Introduction

Artificial Intelligence has rapidly become one of the defining topics across the marketing industry. From conference stages to executive boardrooms, discussions around AI often focus on its transformative potential and its ability to reshape how brands create content, optimize campaigns, analyse data, and engage consumers. Retail Media has naturally become part of this evolution, with retailers, brands, agencies, and technology providers all exploring how Generative AI can improve the way advertising is planned, activated, and measured.

Yet, as is often the case with emerging technologies, the conversation is beginning to mature.

The question is no longer whether AI will influence Retail Media. For many organizations, that transition has already started. Instead, the discussion is shifting towards a more practical challenge: understanding where AI is already creating measurable value and how it can be integrated into everyday business operations.

Recent industry research illustrates this transition. According to a 2025 survey conducted among U.S. marketers involved in Retail Media, 63% are already using Generative AI within their Retail Media programs. While adoption levels naturally vary across organizations, the findings suggest that AI is gradually moving beyond isolated experimentation and becoming part of the operational toolkit used by Retail Media teams.

Perhaps even more interesting than adoption itself is the way organizations are applying the technology. Contrary to the popular narrative surrounding AI-generated content or fully automated marketing, current implementations appear to focus on improving operational efficiency, accelerating repetitive activities, supporting campaign execution, and enhancing analytical capabilities.

This represents an important shift.

Throughout the history of digital marketing, new technologies have rarely generated competitive advantage simply because they existed. Search advertising, programmatic buying, marketing automation and Retail Media itself all followed a similar trajectory. Early adopters experimented with the technology, but sustainable competitive advantage emerged only when organizations successfully integrated those innovations into repeatable business processes.

Generative AI appears to be entering the same phase.

Rather than representing a standalone capability, AI is increasingly becoming an operational layer capable of supporting multiple stages of the Retail Media value chain. The next phase of adoption will therefore be determined less by access to AI tools and more by an organization’s ability to embed those capabilities into planning, execution, optimization and decision-making.

This article explores what recent industry data reveals about the current state of AI adoption in Retail Media, where organizations are already creating value, the challenges that remain, and why the next stage of competitive advantage will likely depend on operational integration rather than technological experimentation.

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AI Adoption Is Becoming Mainstream

Although Artificial Intelligence continues to dominate industry discussions, recent data suggests that Retail Media is gradually moving beyond the early adoption phase.

With almost two-thirds of marketers already using Generative AI within their Retail Media programs, AI can no longer be considered an emerging capability reserved for innovation teams or experimental projects. Instead, it is increasingly becoming part of everyday operations.

This distinction is important because technology adoption and business transformation are not the same process.

Historically, organizations rarely redesign their operating models immediately after adopting a new technology. Instead, they tend to introduce new capabilities into existing workflows, allowing teams to gain experience, build confidence, and identify where the technology delivers the greatest value before expanding its role across the business.

Retail Media appears to be following precisely this pattern.

Rather than replacing existing campaign planning processes or redefining organizational structures overnight, Generative AI is gradually augmenting the work already performed by Retail Media professionals. Teams continue to make strategic decisions, while AI increasingly supports operational activities that previously required significant manual effort.

This transition should not be interpreted as a technological revolution. Instead, it reflects the natural evolution of a rapidly maturing discipline. As organizations accumulate experience and establish governance frameworks, AI becomes less of an experimental technology and more of an operational capability.

In many respects, this represents one of the strongest signals that Retail Media itself is reaching a new stage of maturity.

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Where Retail Media Teams Are Using AI Today

Understanding adoption rates provides only part of the picture. Equally important is understanding where organizations are actually deploying Generative AI.

Current industry research reveals a clear pattern. The most common application today is Creative and Content Production, followed by Campaign Management and Analytics.

At first glance, these findings may appear predictable. However, they reveal something more significant about the current role of AI within Retail Media organizations.

Creative production has traditionally involved numerous repetitive and time-consuming activities. Campaign copy, product descriptions, creative variations, retailer-specific adaptations and briefing documents all require substantial effort while following relatively structured processes. Generative AI naturally lends itself to supporting these activities by accelerating content creation without fundamentally changing the underlying marketing strategy.

A similar observation applies to campaign management.

Launching and maintaining Retail Media campaigns often requires repetitive operational tasks, including campaign setup, keyword generation, audience segmentation, budget adjustments and reporting. While these activities remain strategically supervised by marketing professionals, AI increasingly assists teams by reducing manual workload and improving execution speed.

Analytics represents another area where Generative AI is beginning to demonstrate significant potential.

Retail Media generates an ever-growing volume of performance data across multiple retailers, formats and measurement frameworks. Summarising campaign performance, identifying trends and highlighting actionable insights are activities that can consume considerable time. AI is increasingly helping analysts transform large volumes of data into more accessible information, allowing teams to dedicate greater attention to interpretation and strategic decision-making.

Perhaps the most interesting insight, however, concerns the future rather than the present.

Among the planned areas of future investment, personalization shows one of the strongest expected increases. While today’s adoption is largely concentrated around improving internal productivity, organizations increasingly appear interested in using AI to create more relevant and individualized customer experiences.

This evolution suggests that Retail Media may gradually move through two distinct phases of AI adoption.

The first phase focuses primarily on improving how organizations work.

The second may increasingly focus on improving how consumers experience brands throughout the digital shopping journey.

Both phases are complementary. Operational efficiency creates the organizational capacity required to deliver better customer experiences at scale. Rather than representing separate objectives, they are likely to become successive stages in the broader evolution of AI within Retail Media.

Efficiency Is Driving Adoption

One of the most revealing findings emerging from recent industry research is not how many organizations are adopting Generative AI, but why they are doing so.

When marketers were asked about the primary benefits generated by AI within their Retail Media programs, an overwhelming 85% identified operational efficiency as the most significant advantage. Other frequently mentioned benefits included more accurate audience targeting (49%), improved campaign return on investment (44%), stronger creative performance (42%) and enhanced customer personalization (40%).

These results provide an interesting perspective on the current role of AI within Retail Media.

Public discussions often focus on AI’s ability to generate images, write advertising copy or create entirely new creative assets. While these capabilities certainly attract attention, the data suggests that organizations are assigning greater importance to something considerably less spectacular but arguably more valuable: improving the efficiency of everyday operations.

This distinction is worth emphasizing.

Retail Media has evolved into one of the most operationally intensive disciplines within digital marketing. Campaigns are frequently activated across multiple retailers, each with different advertising products, reporting standards, bidding mechanisms and optimization requirements. As Retail Media networks continue to expand globally, the operational complexity faced by marketing teams increases accordingly.

Within this environment, even relatively small efficiency gains can generate meaningful business impact.

Reducing the time required to prepare campaign briefs, generate creative variations, summarize performance reports or identify optimization opportunities allows teams to dedicate more attention to strategic activities that continue to require human judgement.

In other words, AI is not primarily replacing strategic thinking.

It is reducing the operational friction surrounding it.

This distinction may ultimately define the first phase of AI maturity in Retail Media. Rather than transforming business strategy overnight, Generative AI is helping organizations execute existing strategies faster, more consistently and with greater scalability.

The implications extend beyond productivity alone.

As operational activities become increasingly supported by AI, Retail Media professionals are likely to spend proportionally more time interpreting results, prioritizing opportunities, coordinating cross-functional stakeholders and making commercial decisions. The technology therefore changes not only how work is completed, but also where human expertise creates the greatest value.

Seen from this perspective, efficiency should not be viewed as the final objective of AI adoption.

Instead, it becomes the foundation upon which more advanced organizational capabilities can gradually be built.

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Adoption Does Not Automatically Mean Transformation

Although adoption rates continue to increase, the data also highlights an equally important reality.

Using Artificial Intelligence does not automatically translate into business transformation.

When marketers evaluated the impact generated by Generative AI within their Retail Media programs, only a relatively small proportion described the results as transformational. The largest group instead reported mixed results, while others observed only limited improvements.

At first glance, these findings could be interpreted as evidence that AI has failed to meet expectations.

A closer examination, however, suggests a different interpretation.

Every major technological innovation follows a similar adoption curve. Initial enthusiasm is typically followed by a period of experimentation during which organizations explore different applications, test new workflows and gradually identify where measurable value can actually be created.

Retail Media appears to be experiencing precisely this stage.

Organizations are no longer questioning whether AI should be adopted.

Instead, they are learning how to integrate it effectively into existing operating models.

This distinction fundamentally changes the conversation.

The challenge is becoming less technological and increasingly organizational.

Supporting this interpretation, the most frequently reported barriers are not cultural resistance or lack of executive support. Instead, marketers identify practical implementation challenges such as technical expertise, accuracy of AI-generated outputs, data quality and regulatory considerations.

These barriers are significant.

However, they should not necessarily be interpreted as obstacles preventing adoption. Rather, they reflect the natural evolution of organizations moving from isolated experimentation toward enterprise-wide implementation.

As AI becomes increasingly embedded within campaign planning, optimization and reporting, expectations regarding reliability naturally increase as well. Marketing teams require outputs that are accurate, transparent, consistent and aligned with broader governance standards.

In many respects, these challenges represent a sign of maturity rather than hesitation.

Organizations are no longer simply asking what AI can produce.

They are asking whether AI can consistently support critical business decisions.

That represents a far more advanced discussion.

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AI Adoption Is Only the Beginning

One of the risks when discussing Artificial Intelligence is measuring organizational maturity solely through adoption rates.

While adoption undoubtedly represents an important milestone, it says relatively little about the business value organizations are actually creating.

Two companies may report identical levels of AI adoption while generating completely different outcomes.

One organization may use Generative AI primarily to accelerate content creation or automate reporting. Another may integrate AI throughout campaign planning, optimization, performance analysis and decision support, allowing teams to make faster and more informed commercial decisions.

Both organizations are technically “using AI.”

Yet their level of organizational maturity differs substantially.

This distinction suggests that Retail Media leaders should perhaps begin evaluating AI through a different lens.

Rather than asking “Are we using AI?”, organizations may benefit more from asking “How is AI improving the quality of our decisions, our processes and our execution?”

The answer to that question is likely to become a far more meaningful indicator of competitive advantage than adoption alone.

The AI Value Journey in Retail Media

Looking at the current state of AI adoption, one conclusion becomes increasingly evident.

Organizations do not create business value simply by adopting Artificial Intelligence.

They create value by progressively integrating AI into the way they work.

Recent industry data appears to describe the beginning of this evolution. Adoption is growing, operational efficiency is becoming the primary objective, implementation challenges are gradually being addressed, and organizations are beginning to understand where AI delivers the greatest return.

This progression can be visualized as a simple maturity journey.

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The AI Value Journey

Stage	Primary Objective	Typical Activities	Business Outcome
Adoption	Learn the technology	Pilot projects, testing GenAI tools, experimentation	Organizational learning
Operational Efficiency	Improve productivity	Content generation, campaign setup, reporting automation	Faster execution
Decision Support	Improve decision quality	Performance analysis, insight generation, forecasting, optimization recommendations	Better business decisions
Business Value	Create competitive advantage	AI integrated across planning, activation, optimization and measurement	Sustainable competitive advantage

This framework intentionally places technology at the beginning rather than at the end of the journey.

Too often, AI initiatives are evaluated according to how many tools an organization has adopted. Yet technology, by itself, rarely generates competitive advantage.

Competitive advantage emerges when technology enables better execution.

The findings discussed throughout this article appear to support this interpretation.

Today, most organizations remain concentrated within the first two stages of the journey. They are successfully improving operational efficiency while gradually expanding AI into additional activities.

The next stage will likely require a broader organizational evolution.

As AI becomes increasingly embedded across Retail Media operations, its role will progressively shift from executing individual tasks toward supporting more informed decisions across planning, investment allocation, campaign optimization and performance measurement.

Ultimately, organizations that successfully complete this journey may find that AI becomes almost invisible.

Rather than representing a separate capability, it simply becomes part of how Retail Media teams work every day.

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Beyond Technology: The Human Advantage

One of the most common misconceptions surrounding Artificial Intelligence is that its primary purpose is to replace human expertise.

Current evidence suggests a rather different scenario.

The most valuable applications of AI are concentrated around repetitive, operational and data-intensive activities. These are precisely the tasks that consume significant amounts of time while contributing relatively little strategic differentiation.

Meanwhile, the activities that continue to require human expertise remain remarkably consistent.

Defining business priorities.

Understanding customer behaviour.

Balancing commercial objectives.

Managing retailer relationships.

Interpreting market context.

Making strategic investment decisions.

Leading cross-functional collaboration.

These responsibilities cannot simply be delegated to algorithms.

If anything, Generative AI has the potential to increase their importance.

By reducing the operational workload surrounding campaign execution, AI creates additional capacity for marketers to focus on higher-value activities that require judgement, creativity and business understanding.

This perspective also explains why efficiency emerged as the most frequently cited benefit within recent industry research.

Efficiency is not the final destination.

It is the mechanism that allows organizations to dedicate more time to strategy.

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Conclusion

Artificial Intelligence is rapidly becoming an integral component of Retail Media.

The conversation is no longer centred on whether organizations should adopt AI, but rather on how they can integrate it effectively into their operating models.

Recent industry data paints an encouraging picture.

Adoption is growing steadily.

Operational efficiency is already delivering measurable benefits.

Organizations are expanding AI into campaign management, analytics and content production.

At the same time, implementation challenges remain, reminding us that technological adoption and organizational transformation rarely occur at the same pace.

Perhaps the most important insight is that AI should not be viewed as an isolated capability.

Its greatest value will emerge when it becomes embedded within everyday planning, execution, optimization and decision-making.

The future of Retail Media is therefore unlikely to be defined by the organizations experimenting with the largest number of AI tools.

Instead, it will be shaped by those capable of transforming Artificial Intelligence into an operational capability that consistently supports better execution, stronger collaboration and smarter business decisions.

In many respects, that may represent the true transition from experimentation to everyday execution.