AI as a change agent in an aging society: toward the sustainable behavior of service organizations and customers

The global population is undergoing a significant demographic shift marked by a rapid increase in the proportion of older adults (Bateson, 2021). According to the United Nations, the number of people aged 65 and older is projected to double by 2050 (United Nations, 2019), reaching over 1.5 billion. This unprecedented demographic shift is resulting in numerous economic, environmental, and social sustainability challenges across various service sectors, including healthcare, social services, banking, hospitality, and transportation (McKinsey Global Institute, 2016). The rising demand for accessible and highly personalized services, particularly in healthcare and social services, not only stresses existing service systems but exacerbates resource inefficiencies and escalates costs. Traditional service provision models are becoming increasingly unsustainable due to labor shortages and inefficient resource allocation, putting a strain on service ecosystems and frequently leading to inadequate service delivery to older adults (i.e. individuals over 65 years old (Kabadayi et al., 2020). It is therefore evident that new approaches to service are required. Ensuring that the growing population of older adults can receive essential services while simultaneously maintaining planetary sustainability requires systemic change (Senge et al., 2007).

This need for systemic change is reflected in the 2030 Agenda for Sustainable Development, which aims to guide policy makers, organizations, and individuals toward more sustainable behavior. The agenda’s mission to “transform our world” underscores the urgency of systemic change, as current modes of operation still fall far short of respecting the planet’s ecological limits (Persson et al., 2022). Addressing these grand challenges - “major scientific tasks that are compelling for both intellectual and practical reasons, that offer potential for major breakthroughs on the basis of recent breakthroughs in science and technology, and that are feasible given current capabilities and a serious infusion of resources” (National Research Council et al., 2001, p. 2) - including those brought about by demographic shifts, will require the concerted effort of policy makers, organizations, and individuals (Reppmann et al., 2024). While steps have been taken to mitigate the environmental impact of service provision, the rate of change remains insufficient (Koskela-Huotari et al., 2024). More rapid and effective action is needed not only to sustain the ecosystem but also to ensure the well-being of an aging global population.

The rapid rise of artificial intelligence (AI) and data analytics aid in building the capability of providing hyper-personalized services to fit the unique and complex needs of customers ^[1] (McKinsey, 2021), and the development of social engineering techniques, such as smart nudging (Andersen et al., 2018), may help in achieving some of the sustainable development goals (SDGs) (Vinuesa et al., 2020). Hence, there is a need to better understand how to integrate knowledge of AI, aging, and sustainability to create services that cater more effectively to the needs of service organizations, older adults, and future generations. By reviewing and integrating relevant literature, this article seeks to answer how AI can be used in service innovation and provision to address major threats to sustainability, particularly those caused by an aging global population.

This article explores how AI can facilitate the increased development and use of sustainable services in an aging society through smart nudging (Andersen et al., 2018) and hyper-personalization. Building on the service triangle (Kotler, 1994; Parasuraman, 2000), this article develops a conceptual framework for sustainability and behavioral change, identifying principles and practices that drive behavioral change and learning among four key actors: AI, managers, employees, and customers. In doing so, this article contributes knowledge on how AI can be used to nudge managers, employees, and customers into more sustainable behavior. For example, an AI shopping assistant might highlight eco-friendly products based on a customer’s past purchases and browsing history, thereby encouraging more sustainable consumption patterns, especially for older adults. From an organizational perspective, integrating AI seamlessly into daily workflows can make sustainable practices the default option for managers and employees. For instance, AI-driven procurement tools can automatically suggest eco-friendly alternatives to non-sustainable materials, ensuring green procurement. In meetings, AI assistants can recommend shorter durations or virtual formats to conserve energy. Through an in-depth study of sustainable behavior across service industries, the authors provide knowledge on how society can improve service provision and sustainability by using AI to nudge managers, employees, and customers. This will help in ensuring society is well-positioned to serve the growing population of older adults in a sustainable way. Furthermore, this article seeks to inspire further research and development in this critical area through the provision of a research agenda.

This article addresses the challenges posed by shifting demographics, particularly an aging population. It also explores how AI can facilitate smart(er) nudging and hyper-personalization, proposing “learning” as a foundation for systemic change toward greater sustainability. A conceptual framework integrating these concepts is subsequently applied across various cases, illustrating its potential to support sustainable change in (a) transportation; and (b) healthcare. By examining both the opportunities and challenges of AI-driven smart nudges, this article aims to offer a comprehensive overview of AI’s transformative potential in service provision for an aging population, ultimately contributing to a more inclusive and sustainable world.

In this section, key concepts, such as aging, sustainability, AI, and smart nudging, are discussed. These are the theoretical foundations of the conceptual framework titled “AI service triangle”.

As a consequence of demographic trends, the global population is aging rapidly—that is, the proportion of older adults in society is substantially increasing (Bateson, 2021). Older adults comprise a growing and highly heterogeneous group that increasingly suffers from combinations of chronic illnesses and physical deterioration and that has a greater need for complex and specialized healthcare and medication (Berry et al., 2024). At the same time, this segment often requires adapted transportation and specialized housing and lodging and can have complex dietary requirements. Additionally, this group frequently needs personalized care and other services (Kuppelwieser and Klaus, 2021). The availability and quality of these expensive, increasingly scarce, and often unsustainable services often directly impact the well-being of older adults. Older adults also face greater difficulty in changing their habits and adapting to new circumstances and technologies (Park et al., 2021). In summary, the world has an aging population with increasingly severe medical and material implications, placing significant strain on available societal resources. At the same time, older adults often have difficulty learning new routines or adapting their lifestyles, which threatens their overall well-being.

In service research, sustainability has evolved from a marginal research area initially focused on environmental sustainability (Gummesson, 1994) and green resource integration (Grove et al., 1996; Guyader et al., 2019) into a core research priority that also includes social aspects (Anderson and Ostrom, 2015; Field et al., 2021). The 2030 Agenda for Sustainable Development and the Brundtland Commission have significantly influenced service research. Central to the understanding of sustainability is the idea that service should address the needs of the present while safeguarding the opportunities of tomorrow (Brundtland, 1987; Keiningham et al., 2024).

Improving sustainability has become a critical concern for policy makers, organizations, managers, employees, and citizens. It is common to break sustainability down into three primary dimensions: environmental sustainability, economic sustainability, and social sustainability (Robert et al., 2005; Purvis et al., 2019). Environmental sustainability refers to preserving natural resources, reducing pollution, and mitigating climate change (Purvis et al., 2019). This dimension focuses on the sustainable use of resources, the reduction of harmful emissions, and the conservation of biodiversity. Economic sustainability refers to ensuring long-term economic benefits without depleting resources (Benson and Craig, 2014). This involves practices that promote economic growth and development while ensuring that such growth does not come at the expense of the environment or social equity. Social sustainability refers to social equity, inclusion, and community well-being. It ensures that societal needs are met fairly and inclusively, fostering a society where all individuals have equal opportunities and support (Glavič and Lukman, 2007). Social sustainability addresses issues such as social justice, human rights, and the equitable distribution of resources, which are crucial for maintaining a stable and healthy society.

These three dimensions of sustainability are interrelated and often influence each other (Carlborg et al., 2024). Improvements in one dimension can lead to positive changes in another; for example, the equal distribution of economic welfare can enhance ecological awareness. However, it is also possible for improvements in one dimension to negatively impact another one; for example, economic growth can potentially lead to environmental degradation (Purvis et al., 2019).

Tackling the challenges of an aging population is strongly related—directly and indirectly—to all three dimensions of sustainability. A growing population of older adults leads to an increase in needs, such as healthcare services (Komp-Leukkunen and Sarasma, 2024) and infrastructure, which may boost resource use and increase the burden on environmental sustainability. Innovating for sustainability in this context means creating products and services that cater to the unique needs of older adults in a resource-efficient manner. This includes sustainable housing, age-friendly workplaces, and financial services that support long-term economic stability for both older adults and society as a whole. As people age, social inclusion and community support become increasingly important (Komp-Leukkunen and Sarasma, 2024). Social sustainability ensures that older adults have access to community resources, healthcare, and opportunities for social engagement to maintain and improve their well-being. Many face-to-face services are being replaced by digitized self-service solutions, but to ensure inclusivity, these services need to remain accessible for older adults. This promotes a higher quality of life and well-being for an aging population.

In conclusion, as humanity faces the dual challenges of an aging population and the need for improved sustainability, it is necessary to adopt behavioral changes that align with all three dimensions of sustainability. This approach will not only improve the quality of life and well-being of older adults but will also ensure that resources and opportunities are preserved for future generations.

Changing behavior is the process of altering individuals’ or groups’ actions, habits, and decision-making patterns in a desired direction (Michie et al., 2011). In the context of sustainability, changing behavior involves encouraging and enabling service organizations and their customers to adopt practices that are more environmentally friendly, resource-efficient, and socially responsible. Changing behavior is crucial for promoting sustainability and addressing the needs of an aging population (Barr et al., 2011; Thøgersen, 2014). Understanding how to effectively change service provision and use is essential for developing principles and practices that encourage service organizations and customers to become more sustainable.

Service organizations need to change their behavior to create and provide more sustainable services, but they are often slow to do this unless there is a financial benefit (Bansal and Roth, 2000). Few service firms voluntarily engage in changes that lead to selling fewer services or introducing environmentally friendly services with lower profit margins. This means there must be savings from using fewer resources, benefits from fulfilling new market demand, or coercion from policy makers. Policy makers can initiate systemic change—that is, fundamental shifts in the structures, policies, and practices that define a service system. This can involve rethinking and redesigning service provision models to better align with sustainable practices and the specific needs of older adults. This encompasses changes at various levels, including government policies, industry standards, and community norms.

Customers also need to adopt these more sustainable service options, but they are often unwilling, unable, or slow to do so, especially when the price is higher or when their experience is negatively affected (White et al., 2019). Some customer segments have already adopted sustainable practices, while others choose sustainable services only when the price is right or the value proposition is competitive (Hedvall and Witell, 2025). There is also a group of customers comprised of laggards or even opponents of sustainable behavior who sometimes view sustainability as a hoax. All these customer groups need to be addressed differently to move toward more sustainable consumption. Resources should be allocated based on which behaviors offer the greatest potential for change toward sustainability. To improve the chances that older adults adapt their behavior, nudges could, for example, point them toward more convenient options, better adapted to their unique situation or requirements, while still being more sustainable.

Sustainable consumption focuses on making responsible decisions where customers seek to act responsibly while fulfilling their desire for consumption (see Vermeir and Verbeke, 2006). The sustainability of their product and service choices depends on balancing the willingness to act responsibly with the desire to fulfill their consumption goals (Reppmann et al., 2024). For example, this means that customers balance their desire to spend time with family on vacation with their willingness to travel sustainably, such as choosing a train over an airplane. Each individual customer makes these types of choices daily (e.g. how they travel to work, what food they buy, and when they charge their electric car or bike).

Nudging can be used (1) to influence managers and employees of service organizations to develop more sustainable service options; and (2) to influence customers to make more sustainable choices. Nudging aims to subtly guide behavior by modifying the available choices (Thaler and Sunstein, 2008). It is intended to influence behavior without forbidding any options. In practice, it can involve changing the interface of an online portal, altering how products are displayed in a supermarket, or presenting information in a different way. In marketing, there has been growing interest in “green nudging,” which aims to promote more sustainable customer behavior (von Zahn et al., 2025). It has been used to encourage customers to conserve water, adjust their electricity use, buy more eco-friendly products, or stop using disposable cutlery for takeaway food (von Zahn et al., 2025). In a field experiment, von Zahn et al. (2025) showed that for a fashion retailer, smart nudging by AI can decrease product returns by 2.6% without negatively impacting sales. Aggregated to a societal level, this would result in a reduction of 624,000 metric tons of CO₂ emissions, equivalent to the annual emissions from the electricity consumption of 121,000 US homes. However, existing research suggests that customers respond differently to nudging, which is why the outcomes of a particular nudge cannot be predicted on an individual level (Hummel and Maedche, 2019). Although Mertens et al. (2022) found that the effects of nudging vary widely between individuals, domains, and methods, food use and sustainability appear among the domains where nudging has a positive impact on behavioral change.

A key question is whether and how AI can effectively encourage or nudge managers, employees, and customers of service organizations to change their behavior toward sustainable services. Advancements in AI and large language models (LLMs) have opened significant possibilities in many service sectors. AI can function as intelligent agents, engaging with stakeholders through open-ended conversations, learning from these conversations, influencing actions, and making decisions (Huang and Rust, 2021). Instead of relying on traditional machine-learning benchmarks that assess a model’s data processing or standardized prediction abilities, AI agents represented by chatbots and conversational assistant systems powered by LLMs can enhance language model capabilities by incorporating planning, loops, reflection, and other control structures. These elements leverage the model’s inherent reasoning abilities to complete tasks end-to-end, effectively addressing real-world challenges (Masterman et al., 2024). To tackle real-world issues efficiently, new models of AI agents can reason, plan, and utilize tools that interact with complex service environments.

Nudging has been proposed in marketing as an effective way to change the practices and principles of individuals. It is assumed that the concept of nudging can be applied to managers, employees, and customers of service organizations. But how can AI provide “nudges” that effectively help change the behavior (practices and principles) of firms and, more specifically, older adults? von Zahn et al. (2025) have suggested that AI can be used to differentiate the nudges needed to influence the behavior of different groups of service organizations and customers. Their study employed individual data on customer carts and digital footprints to personalize nudges for different customers. In addition, the browser type, the internet provider, the location, and the day of the week influenced how customers responded to different nudges. In their field experiment on the use of cutlery for takeaway orders in China, He et al. (2023) found that customers from different age groups responded differently to nudges: customers aged 18–24 increased their sustainable behavior by 11.9% points, while older adults increased their sustainable behavior by 30–34% points. In this case, older adults were more responsive to nudges to change toward sustainable behavior.

To initiate behavioral change toward sustainability, service organizations can use smart nudging to influence customers (He et al., 2023). Currently, in several industries, this change is not happening fast enough, as customers are adopting sustainable behaviors too slowly, highlighting the need to accelerate behavioral change. White et al. (2019, p. 124) have described this situation as: “People say they want sustainable products, but they don’t tend to buy them”. However, in some industries, it is the opposite; customers have changed, but there are no sustainable alternatives provided by service organizations. For example, the fast-food industry has continued using single-use plastic packaging and cutlery in serving their food (Hedvall and Witell, 2025). It can be argued that there is a need for AI to act as an agent of change influencing both service organizations (managers and employees) and customers toward more sustainable behavior.

Building on the service triangle (Kotler, 1994) and the service pyramid (Parasuraman, 2000), in this article, a conceptual framework is introduced connecting smart nudging, sustainability, and AI. Parasuraman (2000) introduced technology in the service triangle to take into account the added complexity of technology in service provision. In particular, the introduction of technology emphasized new interaction patterns between the different actors and technology. In the conceptual framework, “the AI service triangle” (Figure 1), four types of actors are present: managers, employees, customers, and AI. The building blocks of the AI service triangle are presented in Table 1. AI can influence the different actors through smart nudging, as well as influencing the interactions between the different actors. In this article, the conceptual framework is applied to promote the sustainable behavior of managers, employees, and older adults (customers), but there are additional situations where the AI service triangle can be useful.

Each set of actors in the AI service triangle is governed by principles and practices, with learning being the process that updates the principles and/or practices of each type of actor. Individuals gain knowledge through operational and conceptual learning. Operational learning involves learning to perform the activities that constitute the practices, while conceptual learning involves challenging the principles concerning why things are done in the first place (Kim, 1993). Principles and practices influence one another mutually, such that a change in one can affect the other, albeit sometimes with a delay. A principle is a set of underlying assumptions about how to view the world (Dean and Bowen, 1994). For a manager, principles may govern how they view their relationships with customers, employees, competitors, suppliers, and the environment. For an employee, principles can involve ethical behavior toward customers, fellow employees, and the organization as a whole. For a customer, principles may influence how they perceive sustainability and use products and services. AI is guided by principles set by either general or specific models; for example, AI can be directed to suggest more sustainable practices for firms and customers.

The principles provide simplified but “workable versions of reality” that guide how managers, employees, customers, and AI interpret and use new information (Gioia and Sims, 1986). Principles are translated into practices (i.e. what each actor does). Practices can be defined as shared templates of organized and recurring activities that actors enact to perform everyday activities and make sense of the world (Skålén, 2024). Practices are neither solely determined by the principles of the actor nor by the context alone. If AI can nudge an actor and induce learning to take place, it can help that actor to (1) change practices by performing new activities, (2) change practices based on new principles, or (3) update principles based on new practices. This implies that if AI can influence an actor’s principles or practices, changes toward more sustainable behavior are likely to occur. Often what makes customers adopt sustainable practices is an awareness of various environmental problems and the consequences of their practices, being concerned with solutions (principles), and a willingness to re-allocate their resources (time, money, and attention) to make their practices more sustainable. Furthermore, the principles and practices of AI can be influenced by access to new training materials that will provide new types of directions for managers, employees, and customers.

As described in the previous section, AI can use smart nudges to influence the principles and practices of managers, employees, and customers. Mele et al. (2021, p. 957) view smart nudging as “the use of cognitive technologies to affect people’s behavior predictably, without excluding options or significantly changing their economic incentives”. Hansen and Jespersen (2013) distinguish between class 1 and class 2 nudges and argue that class 1 nudges influence automatic behaviors (certain practices), while class 2 nudges make people aware of their decisions (certain principles). Mele et al. (2021) argue that most empirical studies of nudges focus on how different customers respond to various forms of nudging, which can then inform how to deliver the most effective nudge in different situations. AI can use hyper-personalization of the nudge to tailor it to a unique context or situation, and to the individual characteristics and needs of managers, employees, and customers. The concept of hyper-personalization has previously been used to personalize the customer experience in retailing to increase product sales (Jain et al., 2021). By using personalized, real-time data, a better fit between the nudge and the individual can increase the likelihood of changes in practices. In this sense, smart nudging enables the design of conditions and contexts that promote a change in practices by increasing capacities for operational and conceptual learning, as outlined in the AI service triangle.

By adopting AI hyper-personalization of nudges, managers and employees of service organizations can improve service offerings, making them more efficient, customized, and sustainable. For example, to employees AI could list potential ways to address a customer need, while indicating the impact on the organization (efficiency), environment and society (sustainability), and customers (customer experience), based on an analysis of available information. Nudging could also – internally–be used to help employees to learn new sustainable principles and practices. This involves integrating more sustainable practices into their operations, from resource management to service design, and ensuring that their services are accessible and tailored to older adults. For managers, the decision to adopt sustainable practices and new services often comes with perceived risks, such as increased costs, uncertain financial returns, or unpredicted shifts in the behavior of customers (Schaltegger et al., 2012). These risks should be considered in developing nudges for their employees and should be regularly reviewed in the light of technological and societal developments. Based on information about these developments managers could be supported – or nudged – toward more sustainable strategic behavior.

In the context of older adults, failure to adopt sustainable practices may pose risks, as these individuals may be less responsive to traditional marketing methods aimed at changing behavior. This group might require specific and personalized nudges to adopt sustainable practices due to their unique physical, cognitive, and social needs. AI can help mitigate this risk by providing recommendations and assessing the potential financial benefits of adopting more sustainable practices (e.g. reducing waste, optimizing resource usage, or increasing customer loyalty through eco-friendly initiatives). For older adults, AI can reduce perceived risk by offering hyper-personalized nudges, ensuring that the suggested sustainable practices (e.g. choosing public transportation over driving) are convenient and cost-effective.

How does the conceptual framework of AI, smart nudging, and sustainability work in practice? One modus operandi is that AI gathers data on service organizations and customer practices, analyzes these data, and establishes a basic understanding of what can be considered sustainable behavior for performing smart nudging through hyper-personalization. AI then nudges managers, employees, and customers of service organizations to influence their principles and practices toward making their behavior more sustainable. AI can then use new data to analyze the efficiency and effectiveness of the smart nudges, updating its guiding principles and translating this to practices (nudging strategies) to achieve even more sustainable behavior of managers, employees, and customers.

To illustrate the use of the AI service triangle and show how AI can be used to nudge managers, employees, and customers of service organizations, two cases will be provided: (a) transportation; and (b) healthcare. These cases were chosen because they represent contexts relevant to the growing proportion of older adults where there is a need for a systemic change toward sustainable behavior—that is, where both service organizations and customers need to change and where there exists a range of possible changes that would contribute to improved sustainability. Each case will focus on how AI can be used for smart nudging older adults, a group in which small changes in behavior can have a large effect on sustainability (see Table 2).

Transportation plays a crucial role in the lives of older adults, affecting their independence, mobility, and well-being (Kim and Ulfarsson, 2013). However, with an aging population we face unique challenges when it comes to sustainable transportation practices. A significant proportion of older adults continue to drive; for instance, approximately 88% of men with a driver’s license continue to drive in their early 70s, with this figure dropping to 55% among men aged 85 or older (Jones and Hoyt, 2025). Many older adults continue to rely on their cars for everyday activities, such as visiting the pharmacy, shopping for groceries, or socializing with family and friends. As individuals age, they often experience physical limitations (e.g. decreased vision, hearing loss, decreased mobility) and cognitive decline, which can impair their driving ability. According to Kim and Ulfarsson (2013), a lack of transportation, especially the inability to drive, is significantly associated with a lower quality of life among older adults. Without sufficient transportation options, older adults are more likely to experience isolation, reduced access to essential services, and decreased participation in social and recreational activities.

One of the main barriers to adopting more sustainable transportation behaviors for older adults is convenience. Driving one’s own car is often seen as the easiest and most familiar option (Banister and Bowling, 2004). This is reinforced by the challenges older adults face, such as limited mobility, reduced access to public transportation infrastructure, and unfamiliarity with newer technologies that facilitate public transportation (Burkhardt et al., 2002). Additionally, the transition to public transportation can be intimidating for those who have spent most of their lives driving their own cars (Metz, 2000). As a result, older adults may be less inclined to switch to more sustainable transportation options despite the broader environmental benefits (Kim and Ulfarsson, 2013).

AI can play a crucial role in more smartly nudging subjects and firms toward adapting these principles and addressing the specific challenges that older adults face when it comes to transportation. AI thus offers a powerful tool for nudging older adults toward more sustainable transportation choices by addressing their unique needs and personalizing their experience. For example, AI can track an older adult’s transportation patterns, preferences, and mobility capabilities. Using this data, it can provide hyper-personalization of nudges, including suggesting public transportation routes that are accessible and convenient, ensuring that an older adult feels confident. For instance, AI can offer real-time information on bus or train schedules, suggesting the most accessible routes, and send reminders about upcoming travel plans. Additionally, AI can highlight the environmental and health benefits of taking public transportation, thereby shifting the principle of convenience toward one that embraces sustainability.

Moreover, AI can use hyper-personalization to adapt its nudges to everyone’s specific circumstances and requirements. For example, if an older adult has difficulty walking long distances, AI can recommend routes that involve minimal walking, ensuring that the transportation option remains accessible. Alternatively, if an older adult is hesitant to give up their car, AI can provide tailored encouragement to transition gradually, suggesting the use of public transportation for specific errands (e.g. grocery shopping) rather than daily commuting. Through these personalized nudges, AI can create a more inclusive transportation experience, reducing barriers to adopting sustainable practices.

In addition to nudging older adults, AI can influence managers of transportation organizations to adapt their practices to better cater to the needs of an aging population. AI can assist managers and employees in analyzing older customers’ transportation patterns, enabling them to adapt services accordingly. By understanding when and where older adults are most likely to use public transportation, managers can make data-driven decisions to increase accessibility, optimize service schedules, and implement discounts to ensure a more sustainable and aging-friendly transportation system. For example, if AI detects that older adults are most likely to use public transportation during certain hours, it can suggest that transportation firms increase the frequency of buses or trains during off-peak times. This would ensure that older adults are not left stranded or inconvenienced, making the shift to public transportation a more attractive and feasible option. Furthermore, AI can nudge managers to adjust transportation routes in relation to where there is infrastructure that can support older adults to take trips that extend beyond the distances covered by an individual transportation firm. As such, AI can suggest how a bus firm needs to connect to the routes of a tram operator to enable sustainable trips door to door for older adults. These systemic changes, driven by AI insights, could create a more sustainable and aging-friendly public transportation system.

AI’s ability to monitor older adults’ transportation choices and adapt its nudging strategies in real time is crucial for promoting long-term behavioral change. For example, if an older adult consistently replaces car trips with public transportation for errands, AI can reinforce this behavior by sending personalized reminders or offering additional rewards. Over time, AI can learn the preferences and habits of older adults, refining its nudges to further encourage sustainable transportation practices. However, as noted in previous studies, unintended consequences must be monitored and addressed. For instance, a shift from car travel to public transportation could lead to a reduction in other sustainable behaviors, such as cycling. In these cases, AI can adjust its nudging strategies to encourage a balance between multiple sustainable behaviors, suggesting alternatives such as combining cycling with bus rides or walking to the bus stop instead of relying solely on public transportation.

The global aging population is placing a significant strain on healthcare systems worldwide, as an increasing proportion of individuals aged 65 and older require specialized care and services (Berry et al., 2024). Many older adults are suffering from chronic diseases, such as multiple sclerosis, Parkinson’s disease, cardiovascular disease, obesity, diabetes, and dementia. This trend places an unsustainable financial burden on national systems, as these older adults need not only medical expertise but also long-term care, adapted housing, and other services to ensure their well-being (Tang et al., 2022).

Given this unsustainable demand, AI-driven interventions have shown promise in optimizing healthcare resource utilization and delivering efficient care to older adults. Sumner et al. (2023) developed an AI-driven nudge intervention to improve medication adherence among older adults, highlighting the potential of AI in improving healthcare provision. AI in telemedicine, such as wearable devices and chatbots, enables continuous monitoring and personalized care, thereby reducing the burden on healthcare providers and ensuring more efficient healthcare provision. Using wearable devices can improve the well-being of older adults if they feel psychological ownership over their well-being. AI can empower older adults to take charge of their own health by offering real-time recommendations that align with their conditions and mobility capabilities. By ensuring that these interventions are hyper-personalized to everyone’s needs, AI can shift patient principles of care toward sustainability, helping older adults adopt healthier habits that reduce their overall healthcare reliance and contribute to the sustainability of the healthcare system.

For managers and employees within healthcare systems, AI can help by providing actionable insights derived from big data, helping them optimize scheduling, treatment protocols, and workflows to improve the delivery of care in a more resource-efficient manner (Schwartz, 2024). AI can also play an important role in nudging healthcare managers and employees to adjust their practices to better cater to the needs of older adults. By analyzing patterns in the behavior of older adults, AI can suggest operational improvements, such as increasing the availability of remote consultations or optimizing care schedules, to meet the unique needs of older adults. AI can also identify inefficiencies in service provision, helping healthcare managers make data-driven decisions to enhance organizational performance while ensuring sustainability. By integrating these AI-driven insights, healthcare systems can create more sustainable and efficient service delivery models.

Moreover, AI nudging can reduce healthcare waste by improving medication compliance and encouraging patients to adopt healthier behaviors, which can prevent hospital readmissions and reduce emergency care visits (McColl-Kennedy et al., 2017). By promoting preventive care through AI, healthcare systems can reduce the environmental impact associated with the need for in-person visits, cutting down on travel-related emissions, and minimizing the energy consumption of healthcare facilities. This focus on sustainability in healthcare provision is essential for managing the growing burden posed by the aging population.

This research on sustainability, AI, and behavioral change, along with empirical illustrations from (1) transportation and (2) healthcare, provide a foundation for discussing the implications of the “AI service triangle” for sustainability in an aging society. However, there are both opportunities and challenges when using AI to promote sustainability, such as how AI can create change in behavior, the role of hyper-personalization, and who is in control of AI.

AI can act in two primary ways as a change agent to promote sustainable behavior. It can nudge practices (class 1 nudges) and principles (class 2 nudges) (Hansen and Jespersen, 2013). Empirical evidence shows that older adults are more likely to change their behavior than younger adults when exposed to a class 1 nudge (He et al., 2023). This is a significant observation because it indicates that older adults can adopt more sustainable behaviors without a substantial negative impact on their experience. Smart nudging is proposed to initiate change among an aging population, with a particular focus on class 1 nudges.

When it comes to shifting the principles of the aging population toward behaving in a more sustainable way, it is more challenging for AI to act as a change agent. One could argue that younger adults can be educated to behave more sustainably, while young to middle-aged adults can be convinced to adopt more sustainable behaviors. However, older adults are less cognitively flexible and face greater difficulty in changing their principles toward more sustainable behavior. However, there may be service sectors where the opposite is true. One such area is personal health, where behavioral changes, such as older adults going to the gym, result from a shift in principles (i.e. the insight that there is no age at which training is not beneficial).

An additional way for AI to support sustainable change among an aging population is to influence firms to provide better and more affordable offerings for older adults. Because the principles of older adults are harder to change, altering offerings to be more sustainable can have a greater impact than trying to change their principles. Following existing knowledge that small details make a big difference in the customer experience (Bolton et al., 2014), AI, acting as a change agent, can influence how services are provided and experienced through nudging, bringing about positive changes toward sustainable behavior.

A key to AI’s success in promoting sustainability is hyper-personalization of nudges to the specific needs of managers, employees, and customers of service organizations. This requires AI to gain access to significant amounts of data, identify which data is crucial for promoting sustainable behavior, and respect the privacy of all individuals. Current AI solutions have learned that sustainable nudges are more effective on weekends, possibly due to lower stress levels among customers (Grahl et al., 2023). Additionally, a customer’s browser choice and digital footprint provide insights into their demographics and other characteristics (Berg et al., 2020). This information helps AI hyper-personalize nudges and determine when to use class 1 or class 2 nudges.

Hyper-personalization of offerings is beneficial for service organizations. McKinsey (2021) shows that customers expect personalization and that hyper-personalization can lead to 40% more revenue. By using data about their customers, hyper-personalization helps organizations determine what kinds of offerings their customers want at specific times and in a specific situation. However, what happens if this knowledge is used to nudge both organizations (i.e. managers and employees) and individual customers toward sustainable behavior? The use of data enabling AI to hyper-personalize nudges raises multiple ethical concerns and intersects with legislation and digital corporate responsibility (Lobschat et al., 2021). In the European Union, for example, individuals have the right to know what information about them an organization or policy maker has stored. Therefore, any entity using AI must be able to provide all the data that AI has access to regarding an individual. Furthermore, even if storing certain data about an individual is legal, the question remains: Is it ethical to store and use this data to nudge managers, employees and customers of service organizations?

Sustainability is multi-dimensional and may have different meanings for different actors. The use of AI as a change agent for promoting more sustainable “systems” behavior can feel rewarding for policy makers, service organizations, and customers, but for others it can feel disconcerting and disempowering (Kissinger et al., 2021). Using hyper-personalized recommendations based on individuals’ data and preferences, AI might suggest one individual take the bus, while recommending their partner to take the car. AI may be more effective at distributing resources, predicting outcomes, and recommending solutions than current systems. However, challenges arise because the way AI is trained directly influences the sustainable behaviors it suggests for individuals. This raises fundamental questions, such as: Who decides what is sustainable, or what is the ‘most sustainable’ option? And what is acceptable for older adults, taking into account their limitations and requirements?

If a service organization is made responsible for nudging their customers toward more sustainable behavior, they can be expected to direct their customers toward their own offerings. If a policy actor nudges a citizen, they would be expected to act in the best interest of the nation. As a result, multiple actors are expected to use AI to nudge individuals toward sustainable behavior, but because they have varying agendas, interests, and desired outcomes, many tensions are likely to emerge. An individual who gets nudged by three different actors can expect to potentially get nudged toward three different practices. This is where AI’s guiding principles and practices become crucial. The data used, the principles shaping AI’s role, and how these are applied to practices will result in different suggested sustainable behaviors. Further research is needed to understand the intended and unintended consequences of using AI as a change agent.

Especially in the case of nudging older adults with (more expensive) options adapted to their individual limitations and requirements, the question of a fair allocation of resources and societally perceived fairness may arise. Research is needed to investigate the trade-offs and how to find and communicate acceptable compromises.

This study makes several theoretical contributions to the understanding of how increasing sustainability can become a key priority in an aging society. First, building on the service triangle and the service pyramid (Parasuraman, 2000), a conceptual framework of the AI service triangle was introduced that focuses on how AI can both influence the interactions among managers, employees, and customers of service organizations and use smart nudging to change the principles and practices of the different actors. This study further builds on existing service research that introduced key concepts, such as sustainability (Gummesson, 1994), AI (Huang and Rust, 2021), and smart nudging (Mele et al., 2021), to apply the AI service triangle to sustainability in an aging society. The integrated framework demonstrates how AI can nudge various actors toward more sustainable behaviors by influencing the principles and practices of individuals. Additionally, it shows how AI’s principles and practices influence the hyper-personalized sustainable behaviors it recommends or nudges for managers, employees, and customers of service organizations.

An aging society will play a crucial role in shaping societal development in the coming decades (Bateson, 2021). This carries significant implications for how service organizations and older adults can adopt more sustainable behaviors. These implications suggest that behavioral changes in an aging society are achieved by either influencing the practices of older adults or encouraging service organizations to adjust their offerings. Because it is more difficult to influence the principles of older adults (He et al., 2023), AI must focus on changing their practices. Empirical evidence shows that older adults are more likely to change their behavior than younger adults when exposed to a class 1 nudge (He et al., 2023). However, this requires a careful balance between promoting sustainable behaviors and adhering to guidelines for digital corporate responsibility and respecting the privacy of individuals (Lobschat et al., 2021).

A key contribution of this study is the development of a research agenda focused on using AI to drive systemic and sustainable changes in an aging society. As the population ages and AI is increasingly integrated into service industries, new challenges arise in achieving the necessary improvements in sustainability. These challenges are influenced by the diversity within the aging population, where some older adults have significant financial resources and will lead long, active lives, while others are financially vulnerable and require improvements in their living standards.

Adopting more sustainable behaviors as a service organization and encouraging older adults to behave more sustainably while maintaining profitability present several challenges for managers. These challenges directly impact their day-to-day decision-making. Hyper-personalizing nudges to promote sustainable behavior is a complex challenge. Currently, service organizations use AI to personalize customer experiences, which results in higher sales and improved loyalty. However, from a sustainability perspective, this approach can be counterproductive, as increased sales often have adverse environmental effects. A more effective approach for service organizations is to use AI to nudge customers toward more sustainable offerings and potentially encourage reduced consumption. To achieve this, firms may need to rethink their business models to optimize resource use.

Nudging older adults requires AI to determine when their principles can no longer be changed, focusing solely on nudging practices to encourage more sustainable behavior. This shift toward nudging practices rather than principles raises important ethical considerations for managers. How can hyper-personalized nudges based on age and other characteristics be implemented without violating privacy? This challenge is particularly significant for firms operating in global markets, where each country has its own legislation, national culture, and business practices. Adhering to ethical guidelines on handling digital data and ensuring transparency in providing effective nudges will require expertise in sustainability, ethics, sociology, business, and engineering.

There are several ways in which service research can contribute to addressing the multiple challenges of older adults. The proposed research agenda highlights important areas of inquiry designed to facilitate a deeper understanding of the complex integration of AI and sustainability in an aging society. The research agenda is outlined below and summarized in Table 3.

Facilitating behavioral change among financially vulnerable customers through AI-driven nudging presents a significant opportunity to improve sustainability and support aging societies. This approach aligns with the United Nations’ SDGs and tackles specific issues faced by low-income aging populations. These communities, often located in less developed regions, experience acute sustainability challenges, such as limited access to clean water, dependence on non-renewable energy, and inadequate healthcare (World Health Organization, 2016). However, this group has historically been difficult to influence in terms of behavioral change. For example, large portions of the population in Africa cook using charcoal fires. Charcoal is produced through unsustainable methods, such as burning large sections of rainforests, resulting in severe environmental damage and significant health risks from the smoke and soot produced during both charcoal creation and use in homes.

Alternatives such as solar energy are available, especially in regions such as Africa, where sunlight is abundant. Yet, the informal institutionalization of the charcoal industry makes it difficult for individual users to transition to more sustainable options (Greene et al., 2025). Wood harvesting, charcoal production, and distribution provide guaranteed income and employment, particularly for vulnerable populations, such as children. Moreover, the high cost of solar cookers is prohibitive for those living at the “base of the pyramid” and who have no savings. Promoting behavioral change in these populations could significantly enhance their quality of life, environmental sustainability, and economic stability. Shifting these populations to more sustainable practices, such as solar cooking, would greatly enhance environmental sustainability and improve health outcomes by reducing deforestation, air pollution, and smoke inhalation risks. It would also decrease the amount of time spent collecting fuel. However, further research is required to understand how behavioral change in older adults can be encouraged in regions where institutional voids and informal practices dominate, creating barriers to adopting sustainable alternatives (Koskela-Huotari et al., 2016).

Individual differences in contexts, customer requirements, and service organizations play a crucial role in the potential effectiveness of behavioral change strategies (von Zahn et al., 2025). Factors such as demographic characteristics, health, socio-economic status, cultural background, and personal values influence both the willingness and ability of individuals to modify their behavior toward sustainability. Understanding these differences is essential for designing effective interventions and fostering meaningful change.

For example, an individual committed to reducing their carbon footprint might invest in energy-efficient appliances, use smart home technology, and choose a green energy provider to minimize consumption. However, their sustainable behavior may not extend to transportation, as they rely on personal vehicles due to inadequate public transportation options. In contrast, another individual might prioritize waste reduction—recycling, composting, and avoiding excess packaging—but neglect water conservation due to a perceived abundance of the resource. These examples highlight the complex and selective nature of sustainability practices, emphasizing that uniform behavioral changes across different sustainability domains are unrealistic. Individuals vary not only in their capacity to change behaviors but also in their willingness, which is often shaped by their immediate environment and personal priorities.

By gaining a deeper understanding of how individual differences and contexts influence the ability and willingness to change, smart nudges can be designed to better accommodate diverse needs and motivations. This approach enables the development of targeted strategies that address personal barriers and leverage individual interests in sustainability. With more knowledge of individual differences, a more nuanced approach can be adopted, allowing for comprehensive strategies to meet global sustainability goals. While individual actions may vary, their cumulative effect can have a substantial impact on sustainability.

It is becoming increasingly clear that several challenges posed by an aging population to the healthcare system and sustainability can be mitigated by adopting healthier lifestyles. Prevention, particularly of chronic diseases such as diabetes, Parkinson’s disease, dementia, cardiovascular disease, and obesity, should begin early and be maintained throughout life. The proposed model can nudge younger adults toward healthier and more sustainable behaviors, while keeping older adults on the “right track,” thus preventing many of the burdens that the aging population imposes on the healthcare system.

It is crucial to understand how to optimize AI-based data analysis and usage to nudge different generations—and service providers—toward more sustainable behaviors and prolonged well-being. For example, this can be explored in the context of transportation choices (e.g. opting for bikes over scooters or cars), tourism (encouraging more active holidays), and food (promoting healthier, lower-calorie options). Smarter choices do not need to be less profitable for firms; therefore, managers, employees, and customers of service organizations should seek customized alternatives to unhealthy activities and foods.

Major lifestyle changes, such as reducing air travel, adopting a plant-based diet, and downsizing living spaces, can significantly reduce individual carbon emissions and contribute to sustainability. For aging populations, these changes can also improve health outcomes and lower living costs. These changes, while highly impactful, are often the most challenging to implement because they involve deeply ingrained habits or acquired privileges that profoundly affect daily life. Additionally, older adults may face unique barriers, such as limited mobility, fixed incomes, or deep-rooted habits, making these changes especially difficult. The authors believe that AI can play a significant role in providing tailored guidance, support, and accessible information to help individuals navigate these transitions. Additionally, AI can help develop customized solutions that reduce environmental impact while making life easier.

Usability, privacy, and security are critical concerns, especially when handling sensitive information about older adults and vulnerable individuals. Organizational, technical and ethical considerations also arise in the deployment of AI technologies (Lobschat et al., 2021; Van Riel et al., 2025). However, the benefits of hyper-personalization, both in terms of enhancing service quality and promoting sustainability, make it a compelling strategy for addressing the needs of an aging population. It is essential to expand knowledge on concepts such as privacy, corporate digital responsibility, and legislation to understand their influence on smart nudging in an aging society.