The Illusion of AI readiness 

Many governments believe they are AI-ready because they’ve:  

• published an AI strategy 

• piloted a chatbot 

• created an ethics framework 

• stood up a data or innovation office 

All of that is important -- none of it, on its own, equals readiness. 

True AI readiness is not about technology adoption--it’s about organizational transformation. AI doesn’t simply automate tasks—it reshapes decision-making, accountability, service models, workforce roles, and citizen expectations. 

This is where many governments run into trouble. Governments try to layer AI onto legacy systems, legacy processes, and—most critically—legacy ways of working. That approach creates isolated wins, but systemic failure. 

 
What is AI readiness?  

A government is AI-ready when it can: 

• deploy AI safely and ethically at scale 

• integrate AI into core service delivery—not just pilots 

• govern AI decisions with clarity and confidence 

• equip its workforce to work with AI 

• continuously adapt as AI capabilities evolve 

What is not on the list? Tools. Vendors. Hype. 

AI readiness sits at the intersection of data, governance, operating models, and culture. If any one of those is weak, AI maturity stalls. 

 
The readiness gaps  

1. Data readiness 

AI runs on data—but many governments still struggle with: 

• fragmented data ownership 

• poor data quality 

• limited interoperability across ministries or agencies 

• unclear rules on data sharing. 

Without trusted, accessible, and well-governed data, AI systems produce unreliable or biased outputs. AI does not fix bad data.  It amplifies it. 

2. Governance and accountability 

Too often AI governance becomes either so restrictive that nothing can move forward, or so vague that accountability disappears. 

Key questions often go unanswered: 

• who is accountable for AI decisions? 

• who approves model use? 

• who monitors bias and drift? 

• who owns outcomes when AI is embedded in services? 

AI readiness requires decision clarity, not just ethical principles. 

3. Operating model misalignment 

This is the biggest gap—and the least discussed. 

Most government operating models were designed for: 

• linear processes 

• human-only decision making 

• static policies and rules. 

4. Workforce confidence 

AI readiness is not just about skills—it’s about confidence and trust. 

Public servants need to know: 

• when to rely on AI 

• when to override it 

• how to explain AI-supported decisions to the public 

• how AI changes—not replaces—their professional judgment 

Without deliberate workforce enablement--AI becomes something that happens to employees, not with them. 

The goal is not speed-- the goal is trust at scale. 

Trust is built when AI is: 

• explainable 

• governed 

• embedded in human-centered service design. 

 
Are governments AI-ready? 

Some are becoming ready. Most are not yet ready at scale. 

Governments are: 

• experimenting responsibly 

• learning what works and what doesn’t 

• building foundational capabilities. 

But readiness is uneven and the risk is not that governments move too fast--it's that they are move too cautiously in the wrong areas—focusing on pilots instead of platforms, tools instead of transformation. 

 
What governments should do next 

1. Shift from AI Projects to AI Capabilities 

Stop thinking in terms of pilots and start building reusable AI capabilities—data platforms, governance models, shared services. 

2. Redesign the operating model 

Explicitly design how humans and AI work together. Define roles, escalation paths, and accountability. 

3. Invest in data as critical infrastructure 

Treat data like roads, bridges, and utilities. 

4. Build workforce fluency, not just skills 

Focus on judgment, ethics, and decision-making—not just prompts and tools. 

5. Anchor everything in service outcomes 

AI is not the strategy. Better, faster, fairer services are.