Figure 1
Our analysis uncovered demographic details that are statistically most likely to be associated with each of these groups. The characteristics are independent of each other, and do not necessarily apply to each consumer in the groups.
Base: 8,451 respondents in the US, UK, Germany and Australia
Source: Cognizant and Oxford Economics
Figure 1
Our analysis uncovered demographic details that are statistically most likely to be associated with each of these groups. The characteristics are independent of each other, and do not necessarily apply to each consumer in the groups.
Base: 8,451 respondents in the US, UK, Germany and Australia
Source: Cognizant and Oxford Economics
As Accelerator and Early Adopter purchasing power grows, both groups will become agents of transformation across industries. However, Anchors will continue to be an important consumer group for businesses to retain and serve.
Meanwhile, our research also reveals insights that are true across all four consumer cohorts. These findings are important for businesses to consider as they determine the best way forward for interacting with consumers using AI:
Convenience drives adoption
For all consumers, convenience is the strongest driver of AI adoption. Frustration with the buying process, a problem noted by 75% of all respondents, will push consumers toward AI solutions that save them time. In fact, 22% of consumers cited time savings as their top reason for using AI compared with just 12% who see it as a way to find the best deal.
Control is critical
Even with their desire for convenience, consumers require a sense of control, especially when it comes to finalizing the purchase selection and transaction. A striking 75% say they are unlikely to allow AI to automatically reorder and pay for high-value items without their direct authorization.
Familiarity breeds trust
Our research suggests that incorporating AI into familiar devices and user experiences such as smartphones and apps could encourage AI adoption across cohorts. Consumers have already become comfortable with a wide range of sensitive and relatively risky activities on their mobile devices, including banking services (which 52% of respondents are comfortable doing) and booking travel (44%). The same dynamic could unfold with AI. As it integrates seamlessly into familiar devices and consumers gain more exposure to AI-powered tools, their distrust and fear may diminish, too, making them increasingly receptive over time.
A composite score of consumers' comfort with using an array of AI tools across the three stages of the purchase journey and 36 products and services in 15 industries, based on survey data and in-depth consumer interviews.
Higher number = more comfortable
Lower number = less comfortable
Consumers are most comfortable in the Learn phase, followed by Use and Buy.
This discovery phase is where consumers feel most comfortable with AI-enabled tools, achieving an average Comfort Quotient of 47. As such, Learn is the first and most critical phase for businesses to focus on. However, there are vast differences among cohorts. Early Adopters and Accelerators have much higher scores of 58 and 50, respectively, while Anchors have a score of 33.
This decision-making phase is where we saw the most hesitation from consumers. The average Comfort Quotient for the Buy phase is just 27. Even Early Adopters show an overall reluctance to use AI to execute on purchases, with a comparatively low score of 34. Anchors are particularly hesitant, with a score of just 16.
Consumer comfort with AI rebounds in the post-sale engagement phase, reaching a Comfort Quotient of 39. This score is buoyed predominantly by an eager Early Adopter cohort, with a quotient of 47, but also by a surprisingly strong showing by Anchors. With a score of 34, Use turns out to be where Anchors are most comfortable using AI.
Figure 2
Base: 8,451 respondents in the US, UK, Germany and Australia, as well as 80 in-depth consumer interviews
Source: Cognizant Research analysis of data collected by Oxford Economics and YouGov
Frustration with traditional search leads to a desire for convenience
Convenience matters at this stage as saving time is the top reason consumers cite for using AI in the Learn phase. In a crowded market, consumers are keen to find what they’re looking for swiftly—whether that’s asking a voice assistant to suggest options based on their personal data or using visual search to identify a brand or product they screenshot on social media.
AI-enabled tools that deliver on consumer desires for a shorter, streamlined discovery process—such as voice assistants, conversational AI, personalized recommendations and visual search—ranked highest in consumer comfort levels (see Figure 3). Here, AI functions as an assistant, curating options and streamlining the discovery process.
In our qualitative research, consumers praised tools such as conversational AI for simplifying complex searches (“It’s a great tool to find and understand something complex with little exploration”) and visual search for speed (“It makes it convenient to find specific products I don’t know the name of”).
Consumers don’t always trust what’s behind AI search results
But there’s another side to this coin: Amid their quest for convenience, even Accelerators and Early Adopters stress the need for privacy, trust and transparency. Dynamic pricing and targeted ads, for example, earned low comfort ratings due to perceptions of the potential for manipulation and profiteering. “It is scary to think that every single thing I search for is being stored … and being used to create targeted digital advertising,” one respondent said. Personalized recommendations, while praised for their relevance, leave little room for error; irrelevant suggestions can quickly erode trust.
Consumers also question the true motivations of AI—is it really making the best choice for them, or is it pushing them to patronize a company’s partners or more lucrative vendors? Across all consumer groups, one of the most commonly cited concerns is that AI isn’t working in their best interests, with 13% of consumers rating it as their main worry.²
Figure 3
Base: 8,451 respondents in the US, UK, Germany and Australia
Source: Cognizant and Oxford Economics
Figure 3
Base: 8,451 respondents in the US, UK, Germany and Australia
Source: Cognizant and Oxford Economics
Business implication
A new front in the battle for mindshare has been opened, and the old ways of getting on consumers’ radars are becoming obsolete. To avoid getting sidelined, businesses will need to orchestrate an operating model that ensures their products, services and experiences are surfaced by consumers using AI-driven tools while also remaining open to traditional avenues of product discovery, at least in the short term.
Consumers fear giving up control
Consumers fear the repercussions of yielding control to AI and allowing it to make purchases on their behalf. They're concerned, for instance, that AI might make incorrect or expensive decisions that are difficult to correct or undo, such as ordering more items than desired. Even consumers who are comfortable with AI automatically selecting which coffee brand to order, for instance, would still want to be in charge of the seemingly trivial action of clicking “buy.”
In our qualitative interviews, consumers voiced such fears, with mentions of errors and inaccuracy coming up frequently. "Voice assistants often misunderstand commands. They don't have a screen for me to validate that I've done the correct thing," one respondent complained. Data security is another concern. “I don’t know if I feel comfortable letting AI use my information and storing it to make payments easier,” another said.
Distrust of what motivates AI decisions is also prevalent. "I have a feeling that the algorithm will follow instructions from the highest bidder and not handle the purchase in my interest,” said one consumer.
Comfort levels plummet, even for popular AI tools
Even AI’s most enthusiastic users are wary of relinquishing decision-making power during the Buy phase. Compared with the Learn phase, Comfort Quotients for Accelerators and Early Adopters drop from 50 to 33 for the former and from 58 to 34 for the latter. More specifically, while 41% of Accelerators (and 52% of Early Adopters) are comfortable using conversational AI during the Learn phase, comfort with AI drops to just 33% and 46%, respectively, in the Buy phase (see Figure 4). A similar dynamic is true of voice assistants, which drops from 38% for Accelerators and 53% for Early Adopters for learning to 30% and 42%, respectively, when it comes to buying.
Interestingly, technologies that add security verifications such as facial recognition compete closely with other technologies, especially among Anchors, in the buying stage. One consumer called it “an extra layer of verification and protection.”
Figure 4
Base: 8,451 respondents in the US, UK, Germany and Australia
Source: Cognizant and Oxford Economics
Figure 4
Base: 8,451 respondents in the US, UK, Germany and Australia
Source: Cognizant and Oxford Economics
Business implication
Consumers are adamant about maintaining control during the Buy stage. While it is possible their reticence may evolve over time as confidence in the security and objectives of AI solutions increases, such distrust currently poses a challenge. Businesses will need to strike a balance between automation and ceding some control to customers.
Consumers like the idea of products and services taking care of themselves
Consumers—perpetually looking to save time—are drawn to the idea of AI automating the upkeep, resupply, updating and enrichment of the products and services they use. Having AI involved at this stage often translates to touchless experiences. In the real world, this could be a refrigerator that restocks commonly used items without supervision, a car adding a personalized feature to save on fuel or a home heating system that senses an error code and emails it to the manufacturer for a specialized fix.
In each example, the consumer experiences significant time savings as well as less aggravation. As one consumer pointed out, “I love the idea of AI helping me use products and services because I can do other things with the time it saves me.”
For consumers, it will feel like AI is an invisible background force simplifying their lives. As one respondent put it, “AI doesn’t even need to be mentioned … it just is.”
But they draw a line at automatic repurchasing
Despite the higher comfort levels in the Use phase, resistance to fully automated post-sale purchases remains. As in the Buy phase, the desire for control kicks in again, with consumers expressing concern that AI won’t act in their best interest, such as reordering supplies at a premium simply to boost revenues instead of serving their needs. One consumer acknowledged that “while the convenience of AI repurchasing products and services is appealing, I have concerns about privacy and control over my purchases. Could it misinterpret my needs and send unwanted products?”
While Accelerators were most likely to be comfortable with automated reorders and payments, their comfort levels decline as prices increase. For instance, 43% of Accelerators are comfortable with AI reordering low-priced items ($25), compared with 36% who are comfortable with reordering medium-priced ($100) items. Only about one-third (32%) felt comfortable with high-priced reorders ($500). Among all consumers, the Comfort Quotient drops to 28%, 22% and 18%, respectively, across the respective price tiers.
Figure 5
Base: 8,451 respondents in the US, UK, Germany and Australia
Source: Cognizant and Oxford Economics
Figure 5
Base: 8,451 respondents in the US, UK, Germany and Australia
Source: Cognizant and Oxford Economics
Business implication
The higher Comfort Quotients in the Use phase are indicative of consumers’ willingness to engage far beyond the sale. This signals an opportunity for businesses to build customer relationships while also driving familiarity with the use of AI tools, which will encourage further adoption.
The Use phase could also introduce new revenue streams both in aftermarket services and automated reordering. But these mechanisms could also prove disruptive for intermediaries. New original equipment manufacturer (OEM)-to-consumer channels will take business from traditional retailers, and those companies that lack partnerships with smart product manufacturers will be at risk of missing out.
* Accelerators will see their consumption levels increase from $4.1 trillion to over $4.4 trillion in the US alone by 2030.
Figure 6
Source: Cognizant Research with economic data provided by Oxford Economics
* Accelerators will see their consumption levels increase from $4.1 trillion to over $4.4 trillion in the US alone by 2030.
Figure 6
Source: Cognizant Research with economic data provided by Oxford Economics
A bed buying journey
Figure 7
Source: Cognizant Research analysis
Figure 7
Source: Cognizant Research analysis
At the Learn phase, consumers gravitate toward AI tools that reduce choice overload frustration. Capturing their attention at this critical junction will require new rules of engagement.
For instance, as AI-powered tools such as voice assistants, visual search and conversational AI are increasingly used to research and discover products, businesses will have to take a multimodal approach to conveying their product and service information. Product descriptions will need to be both concise and easily understood when read aloud by a voice assistant or relayed via a conversational AI tool, while also incorporating high-quality images and videos optimized for visual search.
Once businesses have made themselves known in the discovery phase, it’s time to move to the Use phase, where the consumer Comfort Quotient is also high. Doing so extends the customer relationship to after the sale, giving businesses the opportunity to fortify trust through continued AI-driven engagement with the product or service.
Businesses can then build on that trust to move into the Buy phase, encouraging consumer confidence in using AI to automate purchases but with a sense of user control. By extending the positive experience of AI-assisted learning to encompass product usage and the purchase transaction itself, businesses can create a cohesive customer journey fit for the AI age.
The most important AI project your company will need to undertake may not be implementing AI internally but ensuring your products and services are discoverable on AI-driven platforms. As consumer AI agents become commonplace, businesses must have AI agents ready to interoperate with their consumer counterparts. This will require new infrastructure and data capabilities.
This necessitates a deep understanding of how AI is being used within consumer platforms, and how your product offerings can be embedded within those environments. Simply creating an in-house solution, no matter how robust, will be insufficient if it doesn't interact with platforms where consumers are already engaging with AI.
To make offerings AI-discoverable, businesses should consider partnering with leading AI providers to promote products and services during AI-powered discovery and collaborate on features that ensure visibility in high-value environments.
This level of integration requires businesses to create APIs that integrate with external AI platforms and exchange data with consumer AI agents. Simultaneously, infrastructure investments will be needed to handle the increased data demands of interacting with these agents and platforms. Businesses also need to understand the specific data requirements of disparate AI platforms and adapt their data strategy accordingly. This will mean enriching product data, creating new data sets and implementing real-time data feeds to ensure compatibility and optimal performance.
By embedding their offerings into broader AI networks, companies can maintain visibility and relevance as consumers increasingly rely on AI to guide their purchasing decisions.
For consumers, the primary driver for using AI tools is convenience—not price. While some may question whether consumers will always prioritize time over cost, this dynamic presents a significant opportunity for businesses to attract time-starved customers with a design-for-convenience mindset. Every experience leveraging AI must be fast and frictionless—for the consumer, and not just for the organization.
AI-powered tools are already particularly well-suited to deliver convenience and speed through hyperspecialized recommendations, instant product comparisons and quick and reliable checkout processes. New agentic capabilities will only accelerate this.
By integrating AI functionalities and agents that simplify complexities within the purchasing journey, companies can capture the interest of time-sensitive consumers, regardless of their existing attitudes toward AI or prior experience with the brand.
Just as consumers have grown to trust mobile interfaces for high-stakes tasks, AI will gain acceptance as it becomes accessible through a variety of familiar and reliable devices, apps and platforms. To achieve this, businesses should prioritize an architecturally “headless” approach to AI, making its capabilities available everywhere and seamlessly integrating them into existing platforms and devices that feel natural to consumers, such as smartphones, voice assistants and laptops. This way, users can engage with AI through their preferred channels, mirroring the versatility of mobile internet access.
As consumers become more accustomed to using AI in these trusted environments, their overall comfort with AI-powered decision-making in other areas of their lives will likely increase, even for high-stakes functions, such financial transactions or travel planning.
Rather than feeling forced to use AI in certain contexts, consumers would likely embrace AI applications that feel like a natural extension of the tools and devices they already use.
Even consumers who are comfortable using AI still value human interaction during critical moments. This preference is particularly evident in high-stakes purchases like healthcare or financial services, where trust and accountability are paramount.
A set-it-and-forget it AI strategy simply won't cut it. The winning approach is a hybrid one that blends AI and human interactions. For instance, design customer experiences that incorporate AI for efficient product information gathering and initial inquiries. Then, ensure a smooth transition to human experts for complex questions, nuanced decision-making, and when empathy and a personal touch make all the difference.
By emphasizing human oversight and accountability in their AI implementations, businesses can create a customer experience that works for a wide range of consumers and situations.
One-third of people in the world still have no access to the internet. For these people and other marginalized populations who don’t have the ability to adopt the latest technology, the transition to AI-powered purchase journeys and the agentic internet may feel like a leap they’re unable to make.
This evolution could feel alienating enough when it comes to a favorite retailer or entertainment experience, but the issue becomes more acute when access to vital services becomes AI-influenced by default. Government services delivered via AI-brokered platforms, for example, or healthcare services offered only through the latest wearable technologies all present significant challenges to equitable access.
Chief among business concerns, then, should be avoiding a wider digital divide within both developed markets and globally.
One approach is to continue offering alternatives to AI-enabled experiences at key stages of the purchase journey. For example, businesses could integrate interactive AI tools into their brick-and-mortar locations to familiarize these groups with AI-enabled technology. Such an approach will ensure equal opportunity of access for all consumers until a greater level of education and democratization occurs in the usage of AI-enabled technology for all.
Endnotes
¹ The Comfort Quotient is a score developed by Cognizant Research in order to quantify consumers’ willingness to engage with AI at each stage of the buyer’s journey. It focuses on analysis of the age groups most typically associated with each cohort (see Figure 1), with the exception of Early Adopters, who are identified by their stated willingness to be the first to use technology. The Comfort Quotient is based on Cognizant Research’s analysis of the survey data and qualitative findings, including respondents’ comfort levels with using numerous AI tools in the Learn, Buy and Use phases, and across 36 different products, services and experiences in 15 industries.
² Respondents were asked to select only their top reason for using or not using AI.
Catrinel Bartolomeu
Head of Thought Leadership Editorial
Mary Brandel
Editor
