Reclaiming Older Age Inclusion as a Marketing Imperative: The Case of AI

Few areas have business costs of exclusion as high as those associated with neglecting older consumers. By 2050, one in four individuals in North America and Europe will be age 65 or older, and this demographic shift will also shape many other economies, particularly in East Asia and Europe (Stanford Center on Longevity 2022). Older consumers account for a growing share of spending and remain economically consequential across many categories and life domains (Ofek and Libai 2026). Generative AI has a Janus face in this context: It can reduce age-related exclusion by lowering friction and improving decision support, yet it can also scale exclusion by steering choices and amplifying data-driven bias. Against this backdrop, AI-enabled interfaces and agents are increasingly positioned as “solutions” for accessibility and personalization. This makes it urgent to specify what they should (and should not) be allowed to do in consumer journeys.

Age Exclusion Through Poor Touchpoint Design

Despite these facts, marketing systems often neglect, stereotype, or misrepresent older consumers, suggesting that age inclusion is not merely a moral or social ideal but a matter of touchpoint design failures. Ageism against older adults manifests in many marketing activities, including product design, sales and service encounters, and the tendency to avoid collecting information about older consumers’ opinions and interests (Darveau et al. 2025). Older adults are either erased from advertising or portrayed through tired and negative stereotypes: frail, technophobic, or nostalgically yearning for youth (Eisend 2022). Important progress is underway in research and practice to better serve older consumers. Nevertheless, exclusion persists where friction, defaults, data gaps, and representation repeatedly accumulate across journeys. Some firms already involve older consumers through codesign panels and accessibility testing, demonstrating that inclusion is feasible, though this remains relatively rare. An ageist culture, a misunderstanding of the economic potential of older consumers, and the young age of marketing practitioners can help explain this myopic exclusion, the consequences of which are far-reaching to consumers, brands, and society.

Marketplace exclusion can leave older consumers feeling vulnerable, insecure, misunderstood, exploited, and powerless, and may even lead them to dissociate from other older adults (Darveau et al. 2025). It can result in reduced engagement, trust, and willingness to experiment with brands. It can also damage younger consumers by reinforcing fear and stigma around aging. From a broader social perspective, the exclusion of older adults can harm their education, health care, and employment prospects. Isolated and stigmatized adults are less confident and more dependent. This will affect not only their well-being but also the costs to society.

For brands, the costs of failing to target a significant, potentially profitable segment of the market can be substantial. When marketing communication features only the young, it creates information asymmetry: Products and services for older users are less visible, less normalized, and therefore less adopted, even when they exist. Marketers often fall into the trap of treating “older adults” as a single, homogeneous segment. However, aging unfolds along multiple trajectories, including biological, psychological, and social, which translate into heterogeneity in motivations among older consumers (Carstensen and Hershfield 2021). Distinguishing between lifespan (years lived) and healthspan (years lived independently and actively) allows for more nuanced targeting. The “healthy agers,” that is, active, tech-using, socially connected individuals over age 65, represent a large share of this market and are a key global growth segment in spending and lifetime value (Ofek and Libai 2026). Age-related exclusion can also compound with gender, race, ethnicity, disability, income, and uneven digital access, intensifying friction and mistargeting for some older consumers. Failing to account for these differences means brands would forgo the opportunity to influence consumers intergenerationally (Guillén 2024), such as the successful example of Estée Lauder's positioning of Re-Nutriv as a longevity product across multiple generations. Successful segmentation should thus create personas that reflect the heterogeneity and potential among older adults and serve as the basis for managing the marketing mix to support effective campaigns.

Is AI a Solution? The Janus Face of AI in Age-Inclusive Markets

At first glance, generative AI appears to be a natural (and fashionable) solution for reversing the exclusion of older consumers. AI has the potential to establish autonomy and agency, reduce friction, serve as a traveling companion for the customer journeys of older consumers, and enable better, more informed decisions.

In many customer journeys, especially those conducted online, older consumers face a higher “friction tax” than younger ones. Dense interfaces, multistep authentication, unclear error recovery, and rapid information presentation can lead to cognitive overload. In this context, AI can adapt communication to the user and context. It can simplify language without infantilizing, pace instructions, offer step-by-step guidance, and adjust presentation (e.g., font size, contrast, voice interaction). Importantly, this is less about stereotypes and more about variance: Aging increases heterogeneity in sensory and cognitive load, so a one-size-fits-all interface is likely to fail.

AI enables reducing friction at the exact points where abandonment occurs (e.g., onboarding, payment, customer support). For example, an AI companion can simplify interactions and provide voice-based guidance to reduce interaction friction (Huang, Zhou, and Piper 2025). In addition, older consumers often have to navigate complex, high-stakes categories, such as finance, insurance, and health, in which small misunderstandings can be costly. Here, AI can act as a decision-support system by summarizing options, explaining trade-offs in plain language, flagging hidden fees or conditions, comparing alternatives, and checking consistency across forms and documents. When well implemented, such support can enhance autonomy rather than replace it.

Yet, widespread use of AI also has drawbacks, and the predictable harms mirror the same two dimensions. With respect to autonomy and agency, the risk is bounded-agency violations in which “helpful” agents risk quietly becoming controlling ones that nudge consumers into the firm-preferred path, slowly eroding decision-making abilities (Hermann and Puntoni 2025). Bounded agency occurs when an AI assistant preserves the appearance of choice but steers outcomes through defaults, framing, or procedural dead ends, especially when cognitive load is high. Regarding choice integrity, the risk lies in bias amplification. AI systems learn from baseline data that often underrepresent older adults and other marginalized groups (Huang and Rust 2025). This bias is likely to amplify, leading to systematically worse outcomes and decisions for older adults, even in the absence of any firm manipulation (Chu et al. 2023).

The job of public policy is to keep the benefits while limiting such downsides. Regulators can, for example, require friction equity reporting for key consumer journeys (e.g., onboarding, payment, cancellation, claims/support). Friction equity reporting would require firms to disclose where consumers struggle in key journeys (e.g., completion time, dropout rates, error/retry rates, and human handoff rates), reported by age band or accessibility-need state. Second, policymakers can implement bounded-agency rules for AI agents in sensitive contexts. Such rules can include the mandatory disclosure of AI interaction, a clear separation between “assist” and “sell,” limits on urgency framing and prechecked add-ons, easy undo/reversal, and one-click escalation to a human without penalty. Together, these guardrails preserve AI's capacity to reduce exclusion while preventing it from scaling exploitation.