Developing AI strategy for high-tech industrial company
Kopra, Mikko (2025)
2025
Johtamisen ja tietotekniikan DI-ohjelma - Master's Programme in Management and Information Technology
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
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Hyväksymispäivämäärä
2025-06-16
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202506167200
https://urn.fi/URN:NBN:fi:tuni-202506167200
Tiivistelmä
A successful utilization of Artificial Intelligence (AI) technologies is closely linked to the development and implementation of an effective AI strategy. A well-defined AI strategy helps to identify and prioritize the most suitable AI use cases. This thesis is conducted as a case study for IONCOR, a high-tech industrial company in the battery manufacturing industry. It operates in a data-intensive environment that is highly promising for utilizing AI.
The objective of this thesis is to study: What factors should be considered when creating an AI strategy for a high-tech industrial company? The thesis establishes a theoretical foundation for AI strategy development with a literature review. Based on the literature review results, a seven-step AI strategy development framework was established. Main themes in the AI strategy devel-opment framework are: 1. Setting AI goals, 2. Use case identification and success metrics, 3. Data, 4. People and Skills, 5. Technology selection, 6. Risk analysis and Mitigation plan, and 7. Implementation.
To validate and improve the created AI strategy development framework, the empirical re-search was conducted in a case company. Data were collected through semi-structured inter-views, and the results were compared with the framework that was created. Interview themes and key questions were derived from the AI strategy development framework, and the interview data were analysed using thematic qualitative data analysis. A total of eight participants were inter-viewed in the case company based on their experience in relevant areas. Interview results aligned well with the developed AI strategy development framework. The following are the main empirical findings combined with theory.
As the first step in developing an AI strategy, it is essential to establish AI strategic objectives in collaboration with a cross-functional team and align these objectives with the company’s overall strategic goals and vision. The next step is to identify use cases that are feasible to implement and have clear business value. AI use cases should be prioritized based on value, Return On Investment (ROI), criticality, and complexity. The interview result emphasizes a focus on business value, business-critical topics, and solving business problems. The highest priority use cases should have low complexity and high value. The interview also suggested exploring whether AI presents new business opportunities. Thirdly, you need to identify the available data sources and ensure that the data is of high quality. Data must be clean, well-integrated, and easily accessible. Data security was emphasized in the interview results, along with the importance of privacy, legal, and ethical requirements. The fourth step is to evaluate the organization's current capabilities, maturity, and needed roles. Enhance the organization's AI capabilities through training and work-shops. The fifth step is to do a technology selection with the help of AI experts. The main principle in selecting technology is to select off-the-shelf solutions whenever possible, as there was a clear consensus among interviewees. AI solutions should be flexible, scalable, secure, interoperable with existing systems, and upgradable to accommodate future tasks. The sixth step is to do a risk analysis. Consider the technical, security, operational and financial risks, as well as reputational, ethical and regulatory risks. The latter ones are easy to forget based on interview feedback. The final step is to create an implementation roadmap and plan how to monitor progress. An agile and iterative approach in development should always be preferred when possible.
In conclusion, this study successfully addressed the research question by providing a practical framework for AI strategy development that can be applied in the high-tech manufacturing industry.
The objective of this thesis is to study: What factors should be considered when creating an AI strategy for a high-tech industrial company? The thesis establishes a theoretical foundation for AI strategy development with a literature review. Based on the literature review results, a seven-step AI strategy development framework was established. Main themes in the AI strategy devel-opment framework are: 1. Setting AI goals, 2. Use case identification and success metrics, 3. Data, 4. People and Skills, 5. Technology selection, 6. Risk analysis and Mitigation plan, and 7. Implementation.
To validate and improve the created AI strategy development framework, the empirical re-search was conducted in a case company. Data were collected through semi-structured inter-views, and the results were compared with the framework that was created. Interview themes and key questions were derived from the AI strategy development framework, and the interview data were analysed using thematic qualitative data analysis. A total of eight participants were inter-viewed in the case company based on their experience in relevant areas. Interview results aligned well with the developed AI strategy development framework. The following are the main empirical findings combined with theory.
As the first step in developing an AI strategy, it is essential to establish AI strategic objectives in collaboration with a cross-functional team and align these objectives with the company’s overall strategic goals and vision. The next step is to identify use cases that are feasible to implement and have clear business value. AI use cases should be prioritized based on value, Return On Investment (ROI), criticality, and complexity. The interview result emphasizes a focus on business value, business-critical topics, and solving business problems. The highest priority use cases should have low complexity and high value. The interview also suggested exploring whether AI presents new business opportunities. Thirdly, you need to identify the available data sources and ensure that the data is of high quality. Data must be clean, well-integrated, and easily accessible. Data security was emphasized in the interview results, along with the importance of privacy, legal, and ethical requirements. The fourth step is to evaluate the organization's current capabilities, maturity, and needed roles. Enhance the organization's AI capabilities through training and work-shops. The fifth step is to do a technology selection with the help of AI experts. The main principle in selecting technology is to select off-the-shelf solutions whenever possible, as there was a clear consensus among interviewees. AI solutions should be flexible, scalable, secure, interoperable with existing systems, and upgradable to accommodate future tasks. The sixth step is to do a risk analysis. Consider the technical, security, operational and financial risks, as well as reputational, ethical and regulatory risks. The latter ones are easy to forget based on interview feedback. The final step is to create an implementation roadmap and plan how to monitor progress. An agile and iterative approach in development should always be preferred when possible.
In conclusion, this study successfully addressed the research question by providing a practical framework for AI strategy development that can be applied in the high-tech manufacturing industry.