What retailers need to know about AI and consumer engagement

Retailers need to understand how AI will change consumer behavior

As glimmers of this future take hold, retailers need to understand more about how AI will change consumer behavior. Which consumers are most (and least) inclined to use AI? Which tool would they prefer to use? And where in the purchase process would they be most comfortable using it?

Our recent research uncovered some surprising answers to those questions. Using data from our recent consumer AI study, we developed the AI Inclination Index (AII), which quantifies consumers’ propensity to use AI. While inclination to use AI for retail purchases nearly meets or exceeds the global cross-industry average, there are important variations in AI attitudes across the three key phases of the consumer journey (Learn, Buy and Use) and the five retail segments defined in our study:

• Everyday essentials (e.g., groceries, toiletries)
• Small considered purchases (e.g., shoes, clothing, beauty)
• Large considered purchases (e.g., electronics, appliances, home goods, jewelry)
• Luxury goods (e.g., designer clothing and accessories, artwork)
  
• Unexpected needs

Consider these AI adoption metrics in the retail consumer experience: 

• The Learn phase is where retail consumers show the highest inclination to use AI. AI tools are widely seen as valuable for discovering, learning about and comparing products. AI inclination levels vary, however, among product types. And while AI interest declines in the Buy phase, inclination index scores are higher for retail than the global cross-industry average.
  
  
• Younger and higher-income consumers are the biggest AI enthusiasts. Higher-income consumers consistently demonstrate a greater inclination to use AI throughout the retail journey, especially in the Learn and Buy phases. Age is also a factor. With their greater tech savviness, younger consumers are more willing to leverage AI to discover trends and automate routine purchases.
  
  
• Conversational AI is the tool of choice, especially for Learn and Use. Conversational AI is often preferred for guided discovery or after-sales support. Its ability to understand natural language and provide relevant, contextual responses makes conversational AI particularly effective in fostering ongoing customer engagement, whether by resolving complex queries during product research or by offering personalized assistance, troubleshooting and tailored recommendations in the post-purchase phase.

The AI Inclination Index

To quantify consumers' propensity to adopt AI-driven technology features throughout the consumer journey, we developed the AI Inclination Index. The index was calculated using three measures from our New Minds, New Markets survey data.

AI inclination in retail vs. the global average

Consumers are somewhat less inclined to use AI when purchasing retail goods than other industries’ products and services.

Figure 1
Base: 8,451 respondents in the US, UK, Germany and Australia
Source: Cognizant Research

With these variances across consumer groups and journey stages, it’s clear retailers will need to craft a precise and nuanced AI strategy that captures the greatest areas of opportunity while avoiding low-value pursuits. 

Understanding consumer use of AI, as well as the accompanying pockets of spending power, is essential for leaders in all industries. In our global study “New minds, new markets,” we found that consumers who are enthusiastic about using AI will account for up to 55% of all purchases made across industries. This amounts to $4.4 trillion in spending in the US, $690 billion in the UK, $690 billion in Australia and $540 billion in Germany.

In this report, retail leaders will learn where in the purchase journey consumers are most and least inclined to use AI, the AI tools they would be most apt to use and how this differs across age groups. With this information, businesses can reshape their approach to customer engagement—where and how it matters most.

AI across the retail consumer journey

As our retail AII indicates, consumers’ interest in using AI is especially high in the Learn phase of the purchase journey and surpasses the global average in the Buy phase. However, this overall trend masks crucial differences between distinct types of retail experiences:

• Everyday essentials and small purchases. This category shows exceptionally high AI inclination, especially in the Learn and Buy phases. This reflects a strong consumer desire for convenience and automation in managing frequent, low-consideration purchases.
  
  Much like navigating a crowded wellness products aisle, consumers face numerous choices for meeting their daily needs and seem eager for AI to simplify selection and automate replenishment. This is a significant reason for retail's overall high AI inclination score.
  
  
• Large purchases and luxury goods. These product categories exhibit lower AI inclination, particularly in the Buy and Use phases. For large purchases, the significant financial outlay and product complexity likely increases the desire for human reassurance or detailed personal research over AI-driven decisions.
  
  In luxury goods, the emphasis on bespoke service, curated experiences and human interaction means consumers are far less inclined to value AI, especially for post-purchase engagement. The more cautious approach in these product categories moderates the overall retail AII scores in certain purchase phases. 

This gap in AI inclination—high enthusiasm for automating routine transactions vs. caution around high-value, complex or experiential purchases—is just one of several distinctions in how consumers feel about using AI across the different segments of the retail industry.

The Learn phase: An abundance of options drives interest in AI

The Learn phase is where retail consumers demonstrate significant openness to using AI-enabled tools, with an AII score (88) just two points below the global average. This is particularly true for higher-income consumers, who generally show a stronger propensity to use AI across the board, including in this information-gathering phase. High-income consumers (comprising 29% of respondents) are those with annual household incomes of £40,000 and over in the UK or $80,000 and over in the US.

OpenAI, for example, has recently updated ChatGPT to deliver richer shopping results, such as more detailed product listings that include images, prices, summarized reviews from various sources and direct links to merchant websites. Consumers can ask highly specific questions, such as where to find the best espresso machines under $250 that are great for making lattes and suitable for small counter space. This shifts traditional keyword searching to a more personalized and efficient discovery experience.

With more AI capabilities emerging to support product discovery, the Learn phase represents a prime opportunity for retailers to capture consumer attention and influence subsequent purchase decisions by strategically deploying AI tools. 

Retail AI Inclination Index: The Learn phase

Figure 2
Base: 8,451 respondents in the US, UK, Germany and Australia
Source: Cognizant Research

AI enthusiasm peaks for everyday items and small purchases

Of all the retail product categories in our study, consumers are most interested in using AI to learn about small purchases and everyday essentials. Here, AII scores are above the global average of 90 by five and three points, respectively.

With these high-frequency, low-price-tag items, AI can help navigate the vast array of choices by comparing similar options or quickly finding specific products. These product categories also share a high technology comfort score of 37, further suggesting a positive disposition toward using technology to complete routine discovery tasks. Retailers in these categories have a clear mandate to employ AI discovery tools effectively.

In luxury goods, consumers’ AI attitudes are more reserved

While still showing a reasonable AII score of 83, the luxury segment lags noticeably behind other retail categories in the Learn phase. Although its AI tool comfort score (30) indicates some openness toward AI, its lower technology comfort score of 29 suggests a degree of reservation when compared with 37 for essentials and small purchases.

This likely reflects the importance of delivering curated experiences and brand storytelling in the luxury goods product category via traditional mechanisms. Consumers value human expertise when learning about these products compared with other segments.

AI shows value for navigating complexity or uncertainty

Meanwhile, the large purchase and unexpected needs categories have comparatively high scores in the AI tool comfort component of the index, at 31 and 33, respectively. This indicates the potential for sophisticated AI tools—such as advanced comparison engines, detailed product guides and conversational advisors—to help consumers structure their research and make informed decisions when searching for an item they didn’t anticipate needing or reviewing an extensive number of options that come with a high price tag.

As one consumer said, "AI makes me feel less overwhelmed, especially when there are a lot of options, brands and styles from different decades, countries, cultures and backgrounds."

The Buy phase: High enthusiasm in specific product and demographic pockets

Consumer inclination to use AI dips in the Buy phase, as it also does in other industries. However, at 61, the retail sector’s AII score for purchasing notably surpasses the global cross-industry average of 56.

This finding, however, masks significant variations driven by product type, as well as consumers’ age and income level. Factors such as trust, perceived risk, the demand for convenience and generational comfort with technology all shape a consumer's willingness to use AI when it comes to finalizing the transaction.

Retail AI Inclination Index: The Buy phase

Figure 3
Base: 8,451 respondents in the US, UK, Germany and Australia
Source: Cognizant Research

Younger adults fuel high scores for AI-driven purchasing

AI-driven purchasing in retail shows a sharp generational divide. Younger consumers—those in the 18–24 and 25–34 age groups—are greatly interested in automated purchases, with an AII score of 24. However, this inclination drops off dramatically with older consumers; those aged 45–54 score just 13 for AI use in the Buy phase, and the 55+ age group logs a mere 10.

Industry innovations are moving to meet the demands of the younger generations; for example, Mastercard recently announced its Agent Pay technology, which enables AI agents to make secure payments on behalf of consumers using tokenized credentials. Visa quickly followed with a similar program.

While such developments point to an increasingly automated future, these technologies must be deployed with careful consideration of existing consumer reservations, especially for older age groups.

Small purchases and essentials lead the charge in AI transactions

Consumers of all age groups are selective about the types of purchases they’d automate with AI. The higher-than-average score for the Buy phase is overwhelmingly driven by everyday essentials and small purchases, with AII scores that are 24 and 18 points, respectively, above the global average. Consumers clearly show a strong appetite for AI to handle these frequent, low-risk transactions.

As one respondent said, “I’d be comfortable using AI for retail transactions, [especially for] staple purchases compared to more expensive/one-off purchases where my use would be more targeted.”

Consumers are more resistant to automating large or luxury purchases, with AII scores at or below the global average. The extremely low technology comfort score of just 7 for luxury goods suggests that factors inherent to these purchases, such as high financial value, complexity, the desire for human interaction or validation, and perhaps data security concerns, create significant barriers across age groups.

Consumer income adds a further dimension to AI inclination

Income level is another indicator of AI inclination in the Buy phase. Across the purchase journey, higher-income consumers show a greater propensity to use AI. This suggests factors like disposable income, potential exposure to more sophisticated digital tools and different risk perceptions might shape a consumer's willingness to transact via AI.

The Use phase: Consumers are unexpectedly reserved in AI adoption

Surprisingly, AI inclination scores decline to 51 in the Use phase, from 61 in the Buy phase. This dip in AI interest bucks the trend seen in our cross-industry report, where the Use phase saw an uptick in interest from Buy. It is also contrary to the expectation that AI could markedly simplify these ongoing interactions for consumers.

Automated approaches to tracking orders, reordering, managing returns and subscriptions, and intuitively keeping up with maintenance and resupply could offer touchless experiences that many consumers would likely appreciate. Conversational AI-driven capabilities hold promise for providing real-time troubleshooting, personalized assistance with product usage issues and curated recommendations for relevant add-ons and services.

As such, the Use phase holds substantial potential for AI to enrich and extend the business-consumer relationship through enhanced convenience and proactive support. Retailers would do well to design, demonstrate and communicate the value of AI in this phase to unlock its considerable potential for post-purchase engagement.

Retail AI Inclination Index: The Use phase

Figure 4
Base: 8,451 respondents in the US, UK, Germany and Australia
Source: Cognizant Research

Essentials and small purchases again attract strongest AI interest

As in other purchase phases, everyday essentials and small purchases show the highest AI inclination scores in the Use phase. In this product category, consumers appear open to leveraging AI for automated reordering, managing subscriptions, tracking deliveries and initiating returns. The relatively high AI tool comfort score of 30 for essentials further supports the perceived value of specific AI functionalities for post-purchase management of these products.

Older groups remain hesitant to use AI

The generational pattern observed in the Buy phase persists in the Use phase. Younger consumers, particularly those in the 25–34 age group, show the greatest willingness to use AI for post-purchase activities. Inclination drops significantly among older cohorts, with scores dropping 13 points (from 24 to 11) for the 45–54 age group and 15 points (to 9) for consumers 55-plus.

This suggests older retail consumers may find current AI-powered support tools (like chatbots) less intuitive, prefer familiar human support channels or have greater reservations about AI managing their ongoing relationship with a retailer. This decline among older consumers from the Buy phase to the Use phase contrasts with the findings in our global cross-industry study, where older adults showed a rebound in interest from Buy to Use.

Conversational AI could unlock AI post-purchase interest

Conversational AI achieves its highest scores across all retail product categories for post-purchase engagement. As such, the tool could offer brands a pathway to offering efficient, on-demand support for select interactions without entirely supplanting the human touch.

For instance, even in the low-scoring luxury segment, conversational AI could handle initial queries about product care, schedule appointments for services or provide updates on bespoke orders and then escalate to human agents for more complex or relationship-driven engagement. This approach might address the desire for responsiveness that even luxury consumers expect, while still respecting the core tenets of a high-touch service model.

However, as one respondent observes, the "wholesome" nature of the luxury retail experience, centered on personal interaction, means that AI's role, even best-in-class conversational AI, will likely remain supplementary and highly curated to avoid diminishing brand value. The overarching challenge remains balancing AI efficiency with the deep-seated expectation of personalized human service.

Meeting consumers where they are in retail: Preparing for the agentic age

Consumer use of AI is growing fast, heralding the emergence of consumer AI agents. These agents will increasingly act as personal digital concierges, orchestrating tasks across the complex retail purchase journey, from discovery and comparison to purchase execution and post-purchase management.

So while retailers have been trying to solve for the seamless experience across the physical and digital channels, the internet is now poised to become the agentic internet: an interconnected ecosystem of AI-enabled tools and agents that will autonomously locate, evaluate, purchase and manage products and services, potentially becoming new primary interfaces for commerce.

While AI uptake varies across retail product categories, age groups and income levels, the pace of change necessitates action. We believe retail leaders have less than five years to navigate this transformation effectively, particularly in high-volume categories.

To prepare for the AI-driven consumer era ahead, retail businesses will need to rethink how they operate across these five critical areas:

• Optimize product visibility and data to be discovered by AI agents: Just as the online “everything store” became the dominant point of consumer entry by aggregating offerings and capturing attention, AI agents are set to become the next critical interface. These agents will filter, curate and potentially act as new gatekeepers to product discovery and purchase.
  
  This means big changes for becoming discoverable in the critical Learn phase, where consumers show the highest AI inclination. To capture this interest, and therefore attention, retailers must ensure their product information—including detailed features, specifications, stock levels, pricing and potentially summarized review sentiment—is accurate, structured and easily digestible by AI discovery tools and consumer agents. It will be crucial to optimize for conversational search queries and provide data that facilitates AI-driven comparison.
  
  This is especially true for unexpected needs and large purchases, where research is paramount. Visibility to AI agents will become as critical as visibility on traditional search engines.
  
  
• Aggressively apply AI to automate routine purchases: Young adults (18–34) show an exceptionally high inclination to use AI when purchasing everyday essentials and small items. Retailers should prioritize developing seamless, AI-driven replenishment services, intelligent subscription management and automated checkout processes for these high-frequency, low-risk categories.
  
  Success here requires deep integration with customer preferences and predictive analytics to anticipate needs, offering significant opportunities to build loyalty through unparalleled convenience.

• Integrate consumer-facing AI with back-end operations: The promise of automated purchasing hinges entirely on flawless execution. Retailers must ensure seamless, real-time integration between consumer-facing AI agents or tools and their core operational systems, including inventory management, supply chain logistics, fulfillment centers and payment processing.
  
  Failing to connect the AI front end with operational reality will lead to customer frustration from issues like ordering out-of-stock items or delivery errors, undermining the very convenience AI aims to provide.

• Develop a cohesive omnichannel AI experience: As consumers interact across various touchpoints, the AI experience should feel consistent and intelligent, whether via a mobile app, website chatbot or in-store interactions.
  
  Critically, AI should bridge gaps in the post-purchase Use phase, where inclination currently lags. Retailers need to improve the utility of AI for tasks like managing returns, accessing support (potentially blending AI with human agents for complex issues) and engaging with loyalty programs. Building trust and demonstrating clear value are key to increasing AI adoption here, particularly among older consumers.
  
  
• Segment the AI strategy and prioritize trust: A one-size-fits-all AI strategy will fail in retail. Businesses must tailor their approach based on product category, consumer demographics (age and income) and purchase journey stage.
  
  Deploy automation boldly where consumers demand it (essentials, small purchases, younger consumers). Conversely, for large and luxury purchases, where AI inclination plummets, focus AI efforts on augmenting the human experience, providing rich information in the Learn phase, and ensuring transparency and security. Addressing the trust deficit, particularly around transactions and data use for high-value items, is paramount for these segments.