3 AI Trends Set to Redefine Government by 2035

Artificial intelligence is no longer a futuristic concept for government and regulated sectors—it’s fast becoming a critical tool for streamlining processes, improving services, and making data-driven decisions. From predictive analytics in urban planning, to chatbots that manage citizen interactions, AI is reshaping the way that government functions.  

But just as fast as AI adoption accelerates, so too the challenges. And even more so in heavily regulated and unionised organisations which can’t afford breaches of trust, compliance, or transparency. 

So, as we look toward the next decade, we believe that three key trends are set to define the role of AI in the public sector. Understanding these trends will help leaders to adopt AI responsibly and successfully, ensuring long-term success and trust in their investments. 

Trend 1 - Explainable AI: Building Trust and Accountability 

Governments operate in a high-stakes environment where decisions – foolhardy or sound - can significantly impact citizens’ lives.  

As such the “black box” nature of many AI systems, where the logic and algorithms behind AI’s decisions is opaque, can pose a significant reputational risk. Enter Explainable AI (XAI), a trend that focuses on creating models that are not just accurate but are also transparent and interpretable. 

Why It Matters 

In much of the UK’s public sector, and especially in sectors like healthcare, criminal justice, and social services, transparency is non-negotiable. For example, if an AI system were to deprioritise someone in the social housing queue, or if it identified an individual on behalf of HMRC for deeper tax scrutiny, questions are likely to be asked. And decision-makers must be able to confidently explain why.  

Explainable AI ensures that every recommendation or decision made by AI can be traced back to understandable logic, and can therefore be explained. 

What this Means Over the Next Decade 

It’s highly likely that the public sector will lead the way, increasingly demanding AI systems vendors to ensure their products are built with strong transparency features. This might mean, for example, that machine learning models may come with audit trails, showing the various ways that data inputs can influence outcomes.  

But XAI goes a bit beyond pure technical fixes. Significant investments in training public sector employees on how to interpret AI decisions is likely to become commonplace as a critical path towards maintaining public trust. 

Trend 2 – Ethical AI: Ensuring Fairness and Equity 

We’ve all read the headlines. AI systems are slightly racist and misogynistic.  

They didn’t learn this themselves. AI systems, no matter how advanced, are only as unbiased as the data they are trained on. And when the people training them have unconscious biases, the AI learns these biases too.  

For governments, ensuring fairness in AI outcomes is a moral and legal imperative, particularly in sectors like criminal justice, welfare, and immigration.  

The rise of ethical AI focuses on building systems that mitigate bias, respect privacy, and promote equitable outcomes. 

Why It Matters 

Recent high-profile cases have highlighted how AI can unintentionally perpetuate inequality. For instance, facial recognition systems used in electronic immigration gates have been criticised for higher error rates when identifying people of colour. Similarly, voice recognition systems used by contact centres, have been panned for struggling to interpret strong Scottish accents. 

In government contexts, biases like these could lead to the unfair treatment of citizens of individual communities or socioeconomic groups. This further risks eroding public confidence in government AI systems. 

What this Means Over the Next Decade 

Over the next 10-years, Ethical AI frameworks which are mostly aspirational today, will become mandatory in government settings.  

It means that governments will: 

• Adopt stricter data governance policies to ensure diverse and representative datasets. 

• Require bias detection tools to regularly audit their AI systems. 

• Partner with independent oversight bodies to review AI implementations. 

And the UK Government has already started. The Responsible Technology Adoption Unit leads its work to enable trustworthy innovation using data and AI as part of the Department for Science, Innovation and Technology.  

Governments across the world will rollout similar initiatives to shape policies and ensure compliance with ethical standards. 

Trend 3 – AI-Driven Cybersecurity: Protecting Critical Infrastructure 

As governments become more digitally interconnected, the stakes for cybersecurity have never been higher.  

From protecting sensitive citizen data, to ensuring the integrity of election systems, the rise of AI-driven cybersecurity is a game-changer. Governments are increasingly turning to AI to detect, prevent, and respond to cyber threats in a way that is faster than traditional methods ever could. 

Why It Matters 

The UK faced over 7.8 million cyber incidents in 2023 alone. While many of these targeted public sector infrastructure, attacks on private sector organisations can be even more damaging to the wider economy and infrastructure.  

With malicious actors using AI to launch sophisticated attacks, governments are forced to fight ‘fire with fire’ by deploying AI-powered defence systems. 

What this Means Over the Next Decade 

We can expect greater collaboration between governments and private sector tech specialists to build resilient cybersecurity ecosystems, that will rely on AI to: 

• Predict and prevent attacks by using machine learning to identify – in real time - vulnerabilities and unusual patterns. 

• Enhance response time, mitigate breaches as they occur and reducing any fallout that might occur. 

• Secure citizen and Government Security Classification (GSC) data by identifying and addressing potential data risks before they escalate. 

A Pragmatic Approach to AI innovation in Government 

These trends show that the complexity of implementing AI in government in immense. But by aligning AI with clear objectives, prioritising ethical and explainable systems, and using data responsibly, governments can build trust in their AI initiatives while paving the way for scalable, future-proof implementations. 

This is where Pragmatic AI comes in. The approach isn’t about adopting technology for technology’s sake; it’s about leveraging AI as a digital colleague to address highly-focused use cases.  

By focusing on seamless integration with existing investments in systems and infrastructure, targeted solutions that fit into an existing environments are identified, helping teams to work smarter and achieve measurable outcomes without the cost, risks and complexity of traditional AI rollouts. 

Download the Pragmatic AI whitepaper to learn how public sector organisations, especially those in heavily unionised and regulatory-controlled environments, can navigate these trends and sustainably unlock the full potential of AI.