The golden opportunity: integrating data and AI to improve government services and renew public trust
Public sector organisations must embrace transformation if they are to meet increasing demands. Here, Paul Jones, senior director of customer advisory at SAS, sets out how to make the most of real-time intelligence – including knocking down the barriers that stand in the way
Leaders of government departments, local councils and public service providers are facing an increasingly complex landscape across almost every dimension under their purview.
Citizens are demanding more responsive, personalised and efficient services, while budgets remain constrained, and the need to evolve services to keep up with changing social challenges facing communities is a constant that seems to be accelerating.
For decades, global economic and geopolitical stability meant that the public sector was able to operate using legacy systems that collected and processed data in periodic intervals. This model, while serviceable in a slower, analogue era, is now woefully out of sync with the cadence of the world in almost any scenario.
When these legacy systems remain in place, the risks associated with results based on fragmented data, limited visibility and delayed responses increase with each passing day. If you are making critical decisions using stale information, you will miss key opportunities to improve the citizen experience with more timely interventions or to seize opportunities to optimise service delivery.
In fact, there’s never been a better time for you to make the changes that result in improved services.
In our current environment, this traditional, batch-based approach to data processing is no longer fit for purpose. AI – and AI agents – can be deployed to handle repetitive tasks, allowing employees to focus on more meaningful work instead of performing rote activities or overseeing routine workflows.
Integrate data, systems and processes
One of the primary obstacles to agility in the public sector has been the persistent siloing of data. Departments and agencies often operate in isolation, each with their own systems, databases and reporting protocols.
While this may simplify internal control, it severely limits your ability to share insights across services, identify emerging risks, or respond holistically to citizen needs.
With real-time data integration, this fragmentation begins to dissolve. Technologies such as data streaming platforms, cloud-native analytics environments and AI-driven automation are enabling a more cohesive approach.
You can now ingest data from multiple sources, ranging from internal case management systems to Internet of Things (IoT) devices and third-party services, and transform it into actionable insight within moments.
You can fully realise the value of this intelligence by embedding this technology into automated, responsive processes that can adapt in real time.
This means rethinking workflows to ensure that intelligence flows directly into frontline operations – triggering alerts, informing decisions, or initiating services without delay. From eligibility assessments to emergency response coordination, integrated processes help convert information into timely action.
The result is a more connected public sector ecosystem, where decision-making is informed by a continuous, 360-degree view of the citizen and the wider social context.
Change mindsets
Realising the full potential of real-time intelligence requires more than just new technology – it demands a fundamental shift in leadership mindset. Leaving legacy systems behind also means leaving behind legacy thinking; the risk-averse, process-heavy approaches that were shaped by outdated capabilities and constraints.
Today’s data and AI solutions make it possible to act in the moment, rather than after the fact. In areas like welfare and benefits, for example, AI-powered fraud detection can now analyse claims data and behavioral patterns in real time, flagging anomalies before funds are disbursed.
This isn’t about increasing red tape or suspicion – it’s about ensuring public resources are protected and directed to those who truly need them, with greater speed and fairness.
But to unlock this level of responsiveness, you must move away from rigid, retrospective models of decision-making. Embracing real-time intelligence means enabling teams to act proactively, use data dynamically and rethink outdated workflows.
Changing mindsets is the first step toward modernising service delivery, not just technologically but culturally. The real opportunity lies in reshaping how your organisation thinks, decides and acts in an age of real-time information.
Adopt AI strategically
Strategic AI adoption isn’t about deploying the latest technology for its own sake – it’s about thoughtfully leveraging AI where it can meaningfully solve operational challenges.
A central element of this is the emergence of AI agents – autonomous, context-aware systems that go beyond traditional analytics to act intelligently and independently. These agents can fuse historical and live data, applying predictive models and natural language understanding to detect patterns, generate actionable insights and even initiate automated responses.
But their deployment must be purposeful and grounded in real-world utility, not just technological enthusiasm. In emergency response scenarios, AI agents can analyse real-time incident reports, traffic data and weather forecasts to dynamically reroute ambulances or deploy additional emergency personnel where they are needed most.
These applications exemplify where real-time intelligence – powered by AI – delivers tangible value.
Adopting AI strategically also means assessing whether to build or buy. In some cases, developing custom models in-house will align better with specific policy contexts or data environments. In others, pre-built solutions may provide a faster, cost-effective route to value.
For example, off-the-shelf models like SAS Document Analysis or SAS Tax Compliance for Sales Tax offer proven, scalable capabilities for specialised use cases.
Ultimately, real-time intelligence thrives when you deploy AI in service of clearly defined outcomes – anticipating needs, automating responses and enabling more agile, informed governance.
The shift from descriptive to prescriptive and autonomous analytics is not just technological – it’s a strategic transformation that must be approached with both ambition and care.
Unlock efficiency, transparency and trust
Beyond operational improvements, real-time intelligence offers the potential to address some of the deeper challenges facing the public sector.
With real-time visibility into performance metrics, service demand and financial flows, you are better equipped to allocate resources effectively and streamline operations.
Automated reporting and audit trails enhance accountability, while data-driven insights support evidence-based policymaking. Moreover, when citizens connect with public services that respond quickly, anticipate their needs and deliver personalised support, their trust in the institutions behind them grows.
Whether it’s a council that pre-emptively repairs a pothole identified via citizen reports and sensor data, or a health authority that spots and supports a vulnerable patient early, these experiences foster a sense of care, reliability and responsiveness.
A strategic shift
It’s important to emphasise that this transformation is not purely about technology.
Adopting real-time intelligence requires you to shift your organisation’s approach to data governance, workforce skills and cross-agency collaboration. Investments must be made not only in platforms and AI capabilities, but also in organisational culture and leadership.
Responsible use of this technology, particularly around data privacy, algorithmic bias and digital inclusion, must be considered from the outset. Fortunately, many public sector leaders are rising to this challenge.
Pilot programmes are underway across local authorities, healthcare and central government bodies, demonstrating what is possible when data is treated as a strategic asset and not just a compliance requirement.
These initiatives are showing that real-time intelligence is not only achievable – it is already delivering tangible benefits. In Jakarta, the capital of Indonesia which has a population of 11 million, linking millions of points of government data in real-time gives citizens a one stop shop on vital information like taxes, crime, and air quality, and helps in potential emergencies. With 40% of the city subject to potential flooding, authorities can now react in five minutes rather than an hour. In North Carolina, the Department of Insurance runs its Insurance Crimes Investigation System in the cloud to better detect fraud and track investigations from intake through to prosecution, and has recovered US$6.9m in just seven months.
In a recent global study, 100% of the civil servants surveyed reported that the use of data and AI has already improved their productivity in tackling these irregularities – a welcome shift for teams under constant pressure and working with limited resources.
The future is now
The reimagining of the public sector data journey is well underway.
Leaders who have connected these dots understand how to move their agencies beyond what worked in the past towards what citizens expect today – and will likely need in the future. At the heart of this transformation is the integration of data and AI. These are technologies that, when deployed responsibly, have the power to transform how the government works for its citizens.
As we look ahead, the question is no longer whether you can adopt real-time intelligence, but how quickly and boldly you will do so. The opportunity is clear: smarter services, reduced costs, better outcomes and renewed trust in public institutions.
Read more about reimagining the future of public sector productivity
About the author
Paul Jones, senior director of customer advisory, SAS
Leading the practice for the Northern Europe region and supporting key customers across various industries, Paul has over 30 years of experience in applied analytics and technology, focusing on data strategy modernisation, AI, cloud deployment patterns, big data systems, and enterprise architecture. His mission is to help organisations face their data and AI challenges by adopting transformative enterprise-wide analytical strategies to derive value from their data.
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