The Digital Government Authority (DGA) held two workshops on Wednesday and Thursday, February 11–12, 2026, in collaboration with Amazon and McKinsey & Company. The first workshop, titled “From Vision to Readiness: Building the Strategic Foundations for Cloud Transformation and Generative AI in the Government Sector,” and the second, titled “From Readiness to Execution in Adopting Cloud-Based Generative AI in the Government Sector,” were attended by 70 representatives from 37 government agencies at the Authority’s Innovation Hub.

The workshops aimed to enable government agencies to move from planning to actual implementation in adopting cloud solutions and generative AI technologies by strengthening institutional readiness at the strategic, organizational, and technical levels. They also supported value-based investment decision-making, fostered a shared understanding of governance, risk management, and cybersecurity concepts, and empowered leaders to effectively drive institutional change.

The workshops covered several key themes, including showcasing applied use cases in government services, operating and implementation models, governance and privacy frameworks, and mechanisms for measuring impact and value. They also addressed institutional readiness models, global best practices in the public sector, and the role of senior leadership in enablement and guidance.
The outcomes of the workshops are expected to help identify priority gaps within government agencies, develop a clear roadmap for progressing from vision to readiness and then to execution, establish an approved operating model, and produce actionable recommendations in governance, risk management, and technical and human enablement—supporting alignment with digital government objectives.

These initiatives come as part of the Authority’s efforts to increase cloud adoption rates among government agencies to 80% and raise the number of entities adopting cloud solutions to more than 200, supporting the sustainability of digital transformation and enhancing government performance efficiency.