How a five-step roadmap helps governments succeed with AI

Governments are moving beyond asking whether to adopt artificial intelligence (AI), to focusing on how to implement it responsibly, effectively and at scale

In brief

• Many governments struggle to scale AI beyond pilots due to deployment challenges, cost overruns driven by inadequate planning, and integration complexities.
• A disciplined five-step roadmap helps organizations move from ideas to measurable impact, guiding responsible AI implementation and overcoming scaling barriers.
• Systematic approaches unlock major gains in productivity, service delivery and resilience – while avoiding the pitfalls that derail promising AI initiatives.

Turning AI ambition into real-world value is rapidly rising to the top of the agenda for government leaders. The debate is no longer about AI’s potential – it’s about understanding and preparing for the full complexity of scaling beyond successful pilots to deliver meaningful results for citizens. However, the path from ambition to impact remains fraught with challenges. The global EY organization’s experience suggests only 20% to 25% of AI proofs of concept (PoCs) progress to wider implementation.

Our research shows three fundamental barriers consistently derail AI scaling: deployment challenges that overwhelm technical and operational capacity; underestimated costs and funding gaps; and integration complexities like legacy systems, user resistance and compliance demands. These implementation gaps explain why many promising pilots fail to deliver public value – and why a structured, disciplined approach is essential.

Building on essential foundations

Our previous report, How data analytics and AI in government can drive greater public value, identified five essential foundations for successful government AI initiatives. They were robust data and technology infrastructure, methodical talent development, adaptive organizational culture, comprehensive ethical governance and collaborative ecosystem management.

Organizations that have established these foundations still face a critical question in moving from ideas to enterprise-wide AI transformation: How do you understand and prepare for the complexities of scaling AI to deliver sustained public value?

Our research with 492 government leaders across 14 countries illuminates this challenge. Over 60% cite data privacy and security concerns as a primary constraint, among other systemic barriers, including lack of strategic alignment, inadequate data infrastructure and ethical concerns.