How AI is reshaping software development for Australian enterprises

Australian organisations must rapidly adapt to AI advancements, evolving regulations, and the demand for secure, scalable AI integration to remain competitive. As highlighted at the recent National Tech Summit by the Technical Council of Australia, investment in AI-driven R&D is critical for Australia's position in the global tech landscape. However, success requires more than technology; it demands strategic investment in skills, infrastructure, and measurable ROI.

While AI has already improved productivity, code quality, and innovation in software development, organisations must now expand AI use beyond basic automation to include contextual awareness and proactive decision-making. This will also enable leaders to refine how they measure AI's business impact to optimise future investments.

As AI adoption matures, organisations must focus on the following key areas.

The next evolution in software development is context-aware

While many development teams already embed AI in some of their workflows—such as code completion assistance and code explanation—context-aware AI is the next frontier and a crucial foundation for the development of agentic AI. Agents can only operate effectively if they capture the necessary context, which extends far beyond the codebase itself. Context-aware AI has the potential to reshape software development through applications that understand and adapt to environmental context. 

When AI understands both​​ user and application context, it can automate more complex tasks, anticipate a developer's needs, and make better-informed decisions. This translates into increased efficiency and accuracy and allows developers to apply their expertise to more creative and strategic work. Eventually, AI will go beyond simply adhering to development best practices and optimise code based on different variables such as performance, scalability, or even costs.

Here are several key areas where we anticipate seeing this impact:

• Enhanced code understanding: As AI matures, it will be able to analyse existing codebases and proactively suggest new functionality that integrates seamlessly with existing architecture, infrastructure, and application needs. It will also automatically adhere to an environment's security and compliance guardrails.
• Streamlined code reviews: Code reviews can be a bottleneck, but AI can help streamline the process. AI-assisted code reviews will flag potential issues based on existing standards, best practices, and predicted performance implications, helping development teams better collaborate with shared context.
• Improved testing: By understanding application logic and performance characteristics, AI can generate more comprehensive tests to proactively identify and prevent code defects before they reach production.
• Infrastructure-aware updates: Managing updates to legacy systems can be particularly challenging. AI can help by considering both the codebase and potential implications for the underlying cloud infrastructure and application performance when proposing changes to maintain security and compliance.

Measuring the ROI of AI in software development

Organisations have rapidly integrated AI into their operations, from software development to decision-making and customer service. While tapping into AI's power, they still need to work on measuring its impact across various teams and business functions.

This is partly because they need to ask the right questions. They tend to focus on macro issues that are hard to measure, such as "How is AI helping to increase my bottom line?" Instead, they should focus on specific business outcomes that are easier to measure.

In software development, this means looking at the impact of AI and automation on metrics like time to market for new applications and features, software quality, operating costs, and developer productivity. When senior leaders sharpen their focus on these outcomes, it allows them to accurately quantify the gains from AI and justify further investments by focusing on the tasks where AI excels.

How AI agents are transforming software development

AI assistants are getting smarter, moving beyond prompt-based interactions to anticipate developers' needs and proactively offer suggestions. This evolution is driven by the rise of AI agents, which can independently execute tasks, learn from their experiences, and even collaborate with other agents. These agents are beginning to serve as a central hub for code assistance, streamlining the entire software development lifecycle. AI agents are set to autonomously write unit tests, refactor code for efficiency, and even suggest architectural improvements.

Developers' roles will need to evolve alongside these advancements. AI will not replace them. Far from it, proactive AI assistants and their underlying agents will help developers build new skills and free up their time to focus on higher-value, more strategic tasks. Developers can now act as "AI architects," designing and guiding intelligent agents to tackle complex challenges. The result will be higher productivity, better-quality code, and a greater focus on solving broader business problems.

Moving toward customised, self-hosted AI models

AI models are more powerful when trained on internal company data, which allows them to generate insights specific to an organisation's unique operations and objectives. However, this often requires running models on-prem for security and compliance reasons.

As open-source models rapidly close the performance gap with commercial offerings, businesses are increasingly deploying models on-prem. This strategy enables organisations to fine-tune models with proprietary data and significantly reduce AI application cost.

Critically, this approach offers distinct advantages for highly regulated industries such as banking and healthcare, allowing them to maintain compliance by deploying models in secure, air-gapped environments.

Profound changes ahead for Australia's tech industry

AI's expanding role in software development will bring significant changes this year. Beyond code generation, AI is now embedded across the entire software development lifecycle, enhancing security, boosting performance, and reducing technical debt.

Sydney-based Lendlease has embraced AI as a key enabler of efficiency and innovation in software development, as it helps its DevSecOps teams automate repetitive tasks, streamline code reviews, and accelerate software creation. By leveraging AI-driven pipelines, Lendlease is modernising legacy applications and seamlessly integrating security and compliance into every stage of the development lifecycle. This aligns with broader industry trends where context-aware AI transforms software development, optimises code for performance and scalability, and enables greater collaboration across distributed teams.

As AI adoption advances, organisations like Lendlease demonstrate how integrating AI strategically, while maintaining human oversight, can drive innovation, improve security, and future-proof technology investments.