National Sovereign Artificial Inteligence and Digital Transformation Strategy
National Sovereign AI and Digital Transformation Strategy
The Government of Papua New Guinea is undertaking stakeholder consultations on the National Sovereign AI and Digital Transformation Strategy, which establishes a unified national architecture based on Digital Government, Digital Public Infrastructure (DPI), and Artificial Intelligence (AI).
The Strategy marks a shift from fragmented ICT systems to a coherent, sovereign, and interoperable digital ecosystem to improve governance, drive economic growth, and expand inclusive service delivery, particularly for rural communities.
It builds on existing national frameworks and introduces the Digital Government, Digital Public Infrastructure, and Artificial Intelligence model, emphasizing sovereign, law-governed AI adoption on trusted digital foundations, supported by whole-of-government coordination and strong multi-stakeholder partnerships.
These consultations are therefore intended to:
- validate the proposed national architecture and implementation approach;
- identify risks, gaps, and opportunities across sectors;
- ensure alignment with existing institutional, legal, and operational realities; and
- secure stakeholder ownership and commitment for implementation.
Key Questions for Stakeholder Feedback
- National Strategy and Alignment
- Does the proposed Digital Government × Digital Public Infrastructure and Artificial Intelligence framework adequately reflect PNG’s national priorities and development context?
- How can this Strategy be better aligned with existing sectoral policies, programs, and investments?
- What should be the priority use cases for AI in PNG over the next 3–5 years?
- Sovereignty, Data Governance, and Trust
- What are the key concerns regarding data sovereignty, privacy, and security in adopting AI systems?
- How should government balance sovereign control with partnerships involving global technology providers?
- Are the proposed data governance and classification approaches practical and implementable across institutions?
- Digital Public Infrastructure (DPI) Integration
- Are current platforms (e.g., Digital ID, payments, data exchange) sufficiently mature to support AI deployment?
- What challenges exist in integrating existing systems into a shared national architecture?
- How can we ensure interoperability and avoid duplication across government and sectors?
- Infrastructure and Technology Readiness
- What are the key gaps in connectivity, cloud, data centres, and compute capacity that may constrain AI adoption?
- What role should the private sector and development partners play in building AI-ready infrastructure?
- Is the proposed hybrid sovereign cloud approach feasible and sustainable?
- Institutional and Governance Arrangements
- Are the proposed roles of:
- Ministerial Committee
- Digital Transformation & AI Board
- Implementing Department
- Regulatory Authority
clear and appropriate? - What institutional challenges may arise in implementing a whole-of-government AI governance model?
- How can accountability be strengthened at agency level?
- Legal and Regulatory Framework
- Are the proposed new laws (AI Act, Data Governance Act, Digital Identity Act, etc.) adequate and future-proof?
- What regulatory risks or gaps should be addressed early?
- How can compliance be ensured without stifling innovation?
- Private Sector and Innovation Ecosystem
- How can the Strategy better support local innovation, SMEs, and startups?
- What incentives or frameworks are needed to encourage private sector participation in DPI and AI?
- What are the risks of market distortion or crowding out private innovation?
- Inclusion and Rural Access
- How can AI and DPI be designed to effectively serve rural and remote communities?
- What are the key barriers (connectivity, literacy, affordability, language) that must be addressed?
- How can local languages and low-connectivity solutions be integrated into AI services?
- Workforce, Skills, and Capacity
- What are the most critical skills gaps in government and industry for AI adoption?
- What partnerships (universities, training institutions, private sector) are required to build capacity?
- How can PNG develop sustainable local expertise rather than relying on external providers?
- Implementation and Investment Priorities
- What should be the phasing and sequencing of implementation (short, medium, long term)?
- Which investments should be prioritized first:
- infrastructure,
- DPI platforms, or
- AI applications?
- What financing models (public, PPP, development partners) are most viable?