Integrating Artificial Intelligence and Data Analytics into Marketing Education: The Digital-Ready Marketing Education Framework for Developing Graduates in Emerging Economies

Artificial intelligence (AI) and data analytics are reshaping marketing practice, yet many universities in emerging economies have been slow to reflect these changes in their curricula. Limited digital infrastructure, inadequate faculty expertise, and concerns about the ethical use of AI continue to constrain curriculum reform. This study develops the DigitalReady Marketing Education Framework (DRMEF) to guide the integration of AI and data analytics into marketing education. Drawing on a conceptual theory-build…

By the Truvace deskOctober 22, 20261 min read1 sourcePeer-reviewedTRV-2026-2156· cite & verify

In brief

A conceptual study synthesizes existing literature on AI, marketing education, technology adoption, and digital competency to build the DigitalReady Marketing Education Framework (DRMEF). It is meant to guide universities in emerging economies in bringing AI and data analytics into marketing programs.

The work matters because marketing practice is already being reshaped by AI, so graduates trained on outdated curricula may be poorly prepared. The framework is theory-built from literature rather than tested, so its real-world effect on graduates or institutions remains unknown.

Main points

The framework is called the DigitalReady Marketing Education Framework (DRMEF).
It rests on four connected elements: digital infrastructure, curriculum redesign, faculty capability, and ethical AI governance.
The study uses a conceptual theory-building approach that synthesizes literature on AI, marketing education, technology adoption, and digital competency.
Technology adoption alone is not enough; institutional commitment and continuous curriculum development are also required.

The problem

Universities in emerging economies have been slow to update marketing curricula for AI and analytics because of weak digital infrastructure, limited faculty expertise, and ethical concerns.

The rundown

The article identifies a gap between how AI and data analytics are changing marketing work and how slowly universities in emerging economies have adapted their teaching. Named constraints are limited digital infrastructure, inadequate faculty expertise, and ethical concerns about AI use.

To address this, the author builds a framework from a literature synthesis rather than from empirical data. The analysis concludes that integration depends on infrastructure, curriculum redesign, faculty capability, and ethical governance working together.

The framework is presented as a practical guide for higher education institutions, stressing sustained institutional commitment and ongoing curriculum development rather than one-off technology purchases.