Government leaders worldwide are talking big about AI transformation. In the U.S., Canada, and the U.K., officials are pushing for AI-first agencies that will revolutionize public services. The vision is compelling: streamlined operations, enhanced citizen services, and unprecedented efficiency gains. But here’s the uncomfortable truth—most government AI projects are destined to fail spectacularly. The numbers tell a sobering story. A recent McKinsey analysis of nearly 3,000 public sector IT projects found that over 80% exceeded their timelines, with nearly half blowing past their budgets. The average cost overrun hit 108%, or three times worse than private sector projects. These aren’t just spreadsheet problems; they’re systemic failures that erode public trust and waste taxpayer dollars. When AI projects go wrong in government, the consequences extend far beyond budget overruns. Arkansas’s Department of Human Services faced legal challenges when its automated disability care system caused “irreparable harm” to vulnerable citizens. The Dutch government collapsed in 2021 after an AI system falsely accused thousands of families of welfare fraud. These aren’t edge cases—they’re warnings about what happens when complex AI systems meet unprepared institutions. The Maturity Trap The core problem isn’t AI technology itself—it’s the mismatch between ambitious goals and organizational readiness. Government agencies consistently attempt AI implementations that far exceed their technological maturity, like trying to run a marathon without first learning to walk. Our research across 500 publicly traded companies for a previous book revealed a clear pattern: organizations that implement technologies appropriate to their maturity level achieve significant efficiency gains, while those that overreach typically fail. Combining this insight with our practical work implementing digital solutions in the public sector led to the development of a five-stage AI maturity model specifically designed for government agencies. Stage 1: Initial/Ad Hoc. Organizations at this stage operate with isolated AI experiments and no systematic strategy.  Stage 2: Developing/Reactive. Agencies begin showing basic capabilities, typically through simple chatbots or vendor-supplied solutions.  Stage 3: Defined/Proactive. Organizations develop comprehensive AI strategies aligned with strategic goals.  Stage 4: Managed/Integrated. Agencies achieve full operational integration of AI with quantitative performance measures.  Stage 5: Optimized/Innovative. Organizations reach full agility and influence how others use AI. Most government agencies today operate at stages 1 or 2, but AI-first initiatives require stage 4 or 5 maturity. This fundamental mismatch explains why so many initiatives fail. Without the right cultural frameworks, technological expertise, and technical infrastructure, organization-wide transformation based around AI capabilities stand little chance of success. Start Where You Are, Not Where You Want to Be The path to AI success begins with brutal honesty about current capabilities. A national security agency we studied exemplifies this approach. Despite seeing enormous opportunities in large language models, they recognized serious risks around data drift, model drift, and information security. Rather than rushing into advanced implementations, they are pursuing incremental development grounded in institutional knowledge and cultural readiness. This measured approach doesn’t mean abandoning ambitious goals—it means building toward them systematically. Organizations must select projects that are appropriate to their maturity level while ensuring each initiative serves dual purposes: delivering immediate value and advancing foundational capabilities for future growth. Three Immediate Opportunities For agencies at early maturity stages, three implementation areas offer immediate value creation opportunities while building toward transformation: 1. Information Technology Operations IT represents the most accessible entry point for government AI adoption. The private sector offers a road map— 88% of companies now leverage AI in IT service management, with 70% implementing structured automation operations by 2025, up from 20% in 2021. AI can transform government IT through chatbots handling common user issues, intelligent anomaly detection identifying network problems in real-time, and dynamic resource optimization automatically adjusting allocations during peak periods. These capabilities deliver immediate efficiency gains while building the technical expertise and collaborative patterns needed for higher maturity levels. The challenge lies in government’s unique constraints. Stringent security requirements along with legacy systems at agencies like Social Security and NASA create implementation hurdles that private sector organizations rarely face. Success requires careful navig