Beyond Awareness: Managing Workforce-Related AI Risks in the National Library and Archives of Iran Using Importance– Performance Analysis (IPA) - Nobari, Behnam Zendehdel; Babak Zendehdel Nobari (2025)
As artificial intelligence (AI) technologies rapidly evolve, libraries and archives must proactively address the associated workforce-related risks to ensure sustainable and secure adoption. This study prioritizes AI workforce risks in the National Library and Archives of Iran (NLAI) using the Importance-Performance Analysis (IPA) method, offering a strategic and practical framework for institutional risk management. Through the application of the CATWOE framework, key stakeholders were identified, and 23 AI workforce risks were evaluated. Among them, the most critical risk was identified as “Inadvertent Data Exposure During AI Optimization”, highlighting the urgent need for safeguards against unintended security breaches caused by well-meaning but untrained staff. Beyond presenting a risk prioritization model, this research underscores the broader significance of adopting structured approaches like IPA in libraries and archives. Rather than waiting for disruptive technologies to dictate their trajectory, institutions such as NLAI can serve as role models by taking initiative, anticipating challenges, and shaping their own future in the AI era.