AI-Enabled Public Policy Design and Piloting Training Course

AI-Enabled Public Policy Design and Piloting Training Course is a 5-day advanced course on designing, testing and scaling public policies with the responsible use of artificial intelligence. It is designed for government leaders and senior officials who own policy change in public bodies. You complete and take back the AI Policy Pilot Canvas, built on a policy challenge from your own entity. The course is delivered by Mercury Training Center.

About This Course

Government teams are asked to use AI to shape policy faster, yet many efforts stall over weak evidence, unclear accountability and public trust concerns. To follow this course, you should already lead or advise on policy or service reform. You practice by framing policy problems, building no-code AI prototypes, designing pilots and reviewing them against ethical principles.

Who It Is For

• Officials responsible for setting the direction of policy reform and approving new policy pilots
• Staff responsible for running policy labs, design units or service redesign teams in public bodies
• Analysts responsible for turning data and evidence into policy options and ministerial briefings
• Managers responsible for adopting AI tools inside policy, planning or regulatory functions
• Officers responsible for AI ethics, data governance and risk review of new public initiatives

This course is not for readers who need a technical grounding in building machine learning models, who are better served by a data science course, nor for readers new to public policy concepts, who are better served by an introductory public policy course.

Competencies You Will Build

• AI Use Case Framing: you identify where AI adds public value in a policy problem and where it does not
• Citizen Co-Design: you bring citizens, frontline staff and partners into shaping policy options early
• Policy Experimentation: you design pilots with clear hypotheses, comparison groups and stop rules
• Responsible AI Review: you test AI uses against fairness, transparency, privacy and accountability principles
• Evidence-Based Decisions: you read model outputs, scenario results and pilot data before recommending action
• Pilot Scaling: you secure the mandate, resources and safeguards needed to extend a pilot that works

What You Will Be Able to Do

By the end of the course you will be able to:

• From a policy challenge statement, produce a problem frame that separates root causes, affected groups and decision points suited to AI support
• Given a stakeholder list, plan a co-design session that captures user needs and translates them into testable policy options
• Using a no-code AI tool, build a working prototype that drafts, summarizes or simulates one step of a policy process
• Given a proposed AI use, complete an ethics and risk assessment with named mitigations and a human oversight point
• Using pilot results and scenario data, write a scale, adapt or stop recommendation for senior decision makers
• From a successful pilot, prepare a scaling plan with owners, resources, monitoring indicators and public communication steps

Course Content

Day 1: AI in the Public Policy Cycle

• Public Policy Cycle from Problem Framing to Scaling
• AI Capabilities and Limits in Drafting, Analysis and Simulation of Policy
• Public Value Test for Selecting AI Use Cases in Government
• Policy Lab Models and Design Unit Structures in Public Bodies
• Mandate and Readiness Assessment for AI-Supported Policy Teams

Day 2: Evidence, Data and Co-Design

• Problem Framing and Root Cause Mapping for Complex Public Issues
• Data Sources, Data Quality and Big Data Use in Evidence-Based Policy
• Co-Design Sessions with Citizens and Frontline Staff
• AI-Assisted Analysis of Consultation Responses and Citizen Feedback
• Translating User Needs into Testable Policy Options

Day 3: Prototyping and Pilot Design

• No-Code AI Prototyping for Policy Drafting and Summarization Tasks
• Scenario Modelling and Predictive Tools for Policy Options Appraisal
• Pilot Design with Hypotheses, Comparison Groups and Stop Rules
• Regulatory Sandboxes and Controlled Testing Environments for New Rules
• Measuring Pilot Outcomes with Leading and Lagging Indicators

Day 4: Responsible AI and Policy Risk

• AI Ethics Principles of Fairness, Transparency, Privacy and Accountability in Policy Use
• Bias Detection and Explainability Checks for AI-Supported Decisions
• Human Oversight Points and Responsible AI Officer Roles in Policy Teams
• Pilot Risk Register and Risk Appetite for Public Experiments
• Building Public Trust Through Disclosure of AI Use in Policy

Day 5: Applied Policy Design and Piloting Practice

• Exercise Framing a Policy Challenge and Selecting an AI Use Case
• Exercise Building and Testing a No-Code AI Policy Prototype
• Exercise Running an Ethics and Risk Review of the Prototype
• Scaling Plans, Adoption Barriers and Sustaining Policies That Work
• Completing the AI Policy Pilot Canvas

Case Studies and Exercises

The following are suggested activities used during the course.

• Case study: a municipal services department using AI to analyze citizen complaints before redesigning a permit policy; you decide which findings justify a pilot.
• Case study: a health authority testing a predictive tool to prioritize preventive outreach; you identify the fairness risks and set the human oversight point.
• Exercise: an education ministry team co-designing a student support policy; you plan the session and convert the outputs into two testable options.
• Exercise: a utilities regulator reviewing pilot data on a new tariff support rule; you write a scale, adapt or stop recommendation.

What You Take Back

You return with the AI Policy Pilot Canvas, built around a policy challenge from your own entity. In your first month back, you use it to brief senior officials, agree the pilot scope and run the first ethics and risk review before any AI tool is used on live cases.

• Policy problem frame and AI use case selection sheet
• Co-design session plan and policy options log
• Pilot design with indicators and stop rules
• Ethics and risk assessment with scaling plan

Quick Answers (FAQ)

What should I know before AI-enabled public policy design training?

You should already lead or advise on policy or service reform and be comfortable reading basic data reports. No coding or technical AI background is needed, since prototyping uses no-code tools.

How does AI-enabled public policy design training differ from general strategic policy management training?

Strategic policy management training focuses on planning and aligning policies with institutional priorities. This course centers on designing and piloting new policies, with AI used responsibly at each stage and tested through prototypes before scaling.

How can governments use AI responsibly in public policy design?

Governments use AI responsibly when its purpose is clear, its data is fit for use, its outputs are explainable, affected people are consulted and a named official reviews decisions. Regular bias and risk checks keep it accountable.

What do I take back from AI-enabled public policy design and piloting training?

You take back the AI Policy Pilot Canvas, built on your own policy challenge, with a problem frame, co-design plan, pilot design, ethics and risk assessment and scaling plan.

For Your Manager

After the course, the team member will be able to frame a policy challenge, test a responsible AI-supported solution through a controlled pilot and recommend whether to scale it. They return with the AI Policy Pilot Canvas, first used to agree pilot scope and run an ethics and risk review on a current policy initiative in your entity.