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Copilot use was moderate and focused on a few use cases

Use of Copilot was moderate. However most trial participants across classifications and job families were optimistic about Copilot and wished to keep using it. 

• Only a third of trial participants across classifications and job families used Copilot daily. 
• Copilot was predominantly used to summarise information and re-write content.
• Copilot in Microsoft Word and Teams were viewed favourable and used most frequently.
• Access barriers prevented Copilot use in Outlook.

Perceived improvements to efficiency and quality

Trial participants estimated time savings of up to an hour when summarising information, preparing a first draft of a document and searching for information.

The highest efficiency gains were perceived by APS levels 3-6, Executive Level (EL) 1 staff and ICT roles.

The majority of managers (64%) perceived uplifts in efficiency and quality in their teams.

40% of trial participants reported their ability to reallocate their time to higher-value activities such as staff engagement and strategic planning.

There is potential for Copilot to improve inclusivity and accessibility in the workplace and in government communication.

Adoption requires a concerted effort to address barriers

There are key integration, data security and information management considerations agencies must consider prior to Copilot adoption, including scalability and performance of the GPT integration and understanding of the context of the large language model.

Training in prompt engineering and use cases tailored to agency needs is required to build capability and confidence in Copilot.

Clear communication and policies are required to address uncertainty regarding the security of Copilot, accountabilities and expectation of use.

Adaptive planning is needed to reflect the rolling feature release cycle of Copilot alongside governance structures that reflect agencies’ risk appetite, and clear roles and responsibilities across government to provide advice on generative AI use. Given its infancy, agencies would need to consider the costs of implementing Copilot in its current version. More broadly this should be a consideration for other generative AI tools.

Broader concerns on AI that require active monitoring

There are broader concerns on the potential impact of generative AI on APS jobs and skills, particularly on entry-level jobs and women.

Large language model (LLM) outputs may be biased towards western norms and may not appropriately use cultural data and information.

There are broader concerns regarding vendor lock-in and competition, as well as the use of generative AI on the APS’ environmental footprint.

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