Nonprofit senior living organizations communicate with prospective residents, families, current residents, staff, referral partners, donors, and regulators while operating under mission, accessibility, and data-governance constraints. This article develops an evidence-informed marketing infrastructure for that setting. An integrative conceptual review was conducted on 13 July 2026. The review examined 36 scholarly records with confirmed Crossref metadata and three public institutional documents. Selection focused on nonprofit stakeholder engagement, relationship marketing, market orientation, digital transformation, artificial intelligence in marketing, privacy, technology access in later life, and governance. The synthesis yields a four-part framework: mission and stakeholder needs, consent-aware data, human-reviewed communication, and mission-linked measurement. Governance and risk review operate across all four parts. The article further specifies a traceability path from stakeholder signal to evidence record, accountable decision, and learning measure. This path distinguishes enabling communication outcomes from service and mission outcomes, preventing unsupported causal claims. The framework treats marketing as an organizational capability for listening, coordination, and accountable communication. Generative tools may assist with drafting, classification, and retrieval when a responsible staff member validates facts, audience fit, accessibility, and approval status. They should not determine eligibility, clinical matters, housing placement, or other high-stakes decisions. The article contributes a bounded planning model and a set of testable propositions for future research on adoption, trust, accessibility, staff workload, and mission-related outcomes.

Artificial intelligence, data governance, digital transformation, nonprofit marketing

Acquisti, A., Taylor, C., & Wagman, L. (2016). The economics of privacy. Journal of Economic Literature, 54(2), 442-492. https://doi.org/10.1257/jel.54.2.442

Administration for Community Living. (2024). 2023 profile of older Americans. https://acl.gov/aging-and-disability-in-america/data-and-research/profile-older-americans

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623). Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922

Bleier, A., Goldfarb, A., & Tucker, C. (2020). Consumer privacy and the future of data-based innovation and marketing. International Journal of Research in Marketing, 37(3), 466-480. https://doi.org/10.1016/j.ijresmar.2020.03.006

Cillo, P., & Rubera, G. (2025). Generative AI in innovation and marketing processes: A roadmap of research opportunities. Journal of the Academy of Marketing Science, 53(3), 684-701. https://doi.org/10.1007/s11747-024-01044-7

Cloarec, J., Meyer-Waarden, L., & Munzel, A. (2024). Transformative privacy calculus: Conceptualizing the personalization-privacy paradox on social media. Psychology & Marketing, 41(7), 1574-1596. https://doi.org/10.1002/mar.21998

Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., & Sharit, J. (2006). Factors predicting the use of technology: Findings from the Center for Research and Education on Aging and Technology Enhancement. Psychology and Aging, 21(2), 333-352. https://doi.org/10.1037/0882-7974.21.2.333

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42. https://doi.org/10.1007/s11747-019-00696-0

Ebrahim, A., & Rangan, V. K. (2014). What impact? A framework for measuring the scale and scope of social performance. California Management Review, 56(3), 118-141. https://doi.org/10.1525/cmr.2014.56.3.118

Electronic Code of Federal Regulations. (n.d.). 2 CFR Part 200: Uniform administrative requirements, cost principles, and audit requirements for federal awards. Retrieved July 13, 2026, from https://www.ecfr.gov/current/title-2/subtitle-A/chapter-II/part-200

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People: An ethical framework for a good AI society. Minds and Machines, 28(4), 689-707. https://doi.org/10.1007/s11023-018-9482-5

Fu, J. S., Cooper, K. R., Woo, D., & Kwestel, M. (2024). Beyond stakeholder management: Organizational listening for nonprofit stakeholder engagement. Nonprofit and Voluntary Sector Quarterly, 53(4), 841-865. https://doi.org/10.1177/08997640231201068

Grewal, D., Satornino, C. B., Davenport, T., & Guha, A. (2025). How generative AI is shaping the future of marketing. Journal of the Academy of Marketing Science, 53(3), 702-722. https://doi.org/10.1007/s11747-024-01064-3

Hartmann, J., Exner, Y., & Domdey, S. (2025). The power of generative marketing: Can generative AI create superhuman visual marketing content? International Journal of Research in Marketing, 42(1), 13-31. https://doi.org/10.1016/j.ijresmar.2024.09.002

Heitmann, M. (2024). Generative AI for marketing content creation: New rules for an old game. NIM Marketing Intelligence Review, 16(1), 10-17. https://doi.org/10.2478/nimmir-2024-0002

Hermann, E., Williams, G. Y., & Puntoni, S. (2024). Deploying artificial intelligence in services to aid vulnerable consumers. Journal of the Academy of Marketing Science, 52(5), 1431-1451. https://doi.org/10.1007/s11747-023-00986-8

Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30-50. https://doi.org/10.1007/s11747-020-00749-9

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2

Jones, M., Jallinoja, P., & Pietila, I. (2021). Representing the voice of patients: How third sector organisations conceptualise and communicate experiential knowledge in health service development. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 32(3), 561-572. https://doi.org/10.1007/s11266-020-00296-5

Lovejoy, K., Waters, R. D., & Saxton, G. D. (2012). Engaging stakeholders through Twitter: How nonprofit organizations are getting more out of 140 characters or less. Public Relations Review, 38(2), 313-318. https://doi.org/10.1016/j.pubrev.2012.01.005

Mikalef, P., Conboy, K., & Krogstie, J. (2021). Artificial intelligence as an enabler of B2B marketing: A dynamic capabilities micro-foundations approach. Industrial Marketing Management, 98, 80-92. https://doi.org/10.1016/j.indmarman.2021.08.003

Mishchenko, E. V. (2026). Advanced applications of LLMs in marketing during the purchase stage. In A. Karbekova (Ed.), Ethics of the AI economy: A system view from the positions of the theory of stakeholders (pp. 105-115). Springer. https://doi.org/10.1007/978-3-032-23423-0_11

Mitzner, T. L., Boron, J. B., Fausset, C. B., Adams, A. E., Charness, N., Czaja, S. J., Dijkstra, K., Fisk, A. D., Rogers, W. A., & Sharit, J. (2010). Older adults talk technology: Technology usage and attitudes. Computers in Human Behavior, 26(6), 1710-1721. https://doi.org/10.1016/j.chb.2010.06.020

Modi, P. (2012a). Market orientation in nonprofit organizations: Innovativeness, resource scarcity, and performance. Journal of Strategic Marketing, 20(1), 55-67. https://doi.org/10.1080/0965254X.2011.628405

Modi, P. (2012b). Measuring market orientation in nonprofit organizations. Journal of Strategic Marketing, 20(5), 447-460. https://doi.org/10.1080/0965254X.2012.689991

Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20-38. https://doi.org/10.1177/002224299405800302

National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1). https://doi.org/10.6028/NIST.AI.100-1

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342

Peek, S. T. M., Wouters, E. J. M., van Hoof, J., Luijkx, K. G., Boeije, H. R., & Vrijhoef, H. J. M. (2014). Factors influencing acceptance of technology for aging in place: A systematic review. International Journal of Medical Informatics, 83(4), 235-248. https://doi.org/10.1016/j.ijmedinf.2014.01.004

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33-44). Association for Computing Machinery. https://doi.org/10.1145/3351095.3372873

Rust, R. T., Ambler, T., Carpenter, G. S., Kumar, V., & Srivastava, R. K. (2004). Measuring marketing productivity: Current knowledge and future directions. Journal of Marketing, 68(4), 76-89. https://doi.org/10.1509/jmkg.68.4.76.42721

Sargeant, A., & Lee, S. (2004). Trust and relationship commitment in the United Kingdom voluntary sector: Determinants of donor behavior. Psychology & Marketing, 21(8), 613-635. https://doi.org/10.1002/mar.20021

Saxton, G. D., & Guo, C. (2011). Accountability online: Understanding the web-based accountability practices of nonprofit organizations. Nonprofit and Voluntary Sector Quarterly, 40(2), 270-295. https://doi.org/10.1177/0899764009341086

Saxton, G. D., & Waters, R. D. (2014). What do stakeholders like on Facebook? Examining public reactions to nonprofit organizations' informational, promotional, and community-building messages. Journal of Public Relations Research, 26(3), 280-299. https://doi.org/10.1080/1062726X.2014.908721

Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59-68). Association for Computing Machinery. https://doi.org/10.1145/3287560.3287598

Shumate, M., Cooper, K. R., Pilny, A., & Pena-y-lillo, M. (2017). The nonprofit capacities instrument. Nonprofit Management and Leadership, 28(2), 155-174. https://doi.org/10.1002/nml.21276

Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1-17. https://doi.org/10.1509/jmkg.68.1.1.24036

Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889-901. https://doi.org/10.1016/j.jbusres.2019.09.022

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118-144. https://doi.org/10.1016/j.jsis.2019.01.003