Generative artificial intelligence in content marketing production : Exploring acceptance and adoption in marketing agencies
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Generative AI is fundamentally changing how content marketing is produced. Marketing agencies, which have traditionally provided clients with creative, strategic, and technical expertise, now face a situation where AI tools enable a much broader range of actors to produce professional-quality content.
This thesis examines how generative AI is used and accepted in content marketing production within Finnish marketing agencies. The theoretical framework is the Technology Acceptance Model (TAM), extended with individual, organisational, and environmental factors. The study is qualitative and interpretivist, drawing on eight semi-structured interviews conducted across four Finnish marketing agencies of varying sizes, analysed using thematic analysis.
The findings broadly confirm the relevance of TAM while revealing its limitations. Perceived usefulness is the most significant driver of adoption, and perceived ease of use varies by tool type. Language models integrate easily into daily workflows, while visual and video tools present more varied adoption patterns, shaped by interface complexity, output quality, and the reliability of what the tools produce. Professional identity emerged as a significant factor among creative practitioners, who describe a tension between acknowledging GenAI as useful and feeling that AI-assisted work is less fully their own.
At the organisational level, clear governance and structured knowledge sharing are associated with more consistent use, while data security and copyright considerations form practical boundaries on client work. At the environmental level, competitive pressure and client expectations shape which GenAI applications are prioritised. The key theoretical contribution of the study is the identification of professional identity as a significant factor in GenAI acceptance, extending TAM in the context of creative professional work.
The findings also reveal meaningful variation across agency sizes. Larger agencies have developed more structured governance and dedicated AI training
programmes, while smaller agencies rely more heavily on individual initiative and self-directed learning, with support structures still forming. The study suggests that realising the full potential of GenAI in creative agency work requires deliberate attention to how freed-up capacity is directed, how ethical boundaries around client data and intellectual property are maintained, and how professional confidence is supported during a period of significant occupational change.