INTEGRAL ASSESSMENT OF AI MARKETING MATURITY IN HEALTHCARE INSTITUTIONS AND AN ARCHETYPE SYSTEM
Abstract
Purpose of the article. The purpose of this study is to develop and validate an integral indicator of AI marketing maturity in healthcare institutions, as well as to establish a typology of institutions based on their level of maturity. Methodology of research. The research methodology is based on a systems approach, combining integral assessment with qualitative analytical methods, in particular in-depth interviews. The validation of the proposed indicator was carried out using data from the authors’ empirical study, which covered 34 healthcare institutions and included 34 in-depth interviews. Findings. The study develops and validates an integral index of AI marketing maturity (AIMM), which combines the assessment of AI usage and its effectiveness across individual marketing channels with a managerial control indicator. A mechanism for adjusting the maturity indicator is proposed, taking into account the level of managerial control; in the presence of such control, the adjusted indicator increases by 30% relative to the baseline value. In addition, a system of five archetypes of healthcare institutions according to their level of AI marketing maturity has been сформulated, enabling a structured classification of organizations. Practical value. The practical significance of the study lies in the applicability of the proposed AI Marketing Maturity Integral Index (AIMM) as a self-calibrating diagnostic tool for healthcare institutions. It is the first quantitative measure of AI marketing maturity at the marketing channel level, adapted to the healthcare sector and incorporating managerial control as a multiplicative factor. The AIMM does not rely on external benchmarks and enables data-based self-assessment using internal institutional data. The study also introduces a measurement gap indicator (MGⱼ = Eⱼ / max(Uⱼ, 1)) for identifying mismatches between AI usage and effectiveness, and a validated five-archetype typology supporting managerial decision-making in AI marketing development.
References
Gartner. AI Maturity Model. 2024. URL: https://www.gartner.com/en/information-technology/topics/ai-maturity (дата звернення: 01.03.2026).
Forrester Research. AI Maturity Assessment. 2024. URL: https://www.forrester.com/ (дата звернення: 01.03.2026).
McKinsey & Company. Digital Quotient. 2023. URL: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/how-to-build-a-data-driven-company (дата звернення: 01.03.2026).
Deloitte. AI maturity: Mature adopters capture more value. 2025. URL: https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/state-of-ai-and-intelligent-automation-in-business-survey.html (дата звернення: 01.03.2026).
Think with Google / BCG. Digital Maturity: Benchmark your organization. 2019. URL: https://www.thinkwithgoogle.com/intl/en-154/marketing-strategies/data-and-measurement/digital-marketing-maturity/ (дата звернення: 01.03.2026).
Smart Insights. Digital Marketing Maturity. 2024. URL: https://www.smartinsights.com/ (дата звернення: 01.03.2026).
HIMSS. Digital Health Indicator. 2024. URL: https://www.himss.org/what-we-do-solutions/digital-health-transformation/maturity-models (дата звернення: 01.03.2026).
Damo Consulting. DigiMTM Healthcare Digital Maturity Model. 2024. URL: https://damoconsulting.net/ (дата звернення: 01.03.2026).
Gallo P., Cúg J., Dobrovč J., Balogová B. Perceptions of AI in the Context of Marketing Innovations of Healthcare Institutions. Marketing and Management of Innovations. 2025. DOI: https://doi.org/10.21272/mmi.2025.1-05 (дата звернення: 01.03.2026).
McKinsey & Company. The State of AI. 2026. URL: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai (дата звернення: 01.03.2026).
Yang X. et al. Factors Influencing Adoption of Large Language Models in Health Care. JMIR. 2025. DOI: https://doi.org/10.2196/84918 (дата звернення: 01.03.2026).
Kolasa K. et al. How to build trust toward AI in healthcare: Polish cross-sectional study. Health Policy. 2025. DOI: https://doi.org/10.1016/j.healthpol.2025.105379 (дата звернення: 01.03.2026).
Острозька Г. Й., Олексієнко О. О. Штучний інтелект в умовах сучасних підприємств та маркетингових кампаній. Маркетинг і цифрові технології. 2023. Т. 7. № 3. С. 66–82. DOI: https://doi.org/10.15276/mdt.7.3.2023.5 (дата звернення: 01.03.2026).
Струнгар А. В. Вплив штучного інтелекту на стратегії цифрового маркетингу. Економіка та суспільство. 2024. № 62. DOI: https://doi.org/10.32782/2524-0072/2024-62-160 (дата звернення: 01.03.2026).
Cholewa-Wiktor M., Kozłowski E., Jałowiec T. Marketing Communication as a Tool Supporting AI Acceptance in Medical Services. Silesian University of Technology. 2025. DOI: https://doi.org/10.29119/1641-3466.2025.224.2 (дата звернення: 01.03.2026).
Gartner. (2024). AI Maturity Model. Available at: https://www.gartner.com/en/information-technology/topics/ai-maturity (accessed March 1, 2026).
Forrester Research. (2024). AI Maturity Assessment. Available at: https://www.forrester.com/ (accessed March 1, 2026).
McKinsey & Company. (2023). Digital Quotient. Available at: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/how-to-build-a-data-driven-company (accessed March 1, 2026).
Deloitte. (2025). AI maturity: Mature adopters capture more value. Available at: https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/state-of-ai-and-intelligent-automation-in-business-survey.html (accessed March 1, 2026).
Think with Google, & BCG. (2019). Digital Maturity: Benchmark your organization. Available at: https://www.thinkwithgoogle.com/intl/en-154/marketing-strategies/data-and-measurement/digital-marketing-maturity/ (accessed March 1, 2026).
Smart Insights. (2024). Digital Marketing Maturity. Available at: https://www.smartinsights.com/ (accessed March 1, 2026).
HIMSS. (2024). Digital Health Indicator. Available at: https://www.himss.org/what-we-do-solutions/digital-health-transformation/maturity-models (accessed March 1, 2026).
Damo Consulting. (2024). DigiMTM Healthcare Digital Maturity Model. Available at: https://damoconsulting.net/ (accessed March 1, 2026).
Gallo, P., Cúg, J., Dobrovč, J., & Balogová, B. (2025). Perceptions of AI in the Context of Marketing Innovations of Healthcare Institutions. Marketing and Management of Innovations. DOI: https://doi.org/10.21272/mmi.2025.1-05
McKinsey & Company. (2026). The State of AI. Available at: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai (accessed March 1, 2026).
Yang, X., et al. (2025). Factors Influencing Adoption of Large Language Models in Health Care. JMIR. DOI: https://doi.org/10.2196/84918
Kolasa, K., et al. (2025). How to build trust toward AI in healthcare: Polish cross-sectional study. Health Policy. DOI: https://doi.org/10.1016/j.healthpol.2025.105379
Ostrozka, H. Y., & Oleksiienko, O. O. (2023). Shtuchnyi intelekt v umovakh suchasnykh pidpryiemstv ta marketynhovykh kampanii [Artificial intelligence in the context of modern enterprises and marketing campaigns]. Marketynh i tsyfrovi tekhnolohii – Marketing and Digital Technologies, no. 7(3), pp. 66–82. DOI: https://doi.org/10.15276/mdt.7.3.2023.5 [in Ukrainian].
Strunhar, A. V. (2024). Vplyv shtuchnoho intelektu na stratehii tsyfrovoho marketynhu [The impact of artificial intelligence on digital marketing strategies]. Ekonomika ta suspilstvo – Economy and Society, no. 62. DOI: https://doi.org/10.32782/2524-0072/2024-62-160 [in Ukrainian].
Cholewa-Wiktor, M., Kozłowski, E., & Jałowiec, T. (2025). Marketing Communication as a Tool Supporting AI Acceptance in Medical Services. Silesian University of Technology. DOI: https://doi.org/10.29119/1641-3466.2025.224.2
Copyright (c) 2026 А.В. Фігун, І.О. Дейнега
This work is licensed under a Creative Commons Attribution 4.0 International License.
