The rapid advancement of digital technologies has opened new possibilities for cultural heritage institutions (CHIs), enabling digitization and AI-supported initiatives that improve preservation, accessibility, and public engagement. However, CHIs often face significant challenges in evaluating and selecting appropriate projects due to fragmented resources, institutional complexity, and existing decision-support frameworks prioritize financial and technical feasibility while neglecting cultural and social dimensions. This study addresses a critical gap by presenting a micro-level decision-support framework specifically designed to guide CHIs in the evaluation of digitization and AI-supported projects. The framework was constructed through a three-stage mixed-method research design that combined a systematic literature review (SLR), focus group discussions, and the Delphi technique. It draws on the expertise of professionals from the European cultural and creative industries, including representatives from CH institutions and ethnic minority groups. Their diverse perspectives ensured that the framework reflects both technical and cultural priorities. The process resulted in a validated set of 43 criteria, grouped into six categories: finance and investment, employment and personnel, market, accessibility, social impact, and cultural heritage object. The framework offers CHIs a comprehensive but context-sensitive tool to guide structured, evidence-based decision-making. It captures both tangible and intangible project dimensions and supports strategic planning that aligns with institutional missions and stakeholder expectations. The study contributes theoretically by operationalizing cultural value and institutional priorities in micro-level evaluation, addressing the limitations of market-oriented models. It also offers practical value by enabling CHIs to navigate trade-offs between innovation, feasibility, and cultural integrity. While grounded in project-level application, the framework also serves as a foundation for the application of multi-criteria decision-making (MCDM) methods and future research on institutional-level decision making. The findings improve CHI’s ability to make informed, inclusive, and forward-looking investment decisions in a rapidly changing digital world.