How a five-step roadmap helps governments succeed with AI

Governments are focusing on implementing AI responsibly and at scale.

Building on essential foundations

Data, skills and governance come first.

Methodology

The need for systematic implementation

Pilots alone do not deliver public value.

Identifying and prioritizing opportunities

Example: easing pressure on a transport agency's contact center

AI helped staff answer routine queries faster.

Recommendations

Prioritize high-volume, well-defined processes.

Preparing for responsible AI

Example: transparency in AI decision-making

A government published how its AI systems make decisions.

Recommendations

Build transparency and accountability into AI from the start.

Designing pilots and evaluating rigorously

Example: using frontline expertise in a security agency

Officers helped design and test an AI tool.

Recommendations

Involve users and measure results objectively.

Managing change and preparing to scale

Example: connecting data and AI to service delivery in a tax authority

AI was embedded in core processes.

Recommendations

Invest in change management and infrastructure.

Measuring impact and sharing knowledge

Example: international knowledge sharing

Governments share lessons through international forums.

Recommendations

Measure public value and share what works.

Implications for government leaders

A structured roadmap turns AI ambition into public value.

Summary

Five steps help governments scale AI responsibly.