Think AI for Government: Key Takeaways for Public Sector Teams
AI in UK government is moving from pilots to delivery. The message from Think AI for Government was clear: focus on real problems, build trust into the tech, and scale what works.
Below are the concise takeaways and a practical playbook you can use this quarter.
Government Focus: From Tools to System Change
The opening keynote set the tone: AI is being embedded across services, backed by initiatives such as the £42m AI Frontiers Fund and work from the Government Digital Service (GDS). GDS has moved from basic productivity helpers (transcription, data analysis) to service-level change in health, education, and planning.
A standout example was Extract-an AI tool that digitises complex planning documents to speed approvals and support housing and economic growth. While the Ministry of Justice and HMRC are further along, progress is uneven. GDS is building capability, encouraging quick experiments, and emphasising one principle: tie AI to concrete outcomes that solve real public needs.
From Reactive Services to Agentic Systems
Speakers discussed a shift from reactive delivery to agentic systems that anticipate needs, personalise support, and improve throughput. The goal isn't to replace human-centred design; it's to extend it.
"We need to start looking at re-imagining the experiences we want to deliver for citizens and work from there; production readiness and all the technical pieces will follow," said Deepak Shukla, public sector data & AI strategy lead at AWS.
Partnerships That Deliver at Scale
SMEs bring speed and specialist skills, but face rigid procurement and risk concerns. With new scope under the UK Procurement Act, departments can use more flexible, demo-based, and dynamic approaches to bring in innovation faster. Transparency, explainability, and open-source collaboration were highlighted as essential.
"Trust is non-negotiable in national security… explainability [is] mandated," said Chad Bond, director of strategy and innovation at Zaizi. Officials also need clarity to make well-founded decisions.
Examples from Estonia and Ukraine underline what works: shared goals, technical fluency on both sides, and co-design-where government acts as a partner in delivering public value, not just a buyer.
Building an Ethical, Inclusive Future
Leaders agreed that this moment feels different-attention, investment, and ambition are finally coming together. "AI transformation only succeeds when organisations accept it, adopt it and adapt it," said JP Bhamu, director of data and AI at the NHS Business Services Authority. He pointed to the AI Opportunity Action Plan and AI Growth Labs as signals of intent.
The caution: public sector systems are complex. Data silos, legacy tech, and rigid structures still slow progress. The task is to make adoption both fast and safe-a cultural shift as much as a technical one.
Kanika Joshi, founder of Impact Circle, put inclusion at the centre: "The 'I' in AI has to be the starting line and cannot be the finish line." Putting people from vulnerable communities first in decision-making is non-negotiable.
What To Do Next: A Practical Playbook
- Start with outcomes: Pick 2-3 high-friction workflows (e.g., planning, case triage, correspondence) with measurable time or cost savings.
- Prove value fast: Build thin prototypes, test with real users, measure, iterate, then scale.
- Treat data like a product: Define owners, standards, retention, and access. Prioritise high-signal datasets for early use cases.
- Governance by default: Set policies for human-in-the-loop, model choice, explainability, red-teaming, and audit trails-before deployment.
- Balance build vs buy: Buy commodity capability; build where policy nuance, security, or integration make it unique.
- Use flexible procurement: Pilot with SMEs, use outcome-based specs, and adopt demo-first evaluations under the Procurement Act.
- Prepare people: Upskill product, policy, legal, and operations teams; create an AI guild; agree on playbooks for evaluation and rollout.
- Monitor and improve: Track accuracy, bias, throughput, and user satisfaction. Sunset what doesn't deliver, double down on what does.
For policy and delivery teams, two useful references: the GDS guidance and the Cabinet Office resources on the Procurement Act.
If your team needs structured upskilling on AI fundamentals and applied use cases, explore curated options by role at Complete AI Training.
Bottom Line
Keep the focus on citizen outcomes, trust, and delivery discipline. Build capacity, learn in public, and scale what works-one service at a time.
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