Details

Category: Statistical Software for Quantitative Analysis

Published: Friday, 07 August 2026 09:15

Written by: Matt Pye - HWD

AI-Assisted Analysis for Government Analysts

Overview

Artificial intelligence is rapidly changing the way analysis can be undertaken across government. Used effectively, AI can help analysts explore information more quickly, work with large quantities of data and evidence, generate and test analytical approaches, support coding and modelling, identify patterns, improve quality assurance and communicate findings more effectively.

This practical one-day course will show government analysts how to incorporate AI into their analytical work while retaining the professional judgement, scrutiny and accountability essential to high-quality government analysis.

Participants will explore realistic analytical tasks and consider where AI can genuinely add value, where its use may be inappropriate, and how AI-generated outputs should be checked and challenged. The course will also address the limitations and risks of AI, including hallucination, bias, confidentiality, security, transparency and over-reliance on apparently convincing outputs.

Practical exercises and government-focused scenarios will allow participants to experiment with AI as an analytical assistant while developing a responsible and defensible approach to its use.

Learning Outcomes

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

• identify where AI can add value across different stages of the analytical process
• use AI more effectively to support analytical thinking, investigation and problem solving
• construct effective prompts for complex analytical tasks
• use AI to support data exploration, coding, modelling, evidence synthesis and quality assurance
• critically evaluate and validate AI-generated analysis rather than accepting outputs at face value
• recognise common AI limitations, including hallucination, bias, poor reasoning and inaccurate quantitative outputs
• understand the risks associated with confidential, sensitive and personal data
• apply appropriate principles of transparency, accountability, fairness and human oversight
• recognise when AI should – and should not – be used for an analytical task
• maintain professional responsibility for analytical conclusions and advice when AI has contributed to the work