An AI playbook for retail marketers

Insights

• Three-quarters of retail chief marketing officers (CMOs) plan to increase their marketing technology investments in the coming year.
• Marketers are using artificial intelligence (AI) in a majority of their content creation, campaign management, and personalization.
• CMOs face difficulties in AI deployment related to data and security, risk management, and talent development.
• A comprehensive AI strategy, dedicated teams, and continuous upskilling are essential for overcoming these challenges and maximizing the value of AI investments.

Retailers are increasing their investments in artificial intelligence (AI) — and raising their expectations for this rapidly evolving technology. One study estimates that retailers spend nearly $20 billion annually on AI-driven systems, second only to the financial services sector. A significant portion of this investment flows to chief marketing officers (CMOs) — among the earliest AI adopters — and to their extensive MarTech stack.

This drive for AI adoption shows no signs of slowing. About three-quarters of retail CMOs plan to increase their marketing technology investments in the coming year, and a large majority of new MarTech tools now come AI-enabled.

However, retail CMOs find themselves in a middle ground — reaping significant benefits from AI yet still seeking ways to extract maximum business value from their investments. Infosys research found that just over half (52%) of AI deployments in marketing are delivering measurable impact, highlighting the ongoing challenges of optimizing AI-driven strategies.

AI empowering retail CMOs

The goals of AI transformation are as diverse as the retailers and marketers embracing it. Retail CMOs are using traditional AI and generative AI to drive efficiency, enhance decision-making, and improve customer experiences.

AI now plays a critical role in streamlining and validating vast amounts of data — from demand and supply planning to sales execution — empowering marketers to gain efficiencies and make data-driven decisions with confidence. This is especially crucial as retailers strive to balance online and in-store experiences, ensuring an appealing, personalized journey for customers across both channels.

“When it comes to customer and associate experiences, especially with the rise of omnichannel capabilities, the focus is on enhancing every aspect of the in-store experience, both from technological and operational standpoints,” said Ravindar Vanam, senior director for consumer goods, retail, and logistics at Infosys. “One key trend is infusing smartness into these experiences by leveraging AI, machine learning (ML), and generative AI technologies to help clients enhance their services to keep up with ever-changing consumer needs, and making store associates more productive.”

For example, virtual assistants, smart assistants, and chatbots create personalized shopping experiences, which improve customer interactions and streamline operations. In addition, Vanam said: “Retailers are using AI to analyze retail insights — with data that retailers already have both at a store level and at a corporate level — for appropriate decision-making, be it picking trends or marketing them.”

CMOs’ AI efforts extend beyond just a few isolated areas in the marketing organization. Infosys’s CMO Radar research report found that almost all marketing leaders are using AI to some degree for their most important activities (Figure 1). They are leveraging AI for a range of functions, including managing ad spending, sales enablement, and conversational agents.

Figure 1. How retail marketers are using AI

Source: Infosys Knowledge Institute

For the most part, CMOs have moved past the experimental stage with AI; those who are still just dipping their toes in the water have already fallen far behind. Most retail marketers have implemented AI in five or more of the seven marketing activities that we surveyed (Figure 2). Retailers also expect AI to drive efficiency, productivity, speed to market, creativity, cost savings, and lead generation over the next 18 months.

Figure 2. Most marketers have deployed AI in five or above marketing activities

Source: Infosys Knowledge Institute

“Conversion commerce is emerging,” said Sachin Jangam, global practice leader, consumer goods, retail, and logistics at Infosys Consulting, during a Consumer Spotlight webinar. “While you move from physical commerce to digital commerce to now conversion commerce, it's all based on what you want, and how AI can interpret the customer’s mind, and mindshare, that's helping significantly to gain that market share. As the shelf spaces are changing in retail, what to buy, in what quantities to buy, what forecasting and AI algorithms to use to get right products at right place is becoming key.”

The value of AI

AI-driven personalization is one of the most anticipated benefits of the technology. AI-powered intuitive searches enhance efficiency by reducing the time customers spend searching for products, creating a more seamless and user-friendly shopping experience.

Additionally, AI delivers personalized recommendations, helping customers discover products aligned with their preferences and needs. For retailers, this improves customer engagement and boosts retention rates, ultimately driving higher conversion and loyalty.

At the Consumer Electronics Show in January 2024, Walmart highlighted its use of generative AI search in its iOS app — later introduced to its Android app. The app allows shoppers to search by general needs, such as camping trip or football watch party, to reduce the number of searches needed and provide consumers with options they might not have considered. AI is critical to powering this type of curation at scale. One company executive said that consumers spend an average of six hours per week to search for products on the retailer’s website.

Walmart’s CEO Doug McMillon said during an earnings call that “help me buy a Valentine’s Day gift” was a popular search for the retail giant in February. “And rather than searching separately for things like chocolates, a car, jewelry, flowers, the search returns a list of results that are relevant and curated,” he added.

Virtual search

Perception systems process diverse data types, including audio, visual, language, and other unstructured formats, providing context and input to AI solution components for deeper analysis, decision-making, and automation.

Advancements in deep neural networks over the past decade have significantly improved AI’s ability to process unstructured data. As foundation models continue to evolve, we can expect more powerful AI applications capable of handling and integrating multimodal data.

Additionally, AI-powered chatbots enhance customer support and engagement by resolving queries quickly, leading to improved customer satisfaction and better experience.

Omnichannel customer experience

The integration of customer journeys remains a top priority for CMOs and an area that demands continuous innovation.

Leading brands are leveraging AI-powered solutions to enhance direct-to-consumer engagement. Nike developed an AI-driven app that uses computer vision, AI, and ML to create a precise digital representation of a customer’s foot. This data enables tailored product recommendations, improving the in-store and mobile shopping experience. Under Armour offers similar AI-powered features in its retail locations.

Even quick-service restaurants are adopting AI-driven personalization. Starbucks’ proprietary AI platform, Deep Brew, analyzes customer data to generate personalized menu suggestions and targeted marketing messages, enhancing customer engagement and sales.

Managing marketing campaigns

AI offers rapid insights into consumer behavior, enabling marketers to design personalized campaigns that are most relevant to their audience. It helps determine key campaign details, such as the best channels for execution, creates and distributes targeted marketing communications, and evaluates campaign effectiveness based on reach and outcomes. This continuous feedback loop allows marketers to refine and improve future campaigns.

Content creation

According to recent data, 47% of marketers utilize AI tools to generate content, while 28% use AI to create design elements. Generative AI empowers marketing teams to scale and personalize the production of visual content, tailoring it for diverse audiences and platforms. This technology also facilitates the creation of marketing campaign content, including both text and images. Alibaba has also developed an AI copywriting tool that uses deep learning and natural language processing, capable of generating up to 20,000 lines of content including ad copy per second.

More than half of content creation in the retail industry now relies on AI, according to CMO Radar. A similar percentage of personalization efforts and a slightly higher share of campaign management also incorporate AI. In addition, marketers can leverage AI to assess and refine content effectiveness across various formats and channels. This capability is already deeply embedded in how retail CMOs operate today.

Navigating the challenges

Marketers struggle to effectively capture and utilize data to understand customer behavior while also devising strategies to enhance customer loyalty through campaigns that truly resonate. Balancing online and in-person experiences to foster long-term loyalty remains a significant challenge.

AI helps address these challenges, enabling marketers to analyze customer data, personalize experiences, and optimize campaigns. However, despite these advantages, only 5% of retail firms experimenting with or implementing generative AI are successfully creating value from their use cases — significantly lower than the overall trend of 13%, according to Infosys’ Generative AI Radar research.

Additional challenges include lack of data security, absence of a formal AI strategy, fragmented AI efforts, failure to eliminate overlapping tools, and insufficient training in AI technologies.

Furthermore, CMOs are under pressure to integrate AI to enhance efficiency and performance, yet MarTech utilization rates have declined sharply, dropping from 58% in 2020 to just 33% in 2023. This trend highlights the underuse of existing technologies, suggesting that many organizations struggle to fully leverage AI and automation in their marketing strategies.

The transformative potential lies in AI’s ability to address the issue of data accuracy, which currently hinders effective marketing decisions. The emphasis on high-quality data powered by AI will reshape how businesses operate, offering measurable advantages in efficiency, accuracy, and overall strategic execution.

Studies suggest that 25-30% of marketing data is inaccurate, underscoring the urgent need for systems that prioritize data integrity. AI-driven solutions not only improve reporting processes but also drive growth and transformation across business functions by ensuring reliable, high-quality data.

How marketers can deliver business value

AI serves as a technological solution to a wide range of business challenges. However, to unlock its full potential, a combination of a robust strategy and the right technology is essential. CMOs must ensure their AI deployments are aligned with business objectives, well-integrated into existing systems, and optimized for measurable impact.

By addressing AI-related challenges strategically, they can maximize the value and efficiency of their AI investments.

Comprehensive rollout strategy: Marketers must precede AI deployment with a well-defined AI roadmap. They need a clear understanding of which marketing activities will incorporate AI and which will generate business value. These activities must be aligned with the organization’s business goals and not selected in silos, ensuring they contribute to overall success.

Additionally, full leadership buy-in and active support are essential at every stage to secure the necessary funding and strategic guidance for a successful AI deployment.

Risk management office: Establishing a well-defined team to assess the risks associated with AI deployment is essential. This ensures identification and mitigation of potential risks while maintaining accountability. By closely monitoring AI implementation, organizations can address challenges in advance and ensure compliance with security, ethical, and regulatory standards.

Data governance office: A dedicated data governance office is essential for monitoring data quality used by AI to generate insights. It ensures that data is stored in a centralized platform, allowing for easy access and real-time flow across systems.

Additionally, it plays a critical role in promoting responsible AI usage, ensuring compliance with data regulations, and mitigating risks associated with noncompliance. By maintaining data integrity and security, the governance office helps organizations maximize AI’s effectiveness while minimizing regulatory risks.

Integrated MarTech stack: Marketers should ensure their marketing technology is AI-compatible and scalable. Investing in adaptable, future-ready solutions allows them to maintain long-term efficiency while reducing the need for frequent system overhauls and associated costs.

A flexible MarTech stack enables smoother transitions as AI capabilities evolve, ensuring technology remains aligned with business goals. Additionally, marketers should optimize their MarTech investments by eliminating redundant actions and streamlining processes to maximize efficiency and effectiveness.

Dedicated training programs: Research shows that 69% of marketers are excited about AI technology and its impact on their jobs. However, to build confidence in using AI and AI-driven tools, they must undergo regular training. With technology constantly evolving, well-coordinated training programs ensure that marketers stay upskilled and up to date, allowing them to leverage AI for optimal results.

Maximizing AI’s value

As AI adoption advances — and proofs of concept evolve — CMOs will refine their AI strategies, identifying the most effective use cases and implementation approaches. While challenges will persist, enterprises will gain a clearer understanding of how to overcome barriers that may have previously hindered progress.

For retail CMOs, maximizing AI success requires a holistic approach, integrating AI across the enterprise rather than treating it as a standalone tool. This ensures AI is embedded into core business strategies, operations, and decision-making, driving greater efficiency, innovation, and long-term value.