The Marketing Function refers to the organizational department responsible for developing and executing strategies to promote products, services, and brand positioning within enterprises. In the context of modern digital-native companies, the marketing function represents a critical yet historically non-technical business area that has begun to experience significant transformation through artificial intelligence (AI) adoption and deployment.

The marketing function encompasses a broad range of activities including market research, campaign development, customer segmentation, content creation, performance analysis, and brand management. Traditionally, marketing has relied on creative talent, business acumen, and analytical methodologies rather than deep technical infrastructure. However, the emergence of AI and machine learning technologies has begun to reshape how marketing organizations operate, from automating routine tasks to enabling more sophisticated predictive analytics and personalization strategies ¹⁾.

Despite the growing focus on AI deployment across enterprise organizations, the marketing function faces particular challenges in fully embedding artificial intelligence into its core operations. Research indicates that marketing typically ranks fifth or lower among business functions in terms of AI maturity and integration depth, even within companies explicitly pursuing comprehensive AI deployments ²⁾.

Several factors contribute to this maturity gap. The marketing function operates with different technical requirements compared to data-intensive functions like finance or operations. Marketing teams often lack in-house expertise in machine learning infrastructure, data engineering, and model deployment—competencies that have become increasingly essential as organizations move beyond basic analytics toward predictive modeling and generative AI applications. Additionally, the creative and subjective nature of marketing work creates inherent challenges for standardization and automation compared to more rule-based business processes.

The position of marketing as a lower-maturity function for AI adoption presents both challenges and opportunities. Organizations struggle to leverage advanced AI capabilities for customer journey mapping, demand forecasting, and personalized content generation at scale. This maturity gap can result in marketing teams operating with legacy analytics platforms, limited predictive capabilities, and insufficient integration with broader enterprise data ecosystems.

For digital-native companies—organizations built from inception with technology at their core—the persistent gap in marketing AI maturity is particularly noteworthy. These companies typically excel at AI integration in technical domains like product development and infrastructure, yet encounter unexpected obstacles when attempting to apply similar AI methodologies to marketing operations ³⁾.

Addressing the AI maturity gap in marketing functions requires targeted investment in several areas: development of marketing-specific AI tools and platforms, upskilling of marketing personnel in data literacy and AI fundamentals, integration of marketing data into unified data architectures, and creation of governance frameworks for responsible AI use in customer-facing applications. Organizations that successfully bridge this gap may achieve significant competitive advantages through improved customer targeting, more efficient campaign optimization, and enhanced predictive capabilities in demand forecasting and market analysis.

• Operations and Supply Chain Function
• Sales and Customer Service Function
• Finance Function