Adoption and Effectiveness of AI-Personalized Email Marketing: A Cross-Industry Study of Indian Organizations
This doctoral research examines how organizations adopt artificial intelligence for email personalization and whether its adoption improves marketing effectiveness. It also explores the roles of organizational readiness, internal AI governance and external regulatory pressure.
Researcher: Dr. Kaustubh Patil
Qualification: Doctor of Business Administration
Institution: École Supérieure de Gestion et de Commerce International (ESGCI), Paris
Year: 2026
Research approach: Quantitative, cross-sectional study
230
Survey responses
181
Eligible responses
5
Industries represented
India
Cross-industry research
Examine whether the adoption of AI-personalized email marketing improves perceived marketing effectiveness.
Assess whether AI adoption improves self-reported email campaign KPIs.
Test whether organizational readiness influences the relationship between AI adoption and marketing effectiveness.
Explore the influence of internal AI governance and external regulatory pressure.
Primary factor
AI-personalized email marketing adoption
Effectiveness outcomes
Perceived marketing effectiveness
KPI-oriented self-reported effectiveness
Moderating factors
Organizational readiness
Internal AI governance
External regulatory pressure
Approach: Quantitative and deductive
Design: Cross-sectional survey
Scale: Five-point Likert scale
Sampling: Purposive and convenience sampling
Main analysis sample: 181 eligible respondents
Analysis: Reliability, validity, correlation, regression and moderation testing
Statistical adjustment: HC3 robust standard errors
The study examined relationships and associations; it did not claim that AI adoption directly caused improved marketing results.
Key Research Findings
AI adoption improved perceived effectiveness
Higher adoption of AI-personalized email marketing was significantly associated with stronger perceived marketing effectiveness.
B = 0.359 | p < .001
AI adoption did not significantly improve reported KPIs
The relationship between AI adoption and KPI-oriented self-reported effectiveness was not statistically significant.
B = 0.073 | p = .470
Organizational and governance factors did not change the relationship
Organizational readiness, internal AI governance and external regulatory pressure did not significantly moderate the relationship between AI adoption and either effectiveness outcome.
Central insight: Organizations may perceive AI personalization as effective before they can demonstrate corresponding improvement through measurable campaign KPIs.
Separates perceived marketing effectiveness from KPI-oriented effectiveness instead of treating them as one outcome.
Provides cross-industry evidence on AI-personalized email marketing adoption in Indian organizations.
Examines organizational readiness, AI governance and regulatory pressure within one research framework.
Highlights the possible gap between perceived benefits and measurable campaign performance.
Define measurable KPIs before implementing AI personalization.
Strengthen CRM integration, data quality and employee capabilities.
Connect each personalization initiative to a clear campaign objective.
Use controlled testing to distinguish genuine improvement from perceived value.
Maintain clear AI governance, privacy and approval processes.
Avoid assuming that greater AI adoption will automatically improve campaign results.
AI personalization creates greater value when technology adoption is supported by reliable data, clear measurement, skilled teams and responsible governance.
The research used a cross-sectional design, capturing responses at one point in time.
Findings were based on self-reported survey responses.
Campaign KPI values were reported in categories and were not independently verified.
Convenience and purposive sampling limit broad generalisation.
Internal AI governance and competitive pressure were measured using single items.
Multiple respondents may have represented the same organization.
Conduct longitudinal studies to measure performance changes over time.
Use verified campaign data such as clicks, conversions and revenue.
Compare AI-personalized campaigns with non-AI campaign control groups.
Develop multi-item measurements for AI governance and competitive pressure.
Conduct industry-specific and international comparative studies.
Combine surveys with interviews for deeper organizational insights.
These limitations define the boundaries of the study and provide directions for stronger future research.
The full doctoral dissertation presents the literature review, conceptual framework, research methodology, statistical analysis, findings, practical implications and recommendations in detail.
Suggested citation
Patil, K. (2026). Adoption and Effectiveness of AI-Personalized Email Marketing: A Cross-Industry Study of Indian Organizations. Doctor of Business Administration dissertation, École Supérieure de Gestion et de Commerce International (ESGCI), Paris.