CMOs need to redesign marketing work around AI

CMOs are already putting substantial money into AI. The harder question is whether that spending is changing how marketing work gets done. McKinsey reported several measures in June 2026 that frame the tension.

These findings can coexist without implying causation. Enthusiasm about AI does not establish organizational redesign, and concern about job security does not establish resistance to change. The useful management distinction is between deploying AI and changing the operating model: the roles, decision rights, handoffs, approval paths, and processes through which marketing work gets done. A company can add AI to existing activities while leaving most of that system intact.

CMOs are buying AI faster than they are rebuilding marketing

AI adoption and operating-model redesign are separate management decisions. Technology spending can fit into existing budgets and workflows, while redesign requires decisions about responsibilities, staffing, skills, governance, and work across teams. The McKinsey findings support a limited conclusion: reported enthusiasm can coexist with a comparatively low reported rate of fundamental rewiring.

That distinction changes how a CMO should assess an AI program. Products, pilots, and budget commitments measure deployment activity. Changes to tasks, decisions, and approval paths measure organizational change. The McKinsey figures do not establish a causal relationship between the extent of rewiring and financial returns.

McKinsey has a commercial stake in this framing because the consulting firm sells transformation and AI-related advisory services and can benefit when companies invest in such work. Its findings can inform executive decisions, but its prescriptions and characterizations should be read as those of an interested market participant.

The investment gap is becoming an operating-model question

Gartner reported in May 2026 that CMOs were allocating an average of 15.3% of marketing budgets to AI initiatives. Boston Consulting Group reported in June 2026 that 43% of CMOs had invested more than $15 million in marketing AI that year, compared with 28% the previous year.

These measures are separate datasets. Gartner reports a share of marketing budgets, BCG reports the share of CMOs exceeding an absolute investment threshold, and McKinsey reports perceived organizational rewiring. Their populations, questions, periods, and definitions may differ. Together, they provide separate indicators of AI spending and organizational change rather than evidence of a causal relationship between the two.

For a CMO, the useful discipline is to maintain a financial view and an operating view of the same investment. The financial view tracks money assigned to initiatives, vendors, infrastructure, talent, and experiments. The operating view identifies the decisions, tasks, workflows, and responsibilities expected to change. Connecting the two makes the investment hypothesis explicit and gives management something more concrete to evaluate than adoption alone.

Consider AI in campaign development. The CMO can identify how briefs are produced, how creative variations are developed, who reviews machine-generated output, how approvals work, and which measure should respond if the redesign succeeds. Campaign cycle time is one possible measure when speed is the intended benefit. This connects expenditure to a defined change in work without assuming that every AI deployment requires a larger reorganization.

Staffing belongs in the same analysis because technology and labor operate inside one workflow. A change in the effort required for a task may alter where people spend their time or create review, quality, or workflow-management duties. Executives therefore need to test staffing assumptions for specific use cases.

Gartner and BCG also have commercial interests in enterprise AI adoption and organizational change. Gartner sells research and advisory services, while BCG sells consulting services, so both can benefit from executive demand for AI-related guidance. Their investment findings are best treated as attributed research rather than proof of a universal market pattern.

Rewiring marketing means changing the work

Boston Consulting Group describes the organizational task in broad terms. BCG says CMOs “must reimagine the job to be done at every layer of the function, insights, strategy, creative, planning, production, activation, or measurement, in the context of what AI makes possible.” This is BCG’s prescription, and BCG benefits commercially from demand for the transformation work it recommends.

The prescription offers a useful way to frame an executive decision without requiring a specific organizational design. In insights and strategy, a CMO can examine which tasks AI assists, where human judgment enters, and who remains accountable for a decision. If information gathering or processing changes materially in a workflow, management can assess whether responsibilities and required expertise should change. Start with the work, then consider the organizational design.

Creative, planning, and production require the same task-level analysis. If a specific AI system allows a team to generate more material or produce it faster, management still has to define direction, evaluation, revision, approval, and customer-facing standards. Production output matters when it connects to a stated business measure.

Activation and measurement extend the design problem into execution. When an AI-enabled workflow spans several roles, management must specify who acts on an output, who evaluates the result, and who remains accountable for the underlying business decision. Responsibility can cross team boundaries even when the software sits inside one function. The right arrangement will depend on the campaign, organization, and decision being automated or assisted.

BCG says leading CMOs are creating AI-specific roles and teams and pairing humans with AI across analytics, marketing science, and engineering to build and manage workflows. It also describes smaller, AI-assisted teams working across functions and organizational silos. These claims are more useful as hypotheses to test against a company’s work than as templates every CMO should copy.

McKinsey has reported large potential performance effects from a hybrid human-AI workforce. In April 2026, McKinsey attributed 10% to 30% revenue growth from hyperpersonalized marketing and 10x to 15x faster creation and execution of marketing campaigns to that model. It also described humans as being freed to focus on human tasks with higher ROI from data-driven marketing, media, and creative performance.

Those figures describe McKinsey’s reported potential rather than assured outcomes. The firm sells consulting services connected to AI and business transformation, giving it a commercial interest in enterprise demand for this work. A CMO evaluating similar gains should connect any claimed improvement to the full workflow, its operating costs, and a measurable business result.

AI literacy is now a CMO capability

Organizational redesign also depends on what the CMO can evaluate personally. Gartner reported in June 2026 that 66% of marketers said learning new technologies takes significant time away from day-to-day work. That finding makes capability-building a capacity issue for management as well as a training issue.

A CMO does not need an engineer’s depth of technical knowledge to make executive decisions about AI. The role does require enough understanding to assess where a system changes a workflow, which decisions remain accountable to people, where specialist expertise is necessary, and what evidence would support further investment. Direct use of deployed systems can provide some of that understanding. The appropriate depth depends on the systems and risks involved.

The Marketing AI Institute offers free courses and resources that can provide general AI education. It also operates commercially in AI education and related services, so that business interest provides context for its educational material. General education can establish a baseline, while organization-specific training can address the workflows employees actually use.

Gartner has also reportedly predicted that by 2027, a lack of AI literacy will rank among the top three reasons CMOs are replaced at large enterprises. Separately, Gartner research found that only 32% of CMOs said significant changes were needed to the CMO profile and skill set. The claims measure different things and do not by themselves establish how CMOs perceive their own risk.

For an executive, literacy matters most when it improves decisions that cannot simply be delegated to a specialist. These include capital allocation, organizational design, governance, capability ownership, and standards for measuring success. Specialists can supply deeper technical knowledge, while the CMO remains responsible for deciding how that knowledge affects marketing operations. The substantive test is whether the executive can connect an AI capability to a specific change in work and evaluate the evidence that change produces.

Main highlights

• Redesign marketing around AI: CMOs can assess AI transformation through changes to roles, decision rights, workflows, and approvals. AI deployment and spending alone do not establish that the marketing operating model has changed.
• Connect AI investment to changes in work: CMOs can pair the financial view of AI spending with an operating view that identifies which tasks, decisions, responsibilities, and business measures are expected to change. This makes the investment hypothesis explicit and measurable.
• Start operating-model decisions at the task level: Marketing organizations can examine how AI changes work across insights, strategy, creative, planning, production, activation, and measurement. Define human accountability, handoffs, and success measures for each workflow before deciding whether roles or team structures need to change.
• Build AI literacy into CMO decision-making: CMOs need enough practical AI knowledge to evaluate capital allocation, organizational design, governance, capability ownership, and measures of success. Specialists can provide technical depth while CMOs retain accountability for how AI changes marketing operations.