AI is thus a fundamental technology of our era in the Fourth Industrial Revolution, and the revolution is altering the terrain in which government can conceive, provide and assess services. It offers a hands-on policy implementation framework for governments considering deploying AI, including policy and legal templates, and good practices, ethical principles and illustrative use cases. With concise explanations of key concepts – such as machine learning, generative AI, and agentic systems – and practical advice on policy, procurement, cyber, and audit, public officials will gain the understanding and skills needed to spot high-impact AI uses, assemble and lead interdisciplinary AI project teams, and oversee risk at every point in an AI system’s life. Rather than a step-by-step manual, here’s a quick playbook that can be used as a strategic aide built on knowledge, public value, and public trust to help you begin with AI.

• The playbook adopts the OECD definition: “An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.”
• The UK government’s paper on AI regulation suggests these systems are “‘adaptable’ because they can find new ways to meet the objectives set by humans, and ‘autonomous’ because, once programmed, they can operate with varying levels of autonomy, including without human control.”

• The playbook provides explanations of Neural Networks (NNs), Machine Learning (ML), Deep Learning (DL), Speech Recognition (SR), Computer Vision (CV), Natural Language Processing (NLP), Generative AI, and Agentic AI, outlining their functionalities and potential applications in government (e.g., fraud detection, image processing, document analysis, personalized content generation).
• Generative AI is highlighted as being capable of “generating text, images, video or other forms of output by using probabilistic models trained across various domains.” Its applications extend to drug discovery and financial modeling.
• Agentic AI systems are defined as “autonomous AI systems that can make decisions and perform actions with minimal human intervention.”

• Several guiding principles are implicitly and explicitly mentioned:
• Using AI “only when relevant, appropriate and proportionate.”
• Ensuring AI systems are “secure to use and resilient to cyber attacks.”
• Understanding and managing “drift, bias, and, in the case of generative AI, hallucinations.”
• Working with commercial colleagues “from the start.”

• A “minimum viable AI team” needs skills to: “identify user needs and accessibility requirements,” “manage and report to stakeholders,” “design, build, test and iterate AI products,” “ensure the responsible development of lawful, ethical, secure and safe-by-design AI services,” and “collect, process, store and manage data ethically, safely and securely.”
• The playbook acknowledges the “current shortage of AI talent” and suggests strategies like new hires, contractors, and internal upskilling.
• Civil Service Learning offers free AI courses on topics like “fundamentals of AI and generative AI,” “understanding AI ethics,” and specific technical domains.

• “Like all technology, using AI is a means to an end – not an objective in itself.” The playbook emphasizes defining goals, understanding user needs, and identifying where AI can be most effective.
• User research is deemed “critical to the success of your AI project” to understand needs, values, and priorities.

• Public sector procurement must comply with legislation like the Public Contract Regulations 2015 and the upcoming Procurement Act 2023.
• Various “routes to market” exist, including frameworks and Dynamic Purchasing Systems (DPS) offered by the Crown Commercial Service (CCS).
• When drafting requirements, buyers should “start with your problem statement,” “highlight your data strategy and requirements,” and “underline the need for you to understand the supplier’s AI approach.”
• “It’s important to consider and factor data ethics into your commercial approach from the outset.”

• The playbook stresses the importance of building “safe, secure and robust AI solutions” to promote privacy, reduce harm, and uphold ethical principles.
• Explainability is crucial, describing “the ability to clarify how an AI system arrives at a given output or decision.” Different levels of transparency (technical, process, outcome-based, internal, public) are outlined.
• Accountability and liability need to be clearly defined across all parties involved in the AI lifecycle. “As an end-user, assume responsibility for the outputs and decisions made by the AI systems you use.”
• Potential impacts of AI tools include “justified trust,” “public benefit,” “harm minimisation,” “misinformation and disinformation,” and “sustainability.”

• Relevant provisions include the government’s “pro-innovation approach to AI regulation,” the “Portfolio of AI assurance techniques,” and guidance on AI assurance and management essentials.
• Data protection laws (UK GDPR and Data Protection Act 2018) are central, requiring Data Protection Impact Assessments (DPIAs) for high-risk AI processing.
• Principles like “lawfulness and purpose limitation,” “fairness,” “data minimisation,” and “storage limitation” from the UK GDPR are highlighted as crucial for AI implementation.
• “Article 22 currently prohibits decision(s) based solely on automated processing that have legal or similarly significant consequences for individuals.” Human oversight is often required.
• Intellectual property considerations, including ownership and usage rights, need to be addressed early in AI projects.

• Different deployment models (public applications, embedded AI, APIs, etc.) present varying security challenges.
• The playbook outlines specific threats like “perturbation attack,” “prompt injection” (specific to generative AI), “hallucinations,” and “phishing” facilitated by AI.
• Mitigation strategies are suggested, such as adversarial training, prompt filtering, robust models, and multi-factor authentication.
• “Never enter any official information directly into public AI applications or APIs unless it’s already publicly available or cleared for publication.”

• Organizations should establish an “AI and machine learning (ML) systems inventory” in addition to the Algorithmic Transparency Recording Standard (ATRS).
• Robust “governance structures for teams” are essential, including clear roles, responsibilities, escalation pathways, and risk prioritization plans.
• Continuous monitoring for “model drift” is necessary to ensure the ongoing accuracy and reliability of AI systems.

• GOV.UK Chat: Demonstrates a “retrieval augmented generation (RAG)” approach to improve user interaction with website content, with a focus on business-related information. Ethical, legal, and security considerations included protecting user privacy and red teaming for vulnerability identification.
• Sensitivity Review Tool: Highlights the use of ML to automate the review of unstructured data for sensitive content, leading to significant reductions in human effort and risk. Challenges include the need for large, labeled datasets.
• NHS User Research Finder: Showcases a tool using NLP and embedding models for auto-moderation of user-generated content, emphasizing the need to navigate organizational sign-off for AI solutions.