Strategic Framework for Artificial Intelligence Adoption in Business
The adoption of Artificial Intelligence (AI) in business operations has become an increasingly important strategy for companies striving to remain competitive and efficient in evolving market.
Master thesis study (Ojanen 2025) seeks to provide actionable strategies for integrating AI into project business operations and reshaping leadership practices in an engineering company. The thesis focuses on AI’s impact on operational efficiency and decision-making, challenges in ethics, transparency, data privacy and cybersecurity, particularly in business critical delivery projects. Additionally, it dives into AI training, adaptation, and the obstacles to its successful integration.
AI’s impact on operational efficiency
AI can be seen as a positive force capable of improving operational efficiency and decision-making. In several ways AI could benefit business operations, including automatize routine tasks, optimizing resource distribution, and improving process management. High human resource demand and project prioritization could be improved by utilization of AI automation, which could release time for more strategic tasks. This aligns with Helo and Hao (2022) who emphasize AI’s role in enhancing resource utilization and decreasing cycle time.
Companies must carefully select AI use cases that offer both value and scalability. The absorption of AI into decision-making processes is particularly promising, highlighting AI’s future to help decision-making by integrating and analyzing large datasets across multiple tools. However, companies need to establish understandable procedures on the use of AI in decision-making, especially regarding the types and qualities of data that can be safely and ethically utilized. Understanding risks and opportunities of the AI usage need to be carefully assessed and proper guidelines to be established.
Efficient AI integration for the business activities start from establishing AI team and recruiting AI specialists for the company. The AI team’s key task in the beginning is to establish AI vision and strategy, which guides the company forward in AI integration. Companies need to establish AI training plans and start AI training to improve AI awareness.
Enhancing decision-making
AI can improve decision-making and operational efficiency by combining data from various tools, offering more efficient processing of large data sets. It can automate tasks such as resource planning, finance estimates, presentation creation, competitor analysis, and report generation. Korostin (2024) states that AI can also improve process accuracy and reduce human error, ensuring higher operational safety and quality. This is crucial in contexts where there is no room for mistakes, such as in operational safety, delivery timelines, and cost management. Companies must establish clear guidelines for what kind of data can be used with the AI tools and how AI can be used in the decision-making in the business.
Ethical and transparency challenges
The absorption of AI into decision-making procedures also raises concerns related to transparency and accountability. Concerns exist about trusting AI systems, particularly in areas where human oversight is critical. The importance is to verify that AI models are transparent, explainable, and accountable for their actions. Vilone and Longo (2021) focused attention on the importance of designing AI environments that provide clarity around decision-making processes to foster trust among users.
One of the key threats is AI, which makes decisions based on biased or incomplete data, leading to unfair or undesirable outcomes. Human oversight is needed to verify AI-generated decisions and ensure that final decisions remain in the hands of qualified human leaders. Also, reliance on AI could result in biases and suffocate creativity, as AI lacks the nuanced understanding that humans bring to complex decision-making.
Determining AI governance including ethics, data, and risks in the AI strategy creation is important to keep future AI works diverse and equal. This is a complex task as cultures, norms, rules, and laws are different all around the world.
Data privacy and cybersecurity
AI adoption presents significant challenges related to data privacy and security. Concerns are risks of data breaches, intellectual property leaks, and cybersecurity threats. To address these concerns, companies must implement strong cybersecurity protocols, ensure compliance with relevant privacy regulations, and establish clear guidelines on how sensitive data can be handled by AI tools.
Companies must also take steps to minimize the risk of over-dependence on AI systems and ensure that employees are adequately trained to use AI tools responsibly. Giving extensive training on AI usage and data security will help mitigate fears around AI adoption and build trust among employees. Companies need to communicate the benefits of AI usage and future competencies required. Otherwise, people who start to use AI might get leverage for the data usage compared to non-AI users. People with AI knowledge might surpass people without AI knowledge.
Conclusion
Incorporating AI into business operations requires a systematic strategy that takes into consideration both the opportunities and risks introduced by this transformative technology. Companies must begin by establishing a clear AI strategy, focusing on building an AI-skilled workforce, and fostering a culture of trust and transparency. AI team need to establish AI vision and strategy including use cases selection, AI technology selection, big data integration with AI, AI governance establishment for data, transparency and accountability, and AI roadmap creation. Moreover, AI tools must be integrated into business operations with careful consideration of data privacy, ethical implications, and human oversight. Through the establishment of an AI Center of Excellence and a strategic roadmap for AI adoption, companies can ensure a smooth transition into the digital age while enhancing operational efficiency and leadership decision-making.
The key to successful AI integration lies not only in technological readiness but also in organizational culture, leadership, and the careful balancing of innovation with ethical considerations. Figure 1 below presents AI strategy actions for efficient AI integration in the companies (Ojanen 2025).
References
Helo, P., & Hao, Y. (2022). Artificial Intelligence in operations management and supply chain management: an exploratory case study. Production Planning & Control, 33(16), 1573–1590. Accessed 28 December 2024.
https://doi-org.ezproxy.turkuamk.fi/10.1080/09537287.2021.1882690
Korostin, O. (2024). Analysis of Ai Effectiveness in Reducing Human Errors in Processing Transportation Requests. German International Journal of Modern Science / Deutsche Internationale Zeitschrift Für Zeitgenössische Wissenschaft, 88, 66–69. Accessed 28 December 2024
https://doi-org.ezproxy.turkuamk.fi/10.5281/zenodo.13786097
Ojanen, E-J. (2025). Development of Operational Management and Leadership with Artificial Intelligence. Theseus.
Vilone, G., & Longo, L. (2021). Notions of explainability and evaluation approaches for explainable Artificial Intelligence. Information Fusion, 76, 89–106. Accessed 28 December 2024.
https://doi-org.ezproxy.turkuamk.fi/10.1016/j.inffus.2021.05.009
Figures and Pictures
Hauntedeyes, L. 2017. Free food. Accessed 16 March 2025.
https://unsplash.com/photos/cXMM-gQY6HU
Hauntedeyes, L. 2018. Robot standing near luggage bags. Unsplash. Accessed 15 March 2025.
https://unsplash.com/photos/robot-standing-near-luggage-bags-hND1OG3q67k