How CMOs and CX Leaders Should Work Together on AI

For most of the past decade, the division of labour between marketing and customer experience felt logical. Marketing owned the funnel of awareness, acquisition, and the promise of the brand. CX owned what came after, including onboarding, service, retention, and the moment-to-moment reality of the relationship. Each team had its metrics, its tools, and its seat at the table.

The same technologies that now power personalised campaign content, however, are also shaping how service agents respond to a frustrated customer mid-call. The data that helps a marketing team predict churn is the same data a CX team needs to intervene before it happens. When AI operates across the entire customer lifecycle, functional silos can create both inefficiencies and contradictions.

The potential impact of AI on marketing leadership was reflected in a Gartner survey, which found that 65% of CMOs believe advances in AI will dramatically transform their role within the next two years.

Yet recognising AI’s importance and operationalising it effectively are proving to be very different challenges. A 2025 global study by the IBM Institute for Business Value (IBV), revealed that while 81% of CMO respondents view AI as a game-changer, 84% reported that challenges with rigid, fragmented operations limit their ability to effectively harness the technology.

The cost of this fragmentation is felt by the customer who witnesses a brand that promises one thing and delivers another; one that knows their preferences at the point of acquisition but forgets them the moment something goes wrong. In an era where AI is capable of enabling genuine continuity across every touchpoint, inconsistency is increasingly a choice, and one that customers are less willing to forgive.

Despite operating in different lanes, CMOs and CX leaders are ultimately working toward the same essential outcome of a customer who stays, spends more, and tells others. Marketing measures success at the top and middle of the funnel, whereas CX measures it further downstream but both are accountable to lifetime value, even if neither team always owns that metric cleanly.

The IBM IBV study uncovered that CMOs estimate fully aligning marketing, sales, and operations could unlock a 20% increase in revenue, although only 28% of the organisations surveyed reported that end-to-end customer experience is effectively owned and aligned across functions. There is clearly a big difference between what cross-functional collaboration could deliver and what most organisations have actually built.

Already customer data that was once siloed by a system or team can flow through platforms capable of building a unified view of the individual, which does not just reset when a conversation moves from a marketing email to a support ticket. CMOs and CX leaders who recognise this share common currency in data, and the mutual issue of how to act on it responsibly and at scale. Organisations attempting to unify these capabilities are increasingly investing in a modern CX AI technology stack that connects customer data, orchestration, analytics, and automation across functions.

Several AI capabilities are already demonstrating value precisely because they straddle the marketing and CX boundary, rather than sitting neatly on one side of it. Predictive churn modelling is one example. Marketing may generate the signal through engagement data but CX holds the service history that gives that signal context. Neither team can build an accurate model in isolation, and neither can act on one effectively without the other.

AI-driven personalisation presents a similar case. When marketing and CX share a connected data layer, personalisation becomes something a customer experiences consistently, not just in the inbox. The tone, the offers, the level of support, and the proactive outreach can all reflect the same understanding of who that individual is and what they need. Delivered in isolation, each piece may be technically impressive, but together they form something that actually feels like a relationship. This broader shift towards personalisation at scale with AI is becoming one of the defining competitive advantages in modern customer experience.

Voice of the customer programmes offer a third area of convergence. CX teams have long owned survey feedback and NPS tracking. AI-powered sentiment analysis, however, can be applied across contact centre transcripts, social mentions, and post-purchase surveys, to generate insights that are as relevant to a CMO thinking about brand positioning as they are to a CX leader managing service quality. When both teams see the same signals, they can make decisions that reinforce rather than undermine each other.

Identifying the shared opportunity is the straightforward part. Building an operating model that captures it is considerably harder, and most organisations are not yet close to doing it well. On this issue, IBM’s study found that 54% of CMO respondents admit they underestimated the operational complexity of translating AI strategies into tangible outcomes. Only 24% say they have technology platforms that support consistent cross-functional collaboration.

A starting point for building a unified operating model is joint ownership of the customer data foundation in order for them to be able to produce consistent results across both functions. This does not require a single monolithic platform, but it does require agreement on what a unified customer profile looks like and who is responsible for its integrity. Many organisations still underestimate the role of data in CX AI and the extent to which fragmented data limits AI performance across the customer lifecycle.

Collective governance of AI tools is equally important. When marketing deploys a personalisation engine and the contact centre deploys a separate AI assistant, with no shared logic or data exchange between them, the customer bears the cost of that disconnection. Increasingly, organisations are addressing this by establishing cross-functional AI councils or centres of excellence, bodies that set standards, review use cases, and ensure that new deployments serve the customer rather than just the team that commissioned them.

Shared metrics is the third pillar. When CMOs are measured on customer acquisition cost and CX leaders on CSAT, there is no structural incentive to collaborate. Organisations making real progress tend to have introduced shared accountability for metrics that capture the full relationship, whether that is customer lifetime value, net revenue retention, or a composite loyalty score. When leaders are evaluated on the same number, the motivation to collaborate follows.

The case for CMO and CX leader collaboration on AI is fundamentally about customer experience. Customers do not experience a brand in functional chapters. They experience it as a continuous relationship, and they judge it accordingly. When AI is deployed in silos, that continuity breaks, and the distance between what a brand promises and what it actually delivers widens.

IBM research was able to translate that gap to a tangible cost. Respondents who reported internal collaboration challenges saw marginally lower revenue growth than their higher-performing peers, a seemingly small difference that the study estimated could represent $140 million in potential upside for an organisation with $14 billion in revenues. At that scale, the question of whether marketing and CX should work together becomes a financial one.

Companies with effective AI strategies will not necessarily be the ones with the most sophisticated models or the largest data estates, but those which deploy them effectively, unifying data, governance, and accountability across departments. It is a leadership problem before it is a technology problem, and it is one that CMOs and CX leaders need to solve together.