From pilots to practice: What it takes to make AI work in government

News February 3, 2026

Written by Piret Hirv, Head of Data Management Competence Centre

The conversation around artificial intelligence in government is evolving. Not long ago, the focus was on speed, automation and efficiency – if something could be automated, it should be. Today, that focus has shifted.

Before talking about models or tools, the real question is what problem needs solving and whether AI is the right answer. Experience has shown that many AI pilots fail not because the technology is inadequate, but because governance frameworks, processes and capacities are not yet in place to support their integration into everyday public administration. What is missing is not innovation, but coherence.

The core enablers are missing

In practice, the shortcomings are frequently the same. Data is scattered across institutions and governed unevenly, while issues with data quality often surface only after systems are already in place. Processes are poorly documented, making them difficult to translate into algorithmic logic. Responsibility for AI-supported decisions is blurred and divided among policy units, IT teams and external suppliers. Skills are concentrated in small expert groups rather than embedded across organisations. Under these conditions, scaling AI becomes primarily an organisational challenge rather than a technical one.

This has shaped the way people in the governments and beyond now think about AI projects. They are no longer seen as single interventions, but as journeys that unfold in distinct phases, each requiring different questions and disciplines. Early on, it is essential to assess whether AI is appropriate at all and whether affected users are involved from the start.

During the development of any AI project, data quality, privacy and cybersecurity are not side issues, but core factors that must be addressed by design. AI piloting is not a demonstration exercise, but a moment to surface risks, user concerns and governance gaps. Scaling, when it happens, demands transparency, explainability and clear accountability for outcomes.

Trust as the foundation

Trust runs through all of this. AI in government operates within existing power balances. People accept automation only when decisions remain understandable and contestable. Safeguards, impact assessments and oversight mechanisms are often perceived as slowing innovation, yet they are what enable it. Systems designed to be explainable from the start are easier to govern, audit and trust over time.

Context also matters more than we often admit. Administrative traditions, legal concepts and language shape how public services function. When AI systems fail to reflect this context, services risk becoming less accessible and less fair. This is not an abstract concern: it affects whether citizens understand the decisions made, whether officials trust the tools they use and whether public institutions retain legitimacy in an increasingly automated environment.

Value behind the scenes

There is a strong temptation to focus AI efforts on visible interfaces, especially conversational tools. These can improve access, but the most durable value often emerges behind the scenes. Decision-support systems for caseworkers, analytical tools that improve resource allocation and early warning mechanisms that help governments act proactively often deliver quieter but deeper impact. However, they also require stronger governance and higher-quality data.

AI literacy and risk management are the cornerstones

One of the most important lessons has been about capacity. Sustainable AI cannot be fully outsourced. Governments that invest in digital and AI literacy across the civil service are better equipped to govern technology responsibly. This includes not only technical skills, but the ability to define problems, evaluate outcomes and reflect on unintended effects. Community practices, shared standards and internal learning structures matter more than individual success stories.

Our understanding of risk has evolved as well. Responsible use of AI is not about avoiding uncertainty or embracing technology blindly, but about managing risk deliberately throughout the lifecycle of a system: setting expectations early, monitoring impacts continuously and being willing to adapt or stop when public value is not delivered.

AI as a governance test

What is most valued over time is sustainability and continuity: not of specific technologies, but of principles, institutions and ways of governing digital change. Trust is not built through constant reinvention. It grows when people recognise stable responsibilities, predictable safeguards and familiar rules, even as tools evolve. In this sense, AI is not a rupture with the past, but a test of how coherent and resilient our digital governance really is.

Seen this way, AI is neither a destination nor a shortcut to modernity. AI is simply the next chapter in a longer transformation shaped less by technology than by choices about responsibility, transparency and how people are treated in their everyday interactions with the state. When those choices are made well, AI becomes almost invisible. Not because AI is insignificant, but because it works quietly in the background, supporting institutions that remain stable, humane and worthy of trust over time.

5 policy takeaways

Start with the problem, not the technology. AI delivers value only when it addresses a clearly defined (public service) problem. Automation without purpose rarely scales.
Data quality and data governance matter more than models. Fragmented, poorly governed data limits impact. Treat data as core public infrastructure, not a technical afterthought.
If the process is unclear, AI will amplify the confusion. Understanding and simplifying administrative processes must come before automation.
Trust is a prerequisite, not a bonus. Explainability, accountability, and privacy by design determine whether citizens accept AI in sensitive public services.
Build capacity before scaling solutions. Lasting impact comes from skilled public servants and institutional learning, not from isolated pilots or outsourced expertise.