Generative AI is a form of artificial intelligence that can produce new content—text, summaries, translations, drafts, classifications—based on patterns learned from data. Tools like ChatGPT add a conversational interface, enabling natural language interaction that can feel more like speaking to a human than navigating a form or a call queue.
The question for public services is not whether the technology is impressive (it is), but whether it can be used safely, lawfully and responsibly to improve outcomes for citizens and support public sector staff. Recent UK guidance has moved the conversation from “should we?” to “how do we do this well?”—with clearer expectations around security, data protection, transparency and procurement. (GOV.UK)
Could Generative AI and ChatGPT improve the delivery of Public Services ?
The most obvious benefit of generative AI is its ability to automate or accelerate high-volume, low-complexity work. In a public service context, that often means reducing friction at the front door: answering frequently asked questions, guiding citizens to the right pathway, explaining eligibility rules in plain language, and helping people complete forms correctly the first time. Done well, this can reduce avoidable contacts, shorten waiting times, and free staff to focus on complex or sensitive cases.
Generative AI can also support triage and signposting. Instead of citizens bouncing between channels, a well-designed assistant can clarify intent, gather the right information, and route the enquiry to the correct team, service, or self-service step—while keeping the interaction accessible and available outside office hours. UK government guidance to civil servants has explicitly focused on using large language models in ways that improve work while managing risk, which reinforces that “assistive” use cases are often the safest starting point. (GOV.UK)
A second major benefit is summarisation and synthesis. Public bodies sit on vast quantities of documents, correspondence and guidance. Generative AI can help staff quickly summarise lengthy material, extract key points, compare policy options, draft communications, and convert dense language into citizen-friendly formats—improving both productivity and inclusion, especially where plain English (or alternative languages) are needed.
A third opportunity sits in insight and operational learning. Many services produce high volumes of “signals”: call logs, complaint themes, case notes, incident reports, internal knowledge articles, staff feedback. Generative AI can help cluster themes, identify recurring failure demand, and surface improvement opportunities more quickly—supporting evidence-led service improvement and better prioritisation.
Finally, generative AI can strengthen the Intelligent Client Function (ICF). In multi-supplier environments, a public body’s ability to specify outcomes, measure performance, interpret service data, and drive improvement often determines success. AI can support the ICF by helping teams analyse supplier reports, identify gaps, track commitments, and produce clearer governance packs—improving accountability without simply creating more reporting overhead.
Public sector health
Health and care is a particularly relevant domain because time is scarce, documentation burdens are heavy, and service quality depends on accurate information flow. One of the most immediate and realistic use cases is AI-enabled ambient scribing—tools that help generate structured clinical documentation from consultations, reducing manual admin and improving record completeness. NHS England has published guidance to support adoption of AI-enabled ambient scribing products, reflecting both the opportunity and the need for careful controls in real clinical settings. (NHS England)
This is a useful example because it illustrates the “right shape” of public sector use: assistive capability, strong governance, clear boundaries, and a focus on safety, privacy and accountability rather than novelty.
Some of the risks
As powerful as generative AI can be, public services carry distinctive risks because they often involve vulnerable people, sensitive personal data, and decisions that can materially impact individuals’ lives.
Trust is the first risk. Citizens may be wary of receiving advice from an automated tool, especially where outcomes affect benefits, housing, immigration, safeguarding, or clinical care. Trust is not won by marketing; it is earned through transparency, predictable performance, and safe escalation routes to humans.
Accuracy and “hallucination” is a second risk. Generative AI can produce plausible text that is incorrect, incomplete, or misleading—particularly when asked for authoritative answers. This is why “assistive drafting + verification” and “retrieval-augmented answers from approved sources” are generally safer than unconstrained free-form advice in high-stakes contexts.
Bias and unequal impact is a third risk. If training data or service data reflects societal inequalities, the outputs can reinforce them—especially where automated decision-making or profiling is involved. Public services must also consider equality duties and ensure that AI use does not create unfair outcomes, barriers to access, or discriminatory impacts. Guidance for local government procurement increasingly emphasises building equality and data protection into commissioning decisions. (Local Government Association)
Privacy and data protection risks are significant. Public services frequently handle sensitive data, and the temptation to “paste a case into a chatbot” is real. The ICO’s guidance on AI and data protection sets expectations around fairness, transparency, security, data minimisation and accountability—principles that must be designed into the system, not bolted on later. (ICO)
Security and misuse risks have also grown. Generative AI can be used maliciously (fraud scripts, impersonation, scalable misinformation). Recent UK work on deepfake detection reflects the reality that synthetic content can undermine trust and enable harm—issues public bodies must plan for, particularly in identity, payments and safeguarding workflows. (Reuters)
Generative AI is not an expert system
A crucial point: generative AI is not an expert system. It does not “know” in the same way a rules engine or curated knowledge base does, and it can struggle to reliably distinguish a widely repeated myth from a verified fact unless it is grounded in trusted sources at the point of use.
Stephen Wolfram’s well-known discussion on combining ChatGPT with computational knowledge is still a good primer on why blending generative AI with structured knowledge and verification can produce better outcomes than relying on pure text generation. (ICO)
For public services, this leads to a practical design principle:
Use generative AI as an interface and accelerator, but anchor outcomes in authoritative sources, rules, and human accountability—especially where advice or decisions affect people’s rights, eligibility or safety.
In practice, that often means:
- retrieval from approved guidance and policy sources (so the model cites the “house view”);
- decision support rather than decision replacement;
- human review for high-impact outputs; and
- clear escalation routes when confidence is low or stakes are high.
Policy, governance and transparency
To realise the benefits while minimising the risks, public bodies need a governance approach that is proportionate, repeatable, and operational (not just policy documents).
UK government has published practical guidance to support safe and effective adoption—including the AI Playbook for the UK Government and guidance for civil servants on using generative AI. These emphasise responsible use, risk management, security, and alignment with public sector expectations. (GOV.UK)
A workable public sector governance posture typically includes:
1) Clear use-case boundaries Start with low-risk, high-value applications (drafting, summarising, internal search, triage), then expand only with evidence.
2) Data controls Strong rules for what data may be entered, where it is processed, how it is retained, and how it is protected—aligned to UK GDPR and public sector security expectations. ICO guidance is a key reference point. (ICO)
3) Transparency and explainability People should understand when they are interacting with AI, what it can/can’t do, what sources it uses, and how to challenge or escalate outcomes. The ICO has extensive material on explaining AI-assisted decisions. (ICO)
4) Assurance and monitoring Testing for bias, error modes and security; logging and audit; continuous monitoring; and regular review of performance against service outcomes.
5) Supplier and procurement discipline Avoiding vendor lock-in, ensuring contractual clarity on data use, model updates, security controls, and accountability—particularly important in multi-supplier architectures.
Summary
Yes—generative AI and ChatGPT can improve the delivery of public services, especially where the aim is to reduce friction, improve access, support staff productivity, and strengthen evidence-led improvement. The biggest wins are likely to come from assistive uses: summarising, drafting, triage, guided self-service, and operational insight—combined with strong controls.
The risks are real: trust, accuracy, bias, privacy, and misuse. But these risks are also manageable when public bodies treat generative AI as part of a governed service model: clear boundaries, grounded answers, human accountability, and transparent, auditable operations—supported by existing UK guidance and regulator expectations. (GOV.UK)
In a world of shrinking funding, rising demand, and urgent service pressures, generative AI is a compelling opportunity—but only if implemented with the structure and discipline that public services (and citizens) rightly expect.
References
- Guidance to civil servants on use of generative AI (UK Government): https://www.gov.uk/government/publications/guidance-to-civil-servants-on-use-of-generative-ai/guidance-to-civil-servants-on-use-of-generative-ai (GOV.UK)
- AI Playbook for the UK Government (UK Government): https://www.gov.uk/government/publications/ai-playbook-for-the-uk-government/artificial-intelligence-playbook-for-the-uk-government-html (GOV.UK)
- Generative AI framework for HM Government (Cabinet Office / CDDO PDF): https://assets.publishing.service.gov.uk/media/65c3b5d628a4a00012d2ba5c/6.8558_CO_Generative_AI_Framework_Report_v7_WEB.pdf (GOV.UK)
- ICO — Guidance on AI and data protection: https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/guidance-on-ai-and-data-protection/ (ICO)
- NHS England — Guidance on the use of AI-enabled ambient scribing products: https://www.england.nhs.uk/long-read/guidance-on-the-use-of-ai-enabled-ambient-scribing-products-in-health-and-care-settings/ (NHS England)
- Responsible buying: equality and data protection in AI commissioning (Local Government Association): https://www.local.gov.uk/publications/responsible-buying-how-build-equality-data-protection-your-ai-commissioning (Local Government Association)
- Wolfram — Wolfram|Alpha as the Way to Bring Computational Knowledge Superpowers to ChatGPT: https://writings.stephenwolfram.com/2023/01/wolframalpha-as-the-way-to-bring-computational-knowledge-superpowers-to-chatgpt/ (ICO)