Introduction
With the rapid expansion of artificial intelligence (AI) systems across public and private sectors, coordinated governance and purposeful stakeholder engagement have become essential. This study introduces a compact framework that combines SWOT analysis with stakeholder prioritization to support transparent, effective, and sustainable AI governance.
Methodology
The research follows three main steps: (1) Identifying internal and external drivers of AI governance through SWOT analysis; (2) Recognizing key stakeholders—including regulators, technology companies, research institutions, civil society organizations, and end users—and assessing them according to five criteria: influence, importance, financial capacity, technical expertise, and legitimacy; (3) Quantifying expert survey responses and applying the Analytic Hierarchy Process (AHP) to weight the criteria and rank the stakeholders. The outcomes include priority matrices and comparative tables.
Findings and Discussion
The AHP results show that governments/regulators and technology companies rank highest due to their legal authority, resources, and technical capabilities. Research institutions and civil society organizations occupy a middle tier—they play a key role in legitimacy and oversight but often face limited resources. End users have less influence in setting agendas but are crucial for ensuring fairness, safety, and accountability. The SWOT analysis reveals key strengths (growing institutional awareness), weaknesses (fragmented missions, uneven literacy), opportunities (standardization, public–private partnerships), and threats (regulatory disputes, concentration of capabilities). Aligning powerful actors with those that strengthen legitimacy can reduce institutional capture and promote responsible AI adoption.
Conclusion
The proposed framework offers a practical tool for organizing stakeholder interactions, defining roles, and tracking governance maturity. Cross-sector collaboration—especially between regulators and technology companies—should be supported by structured mechanisms for including input from research institutions, civil society, and end users. Future studies can test alternative evaluation criteria and explore cross-national case examples.