How AI will revamp the healthcare customer journey

For consumers, the pursuit of healthcare is often a high-stakes endeavor, fraught with anxiety. Many struggle to navigate complex health systems or endure long waits and care gaps, often reflective of overburdened healthcare resources. Even routine tasks like booking an appointment, understanding test results or monitoring a chronic condition can be confusing or time-consuming.

Especially as more care is shifted from medical facilities into homes, consumers would likely welcome a newly intelligent way to manage their health. That way is emerging as consumers increasingly use AI. Whether it’s helping people interpret medical jargon, obtain personalized and proactive health insights, get a preliminary diagnosis or stay on track with a care plan, AI can benefit consumers and health systems alike. 

But the growing use of AI among consumers poses a challenge for healthcare businesses. Who is most (and least) inclined to use AI? Which tool would they prefer to use? And where in the healthcare journey would they be most comfortable using it?  

Our latest research surfaced some unexpected insights into those questions. Using data from our recent consumer AI study, we developed the AI Inclination Index (AII), which quantifies consumers’ propensity to use the technology (see explainer box below). 

While the index reveals that consumers are somewhat less inclined to use AI in healthcare than in other industries (see Figure 1), that broad finding masks important variations. Consumer attitudes toward AI differ significantly depending on where they are in the healthcare journey (that is, whether they’re learning about, buying or using products and services) and across the three healthcare categories included in our study: 

• Healthcare services (primary, specialty and emergency care, physical therapy)
• Condition diagnosis (online services, at-home testing kits)
• Healthcare monitoring (wearables, in-home monitoring)

Consider these metrics in the healthcare consumer experience:

• Healthcare consumers are most apt to use AI in the Learn phase. As consumers are increasingly called upon to be their own health advocate, they see AI’s value in helping to identify the right monitoring device, the best place to get medical care or the most accurate condition diagnosis product or service. Healthcare consumers are also more inclined than the global average to see the value of AI in the Use phase, when they are engaging with treatment plans or at-home devices.
  
  
• Older consumers are more inclined than younger ones to use AI when learning about and using healthcare products and services. While people 55+ might be considered less tech-savvy than younger consumers, they are generally more intensive users of healthcare services. As such, they have a greater interest in tools that would improve their healthcare experience.
  
  
• Conversational AI is often the tool of choice. Whether through chat or voice, consumers want experiences that feel human, especially when dealing with complex or sensitive health concerns.

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 healthcare vs. the global average

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

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

With these variances, it’s clear healthcare businesses will need to craft a highly 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 its impact on consumer spending power, is essential for leaders in all industries, including in healthcare. In our global cross-industry study “New minds, new markets,” we found that consumers who are enthusiastic about AI will soon 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, healthcare leaders will learn about where consumers are most and least inclined to use AI, the AI tools they are most apt to use and how this differs across age groups. With this information, businesses can reshape their approach to patient engagement, where and how it matters most.

AI across the healthcare consumer journey

The consumer journey—discovery, purchase and post-sale engagement—is at the heart of the business-customer relationship. While the “Learn, Buy, Use” framework might not seem immediately applicable to the healthcare journey, these three stages offer a useful way to explore where AI might support people in managing their health.

In healthcare, “Learn” is the information-gathering stage, when patients are trying to make sense of symptoms, weighing treatment options and identifying the best service or device for their specific needs. Our AII reveals that consumers are most enthusiastic about using AI in this stage. AI can help personalize and streamline this research and help consumers feel more supported and informed than doing an elongated web search.

The Buy phase is where decisions get made. That might mean booking an appointment, ordering a condition diagnosis product or service, or purchasing a monitoring device. Consumers are more wary of using AI in this phase, which often involves complex, emotionally loaded choices that require a high level of trust.

The Use phase encompasses everything that happens during and after treatment: understanding test results, following a treatment plan, accurately logging results from a wearable health monitor, booking follow-up appointments and more. It’s the long tail of care, often managed at home. Our research shows that consumers see AI’s potential to provide practical, everyday help in this post-purchase stage.

This patient-centered framework is important to keep in mind as we review how consumers feel about using AI across all three product categories of the healthcare industry.

The Learn phase: Complex healthcare choices drive interest in AI

• Older consumers are the biggest AI enthusiasts in the Learn phase
  
  
• Health monitoring captures the highest AI interest
  
  
• AI in healthcare services is close behind
  
  
• Conversational AI is the tool of choice

The Learn phase is when consumers show the greatest openness to AI-enabled tools in healthcare—by far. While the AII score for Learn (averaged across all three product areas) is 86, it plummets to just 48 for the Buy phase, then rises to 54 for Use. 

Consumers daunted by the complexity of health choices are drawn to the idea of AI making the process less overwhelming.

This highlights the Learn phase as a critical window for healthcare businesses; it’s their opportunity to use AI to engage with consumers and help shape their health decisions.

Healthcare AI Inclination Index: The Learn phase

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

Older consumers are the biggest AI enthusiasts in the Learn phase

Of all the age groups, consumers 55+ demonstrate a greater interest in using AI tools to learn about their healthcare options. This is likely due to their more intensive experience with using healthcare. In the US, individuals 55+ account for less than one-third of the population but over half of total health spending. So while the older cohort may be less tech-savvy than younger consumers, it stands to reason they will have a greater interest in tools that would improve their healthcare experience. 

This cohort also recognizes AI's potential to reduce the burden on increasingly understaffed health systems and enable clinicians to spend more time on direct patient care. They see the value of AI helping them find information, understand their options, cut through medical jargon and streamline their path to receiving care.

Health monitoring captures the highest AI interest

Among the three product categories in our study, consumers are most interested in using AI to learn about health monitoring devices, with an AII score nearly equal to the global average. This is likely due to the wide range of monitoring technologies available, from specialized sensors that track glucose levels and blood pressure, to non-health-specific devices that measure heart rate and sleep cycles. 

AI tools could help cut through the noise of often contradictory product claims and offer personalized selection guidance tailored to individual health profiles, lifestyle preferences and medical conditions.

AI could also provide needed patient education for the increasing array of remote home monitoring devices geared toward chronic disease management and treatment adherence. Consider a patient learning about the use of a “digital pill,” which uses an ingestible sensor and a tracking device to precisely record the date and time the medication was taken. Such advanced monitoring technologies will require full patient compliance and understanding. AI could reduce uncertainty by providing clear explanations about how such systems work, helping patients feel more informed and comfortable.

AI in healthcare services is close behind

Consumers are almost as enthusiastic about using AI to learn about the various healthcare service options available to them, navigate appointment systems or simply figure out the right type of care for their needs. This interest in AI likely reflects the common challenges patients face in accessing clear, relevant information about the support available across the healthcare spectrum.

For example, a conversational AI interface could help determine whether a general practitioner or specialist is needed, or which walk-in center offers the services the consumer needs.

Conversational AI is the tool of choice

Conversational AI emerges as the preferred tool in the Learn phase, with high AII scores across all three product categories. This preference isn't confined to tech-savvy consumers; individuals who don’t tend to embrace the latest technology are even more inclined to use conversational AI tools to learn about their healthcare options than self-professed tech enthusiasts.

This could be because less technically adept consumers are especially apt to find the dialog format non-intimidating and reassuring. As one older and admittedly tech-averse respondent said, “AI could provide an outline of services to be considered. The benefits might be to alert you to different options available.”

Voice assistants, meanwhile, earn low scores for learning, as do chatbots, especially among younger cohorts, who likely prefer a more sophisticated interaction when exploring service details. Especially for sensitive health queries, typing into a conversational AI interface might feel more private or allow for more detailed information exchange compared with speaking aloud to a voice assistant.

Trust in the specific platform (such as a dedicated health app featuring conversational AI capabilities) and the quality of the AI-driven responses also play crucial roles. In the end, simply having “conversational” technology isn't enough; the design, perceived intelligence, appropriateness for the task and the sense of trust a product engenders will be key differentiators driving consumer comfort during healthcare learning.

The Buy phase: A sharp drop in AI inclination

• AI in medical diagnosis triggers the greatest consumer caution
  
  
• Younger consumers are the least comfortable with AI in this phase
  
  
• AI tool comfort scores offer a vision for the future

The Buy phase is when consumers are completing a healthcare transaction, such as finalizing the choice of provider, booking an appointment, ordering a condition diagnosis product or purchasing a monitoring device. This is where we see the most pronounced hesitation from consumers about using AI in healthcare. 

Although scores for all three components of the AII drop significantly compared with the Learn phase, the 20-point decline in the technology comfort score is particularly notable. This drop in comfort at the Buy phase aligns with the findings of our original study, where consumers in all industries were reluctant to give up control to AI when finalizing a decision.

The industry comfort score is also notably lower for healthcare compared with the global average, which reflects an acute sensitivity to data privacy in this domain. As one consumer said, "I’m a little worried that when someone I don’t know or trust has access to my medical records, they could monitor me, or the information could be used against me at a later date."

Healthcare AI Inclination Index: The Buy phase

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

AI in medical diagnosis triggers the greatest consumer caution

Consumers are particularly sensitive about the use of AI when deciding on and purchasing home diagnostic products and services. Here, patients strongly prefer direct involvement from a healthcare professional. This caution likely stems from underlying concerns about the accuracy, privacy and potential consequences of misdiagnosis. 

The array of home diagnostic tests is expanding; they can detect everything from diabetes to fertility to genetic ailments to skin cancer. Deciding on one of these products is a high-stakes action, with significant health implications. Research consistently shows patients require a high degree of trust and transparency before committing to medical procedures or tests; it would take a very sophisticated AI system to convey the clear, empathetic explanations that a medical professional can offer when making a diagnosis. Moreover, the purchase itself may heighten privacy concerns, as consumers might worry about sensitive diagnostic choices being linked to transactional data within an AI-driven system. 

Of course, healthcare providers are increasingly using AI for medical diagnostics, without ever informing the consumer or asking for consent. The difference is that a human medical practitioner is always involved, unlike how it would work in a home setting with the consumer interacting just with the AI.

Younger consumers are the least comfortable with AI in this phase 

Typically seen as digitally fluent and comfortable with new technologies, the youngest consumer cohort (aged 18–25) expresses surprisingly significant reservations about relying on AI at the Buy stage. Their technology comfort score for healthcare services, for instance, is just 23, markedly lower than the 25–34 age group’s score of 34 and the 35–44 cohort’s score of 39. This finding challenges assumptions about digital natives' universal acceptance of tech. 

While younger generations enthusiastically embrace digital tools in other contexts, their lower use of healthcare services overall could make them less confident when making these types of decisions—and thus more likely to prefer human interaction.

AI tool comfort scores offer a vision for the future 

Even as overall AII scores drop notably during this phase, a closer look at the AII reveals an important nuance: In all three product categories, consumers’ comfort with using AI (reflected in the AI tool comfort component in the index) is much higher than their historic comfort with using other digital technologies, such as their mobile phones (reflected in the technology comfort component).  

There is a wide discrepancy—anywhere from 18 to 20 points—between the technology comfort and AI tool comfort scores. For healthcare services, the AI tool comfort score is 28, and that plummets to 10 for technology comfort. A similar pattern exists for both monitoring and condition diagnosis, where AI tool comfort scores are 28 and 26, respectively, and the technology comfort scores are 9 and 6.

These score gaps are much narrower in the Learn phase. There, the gap between AI tool comfort and technology comfort scores is just a couple of points.

This suggests that consumers see the potential AI has to offer at this stage and that a well-designed AI interface could help individuals overcome a historical aversion to engaging digitally at this critical, sensitive stage of the healthcare journey.

The Use phase: The value of AI is clear for ongoing health management

• Older consumers’ interest in AI increases significantly
  
  
• Health monitoring devices emerge again as the most promising area for AI
  
  
• Significant trust challenges remain for AI in medical diagnosis

Consumer interest in AI rebounds during the Use phase. In fact, for healthcare services and monitoring, scores exceed the global average. 

Consumers, especially older generations, clearly see the value of using AI for ongoing support in managing their treatments, whether through reminders, follow-up scheduling or assistance in using medical or monitoring devices correctly. Such ongoing support is particularly valuable, as it can help maintain adherence to medication regimens and potentially prevent costly hospital readmissions.

In condition diagnosis, however, enthusiasm remains muted, reflecting broader concerns about data sensitivity and the perceived need for professional oversight in the diagnostic process.

Healthcare AI Inclination Index: The Use phase

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

Older consumers’ interest in AI increases significantly

Older consumers (55+) are much more interested in using AI than younger ones during the Use phase, particularly in healthcare monitoring, where there’s a 14-point score gap between the oldest and youngest groups. In health services, older consumers similarly are more likely to see the value in AI-driven support, such as medication reminders or automated scheduling based on their real-time health data. 

Here again, consumers are aware that by using AI, they could free their physicians to give them more attention and personal care. In one study, 72% of patients said it would be worth using AI tools that would help health practitioners focus more on quality patient care.

Health monitoring devices emerge again as the most promising area for AI

Similar to the Learn phase, consumers in all age groups express the greatest enthusiasm for AI when it comes to using health monitoring devices to increase the usability, accuracy and usefulness of these devices. As one consumer said, "They could offer personalized health insights and even predict potential health issues.”

AI could provide them with personalized health recommendations and alerts based on health data collected by the monitoring device. AI-powered conversational interfaces and virtual assistants could also instruct patients on the correct use of devices and provide timely feedback to improve accuracy and health outcomes. Given the increased prevalence of chronic health conditions that are managed at home, AI-enabled monitoring could play a critical role in proactive, rather than reactive, health management.

Significant trust challenges remain for AI in medical diagnosis

Consumers, once again, are less sure of using AI for condition diagnosis—the one product category whose AII score falls below the global average in this phase. This is likely attributable to the perceived risk of highly sensitive diagnostic data being misused by third parties or exposed in a security breach. Unlike health monitoring solutions, which measure biological markers, condition diagnosis products and services draw conclusions that, in some cases, could have a stigma attached. Consumers are unwilling to share that type of information without clear oversight of how it will be used. 

At the same time, there is some recognition of the benefits of incorporating AI into diagnostic processes. As one consumer said, when AI is presented with a wide range of different symptoms, “it can provide a possible diagnosis and reasoning for that diagnosis. And then I could just keep on providing information to narrow down what's my best course of action.”

Meeting consumers where they are in healthcare

Consumer use of AI is growing fast, and so is the use of consumer AI agents. These agents will act like a personal digital concierge, orchestrating complex tasks across the purchase journey. Soon, the internet as we know it will become the agentic internet: an interconnected ecosystem of AI-enabled tools and agents that autonomously locate, evaluate, purchase and maintain the products and services we rely on.  

While consumer AI uptake may be somewhat slower in the healthcare industry, we believe leaders have less than five years to navigate this change.  

To prepare for the AI-driven consumer era ahead, healthcare organizations will need to rethink how they operate across these four areas of consumer engagement: 

1. Tailor strategies by age group and AI inclination levels. When developing AI strategies, healthcare organizations need to be sensitive to the differing attitudes of various consumer demographics when it comes to technology and AI.
   
   With their more intensive use of healthcare resources, older consumers (55+) often value tools that make health management more straightforward. Younger age groups (25–44), meanwhile, tend to favor AI tools that increase speed and convenience.
   
   Aligning AI features with real-life routines, whether through mobile-first interfaces, integrated symptom triaging or quick appointment booking, will increase relevance and usability across demographics.
   
   
2. Prioritize trust-building in condition diagnosis. Even while AI is increasingly used in diagnostic support, patients remain wary of AI-infused diagnostic devices, often due to concerns around misdiagnosis, lack of oversight or misuse of sensitive data. Addressing these concerns requires deliberate transparency about when and how AI is being used, what data it draws on and the extent to which a clinician will be involved.
   
   Trust can be further strengthened by offering AI-assisted diagnosis as part of a blended care experience, where patients know that a healthcare professional will review, confirm and contextualize AI recommendations.
   
   
3. Bridge the gap between high comfort and low usage. Consumers’ inclination to use AI will not always supersede their comfort with traditional approaches to learning about, buying and using healthcare products and services. This gap is particularly evident in healthcare services, where consumers tend to default to manual or offline methods.
   
   To help them make the transition to AI, healthcare organizations should assess where consumers are most inclined to use AI and reduce friction through better integration, onboarding and support. Making AI a seamless extension of everyday healthcare behaviors, rather than a separate digital destination, will be key to driving uptake.
   
   
4. Leverage AI to reduce administrative friction and improve access. Healthcare organizations should strategically implement AI-powered solutions to diminish the administrative load associated with patient interactions, particularly in areas like appointment scheduling and management. These will be especially welcomed by older demographics, who appreciate tools that simplify their healthcare experience and recognize AI’s potential to ease system pressures. 
   
   Deploying intuitive conversational AI interfaces for booking, modifying or confirming appointments will not only enhance patient convenience and satisfaction by offering 24x7 access and immediate responses, but it will also free up valuable administrative staff time. This allows personnel to concentrate on more complex patient needs and direct care activities, ultimately contributing to a more efficient and patient-centered healthcare journey. 
   
   This approach directly addresses the need to bridge the gap between consumer comfort with AI and its practical usage, making routine tasks less time-consuming for patients and less burdensome for the healthcare system.