Why Marketing Is Seeing AI Impact First
What Cognizant and McKinsey reveal about where enterprise AI value actually appears.
AI adoption is no longer the challenge
Artificial intelligence is now widely used across enterprises.
According to the latest McKinsey Global Survey on AI, 88% of organizations report using AI in at least one business function, and many have begun experimenting with AI agents capable of executing multi-step workflows.
Yet enterprise-level impact remains uneven.
While adoption is broad, nearly two-thirds of organizations have not yet begun scaling AI across the enterprise, and only 39% report measurable EBIT impact from their AI initiatives.
The gap between experimentation and enterprise value remains real.
But one function is beginning to close that gap faster than others.
Marketing.
Marketing is emerging as an early AI value driver
Research from Cognizant (2025) highlights an unexpected pattern.
Marketing teams are among the most active adopters of generative AI across enterprises, second only to IT in funding and experimentation.
But the more interesting finding is not adoption.
It’s measurable impact.
According to McKinsey, among marketing and sales teams already using generative AI:
37% report cost reductions of at least 10%
53% report revenue increases of 5% or more
That is a rare signal in enterprise AI adoption, where many initiatives still struggle to demonstrate clear financial results.
Why does marketing see value sooner?
The answer lies in how marketing teams already operate.
Experimentation is already built into marketing
Marketing functions are accustomed to rapid experimentation.
Campaign testing, audience segmentation, personalization and content iteration have long required teams to test ideas quickly and measure results.
Generative AI fits naturally into this environment.
It accelerates processes that marketers already run:
content production
audience analysis
campaign optimization
customer engagement
Rather than replacing workflows, AI amplifies them.
This allows marketing teams to translate AI capabilities into measurable outcomes faster than many other functions.
The marketing + IT partnership
Cognizant’s research also highlights another factor: collaboration.
Successful generative AI initiatives in marketing typically involve close coordination with IT teams.
Marketing identifies use cases and business problems.
IT enables the infrastructure, data pipelines and integration required to deploy solutions safely and at scale.
This combination — problem-driven experimentation and technical implementation — creates a natural environment for innovation.
It also offers an important signal for the rest of the enterprise.
AI transformation rarely succeeds in isolation.
It succeeds when business functions and technology teams move together.
What this means for enterprise AI strategy
The early success of marketing teams does not mean that AI transformation is complete.
It highlights something else.
AI impact tends to appear first in environments where experimentation, revenue accountability and rapid feedback loops already exist.
Marketing simply happens to have all three.
But the broader lesson applies to the entire organization.
AI adoption is not just about deploying tools.
It is about redesigning workflows, clarifying ownership and aligning technology with real business outcomes.
This is the stage where many organizations still struggle.
The next phase of AI maturity
As AI moves from experimentation into operational systems — including the rise of AI agents embedded into enterprise workflows — the challenge will shift again.
Early wins in functions like marketing provide valuable signals.
But scaling those results across the enterprise requires something more:
clear data foundations
well-designed operating models
and cross-functional collaboration.
The Companies that recognize this early are the ones most likely to translate AI adoption into sustained enterprise value.
AI is already reshaping how revenue teams operate.
The real opportunity is understanding where impact appears first, and how to scale it across the organization.
If you’re exploring that transition, our team is always open to the conversation.
