Category: AI

As generative AI becomes more prevalent in advertising, firms increasingly face requirements to disclose AI involvement. Although prior research shows that such disclosures may generate negative consumer responses, it remains unclear whether explanatory disclosures can mitigate these effects. This research examines beneficiary-framed AI disclosures, explanations communicating why AI was used and who benefits, across three studies. In a controlled text-based experiment (Study 1), more specific explanations improve evaluations relative to minimal and less specific AI labels. However, beneficiary framing effects are not systematic, and practically negligible. Moreover, these benefits do not generalize to more realistic advertising contexts. Across two Instagram-style ad studies (Studies 2A and 2B), explanatory disclosures fail to improve consumer responses and, in some cases, lead to more negative evaluations with effects that are either statistically equivalent to zero or significantly negative. Across studies, AI aversion emerges as a robust predictor of negative responses, suggesting that disclosure effects are driven more by consumers’ prior beliefs than by the specific framing of explanations. The findings suggest caution in adding explanatory disclosures, as default inferences of firm-serving motives are difficult to override. https://link.springer.com/article/10.1057/s41270-026-00534-7 s41270-026-00534-7

Despite massive investments in artificial intelligence, most pilots fail to achieve full business impact. We argue these failures stem from a critical oversight: Organizations treat employee and customer AI adoption as separate challenges rather than recognizing their interdependence. By analyzing past technology adoptions and interviewing employees and customers on current AI considerations, we identify consistent patterns where success hinges on simultaneously addressing both stakeholder groups’ needs across different implementation stages. This paper introduces an integrative framework that maps management strategies to the intersection of the Gartner Hype Cycle’s stages (Hype, Disillusionment, Enlightenment) and key stakeholders (Employees, Customers). Informed by theories of social contagion, socio-technical systems and the service-profit value chain, we distill our findings into a managerial toolkit. Our framework yields five recommendations: (1) set realistic expectations early to avoid credibility-damaging overhype; (2) invest in employee reskilling during disillusionment; (3) offer tangible customer benefits to maintain trust; (4) give employees agency in shaping AI applications; and (5) maintain competitive customer value. We demonstrate how organizations ignoring these principles experienced failures, while those applying them achieved sustainable integration. Our framework offers managers actionable guidance for navigating AI’s unique challenges. Unlike previous specialized technologies, AI broadly touches customer interactions and employee workflows simultaneously, meaning failures in one group rapidly cascade to the other. Organizations recognizing and managing these interdependencies from the outset can overcome daunting failure statistics and realize AI’s transformative potential.https://www.sciencedirect.com/science/article/pii/S00076813260010351-s2.0-S0007681326001035-main