| Yhteenveto: | This thesis investigates how business-to-business (B2B) companies leverage artificial intelligence (AI) to personalize the B2B customer journey and enhance data-driven decision-making in marketing. While the adoption of AI-powered personalization is widespread in business-to-consumer (B2C) contexts, its application in B2B remains underdeveloped because of complex sales cycles, multiple stakeholders and fragmented customer data. This study addresses that gap by exploring how AI and data technologies enable scalable personalization particularly during the early engagement and pre-purchase stages where personalization has the greatest strategic impact.
Based on a qualitative study involving semi-structured interviews with AI and marketing professionals from various B2B industries, the research identifies four key enablers of effective AI-driven personalization: customer insight activation, data-driven personalization, AI-enabled execution, and organizational alignment. The findings reveal that AI supports B2B personalization throughout the entire customer journey by enabling predictive profiling, dynamic content delivery, and real-time behaviour adaptation. However, its successful implementation requires proper data infrastructures, cross-functional collaborations, and balancing automation with human oversight.
The research contributes theoretically by extending personalization and AI frameworks from B2C to B2B contexts to highlight how the strategic role of personalization builds trust and improves engagement. The research provides practical advice to practitioners who want to deploy AI-powered personalization at scale by highlighting early engagement, predictive tools, and organizational readiness as critical enablers for implementing personalization at scale. This study positions AI as a strategic enabler of scalable, personalized customer engagement in B2B marketing.
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