Strategic Roadmapping for AI Integration in Multinational Retail: Governance, Change Management, and Performance Outcomes
DOI:
https://doi.org/10.61978/novatio.v2i2.848Keywords:
Artificial Intelligence, Retail Innovation, Strategic Roadmapping, Governance, Change Management, Personalization, Technology AdoptionAbstract
The adoption of Artificial Intelligence (AI) in the retail sector has become a crucial driver of transformation, enabling new forms of customer engagement and operational efficiency. This study investigates strategic roadmapping for AI integration in multinational retail by applying the Technology–Organization–Environment (TOE) framework. The research specifically addresses the gap in understanding how governance mechanisms and change management practices shape sustainable AI adoption across diverse markets. The study employs a mixed methodological approach, combining multi-case qualitative analysis with longitudinal data and quantitative performance metrics. Data include industry benchmarks, customer engagement indicators, operational efficiency measures, and revenue outcomes. This design allows the assessment of AI’s influence on both customer-facing innovations and backend optimizations. Findings indicate that phased AI adoption, clear governance, and measurable KPI milestones are essential to move from pilot projects to full-scale deployment. Case evidence highlights benefits such as revenue gains through personalization, improved supply chain efficiency, and margin enhancement. Challenges including model drift, bias, and talent shortages are also identified, along with strategies for mitigation such as continuous monitoring and targeted upskilling. The study concludes that scaling AI requires balancing innovation with ethical and regulatory compliance. Effective change management, strong stakeholder engagement, and a culture of continuous learning are crucial to maintain momentum. Importantly, the lessons from retail AI adoption are transferable to other industries such as healthcare, finance, and education. Findings indicate that phased AI adoption, clear governance, and measurable KPI milestones are essential to move from pilot projects to full-scale deployment. Case evidence highlights benefits such as revenue gains through personalization, improved supply chain efficiency, and margin enhancement. Challenges including model drift, bias, and talent shortages are also identified, along with strategies for mitigation such as continuous monitoring and targeted upskilling.
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