From Concept to Reality: Integrating AI-Powered Tools and Database Technology in a Renewable Energy Consultancy Company
Amirnia, Ali (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024110627485
https://urn.fi/URN:NBN:fi:amk-2024110627485
Tiivistelmä
This explores the integration of Artificial Intelligence (AI) and advanced database management systems into the operations of a leading Finnish renewable energy consultancy company. In an industry where efficiency and innovation are crucial, AI is becoming increasingly essential for staying competitive. The research assesses the company’s readiness for AI adoption, focusing on its technological infrastructure, organizational culture, people, and alignment with strategic business goals.
Using mainly quantitative but also qualitative methods—such as surveys, interviews, and reviews of relevant literature—the study identifies key constructs that impact AI implementation. To guide this, an integrated framework combining the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Cross-Industry Standard Process for Data Mining (CRISP-DM) was developed. This framework examines both human and technical elements involved in AI adoption.
The analysis reconfirms the current strength in all four main focusing areas of the study and also reveals several challenges, including technological gaps, resistance within the organization, and the need for training and a new set of skills. Phased approaches, known as the “On the Move” 2-10-3 strategy and R.E.A.C.H. program, are proposed to address these issues and ensure successful AI integration over time. The study concludes that while the company is well-positioned to benefit from AI, it must prioritize staff training, improve the integration of digital tools, and strengthen leadership engagement. Implementing these steps will enhance operational efficiency, improve decision-making, and support the company’s broader goals in renewable energy.
Using mainly quantitative but also qualitative methods—such as surveys, interviews, and reviews of relevant literature—the study identifies key constructs that impact AI implementation. To guide this, an integrated framework combining the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Cross-Industry Standard Process for Data Mining (CRISP-DM) was developed. This framework examines both human and technical elements involved in AI adoption.
The analysis reconfirms the current strength in all four main focusing areas of the study and also reveals several challenges, including technological gaps, resistance within the organization, and the need for training and a new set of skills. Phased approaches, known as the “On the Move” 2-10-3 strategy and R.E.A.C.H. program, are proposed to address these issues and ensure successful AI integration over time. The study concludes that while the company is well-positioned to benefit from AI, it must prioritize staff training, improve the integration of digital tools, and strengthen leadership engagement. Implementing these steps will enhance operational efficiency, improve decision-making, and support the company’s broader goals in renewable energy.