From individual chat use to workflow automation: Leveraging generative AI to automate marketing activities in SMEs to create efficiencies
Juhola, Tiina (2026)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202603033674
https://urn.fi/URN:NBN:fi:amk-202603033674
Tiivistelmä
This thesis is a research-based development project that investigates how small and medium-sized enterprises (SMEs) can leverage generative artificial intelligence (GenAI) to enhance marketing efficiency, what steps they should follow as they plan to automate marketing workflows and processes with GenAI, and which factors SMEs should consider when moving from individual use of GenAI tools to adopting GenAI at the team and organisation levels within their processes.
The study draws on existing theoretical frameworks including process automation frameworks (such as Business Process Automation and Robotic Process Automation) and technology adoption models (such as the Technology–Organisation–Environment framework and the Technology Acceptance Model), as well as emerging literature on the use of GenAI in marketing and GenAI adoption strategies.
The empirical study comprised eleven semi-structured interviews with marketing practitioners and AI experts from SMEs. The findings show that SME marketers start their GenAI journey individually with chat-based tools, and that GenAI-embedded marketing workflow automation (with no-code/low-code agent builders or AI agent/Agentic platforms) is not yet common. Successful adoption of GenAI technology depends heavily on organisational readiness, technical foundations such as data and process readiness, and the ability to identify use cases that balance value, feasibility and risk. A learning-by-doing approach is emerging as an important factor in GenAI adoption. The study also highlights the importance of human governance and the role of leadership in building understanding and fostering a culture that supports GenAI adoption through bottom-up ideation and learning opportunities. For marketers, GenAI can support efficiency across content creation, content repurposing, operational, analytical and data-focused work, market research, and idea generation.
As an exploratory study in a rapidly developing area, future research could include longitudinal studies to observe how GenAI use and adoption evolve over time or focus on more specific SME contexts. A deeper assessment of existing adoption frameworks is also needed to better understand their suitability for the iterative, experimental, and rapidly changing nature of generative AI adoption.
The study draws on existing theoretical frameworks including process automation frameworks (such as Business Process Automation and Robotic Process Automation) and technology adoption models (such as the Technology–Organisation–Environment framework and the Technology Acceptance Model), as well as emerging literature on the use of GenAI in marketing and GenAI adoption strategies.
The empirical study comprised eleven semi-structured interviews with marketing practitioners and AI experts from SMEs. The findings show that SME marketers start their GenAI journey individually with chat-based tools, and that GenAI-embedded marketing workflow automation (with no-code/low-code agent builders or AI agent/Agentic platforms) is not yet common. Successful adoption of GenAI technology depends heavily on organisational readiness, technical foundations such as data and process readiness, and the ability to identify use cases that balance value, feasibility and risk. A learning-by-doing approach is emerging as an important factor in GenAI adoption. The study also highlights the importance of human governance and the role of leadership in building understanding and fostering a culture that supports GenAI adoption through bottom-up ideation and learning opportunities. For marketers, GenAI can support efficiency across content creation, content repurposing, operational, analytical and data-focused work, market research, and idea generation.
As an exploratory study in a rapidly developing area, future research could include longitudinal studies to observe how GenAI use and adoption evolve over time or focus on more specific SME contexts. A deeper assessment of existing adoption frameworks is also needed to better understand their suitability for the iterative, experimental, and rapidly changing nature of generative AI adoption.