Artificial Intelligence (AI) is transforming industries through automation, improved decision-making, and human-like interactions. However, many organizations struggle to fully harness its potential due to its complexity and the need for specialized expertise. In her doctoral thesis, Maria Kandaurova explores how large organizations turn to low-code AI platforms to bridge this gap. While these platforms promise to simplify AI adoption, their implementation is often more complex than expected. Her research examines how organizations navigate these challenges and what factors influence the ability of LC AI platforms to drive value creation.
What challenges do you focus on in your research?
“In my doctoral thesis, I investigate how large organizations pursue value creation when implementing low-code AI (LC AI) platforms and how the platform influences this process.
Low-code AI platforms are digital tools that combine AI capabilities with low-code development environments, to enable organizations to create AI-based applications that mimic human cognition. By offering features like drag-and-drop interfaces, prebuilt components, and AI-powered functionalities, these platforms simplify development and make advanced AI accessible to a broader audience, including non-technical users.”
How do you address the problem?
“My research is based on a qualitative case study of a specific low-code AI platform used by eight large organizations across different industries. I examine how these businesses navigate the complexities of implementation, uncovering key sociotechnical adaptation processes that enable organizations to move beyond initial efficiency gains and unlock long-term value creation. By developing a conceptual process model, the research provides a structured understanding of how organizations engage with LC AI platforms and the factors that influence their successful adoption.”
What are the main findings?
“The research identifies three key adaptation processes:
- Cognitive Understanding – developing a shared understanding of the technology’s capabilities beyond the hype
- Contextual Adaptation – customizing it to fit unique business needs
- Infrastructure Compatibility Evaluation – assessing and adapting existing IT infrastructure for seamless integration.
The study also highlights the dual role of LC AI platforms: as drivers of organizational change, pushing businesses to rethink processes and embrace flexibility, and as enablers of data-driven learning and innovation, allowing organizations to refine AI applications and continuously improve business operations over time. Additionally, the research cautions against a narrow focus on short-term efficiency gains, emphasizing the need for organizations to take a long-term approach to unlock sustained business impact through iterative adaptation and engagement.”
What do you hope your research will lead to?
“By providing empirical insights into the implementation of low-code AI platforms, this research contributes to a deeper understanding of how organizations can unlock their full potential. The findings are valuable for both academics and practitioners. For researchers, it advances knowledge on generative systems and digital transformation, offering a new process model that explains the sociotechnical dynamics of LC AI implementation. For business leaders, it provides practical guidance on how to move beyond short-term efficiency gains and leverage low-code AI for long-term innovation and strategic transformation. This research underscores that successful adoption of low-code AI platforms is not a one-time event but an ongoing process of learning, adaptation, and integration that shapes an organization’s ability to create value.”
Read the thesis: Pursuing Value Creation through Low-Code AI: Sociotechnical Dynamics of Low-Code AI Platform Implementation in Large Organizations
Public defence: 28 March 2025 at 13.15, see link above.
Thesis supervisor
- Head of Unit, Innovation and R&D Management, Technology Management and Economics