Generative AI is a branch of artificial intelligence that can produce new content—text, images, code or summaries—based on patterns learned from existing data. ChatGPT is a well-known example: a natural language system that can hold a conversation, answer questions, and help people find or organise information in a way that feels familiar and human.
In this post, we explore what generative AI could mean for public services—where it can genuinely help, where it can create new risks, and what needs to be in place for it to be used responsibly.
The Benefits
The most visible benefit of generative AI is its ability to reduce the burden of repetitive work—particularly in high-volume, predictable interactions. In public services, that often starts with customer service: answering common questions, guiding citizens through processes, and providing clear signposting to the right service or policy.
Used well, a conversational assistant can help people self-serve at any time of day, reduce waiting times, and free up human teams to focus on cases that need judgement, empathy or specialist decision-making. It can also help improve consistency: the same baseline guidance is offered every time, without “knowledge gaps” that come from turnover or stretched teams.
The opportunity is not limited to front-door interactions. For more complex needs, generative AI can help analyse and summarise large volumes of information quickly—policy, case notes, correspondence, meeting transcripts, incident logs, survey feedback—turning long-form content into structured summaries, themes and options. That matters because public sector decisions are often slowed by the time it takes to assemble and interpret information, rather than the absence of information itself.
There is also a broader accessibility angle. Combining advanced natural language processing with translation, summarisation and rephrasing can make services easier to use—supporting clearer wording, plain-English guidance, and alternative formats that match different needs. In practice, that could mean a citizen can ask a question in their own words and receive a tailored response that points them to the right process, the right form, and the right evidence requirements.
Finally, the same capability can support more user-centred service design. If the system can understand a citizen’s intent, capture key constraints, and adapt the guidance to the situation (for example, accessibility needs, communication preferences, or the complexity of the case), it becomes possible to deliver a more personalised experience without creating a costly, bespoke human process for every interaction.
Public Sector Health
Healthcare is often cited as the most transformative use case—and it’s easy to see why. The NHS and wider health ecosystem generate huge volumes of data and documentation every day: appointments, referrals, symptoms, results, care plans, discharge summaries, correspondence and operational reporting. Generative AI can help clinicians and teams navigate that information more efficiently, identify patterns that might be missed in manual review, and prioritise work.
However, the most immediate and realistic opportunities may be less “diagnose everything” and more “reduce administrative load safely”. A generative system that can listen to a consultation (with consent), summarise it accurately, and structure the notes into the right record format could remove a significant burden from clinical staff. In that model, the clinician remains responsible for judgement and decisions, while the AI supports documentation, recall and workflow—capturing key observations, suggested follow-ups, medication changes and next steps.
If done carefully, this could improve record quality, reduce omissions caused by time pressure, and support continuity of care. It could also allow more time for patient interaction—often the part of the service that matters most, and the part that gets squeezed when administrative tasks expand.
It’s worth saying explicitly: this is not a “replace the GP” story. The nearer-term value is more practical—helping professionals spend less time transcribing, copying, pasting and searching, and more time applying judgement and care.
Some of the Risks
As impressive as generative AI can be, it introduces risks that are particularly sensitive in public services.
One of the biggest is trust. Citizens may be uncomfortable when a system feels human but is not. People often want to know: Am I speaking to a person? Who is accountable? Can I challenge the answer? What happens if it’s wrong? In public services—where decisions can affect entitlements, safety, liberty or wellbeing—those questions are not optional.
Privacy and data protection is another major risk. Personal data may be used to personalise responses or triage cases, which can be helpful, but it must be done with clear boundaries: minimisation, retention controls, security, and lawful basis for processing. Citizens need to understand what data is used, why it is used, and what safeguards are in place.
There is also the risk of bias. If training data or organisational datasets reflect unequal outcomes—or under-represent some groups—the system may produce responses that are unfair, inconsistent or discriminatory. Bias can be subtle: it may appear in wording, assumptions, risk scoring, prioritisation or the “helpfulness” of the guidance. Managing this requires deliberate testing, monitoring and governance—not just good intentions.
Generative AI is not an Expert System
It’s important to be clear about what generative AI is—and what it is not.
Generative AI and ChatGPT are not expert systems. They do not “know” facts in the same way a curated knowledge base or rule engine does. They generate responses based on learned patterns and probabilities. That means they can be fluent and convincing even when they are wrong, incomplete or out of date.
Stephen Wolfram’s article is a helpful way to think about this boundary, and why combining generative AI with computational and verified knowledge can unlock more reliable outcomes: Wolfram|Alpha as the Way to Bring Computational Knowledge Superpowers to ChatGPT
Because these systems are trained on large corpora of text (from many sources), they may fail to distinguish between widely repeated myths and verified facts. They can misunderstand context. They can “hallucinate” details that sound plausible. They can also inherit the limitations of the data they were trained on—especially if the model has not been grounded in authoritative sources for the specific domain.
That matters in public services. A system that gives a confident but incorrect answer can misdirect a citizen, delay access to support, or undermine trust in the service.
In practice, the safest approach is to treat generative AI as a language and reasoning layer, not the final authority. It should be used alongside:
- verified knowledge sources (policy libraries, service directories, eligibility rules);
- fact-checking and validation steps where appropriate;
- and human oversight for complex, sensitive, high-impact decisions.
This is where “hybrid” designs become powerful: generative AI handles the conversation, the summarisation and the user experience, while authoritative systems (and people) handle policy logic, eligibility decisions, and safety-critical judgement.
Returning to healthcare: there is a very real risk that a generative system could miss important context or fail to highlight a risk factor, especially if it is asked to operate beyond its design boundary. Ethical and legal responsibilities must be explicit: who is accountable, what the system is allowed to do, how uncertainty is handled, and how citizens can escalate or appeal.
Policy, Governance and Transparency
To realise the benefits while controlling the risks, agencies need more than a pilot and a set of prompts. They need an operating model.
At a minimum, that includes:
- ensuring any data collected from citizens is stored securely and processed lawfully;
- ensuring automated decisions are explainable, auditable and contestable;
- designing the experience to be human-centred—clear boundaries, clear escalation routes, and language that supports trust rather than mimics human authority.
It also includes practical controls: model evaluation, red-teaming, bias testing, logging, monitoring, incident handling, and change management. In other words, the same discipline we expect in other critical service components—adapted for AI systems.
Summary
Generative AI and conversational systems like ChatGPT offer real opportunities to improve public services: faster access to information, reduced administrative burden, better use of organisational knowledge, and a more responsive service experience.
But these benefits are only sustainable if the risks are taken seriously—trust, privacy, bias, accountability and accuracy. Governments and public bodies will need clear ethical frameworks, strong governance, and implementation approaches that combine the strengths of AI with verified knowledge and human judgement.
In a world of shrinking budgets, rising demand and urgent expectations for improvement, generative AI is an exciting opportunity—but not a shortcut. The organisations that benefit most will be those that invest in clarity: what the system is for, how it is controlled, and how it supports citizens safely, fairly and transparently.