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Government 2.0 is the broader transformation of public institutions through digital infrastructure, better-managed data and, where appropriate, artificial intelligence (AI). The aim is government that is easier to use, more coordinated and more accountable—not simply government that has added AI tools. AI can support that change, but it cannot compensate for fragmented data, weak infrastructure, unclear responsibility or services designed around internal processes instead of people.
What does digital transformation mean for government?
Digital transformation changes how government works and how people experience public services. It can connect information and processes across agencies, make services easier to access, and help institutions use evidence to improve decisions. The technology matters, but so do the rules, skills, infrastructure and public accountability around it.
The OECD describes digital government through six connected dimensions. Together, they offer a practical way to distinguish lasting transformation from a collection of disconnected digital projects.
| Dimension | What it means in practice |
|---|---|
| Digital by design | Build digital capabilities into policy and service design from the outset, rather than treating technology as a late-stage add-on. |
| Data-driven public sector | Manage and use data as a strategic resource, with appropriate quality, governance, access and safeguards. |
| Government as a platform | Provide shared capabilities that agencies can use to build coherent services, instead of making each institution solve common problems independently. |
| Open by default | Make government information and processes more accessible where it is appropriate and lawful, while protecting sensitive information. |
| User-driven | Design services around people’s needs and experiences, using feedback to improve them. |
| Proactiveness | Use government’s knowledge and capabilities to anticipate needs and make services easier to access, with safeguards appropriate to the context. |
This six-part framework comes from the OECD’s Digital Government Outlook 2026. It emphasizes that transformation involves service design, institutions and shared foundations as well as software.
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Where does AI fit in Government 2.0?
AI is one capability within digital government, not a synonym for it. It may help staff process information, support public-service delivery or assist with analysis. Whether it improves a service depends on the task, the data available, the way the system is governed and the ability of people to scrutinize its effects.
The OECD’s Digital Government Outlook 2026 reports that AI was used in at least one area of government in 35 of 36 OECD countries (97%). The same publication reports that 30 of 36 OECD countries (83%) had at least one institution responsible for governing AI in the public sector. These are findings about OECD countries, not global estimates. They indicate that adoption and institutional governance arrangements are widespread in that group; they do not establish that AI use is mature, effective or safe in every country or application. For related analysis, the Outlook identifies the 2025 Digital Government Index analysis window as 1 January 2023 to 31 December 2024; that window should not be mistaken for a global or timeless measure of AI adoption.
In practice, a public agency should begin with the service or decision it wants to improve—not with a model it wants to deploy. For example, an agency might explore whether automation could reduce avoidable delays in handling routine requests. That is a hypothetical starting point, not evidence that AI is the right solution: the agency would still need to establish that the process, information and safeguards are suitable.
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How can data improve government services?
Well-governed data can help institutions understand service needs, coordinate work and make processes more consistent. But data is not automatically useful just because it is digital or plentiful. If records are inaccurate, incomplete, hard to access or incompatible across systems, they can undermine analysis and make automated outputs less reliable. Poorly managed data can also contribute to skewed outcomes or inaccurate results.
The OECD defines public-sector data governance as “diverse arrangements, including technical, policy, regulatory and institutional provisions, that affect data and their creation, collection, storage, use, protection, access, sharing and deletion, including across policy domains and organisational and national borders”. The OECD (2022) definition is quoted in the OECD’s 2025 report Governing with Artificial Intelligence: The State of Play and Way Forward in Core Government Functions. Its breadth matters: data governance is not only a technical question about storage. It includes who may use data, for what purpose, how it is protected and when it should be deleted.
Before relying on data to guide a service or an AI system, an agency should consider:
- Fitness for purpose: Is the information relevant, current and sufficiently accurate for the decision or task?
- Coverage: Which people, cases or circumstances might be missing or poorly represented?
- Interoperability: Can systems exchange and interpret information consistently, where sharing is authorized?
- Access and protection: Who needs access, under what authority, and how will sensitive information be protected?
- Lifecycle management: How are data collected, maintained, used, shared and ultimately deleted?
These checks are particularly important when a system could affect people’s access to a service or the way their case is handled. A result based on unreliable inputs should not be treated as reliable simply because it was produced by an algorithm.
How can governments use AI responsibly?
The OECD groups public-sector AI governance into three linked pillars: enablers, guardrails and engagement. Enablers make responsible use possible; guardrails manage risks and accountability; engagement brings affected people and partners into the process. Treating only one pillar as important—for instance, buying a tool without building the capacity to oversee it—leaves the others unfinished.
| Pillar | What it covers | Questions for an agency |
|---|---|---|
| Enablers | Governance, data, digital infrastructure, skills and talent, investment, procurement, and partnerships with non-government actors. | Is there clear ownership? Are the data and infrastructure fit for the purpose? Can staff evaluate, operate and oversee the system? Can procurement support responsible implementation beyond a pilot? |
| Guardrails | Policy instruments, transparency, risk management and oversight. | What could go wrong, how serious would the effects be, and what controls are proportionate? Can affected people understand how the system is used and seek review? |
| Engagement | Participation by citizens, civil servants and cross-border collaboration. | Have the people who use, deliver or may be affected by the service had a meaningful way to inform its design and evaluation? |
The OECD framework does not treat every application as identical. An internal tool that helps staff organize documents and a system that informs a consequential public decision may call for different levels and forms of transparency, risk management and oversight. Safeguards should match the system’s purpose and the potential consequences of error or misuse.
What can prevent a promising pilot from becoming a better service?
A successful demonstration does not by itself show that a system can be sustained or scaled. The OECD’s 2025 and 2026 publications identify enabling conditions that can vary across countries and institutions, including data governance, infrastructure, skills and organizational capacity. The 2026 Outlook also flags weak data governance and data reuse, underused digital public infrastructure, rigid investment and procurement systems, and trust mechanisms that may lag AI adoption.
- Fragmented or poorly governed data: Information may be difficult to reuse responsibly or may not support reliable outputs.
- Insufficient shared infrastructure: Agencies may duplicate effort or struggle to connect a pilot to the systems needed for delivery.
- Skills and capacity gaps: Staff need the ability to define requirements, assess suppliers, oversee use and respond when a system performs poorly.
- Investment and procurement constraints: Rigid processes can make it difficult to adapt, maintain or scale a solution responsibly.
- Weak accountability or trust: People may not know when AI is involved, who is responsible or how to question an outcome.
These are system-level challenges, not problems an AI model can fix on its own. An agency that pilots a tool without addressing them may produce a demonstration that cannot be integrated into a dependable public service.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should an agency assess a public-sector AI proposal?
A useful assessment starts with the service problem and follows the decision through its full lifecycle. The questions below translate the OECD’s emphasis on enablers, guardrails and engagement into a practical sequence. They are a planning framework, not a substitute for applicable law or sector-specific requirements.
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- Define the service problem. Specify what is not working, whom it affects and what improvement would count as meaningful. Consider whether a process change or non-AI digital tool could address the problem.
- Check data and infrastructure. Establish whether information is fit for the intended use, whether relevant systems can work together and whether access and protection are properly governed.
- Assign responsibility. Name who owns the service, who approves the system’s use, who monitors it and who must act if it causes harm or stops meeting requirements.
- Assess and manage risk. Identify likely errors and other harms in context, then set controls proportionate to the consequences. Specify what human review or alternative route is available where needed.
- Make the use understandable and contestable. Decide what people and staff need to know about the system’s role, how an outcome can be questioned, and how a challenge will be handled.
- Involve affected people and staff. Use engagement to test whether the service works for real users and whether the people delivering it can use and oversee the system appropriately.
- Monitor performance and effects. Set measures before deployment, check whether the service is meeting its goals, and look for errors or harms that require changes, suspension or withdrawal.
- Plan for delivery beyond the pilot. Account for ongoing skills, investment, procurement, infrastructure, maintenance and coordination across institutions before treating a trial as ready to scale.
The order is important: deciding how to deploy a model before defining the service problem, accountability and review process can lock an agency into a technical solution without resolving the underlying need.
How can countries or initiatives be compared fairly?
A comparison should assess the conditions and safeguards behind digital government, not just count AI projects or rank countries by adoption. The OECD’s framework supports comparison across these dimensions, but it does not provide a sourced ranking in the material summarized here.
| Comparison dimension | What to examine |
|---|---|
| Coordination and accountability | Whether responsibilities are clear and agencies can coordinate across government. |
| Data foundations | Data quality, interoperability, authorized access and responsible reuse. |
| Capacity | Shared infrastructure, workforce skills and organizational ability to sustain delivery. |
| Safeguards | Whether transparency, risk management and oversight are proportionate to the use. |
| Service design and engagement | Whether services respond to users’ needs and whether citizens, civil servants and other relevant participants can contribute. |
| Delivery beyond pilots | Whether a promising initiative can be maintained and integrated into dependable services. |
The World Bank’s 2025 update to its GovTech Maturity Index offers a complementary comparative frame. It covers 198 economies and uses 48 indicators across four areas: core government systems and shared infrastructure; online public-service delivery; digital citizen engagement; and GovTech enablers, including strategies, institutions, laws, skills and innovation policies. The index’s scope makes it useful for looking beyond AI, but its coverage does not turn it into a direct ranking of public-sector AI safety or effectiveness.
What does successful Government 2.0 look like?
Success is not the presence of an AI system or the number of digital projects. It is a public service that works better for people and can be operated responsibly over time. That means a clearly defined need, dependable data and infrastructure, staff able to do their jobs, clear accountability, and safeguards that let people understand or challenge relevant outcomes.
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The OECD’s 2025 and 2026 frameworks point to a central test: can institutions connect technology with sound governance, meaningful engagement and sustained delivery? When they can, AI may contribute to more coherent and responsive government. When they cannot, adoption risks becoming a visible layer of technology over unresolved problems in services, data and accountability.
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