How a five-step roadmap helps governments succeed with AI

The imperative for systematic implementation

Traditional technology deployment methodologies prove insufficient for AI implementation. Unlike conventional IT systems, AI systems require iterative development, continuous learning, and adaptive governance. They involve organizational change management, regulatory compliance, and ethical oversight that extends well beyond technical deployment. This creates an imperative for structured implementation approaches. Organizations need methodologies that account for AI’s unique characteristics while ensuring sustainable value delivery.

The five-step roadmap we’ve developed addresses this need by providing a framework based on the experience of pioneering AI systems at government organizations globally. The framework specifically addresses the primary failure modes in AI scaling: unclear cost estimation and value proposition definition, insufficient operational preparation, inadequate pilot design, organizational resistance, and limited learning from pilots. Addressing each step builds systematically toward sustainable AI transformation.

Successful AI transformation begins with an outcome-focused mindset rather than technology-first approaches. Successful organizations focus on identifying operational challenges and potential citizen service improvements before evaluating specific AI applications.

This changes fundamentally how governments approach AI investment. Instead of asking “Where can we apply AI?”, forward-looking leaders should ask, “What strategic outcomes are we aiming for, and how can AI help achieve those goals?” This approach helps ensure that AI initiatives have a clear business need and quantifiable success metrics.

The most effective strategies combine top-down strategic alignment with bottom-up operational insights. Organizations that align AI use cases with their key policy priorities while also capturing frontline perspectives build strong pipelines of valuable opportunities. The State of Maryland exemplifies this top-down approach by assessing all AI use cases against the Governor’s 10 priorities, ensuring they support broader policy goals. As one senior official said, “The most important thing with the implementation of any artificial intelligence project is that it's purpose-led. ... AI is just a tool that needs to be directed to a purpose.”

Japan’s Digital Agency illustrates the bottom-up approach through its child consultation digitization initiative, which enabled counselors to voice-record consultations while on the go, helping reduce burnout and improve team-based care. This addresses immediate operational needs while creating structured, searchable data that lays the groundwork for more advanced analytics. As Director General Keisuke Murakami explained, “If we only impose a top-down approach without knowing the actual situation of the bottom-up, it will not work on the ground.”

Leading government organizations create robust prioritization criteria – evaluating feasibility, cost, risk and impact – to make informed investment choices. They set realistic expectations around PoCs, which typically have a low production conversion rate but offer valuable insights.

Key recommendations for governments implementing AI:                      

• Focus on outcomes first – Define the public value you want to achieve before considering AI solutions.
• Combine top-down and bottom-up approaches – Balance strategic priorities with operational insights from frontline workers and citizens to ensure relevance and practical impact.
• Replace ad hoc efforts with structured, transparent processes – Use idea funnels, prioritization frameworks and governance oversight to evaluate potential use cases.

Key recommendations for governments implementing AI:

• Evaluate infrastructure and data readiness – Before piloting, assess whether your technology architecture can support AI workloads securely, and validate data quality.
• Embed privacy and ethical governance from the start – Adopt privacy-by-design principles and establish clear ethical guidelines.
• Engage regulators early and continuously – Involve regulatory bodies from the design phase to avoid costly revisions later.

The next step is turning ideas into action. Pilots are essential to test solutions in real-world or simulated conditions to learn what works and validate their value. Only the most promising pilots scale for broader implementation.

Creating the right conditions for effective pilots is critical. Leading governments establish controlled environments that emulate operational settings, including regulatory sandboxes that provide temporary flexibility to test AI solutions under actual policy constraints, and live data testbeds where anonymized datasets simulate real service delivery. Some agencies create simulated service workflows – digital twins of frontline operations – to observe how AI tools perform in realistic scenarios without disrupting live systems. These setups enable safe experimentation while ensuring oversight and compliance and maintaining public trust.

The most successful pilot designs focus on human-AI collaboration rather than automation, creating workflows that amplify rather than replace human expertise. Involving people in the design process also helps ensure AI pilots integrate into existing workflows and supports iterative development.

Critical to pilot success is a realistic assessment of scaling economics. Pioneering organizations anticipate long-term costs and scaling needs and build them into business cases that account for the full lifecycle costs to secure the necessary funding. However, some estimates suggest organizations routinely underestimate AI project costs by five to 10 times when scaling from pilot to production. Cost estimates should account for several categories of expenses:

• Technology and data, including network infrastructure, data preparation and storage, compute needs, software and licensing, and energy use
• Talent and human capital, including hiring or upskilling staff and change management
• Governance, security and compliance, covering cybersecurity, privacy, legal and ethical oversight
• Operational integration and maintenance, covering workflow redesign, user support, model retraining, and long-term upkeep.

Understanding all these elements is essential for proper resource allocation and to avoid costly surprises.

Finally, high-performing agencies establish clear evaluation criteria for pilots that focus on measurable outcomes rather than technological novelty. Estonia’s methodology exemplifies this discipline by evaluating every pilot against four criteria: time efficiency, cost-effectiveness, innovation potential and measurable impact. As Estonia’s Government Chief Data Officer Ott Velsberg emphasized, “Cool AI ideas aren’t enough. We’re after numbers.”

Key recommendations for governments implementing AI:

• Plan for scaling from day one – Build comprehensive cost models into pilot business cases and estimate full production costs upfront.
• Design with people at the center – Involve end-users early and apply human-centered design principles with iterative development.
• Set clear, actionable goals and evaluate rigorously – Define specific, measurable objectives and use quantitative criteria to assess pilot outcomes.

Successful scaling requires integrated management of technical infrastructure and organizational change. Organizations must establish operating models that support AI deployment while managing the cultural adaptation necessary for sustained adoption. Effective approaches strike a balance between centralized technical expertise and embedded domain knowledge, ensuring that solutions are both technically robust and operationally relevant.

Workforce engagement is vital, with 31% of organizations citing job security concerns as barriers to implementation. The most successful strategies address these worries through clear communication about AI’s supportive role and by actively involving employees in system design. This people-centered approach recognizes that sustainable AI adoption requires workforce partnership rather than just training.

With 38% of organizations citing talent shortages as a major constraint, forward-looking leaders build long-term capability pipelines. Australia’s Digital Transformation Agency exemplifies this approach through capability assessment and targeted development programs. As Lucy Poole, General Manager, Strategy, Planning and Performance Division, explains, “From a workforce capability and planning perspective, we are currently working to deliver initiatives outlined by the Australian Public Service (APS) Data, Digital, and Cyber Workforce Plan 2025-30. The goal is to identify key challenges across all three disciplines and to start building stronger knowledge, literacy, and practical technical skills. This is fundamental to ensure APS talent is ready to respond to the needs of today, and into the future.”

Procurement transformation supports sustainable scaling by accommodating AI’s iterative nature rather than forcing it into traditional contracting models. This evolution enables access to a broader ecosystem that combines established technology firms with innovative startups, bringing together deep domain expertise and cutting-edge capabilities. As the now retired Captain (Ret.) Manuel Xavier Lugo, Senior Military Advisor, Chief Digital and Artificial Intelligence Office, Office of the Secretary of Defense, former Commander of Task Force Lima, US observed: “The barrier to entry is no longer there. You can be a small company addressing a particular challenge and you’re in.” Success requires developing internal procurement expertise to effectively evaluate AI solutions and foster collaborative partnerships that extend beyond traditional vendor relationships.

Key recommendations for governments implementing AI:

• Choose appropriate operating models – Balance centralized technical expertise with embedded domain knowledge.
• Prioritize workforce engagement – Communicate clearly about AI’s supportive role and involve employees in system design.
• Modernize procurement approaches – Shift to agile, inclusive approaches that engage diverse suppliers and build internal evaluation capabilities.

Systematic measurement and knowledge sharing transform individual implementations into organizational capabilities. These practices help build institutional learning and scaling capacity across government.

Leading governments establish comprehensive performance indicators and stakeholder feedback mechanisms that capture AI’s full operational value rather than focusing exclusively on cost metrics.

The most advanced approaches embed measurement into continuous program management instead of viewing it as a separate post-implementation step. The Department of Home Affairs in Australia demonstrates this approach. Teams define program outcomes, indicators and evaluation measures upfront and monitor both intended and unintended impacts of AI initiatives through real-time portfolio analytics. According to Pia Andrews, this kind of capability allows the department to adjust its course based on live data rather than waiting for evaluation checkpoints. 

Regularly reporting to leadership, employees and the public on AI implementation progress and impact promotes transparency and accountability while strengthening stakeholder confidence. 

Knowledge sharing accelerates organizational adoption through structured communities of practice that prevent duplication while building institutional capability. This collaborative approach creates exponential value, as one State of Maryland official explained, “We set up this community of practice, which is essentially all the folks leveraging AI, forming a joint working group. We all talk to see, ‘What are the issues you’re facing? Can we do things to not reinvent the wheel?’”

By making learning a shared journey — celebrating wins, dissecting failures and opening up the process — government leaders are turning AI pilots into launch pads for transformation.

Key recommendations for governments implementing AI:

• Define KPIs and baselines before implementation – Establish measurement frameworks that capture the full range of benefits.
• Create continuous feedback loops – Monitor progress and gather stakeholder input to enable real-time refinement.
• Share learnings across government – Organize cross-agency events and maintain knowledge management systems to accelerate adoption.

Implications for government leaders

The stakes are high. Those who act decisively to overcome these barriers can realize transformative potential across multiple dimensions – from efficiency gains and enhanced employee experience to improved citizen engagement, strategic service planning, financial optimization and organizational resilience. Those who delay risk rising costs, missed opportunities for productivity gains, and unmet public expectations. 

For government leaders, the window for action is narrowing – making it more urgent than ever to master the complexity of scaling AI to achieve sustained public value. The five-step framework provides a structured path to move from AI strategy through piloting, to full deployment and measurable impact.