Government AI Predictions For 2026

Predicting the trajectory of artificial intelligence in government has become increasingly complex. Political shifts, regulatory uncertainty and rapid advances in AI technologies made 2025 a year of disruption for the public sector. Yet despite the volatility, many analysts correctly anticipated how AI adoption would accelerate across government agencies. As 2026 unfolds, the central question is no longer whether governments will invest in AI, but how responsibly and effectively those investments will be managed.

Government AI investments are increasing even though trustworthy AI efforts are not keeping pace. This was a finding from the recent  IDC Data and AI Impact Report: The Trust Imperative, commissioned by SAS. Government agencies are using generative AI as much or more than other industries and are leaders in the use of traditional and agentic AI. And yet, public sector investments in trustworthy AI infrastructure and software lag behind the private sector.

Recent findings from the IDC Data and AI Impact Report: The Trust Imperative, commissioned by SAS, highlight a growing imbalance. Public sector organisations are among the most active adopters of generative, traditional and agentic AI, often matching or exceeding private-sector usage. However, investment in trustworthy AI frameworks, such as governance, oversight and risk management, continue to lag behind. This gap is likely to shape the next phase of government technology adoption.

So, what does that mean for technology in 2026? Will governments match their AI innovation with an equal focus on trustworthy practices? With an inconsistent regulatory environment, will agencies adopt stronger AI governance to guide their AI use? Will these investments spark the promised productivity and efficiency gains, or will there be a more pragmatic approach to adoption?

One of the clearest trends emerging in 2026 is a move away from costly, highly customised technology projects that rely heavily on external consultants. Governments are increasingly prioritising tools that empower internal teams, enabling public servants to analyse data, automate workflows and make informed decisions with fewer external dependencies. This shift places greater emphasis on usability, workforce enablement and the integration of technology into everyday operations.

As AI systems transition from pilot projects to operational use, transparency is becoming a non-negotiable requirement. Governments are beginning to deploy AI agents capable of making decisions and executing actions with limited human intervention. In this context, algorithmic transparency (ensuring systems are explainable, auditable and understandable) will be essential for maintaining accountability and public trust.

Regulation is expected to play a defining role in 2026. With global AI frameworks emerging and enforcement timelines approaching in regions such as the European Union, governments are under pressure to formalise AI governance. Beyond compliance, many administrations are also exploring “sovereign AI” strategies, aimed at retaining control over data, infrastructure and compute resources within national or regional boundaries. These efforts are driving the development of local AI ecosystems and data centres, while elevating governance from a compliance exercise to a strategic capability.

AI literacy is also gaining recognition as a core component of governance. Rather than relying solely on technical controls, public sector leaders are increasingly focused on building shared understanding and accountability across organisations, positioning literacy as a foundation for responsible innovation.

Citizen-facing services are expected to see expanded use of agentic AI frameworks as large language models become more commoditised. Virtual assistants, combining traditional and generative AI, are being deployed to handle complex queries across multiple languages. These systems aim to reduce wait times, improve accessibility and orchestrate more complex service workflows, although their success will depend heavily on data quality and governance.

Data availability remains a persistent constraint for public sector innovation. Political changes, privacy requirements and digital sovereignty concerns can limit access to real-world datasets. In response, synthetic data is gaining traction as a way to support AI development while maintaining compliance. With appropriate safeguards, agencies are beginning to use AI-generated structured and unstructured data for research, testing and training purposes.

AI-driven transformation of the public sector workforce is expected to be uneven. Governments are experimenting with systems that capture institutional knowledge (such as retrieval) augmented generation tools trained on documented expertise to support younger or less experienced staff. At the same time, concerns about job displacement are creating new risks, including resistance to adoption and deliberate degradation of training data.

More broadly, automation and AI are reshaping job roles across government. While some positions may be reduced, new roles are emerging, particularly in technology-intensive and service-oriented areas. This transition is increasing pressure on governments to invest in large-scale upskilling and reskilling initiatives.

In areas such as tax administration and fraud prevention, AI is proving to be both a challenge and an enabler. Fraud networks are using generative AI to create more convincing identities and transactions, while agencies are responding by strengthening identity management, analytics and real-time monitoring. Improved data sharing across agencies is expected to play a key role in detecting fraud, waste and abuse, while real-time analysis in tax systems aims to reduce errors, account takeovers and revenue leakage.

Healthcare and public health agencies are also looking to AI to modernise legacy systems. Large volumes of patient and surveillance data remain locked in paper records or manual processes. AI-driven data extraction and entity resolution tools are beginning to streamline reporting, reduce duplication and strengthen disease surveillance, potentially enabling earlier detection and response to outbreaks.