Using AI for Policy Research and Analysis in the Australian Public Service (2026)

Policy research and analysis is one of the most intellectually demanding parts of public service work. It requires synthesising large volumes of evidence, understanding complex systems, engaging with diverse stakeholder perspectives, and producing clear, well-reasoned analysis that can inform decisions at the highest levels of government.

AI tools are beginning to change how this work is done. They cannot replace the judgement, expertise, and accountability that good policy analysis requires — but they can significantly reduce the time spent on the more mechanical aspects of research and writing, freeing policy officers to focus on the analytical work that actually requires them.

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Where AI Adds Value in Policy Research

1. Literature Review and Evidence Synthesis

Policy analysis typically begins with a review of the evidence — academic research, evaluation reports, international experience, and expert opinion. Reviewing this literature is time-consuming.

AI tools can help by:

• Summarising academic papers and research reports
• Identifying the main themes and findings across a body of literature
• Flagging areas of agreement and disagreement in the evidence
• Producing structured summaries that policy officers can use as a starting point

Tools like Elicit are specifically designed for research synthesis, allowing users to search for and summarise academic papers on a topic. ChatGPT and Claude can also summarise documents when the text is pasted into the tool.

Important caveat: AI tools can make errors when summarising research, including misrepresenting findings or omitting important caveats. Policy officers must read the original sources for any evidence that will be relied upon in policy documents.

2. Analysing Stakeholder Submissions

Public consultations often generate large volumes of written submissions. Manually reading and categorising hundreds of submissions is extremely time-consuming.

AI tools can help by:

• Identifying the main themes across a large volume of submissions
• Categorising submissions by topic or stakeholder type
• Flagging submissions that raise specific concerns or novel arguments
• Producing a structured summary of the consultation feedback

This is one of the most practical applications of AI in policy work. The AI does the initial categorisation and summarisation; policy officers review the output and exercise judgement about how to interpret and respond to the feedback.

3. Comparative Policy Analysis

Policy officers frequently need to understand how other jurisdictions have approached a policy problem — what approaches have been tried, what has worked, and what lessons Australia can draw.

AI tools can help by:

• Summarising publicly available information about policy approaches in other countries
• Identifying common themes and differences across jurisdictions
• Producing structured comparisons that policy officers can use as a starting point

This is most useful for getting a quick overview of international experience. For detailed comparative analysis, policy officers should verify AI summaries against primary sources.

4. Drafting Policy Documents

Policy documents — options papers, cabinet submissions, regulatory impact statements — follow established structures and conventions. AI tools can help draft these documents based on information and analysis provided by the policy officer.

The policy officer provides the substance — the evidence, the analysis, the options, the recommendation. AI helps with the structure, the writing, and the consistency of the document.

5. Data Analysis and Visualisation

Policy analysis increasingly involves quantitative data — economic data, social indicators, program evaluation data. AI tools can help policy officers who are not data specialists to analyse and interpret data.

Microsoft Copilot in Excel can help with data analysis tasks. Power BI with Copilot allows natural language querying of data. For more sophisticated analysis, agencies with data science teams use Python and R with AI-assisted tools.

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Maintaining Rigour and Accountability

Verify AI Summaries Against Primary Sources

AI tools can produce plausible-sounding but inaccurate summaries of research. Any evidence that will be relied upon in a policy document must be verified against the original source. Do not cite research based solely on an AI summary.

Disclose AI Use Where Required

Some agencies and some document types require disclosure of AI use. Check your agency's policy on AI disclosure. As a general principle, if AI was used to draft a significant portion of a document, this should be noted.

Maintain the Policy Officer's Analytical Role

AI can help with the mechanical aspects of policy research — summarising, categorising, drafting. The analytical work — weighing evidence, assessing options, making recommendations — must be done by the policy officer. AI output is a starting point, not a conclusion.

Protect Sensitive Information

Policy work often involves sensitive information — cabinet-in-confidence material, commercially sensitive information, personal information. This information must not be entered into AI tools that have not been approved for those purposes.

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Practical Workflow for AI-Assisted Policy Research

1. Define the policy question — Be clear about what you are trying to answer before using AI tools.
2. Use AI to get an overview — Use AI to summarise key documents and identify the main themes in the evidence.
3. Verify against primary sources — Read the original sources for any evidence you will rely on.
4. Use AI to draft the document — Provide the AI with your analysis and ask it to draft the relevant sections.
5. Review and refine — Review the AI output carefully, correct any errors, and ensure it accurately reflects your analysis.
6. Apply your judgement — The recommendation and the reasoning behind it must be yours, not the AI's.