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Executive summary

Preface

The uptake of publicly available generative artificial intelligence (AI) tools, like ChatGPT, has grown. In the few years since its public introduction, generative artificial intelligence has become available and accessible to millions. 

This meant the Australian Public Service (APS) had to respond quickly to allow its workforce to experiment with generative AI in a safe, responsible and integrated way. To make this experimentation possible, an appropriate generative AI tool needed to be selected. 

This decision was dependent on:

• how swiftly and seamlessly the tool could be deployed for rapid APS experimentation purposes
• the ability for staff to experiment and learn using applications familiar to them.

One solution to enable the APS to experiment with safe and responsible generative AI was Microsoft 365 Copilot (formerly Copilot for Microsoft 365). On 16 November 2023, the Australian Government announced a 6-month whole-of-government trial of Copilot. Copilot is a supplementary product that integrates with the existing applications in the Microsoft 365 suite and it’s nested within existing whole-of-government contracting arrangements with Microsoft. This made it a rapid and familiar solution to deploy.

Broadly, the trial and evaluation tested the extent the wider promise of generative AI capabilities would translate into real-world adoption by workers. The results will help the Australian Government consider future opportunities and challenges related to the adoption of generative AI. 

This was the first trial of a generative AI tool in the Australian Government. The future brings exciting opportunities to understand what other tools are available to explore a broad landscape of use cases.

Overarching findings

Evaluation findings

Employee related outcomes

• 77% were optimistic about Microsoft 365 Copilot at the end of the trial.
• 1 in 3 used Copilot daily.
• Over 70% of used Microsoft Teams and Word during the trial, mainly for summarising and re-writing content.
• 75% of participants who received 3 or more forms of training were confident in their ability to use Copilot, 28 percentage points higher than those who received one form of training.

Most trial participants were positive about Copilot and wish to continue using it 

• 86% of trial participants wished to continue to use Copilot.
• Senior Executive Service (SES) staff (93%) and Corporate (81%) roles had the highest positive sentiment towards Copilot.

Despite the positive sentiment, use of Copilot was moderate

Moderate usage was consistent across classifications and job families but specific use cases varied. For example, a higher proportion of SES and Executive Level (EL) 2 staff used meeting summarisation features, compared to other APS classifications.

Microsoft Teams and Word were used most frequently and met participants’ needs. Poor Excel functionality and access issues in Outlook hampered use.

Content summarisation and re-writing were the most used Copilot functions.

Other generative AI tools may be more effective at meeting users’ needs in reviewing or writing code, generating images or searching research databases.

Tailored training and propagation of high-value use cases could drive adoption

Training significantly enhanced confidence in Copilot use and was most effective when it was tailored to an agency’s context.

Identifying specific use cases for Copilot could lead to greater use of Copilot.

Productivity

• 69% of survey respondents agreed that Copilot improved the speed at which they could complete tasks.
• 61% agreed that Copilot improved the quality of their work.
• 40% of survey respondents reported reallocating their time for:
  • mentoring / culture building
  • strategic planning
  • engaging with stakeholders
  • product enhancement.

Most trial participants believed Copilot improved the speed and quality of their work

Improvements in efficiency and quality were perceived to occur in a few tasks with perceived time savings of around an hour a day for these tasks. These tasks include: 

• summarisation
• preparing a first draft of a document 
• information searches. 

Copilot had a negligible impact on certain activities such as communication.

APS 3-6 and EL1 classifications and ICT-related roles experienced the highest time savings of around an hour a day on summarisation, preparing a first draft of a document and information searches.

Around 65% of managers observed an uplift in productivity across their team.

Around 40% of trial participants were able to reallocate their time to higher value activities.

Copilot’s inaccuracy reduced the scale of productivity benefits.

Quality gains were more subdued relative to efficiency gains.

Up to 7% of trial participants reported Copilot added time to activities.

Copilot’s potential unpredictability and lack of contextual knowledge required time spent on output verification and editing which negated some of the efficiency savings.

Whole-of-government adoption of generative AI

61% of managers in the pulse survey could not confidently identify Copilot outputs.

There is a need for agencies to engage in adaptive planning while ensuring governance structures and processes appropriately reflect their risk appetites.

Adoption of generative AI requires a concerted effort to address key barriers.

Technical

There were integration challenges with non-Microsoft 365 applications, particularly JAWS and Janusseal, however it should be noted that such integrations were out of scope for the trial. Note: JAWS is a software product designed to improve the accessibility of written documents. Jannusseal is a data classification tool used to easily distinguish between sensitive and non-sensitive information.

Copilot may magnify poor data security and information management practices.

Capability

Prompt engineering, identifying relevant use cases and understanding the information requirements of Copilot across Microsoft Office products were significant capability barriers.

Legal

Uncertainty regarding the need to disclose Copilot use, accountability for outputs and lack of clarity regarding the remit of Freedom of Information were barriers to Copilot use – particularly in regard to transcriptions.

Cultural

Negative stigmas and ethical concerns associated with generative AI adversely impacted its adoption.

Governance

Adaptive planning is needed to reflect the rolling release cycle nature of generative AI tools, alongside relevant governance structures aligned to agencies’ risk appetites.

Unintended outcomes

Appendix

Methodological limitations

Evaluation fatigue may have reduced the participation in engagement activities.

Several agencies conducted their own internal evaluations over the course of the trial and did not participate in Digital Transformation Agency’s overall evaluation.

Mitigations: where possible, the evaluation has drawn on agency-specific evaluation to complement findings.

The non-randomised sample of trial participants may not reflect the views of the entire APS.

Participants self-nominated to be involved in the trial, contributing to a degree of selection bias. The representation of APS job families and classifications in the trial differs from the proportions in the overall APS.

Mitigations: the over and underrepresentation of certain groups has been noted. Statistical significance and standard error were calculated, where applicable, to ensure robustness of results.

There was an inconsistent roll out of Copilot across agencies.

Agencies began the trial at different stages, meaning there was not an equal opportunity to build capability or identify use cases. Agencies also used different versions of Copilot due to frequent product releases.

Mitigations: there is a distinction between what may be a functionality limitation of Copilot and when a feature has been disabled by an agency.

Measuring the impact of Copilot relied on trial participants’ self-assessment of productivity benefits.

Trial participants were asked to estimate the scale of Copilot’s benefits, which may naturally under or overestimate its impact.

Mitigations: where possible, the evaluation has compared productivity findings against other evaluations and external research to verify its validity.

Statistical significance of outcomes

The trial of Copilot for Microsoft 365 involved the distribution of nearly 5,765 Copilot licenses across 56 participating agencies. As part of engagement activities — consultations and surveys — the evaluation gathered the experience and sentiment from over 2,000 trial participants representing more than 45 agencies. Insights were further strengthened by the findings from internal evaluations completed by certain agencies. The sample size was sufficient to ensure 95% confidence intervals of reported proportions (at the overall level) were within a margin of error of 5%.

There were 3 questions asked in the post-use survey that were originally included in either the pre-use or pulse survey. These questions were repeated to compare responses of trial participants before and after the survey and measure the change in sentiment. A t-test was used to determine whether changes were statistically significant at a 5% level of significance.