How to Optimise AI Adoption in Public Sector Organisations

The UK government has announced a landmark multi-sector partnership with OpenAI to “increase productivity in the UK’s public services”. According to Peter Kyle, Secretary of State for Science, Innovation and Technology, the deal stems from a shared belief that “AI will be fundamental in driving change…and driving economic growth”.

This echoes the primary aim of deploying AIOps (Artificial Intelligence for IT Operations) within any organisation: to deliver improved outcomes for the business and the end user. But in the public sector, the stakes are higher. AI not only promises to boost employee productivity but also offers genuine social value to citizens who rely on digital platforms.

That’s why proper deployment is crucial. To succeed, organisations must recognise that AI will be difficult to optimise without the right digital infrastructure around it. Therefore, to unlock the transformative potential of AIOps, public sector enterprises need to prioritise smarter, faster and more transparent networks.

Data Overload Meets Outdated Infrastructure

As it stands, many public sector networks are contending with legacy systems that creak under the weight of increasing data volumes. The situation can create a cycle in which poor day-to-day performance can hinder an AIOps deployment intended to offer a fix.

For example, imagine a local council that rolls out AIOps to improve the reliability of its digital services. If its infrastructure is too sluggish to quickly funnel the real-time data the AI relies on, the new investment will get trapped in the bottleneck it was meant to remove, exacerbating the challenge.

This lose-lose scenario is playing out in the IT departments of the UK Government right now. Recently, the Public Accounts Committee (PAC) warned that “while AI has the potential to radically change public services, the scale of the task is ‘concerningly great’” – citing poor data quality, outmoded tech stacks and a shortage of digital skills leaders as serious obstacles. Clearly, wholesale infrastructural changes are needed before things can be supercharged by AI.

Because AI thrives on relevant and accurate information, even the most advanced models will struggle to perform to the required standard if the network is unable to process and transport data in real-time. Unchecked, these limitations will result in inconsistent user experiences, system blind spots and – as outlined by the PAC – stalled innovation.

The Case for AIOps

To future-proof their digital operations, public bodies must concentrate on streamlining how data flows through their networks. This should be the first consideration when strategising how to overhaul outmoded forms of technology, particularly within complex networks.

For instance, application acceleration technologies help essential data move quickly and reliably across fragmented IT estates – whether that’s hybrid workplaces, cloud architecture or remote end users. With this, bottlenecks that restrict service availability are eliminated, enabling public sector employees to respond faster to public needs.

Once these smoother data movement processes are in place, unified observability platforms can then be integrated to provide much-needed visibility and efficiency. Beyond monitoring traditional telemetry data (such as error logs or user activity), these platforms also measure the specific impact of AI applications on the network and the resulting effects on end-user experiences. With their ability to automatically sift through vast datasets across digital ecosystems, they detect anomalies, identify blind spots, prevent downtime, and even predict imminent failures before they affect users. For IT teams, this automation reduces manual triage efforts and frees up more time to focus on driving productivity.

Real Results From AI-Readiness

Clearly, preparing an AI-ready infrastructure can help the public sector offer faster and more reliable online systems that put less strain on overstretched IT departments. This is the best way to protect service continuity while also earning precious trust among citizens who expect effective digital experiences.

Case in point: The Princess Alexandra Hospital NHS Trust used observability tools to identify the root source of issues and automate their resolution. In turn, they saved “approximately £3 million over a 5-year period” – all while continuing to prioritise employee wellbeing and patient care.

Similarly, by integrating more visibility and efficiency into their digital estate, the Richmond and Wandsworth Councils were able to “improve network application performance and employee experience across social care departments in a complex hybrid working environment”. By modernising their infrastructure, both organisations delivered meaningful digital improvements to their staff and service users.

Laying the Foundation

As digital transformation continues, decision-makers must remember that infrastructure readiness is not an afterthought; it is the starting point. To truly deploy AI in a way that delivers real value to users and the public, it’s essential to create a digital backbone that facilitates speed, transparency and control.

By investing in network acceleration, observability platforms and intelligent automation tools, public sector organisations can unlock the agility and security needed to modernise their digital systems at scale. And the faster these foundations are laid, the sooner decision-makers can convert their AI ambitions into measurable impact – for their operations, for their teams and for the citizens they serve.