Pursuing Value Creation through Low-Code AI: Sociotechnical Dynamics of Low-Code AI Platform Implementation in Large Organizations
Doctoral thesis, 2025
Low-code AI platforms, recognized as “next-generation” digital tools, 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. Characterized by generativity, low-code AI platforms hold significant potential for value creation. However, despite their promise, many projects fall short of expectations, often due to implementation challenges and limited understanding of the processes required to realize their transformative potential.
This thesis investigates how large organizations pursue value creation through the implementation of low-code AI platforms and how these platforms influence this process. Based on a qualitative, embedded case study of a specific low-code AI platform that integrates machine learning (ML), natural language processing (NLP), and a low-code (LC) software development environment, this thesis examines how eight large organizations across diverse industries engage in value creation during platform implementation.
Key findings of this thesis are synthesized into a conceptual process model that highlights three adaptation processes that organizations must engage in before value creation can occur: cognitive understanding, contextual adaptation, and infrastructure compatibility evaluation. The model also emphasizes the dual role of low-code AI platforms as: (1) drivers of organizational change, and (2) enablers of data-driven learning and innovation. Finally, the findings caution against a narrow focus on efficiency gains and cost reduction, which are typically associated with low-code AI. Instead, they emphasize the distinction between short-term and long-term value paths.
This thesis responds to calls from information systems (IS) and management scholars for a deeper understanding of low-code AI platforms by addressing gaps in the existing literature. It provides insights into the sociotechnical dynamics underpinning their implementation and offers practical guidance for leveraging their generative potential to drive organizational transformation and long-term value creation.
This thesis investigates how large organizations pursue value creation through the implementation of low-code AI platforms and how these platforms influence this process. Based on a qualitative, embedded case study of a specific low-code AI platform that integrates machine learning (ML), natural language processing (NLP), and a low-code (LC) software development environment, this thesis examines how eight large organizations across diverse industries engage in value creation during platform implementation.
Key findings of this thesis are synthesized into a conceptual process model that highlights three adaptation processes that organizations must engage in before value creation can occur: cognitive understanding, contextual adaptation, and infrastructure compatibility evaluation. The model also emphasizes the dual role of low-code AI platforms as: (1) drivers of organizational change, and (2) enablers of data-driven learning and innovation. Finally, the findings caution against a narrow focus on efficiency gains and cost reduction, which are typically associated with low-code AI. Instead, they emphasize the distinction between short-term and long-term value paths.
This thesis responds to calls from information systems (IS) and management scholars for a deeper understanding of low-code AI platforms by addressing gaps in the existing literature. It provides insights into the sociotechnical dynamics underpinning their implementation and offers practical guidance for leveraging their generative potential to drive organizational transformation and long-term value creation.
digital platforms
generativity
value creation
low-code AI platforms
Vasa C salen, TME, Vera Sandbergs Allé 8, Göteborg
Opponent: Associate Professor Anna Essén, Handelshögskolan i Stockholm
Author
Maria Kandaurova
Chalmers, Technology Management and Economics, Entrepreneurship and Strategy
GOVERNANCE IN IMPLEMENTING WEAKLY STRUCTURED INFORMATION SYSTEMS
Information Systems,;Vol. 5(2023)p. 1-16
Paper in proceeding
Initiating and expanding data network effects: A longitudinal case study of generativity in the evolution of an AI platform
Proceedings of the Annual Hawaii International Conference on System Sciences,;(2024)p. 6250-6259
Paper in proceeding
The Promise and Perils of Low-Code AI Platforms
MIS Quarterly Executive,;Vol. 23(2024)p. 275-289
Journal article
Mansoori, Y., Kandaurova, M., & Bumann, A. ‘Everyone’Can Be an Entrepreneur: The Rise of Low-Code/No-Code Entrepreneurship.
Artificial Intelligence (AI) is transforming industries by automating tasks, enhancing decision-making, and enabling human-like interactions. However, many organizations struggle to unlock AI’s full potential due to its complexity and the need for specialized expertise. Low-code AI (LC AI) platforms aim to bridge this gap by combining AI with user-friendly development tools, allowing organizations to build AI-powered applications without extensive programming skills. While these platforms promise to make AI more accessible, successful implementation is not guaranteed. Despite predefined features and pretrained capabilities, LC AI platforms are not plug-and-play solutions.
This research explores how large organizations implement LC AI platforms to create value. Based on a case study of eight companies, this thesis identifies three key adaptation processes: (1) developing a shared understanding of the technology’s capabilities beyond the hype, (2) customizing it to fit unique business needs, and (3) assessing and adapting existing IT infrastructure for seamless integration. The study also reveals that LC AI platforms serve a dual role: they drive organizational change, pushing businesses to rethink processes and embrace flexibility, and enable data-driven learning and innovation, allowing organizations to refine AI applications and business operations over time.
This thesis offers new insights for researchers on how organizations implement LC AI, highlighting the key processes that shape success. For business leaders, it warns against focusing solely on efficiency gains and cost reduction associated with LC AI. Instead, it emphasizes that the real value of LC AI lies in its long-term impact through continuous data collection, learning, and innovation.
This research explores how large organizations implement LC AI platforms to create value. Based on a case study of eight companies, this thesis identifies three key adaptation processes: (1) developing a shared understanding of the technology’s capabilities beyond the hype, (2) customizing it to fit unique business needs, and (3) assessing and adapting existing IT infrastructure for seamless integration. The study also reveals that LC AI platforms serve a dual role: they drive organizational change, pushing businesses to rethink processes and embrace flexibility, and enable data-driven learning and innovation, allowing organizations to refine AI applications and business operations over time.
This thesis offers new insights for researchers on how organizations implement LC AI, highlighting the key processes that shape success. For business leaders, it warns against focusing solely on efficiency gains and cost reduction associated with LC AI. Instead, it emphasizes that the real value of LC AI lies in its long-term impact through continuous data collection, learning, and innovation.
Areas of Advance
Information and Communication Technology
Subject Categories (SSIF 2025)
Business Administration
Science and Technology Studies
Information Systems
Artificial Intelligence
ISBN
978-91-8103-188-1
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5646
Publisher
Chalmers
Vasa C salen, TME, Vera Sandbergs Allé 8, Göteborg
Opponent: Associate Professor Anna Essén, Handelshögskolan i Stockholm