddenly become scarce, how org design changes, and how to recruit for AI-fluent marketing without creating a “tools-first” mess.

Why a CMO change is an AI adoption event (not just a brand refresh)

A CMO transition is one of the few moments a company can change its marketing operating system without a multi-year political fight. New leaders come in with a mandate to show impact quickly—often within 2–3 quarters—which naturally pushes teams toward automation, faster experimentation, and clearer attribution.

In media and entertainment, that pressure is even sharper because:

• Audience attention is fragmented across streaming, social video, gaming, podcasts, and live experiences.
• Content supply is high and discovery is the bottleneck.
• Churn sensitivity is brutal: small retention improvements are worth real money.

When CMOs change, the first questions they ask tend to map directly to AI use cases:

• “What do we know about our audience—and how confident are we?” (audience analytics)
• “How fast can we test creative and offers?” (creative analytics, multivariate testing)
• “What’s our personalization strategy across channels?” (recommendation and targeting)
• “Are we measuring the right thing?” (marketing mix modeling, incrementality)

Snippet-worthy truth: A new CMO doesn’t start by buying AI. They start by demanding answers that are hard to produce without it.

The hidden story: CMO churn drives HR urgency in marketing teams

The fastest way to derail an AI roadmap is to ignore workforce readiness. Many marketing orgs still treat AI as a side project owned by one “marketing ops wizard.” That doesn’t scale—especially when the executive sponsor changes.

The marketing roles that shift first

In practice, CMO transitions put pressure on a few roles immediately:

1. Marketing Operations & Automation: owners of CDPs, journey orchestration, tagging, and campaign QA.
2. Performance Marketing & Measurement: people who can run incrementality tests, interpret lift, and reconcile platform reporting.
3. Creative Ops & Content Systems: teams managing asset libraries, versioning, rights, and production workflows.
4. Data Science & Analytics Translators: not pure data science, but people who can turn models into decisions.

If you’re in media and entertainment, add a fifth: growth + lifecycle. Subscription and ad-supported businesses live or die on retention, frequency, and LTV.

A practical HR lens: “AI fluency” beats “AI specialist” hiring

A common mistake after a new CMO arrives: hiring one “Head of AI Marketing” and hoping the rest of the org magically adapts. What works better is building AI fluency across core roles.

Here’s a simple competency model I’ve found useful:

• Level 1 (Aware): understands what AI can/can’t do; can follow governance and brand safety rules.
• Level 2 (Operator): can use AI tools for campaign workflows (briefs, variants, QA), with human review.
• Level 3 (Designer): can redesign processes around AI (experimentation cadence, content pipelines, measurement).
• Level 4 (Owner): can set AI strategy, budgets, vendor selection, and risk controls.

A CMO change is the moment to formalize this—because training programs, job ladders, and hiring plans are easier to reset when leadership is already reshaping priorities.

What new CMOs tend to do with AI in media & entertainment

CMOs rarely introduce totally new AI categories; they prioritize a few that create visible momentum. In media and entertainment, the “first 180 days” AI pattern is surprisingly consistent.

1) Personalization that reaches beyond the homepage

Most streaming and content brands already have some recommendations. The marketing opportunity is extending personalization into:

• Lifecycle messaging (winback, onboarding, post-watch suggestions)
• Paid media audiences (value-based lookalikes, churn-risk suppression)
• Creative personalization (dynamic variants by segment)

The HR implication: you need people who understand both audience strategy and data constraints. Personalization fails when marketers ask for micro-segmentation that the data can’t support—or when data teams build models nobody uses.

2) Faster creative experimentation (and fewer opinion fights)

CMO transitions often expose a painful truth: teams debate creative because they don’t have clean feedback loops. AI helps here, but not the way vendors pitch it.

What tends to work:

• Using AI to generate structured creative variants (different hooks, CTAs, formats)
• Running short-cycle tests (48–96 hours) with clear decision rules
• Building a creative learnings library that’s searchable and reusable

What tends to backfire:

• Flooding channels with infinite variants without a measurement plan
• Ignoring brand tone and legal review, especially with regulated products

One-liner: AI doesn’t fix bad creative strategy; it makes bad strategy run faster.

3) Audience analytics that the business actually trusts

A new CMO usually wants a single view of performance, but 2025 reality is messy: privacy limits, platform reporting gaps, and fragmented identity.

So the “trust-building” analytics moves are:

• Incrementality testing for key channels
• Better first-party data capture (value exchange)
• Stronger data governance and taxonomy

This is where AI in workforce management shows up: analytics trust improves when roles and responsibilities are clear. If three teams “own measurement,” nobody owns it.

The 90-day playbook for CMOs inheriting AI debt

If you’re a new CMO (or you’re supporting one), the right early moves are about focus and operating rhythm—not tooling. Here’s a pragmatic 90-day plan that aligns marketing, data, and HR.

Days 1–30: Audit the system, not the slogans

Prioritize these outputs:

• A list of the top 10 customer journeys (acquisition → activation → retention)
• A map of your marketing data flows (where data comes from, where it breaks)
• A baseline for 5 metrics: CAC, LTV, churn, ROAS (or MER), and retention cohorts

HR/workforce actions:

• Identify “single points of failure” people (the one person who knows tagging, CDP, or attribution)
• Start cross-training immediately
• Create a lightweight AI usage policy for marketing (privacy, IP, approvals)

Days 31–60: Pick two AI bets that prove value

Two bets is the sweet spot: enough to show traction, not so many that you drown.

Good media & entertainment bets:

1. Churn-risk segmentation + winback testing (email/push/paid)
2. Creative variant system for a flagship title or tentpole campaign

Define success in numbers before you start. Examples:

• Reduce churn by 0.5–1.0 percentage points in a target cohort
• Improve paid conversion rate by 10–20% on tested creative themes

Days 61–90: Make it operational (where most AI programs die)

This phase is about turning “project” into “practice.”

• Create a recurring experiment review (weekly) with decision rights
• Build a small AI Marketing Council (marketing, legal, data, security)
• Update job descriptions to reflect reality (measurement, experimentation, AI-assisted workflows)

If you do only one thing here: institutionalize the cadence. AI progress is mostly a scheduling problem.

Hiring and org design: what changes after a CMO transition

A new CMO can either centralize AI capability or distribute it—but they have to choose. The worst structure is accidental: scattered tools, inconsistent governance, and duplicated effort.

Option A: Centralize with a “Growth & AI” pod

This works when the org is chaotic or measurement is weak.

• Small cross-functional team (growth lead, marketing ops, analyst, creative ops)
• Owns experimentation standards, measurement, and enablement
• Partners with channel teams rather than replacing them

Option B: Distribute AI operators into channel teams

This works when the org already has strong process discipline.

• Each channel team has at least one AI-fluent operator
• Shared governance and playbooks
• Central team focuses on platforms, training, and risk controls

Either way, HR should track:

• Time-to-fill for analytics and marketing ops roles
• Training completion and proficiency (not just attendance)
• Retention risk in high-leverage roles (CDP, attribution, lifecycle)

People also ask: what do CMO shakeups mean for teams?

Does a new CMO usually replace the whole marketing team?

Not usually. What’s more common is replacing or re-scoping a few keystone roles (ops, measurement, brand lead) and changing agency/vendor relationships.

Should marketing teams pause AI initiatives during leadership change?

No. They should pause tool sprawl and keep two value-driven pilots running with clear metrics. Momentum matters, and clean reporting builds credibility with new leadership.

What’s the biggest risk of pushing AI too fast in marketing?

Brand and compliance failures—especially in regulated categories and youth-focused entertainment. The fix is governance plus human review, not banning AI.

Where this lands for 2026 planning

CMO shakeups in 2025 weren’t just a headline reel of leadership changes. They were a reminder that strategy resets happen through people, and AI acceleration depends on whether the workforce is ready when that reset arrives.

If you’re planning for 2026, treat marketing leadership transitions like you’d treat a major systems migration: you need an adoption plan, training paths, clear ownership, and a short list of measurable outcomes. That’s the heart of AI in Human Resources & Workforce Management—putting the right skills in the right roles, fast.

The question I’d end on is simple: if your CMO changed next month, would your AI marketing program speed up—or stall because only two people understand how it works?