Solution
Technology expertise and acceptance
Often digital projects involve innovating with technologies that are unfamiliar or untested, which can affect delivery confidence.
Strategies to elevate confidence include iterative deployment strategies that build capability and confidence. Other areas for attention include interfaces with legacy systems, including the ways new systems interact with, or replace, aging legacy systems, while maintaining essential services.
Aging legacy systems can affect the system stability upon which the transformation may be reliant.
Legacy system dependencies need thorough analysis.
Assumptions about legacy data coherence and consistency can be particularly problematic, especially for projects involving transfer to cloud services.
More generally, the solution needs to conform to the technical architecture of the business area and the quality requirements of the business.
For commercial off the shelf software, the degree of fit with business requirements and degree of change that will be required to software or service can reduce delivery confidence.
For AI-based transformation, detailed understanding of how artificial intelligence (AI) will be used within the business environment is essential, as is consideration of human rights, privacy and ethics implications, particularly for AI.
Consideration should be given for AI solution reliability and safety, and the transparency, explainability and contestability of decisions made using AI solutions. For reference, the Australian Government has developed Australia’s AI Ethics Principles which are foundational to Australia’s safe and responsible adoption of AI.
The policy for the responsible use of AI in government builds on this foundation and aims to ensure that government plays a leadership role in embracing AI for the benefit of Australians.
Delivery Confidence Assessment (DCA) tolerances
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In-house expertise in the technology. Ability to challenge supplier expertise. User excitement with the solution.
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Significant in-house familiarity with the technology. User acceptance of the solution.
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Some in-house familiarity with the technology. User tolerance of the solution.
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Low in-house experience with the technology. Largely reliant on supplier capability. Users have had minimal exposure to the technology.
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No in-house familiarity with the technology. Complete reliance on contractor expertise.
Solution context
Because digital solutions are highly interconnected, delivery confidence can be impacted by organisational, procedural, policy, regulatory and human system interdependencies.
Delivery confidence can be improved when there is evidence of strong alignment with technical architecture, policy and standards, and active management of interdependencies beyond the project’s control.
Assessment of delivery confidence should consider adherence to relevant policies and frameworks, such as security policies, that may affect the ability to go-live.
Delivery confidence can be reduced where important factors are outside the project’s control, particularly where policy or legislative reform is required, or where delivery and operational responsibilities are in separate agencies. Solution context has been addressed through assessment related to project interdependencies and legacy dependencies.
Project interdependencies
DCA tolerances
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Project is largely a closed system with tight boundaries and no critical dependencies outside the project’s control.
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Project is largely isolated from external influence but has minor well-managed interdependencies outside project control.
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Project is subject to some external influence, but interdependencies are being actively managed.
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Project is affected by external influence and is aware of interdependencies.
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Projects spans multiple departments or agencies, with unclear or complex interdependencies.
Legacy interdependencies
DCA tolerances
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No legacy dependencies, or dependencies are fully understood and the transformation is near complete.
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Minimal legacy dependencies that are generally understood.
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Some legacy dependencies with some testing of assumptions about implications.
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Significant legacy dependencies, with minimal testing of assumptions about implications.
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Major legacy dependencies with uncertain implications for the project.
Deployment and sustainability
Delivery confidence can be impacted by the deployment strategy. High levels of hyper care, iterative deployment to refine the solution and effort to build capability can improve confidence and minimise risk.
Large scale, big-bang or fast-paced deployment can lower confidence due to unrealistic estimates of time, cost and benefits, reducing opportunity to develop a solution that suits different business area needs.
DCA tolerances
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Transition to BAU co-created with business, scoped, budgeted and scheduled.
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Consultation with business on BAU and accounted for in business case.
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Consultation with business on BAU and partially accounted for in business case.
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Some consideration of transition to BAU, but not sufficiently accounted for in planning. No consultation with business on BAU.
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Transition to BAU considered out of scope.
Relevant policy
- Policy for the responsible use of AI in government: the policy for the responsible use of AI in government positions the Australian Government to be an exemplar of safe, responsible use of AI. It aims to create a coordinated approach to government’s use of AI and has been designed to complement and strengthen – not duplicate – existing frameworks in use by the APS. The policy is designed to evolve over time as the technology changes, leading practices develop, and the broader regulatory environment matures.
- Technical standard for government’s use of artificial intelligence: to support the safe and responsible use of artificial intelligence, the DTA has developed the AI technical standard for Australian Government. This standard provides practical guidance for technical specialists and business owners embedding AI in government systems, enabling agencies to confidently experiment with and develop AI use cases.
- Australian Government Architecture (AGA): the AGA facilitates capability-based information and guidance (policy, standards and designs) to promote opportunities for re-use and make it easier to understand how the directions and decisions of government for digital fit together.