Digital Twin of an Organization for Public Sector

The brief identifies five primary use cases: translating policy into operational execution, modernizing citizen and resident service delivery, increasing transparency and accountability, modernizing legacy processes and systems, and coordinating organizational change. It also describes DTO as a foundation for AI readiness in public services.

Mavim can connect policy mandates and strategic objectives to services, eligibility rules, processes, responsibilities, systems, data, controls, and transformation initiatives. This provides traceability from public intent to the operational activities through which it is implemented.

A DTO can show the end-to-end processes, owners, systems, controls, and dependencies involved when services span departments, agencies, external partners, and applications. This helps teams identify handoffs and coordinate improvement across organizational boundaries.

Mavim makes relationships between policy, processes, responsibilities, controls, systems, and transformation explicit. Organizations can assign ownership, manage governance and versioning, and maintain a more connected source of operational knowledge for transformation governance.

Yes. A DTO connects applications and technology dependencies to the public services, case-management processes, roles, data flows, controls, and reporting requirements they support. This helps teams redesign processes alongside technology rather than simply digitizing outdated ways of working.

The brief does not position Mavim as requiring those systems to be replaced. It states that existing repositories, architecture tools, process tools, project tools, wikis, and transformation spreadsheets can remain valuable. Mavim's role is to provide the governed connective context showing how information across those perspectives relates.

Yes. A DTO can support context-aware employee guidance, AI-assisted process and impact analysis, policy-aware service navigation, transformation copilots, and more governed use of agents within appropriate public-sector processes.

No. The brief explicitly states that the objective is not to automate policy decisions or replace human accountability. AI should remain grounded in trusted public-sector context, with controls visible and humans accountable for decisions.

Measures should reflect the specific transformation scenario. Examples in the brief include service cycle time, case backlog, handoffs, channel completion rates, mandate-to-process traceability, compliance coverage, implementation rework, process adoption, training completion, time to proficiency, controls defined for AI use cases, and identified human-oversight points.

No. Organizations can begin with a focused priority such as citizen service modernization, policy implementation, legacy-system transformation, process improvement, or AI readiness and connect the services, processes, systems, roles, controls, and dependencies required for that priority.