ClearPoint recently brought together a select group of Melbourne's senior engineering and product leaders for an exclusive roundtable on AI as an Enabler for Engineering Leaders. The session focused on the practical application of AI in achieving software engineering excellence, with leaders sharing openly about what’s worked and what hasn’t on the path to AI-enabled delivery.
ClearPoint CTO Rob Cleghorn, Trustport Co-founder Cat Munro, and Product Transformation Expert Ross Stanley, led the conversation – unpacking common friction points and exploring how AI agents, paired with performance enablement, can deliver value from initial idea right through to production.
The roundtable concluded that navigating this disruption requires a profound shift in leadership and a willingness to challenge established practices. "For me, the key thing is as software leaders is how do we adapt, how do we learn, and how do we grow," Cleghorn stated.
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For today's engineering leaders, code generation is just the tip of the iceberg. The opportunity is to apply AI across the entire software development lifecycle, from initial idea to code generation and the realised business value.
"It's crucial for software leaders to comprehend AI's broad impact –not just on code, but on software quality, maintainability, security, and support," said Rob Cleghorn. "We have to consider its influence on our team's capabilities, how they collaborate, and ultimately, the value we deliver to our customers."
This requires a total rethink of how products are built, acknowledging the changing lifespan of code. Tomorrow's legacy tech is being created faster than ever, forcing a new mindset where teams are accepting more tech debt with the expectation of rebuilding in months rather than years.
A primary topic of conversation was how to drive faster, safer AI-enabled delivery, with a leadership mindset focused on empowering engineering teams from the bottom-up. This empowerment, combined with the right cultural support and tools like AI agents, allows organisations to unlock major improvements in their flow, quality, and throughput.
Cognitive delivery
Cognitive Delivery (CD) is the pragmatic, hybrid evolution of Agile required to handle the speed and complexity of generative AI. It intelligently upgrades the traditional playbook by building on Agile's core focus on iteration and flexibility, but integrates strategic, upfront planning (borrowing from older methods) to provide AI agents with clear, well-defined instructions before switching to a fluid execution model. This methodology is built on three key ideas: continuous learning (because AI models are always changing), data-centric development (because data is the fuel for AI), and ethical guardrails (because trust is the new quality metric).
The developer career path
AI tools are now proficient at writing boilerplate code, unit tests, and routine bug fixes, automating many of the tasks traditionally given to junior developers. This means new developers must be "AI-native," focusing on skills that AI can't yet master: prompt engineering and AI orchestration, code review and verification, and system awareness. As they progress, new graduates will transition from verifying AI code to designing and debugging complex systems. They will learn to identify edge cases, plan data flows, and make architectural decisions that AI tools cannot. Seniority will be marked by the ability to mentor other AI-native developers, and the ability to translate a vague business problem into a concrete AI-driven solution.
Adapting to change as leaders
The massive productivity shift from AI necessitates that software leaders actively adapt, learn, and grow, understanding AI's profound impact on software quality, security, team capabilities, and the businesses and customers they serve. This transformation requires rethinking established practices and organisational "muscle memory" that may no longer be effective. To successfully integrate AI, they must find a balance between the pursuit of speed and necessary safety precautions, ensuring they safeguard company and client interests. Adoption can be fostered by supporting a bottom-up approach, and providing teams with genuine, challenging problems to solve rather than setting top-down OKRs for AI adoption. Ultimately, success requires ensuring support from senior management to overcome organisational barriers of fear, lack of knowledge, and extensive governance hurdles.
We look forward to hosting similar discussions and continuing the conversation on how to build the future of software engineering. Register your interest in ClearPoint’s future events.
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