Exploring AI and automation maturity levels across select sectors in Malta

Abstract

This study explores artificial intelligence and automation maturity levels across select public and private sector organisations in Malta. While AI and automation are increasingly used to improve efficiency, decision-making and service delivery, organisational progress remains uneven. Existing literature often focuses on adoption or technical capability, but less attention is given to how maturity develops in practice, particularly within small national contexts such as Malta. A qualitative research approach was adopted, based on eleven semi-structured interviews with managers and senior professionals involved in AI, automation, digital transformation, governance or technology-related decision-making. The findings were analysed thematically and used to assess maturity through a qualitative, literature-informed framework covering technology use, workflow integration, leadership support, data readiness, skills and awareness, governance, trust and human oversight, and scalability. The findings show that all participating organisations are engaging with AI and automation, although maturity levels vary significantly. Automation is generally more embedded than AI, while advanced AI applications remain more selective and often limited to specific functions or technical teams. Private sector organisations demonstrated higher maturity overall, supported by stronger leadership commitment, faster decision-making, greater investment and competitive pressure. Public sector organisations showed more cautious progression, shaped by governance requirements, accountability, regulatory scrutiny and competing priorities. Across both sectors, leadership support, data readiness, employee skills, governance structures and trust emerged as key factors influencing maturity. A consistent finding was that AI outputs are not trusted blindly; participants emphasised the continued importance of human review and accountability. The study concludes that AI and automation maturity should be understood as an organisational and managerial process rather than a purely technical issue. It recommends that organisations prioritise practical use cases, stronger data foundations, employee training, clear governance arrangements and public-private knowledge sharing to support future maturity development.