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Core Problem Areas for NZ Public Service AI Enablement - Coggle Diagram
Core Problem Areas for NZ Public Service AI Enablement
Procurement and Vendor Management Crisis
Problem Statement:
New Zealand's public sector faces significant challenges in procuring AI technology effectively, with 80% of digital transformation projects failing globally, and procurement processes not fit for AI's unique requirements.
Research Opportunities:
Developing implementation frameworks for AI governance in small nations
Creating metrics and KPIs for national AI strategy success
Designing policy tools that bridge the gap between principles and practices
Why This Matters:
Traditional procurement is too slow and rigid for AI adoption. The Government Electronic Tender Service shows 94% of emergency procurements failed to publish contract award notices, indicating systemic procurement failures.
Digital Skills and AI Literacy in the Public Service
Problem Statement:
The public service workforce lacks the digital capabilities needed for AI implementation, with significant gaps in technical expertise and AI understanding across all levels.
Research Opportunities:
Developing AI competency frameworks for different public service roles
Creating AI literacy programs tailored to government contexts and constraints
Designing AI training curricula that bridge technical and policy understanding
Building career pathway models for AI specialists in government
Why This Matters:
Between 2010-2023, domestic training in digital technology declined by 33%. The public service has only 2,226 ICT professionals and 250 ICT managers total, insufficient for AI transformation.
Legacy System Integration and Modernisation
Problem Statement:
Government agencies rely on outdated legacy systems that are incompatible with modern AI technologies, creating barriers to implementation and integration
Research Opportunities:
Developing AI integration strategies for legacy government systems
Creating modernization roadmaps that prioritize AI-ready infrastructure
Designing hybrid approaches that layer AI onto existing systems
Building risk mitigation frameworks for legacy system AI adoption
Why This Matters:
Over 78% of Commonwealth entities lack appropriate IT systems to monitor user access, indicating widespread legacy system issues that will impede AI adoption
Data Governance and AI-Ready Data Management
Specific Research Opportunities:
Developing AI-ready data governance frameworks for government
Creating cross-agency data sharing protocols for AI applications
Designing data quality standards specifically for AI use cases
Building automated data preparation pipelines for government AI
Why This Matters
: Only 11% of public servants report seamless data access across agencies, and fragmented systems prevent the data integration necessary for effective AI implementatio
Problem Statement:
Government data is fragmented, siloed, and not structured for AI use, with poor data quality and inconsistent standards across agencies
Public Service AI Risk Management and Compliance
Research Opportunities:
Developing risk assessment methodologies specifically for government AI use
Creating AI audit and compliance frameworks for public sector accountability
Designing bias detection and mitigation strategies for government AI systems
Building public trust measurement frameworks for AI implementations
Why This Matters
: 62% of government officials cite data privacy and security concerns as high barriers to AI adoption, while maintaining public trust is crucial for government AI legitimacy.
Problem Statement:
Government agencies struggle to balance AI innovation with risk management, lacking frameworks to assess and mitigate AI-specific risks while maintaining public trust
Inter-Agency AI Coordination and Governance
Problem Statement:
Lack of coordinated approach to AI across government agencies leads to duplicated efforts, inconsistent standards, and missed opportunities for shared solutions
Research Opportunities:
Developing whole-of-government AI governance models
Creating shared AI service platforms for multiple agencies
Designing AI project coordination mechanisms across departments
Building knowledge sharing frameworks for AI best practices
Why This Matters:
New Zealand's light-touch approach may lack the coordination needed for effective AI deployment across government, with agencies operating in silos.