Year: 2026
Type: dissertation
Abstract: Artificial intelligence is being adopted in organizations at a rapid pace. However, many struggle to move from pilots to AI value creation. Prior research highlights the importance of organizational capability, over access to technologies, in suc-cessfully deploying AI. The energy sector specifically is an interesting setting for this challenge. The changes that occur in the sector are making the system more complex while regulation and a mandate for security-of -supply shape how AI can be introduced. This thesis assesses the state of AI capability maturity across Finnish elec-tricity and heat companies and examines what drives capability development within them. The study applies an existing AI capability maturity model as an analytical tool. The empirical data come from semi-structured interviews with manager and executive-level professionals responsible for data, analytics, and digital develop-ment in ten case companies. The research follows a pragmatist, abductive, quali-tative case-study design, combining a category analysis with a cross-case the-matic analysis. Firstly, the results show that companies vary in their AI capability maturity: companies could be grouped into three distinct maturity categories, where maturity increased relatively evenly across capabilities from the lowest to the highest cate-gory. Secondly, the industry-specific enablers of AI adoption and capability includ-ed the increasing level of complexity in the industry, its experience with regulation, and its rich measurement data. Obstacles included the uneven distribution of benefits across the workforce, the risk of errors in a low-margin critical-infrastructure environment, and weak competitive pressure due to industry struc-tures. Leadership, competence, and staff capacity were seen as decisive for ca-pability development, more so than technological aspects. The study's contribution is fourfold. Theoretically, it reinforces the resource-based view in that value from AI is harnessed from tangible resources through intangible and human resources. Empirically, it provides a descriptive picture of AI capability of Finnish energy sector across different nine dimensions. Methodologi-cally, it tests an existing maturity model and shows that a research design incorpo-rating an a maturity model as an analytical tool can produce nuanced findings. Practically, it suggests that work towards better AI capabilities can begin with an act of defining what AI means for an organization.
Source: Aaltodoc (Aalto University)
Author: Rantala, Eemil
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204017692