Every organisation's modernisation journey starts from a different place. Some are managing decades-old systems that are costly to maintain and difficult to change. Others have already begun updating platforms but need to move faster, with greater confidence, as the demands on their technology grow.
What's changing is the role AI can play in that journey. AI-enhanced modernisation applies AI-assisted analysis, development and testing within a human-led framework. AI can take on repeatable, time-intensive tasks like code analysis, remediation and regression testing, while experienced engineers focus on design, architecture, security and the decisions that require human judgement.
The result is an opportunity to modernise faster, reduce the cost and risk associated with legacy technology and create stronger foundations for future innovation.
Datacom is working with enterprises and government organisations across Australia and New Zealand to put this into practice. Here's how some of that work and thinking is taking shape.
Datacom's Director of AI, Lou Compagnone, says the organisations getting the most from AI are the ones that invest in foundations first, including data governance, clear policies and staff training, before rolling out tools at scale.
"I always say, 'go slow to go fast'. If you launch a tool before checking your data governance or training staff, you risk what I call organ rejection. The technology doesn't land – and you don't see the benefits."
She recommends starting with internal use, or "backstage AI," giving teams tools to work more efficiently behind the scenes before expanding outward. Datacom has integrated over 90 internal AI productivity tools and built a hybrid workforce of AI agents and humans across its engineering services.
"Let your people co-design the solutions. They know where trust, empathy and judgement are needed – and which tedious tasks they'd love to hand off to an AI intern."
Many organisations are working with systems that are decades old, poorly documented and expensive to maintain. Security and compliance requirements continue to rise, skilled engineers are in short supply, and any disruption during a transition carries real consequences, particularly in government and essential services.
AI-enhanced modernisation is built for these conditions. It integrates AI safely into trusted engineering frameworks and governance practices. The approach includes:
At every stage, experienced engineers retain accountability for critical decisions.
Joel Macfarlane, Datacom's Director of AI and Engineering, describes the shift as "the largest change management exercise in the history of IT.”
His team of 300 engineers now works alongside AI agents that access code repositories, documentation and project management software, even updating their status on kanban boards like their human colleagues.
"We emulate the way human teams work," says Macfarlane. "We give the AI agents access to the same tools so they can collaborate and break up challenges."
The approach treats modernisation as an assembly-line process. AI agents generate detailed specifications from 20-year-old systems in hours rather than months, AI tech leads review code commits and maintain consistency, and AI testers run hundreds of test cases simultaneously across old and new systems.
"For years, app modernisation projects have been plagued with failed implementations, delays and cost overruns," says Macfarlane. "It doesn't have to be that way anymore."
The shift is reshaping development roles rather than eliminating them.
Senior developers are spending less time writing code and more time orchestrating AI agents. Graduates enter the workforce as AI engineers rather than traditional programmers. Project managers oversee agent workflows alongside human teams.
"You're either communicating with humans or corralling AIs – there's no middle ground anymore," says Macfarlane.
This evolution also reflects a practical challenge facing many organisations: skilled tech talent is in short supply, while demand for modernisation continues to grow.
AI agents can enable smaller teams to deliver at a scale that would previously have required significantly more people.
Datacom is investing in reskilling programmes internally, but Macfarlane says the cultural shift matters as much as mastering the tools. Teams need confidence that their roles are evolving, not disappearing, and that the judgement they bring remains central to delivery.
Sunny Katira, Datacom's Managing Director of Professional Services, says the response from customers experiencing AI-enhanced modernisation has been "overwhelmingly positive" across Australia and New Zealand.
"We've got up to 70 percent of code being written by agents on various projects. This is not R&D or experimentation; we are implementing this in real-life production scenarios for one of our largest public sector customers in Australia."
Cost reductions of up to 50 percent are being delivered in some cases, with development timelines cut significantly.
But Katira sees the opportunity extending beyond savings. "The legacy-to-modernisation conversation is definitely a cost-saving thing. But crafting new software is how you gain a competitive advantage.”
Underpinning this is Datacom's investment in local GPU infrastructure across its data centres, ensuring AI workloads are processed within Australia and New Zealand to address both performance and data sovereignty requirements. As AI workloads grow, the quality of the underlying GPU makes a tangible difference to how well software performs – and having that processing power in-country means lower latency and full sovereignty.
There is no single pathway to AI-enhanced modernisation. Datacom combines technology and engineering capability with deep, local experience to help organisations determine where modernisation can create the greatest value:
Modernisation doesn’t need to begin with large-scale transformation. A practical starting point is identifying where legacy technology is creating the greatest cost, complexity or constraint on growth and innovation. From there, organisations can assess which opportunities are suitable for AI-enhanced modernisation and develop a roadmap based on business value, risk and readiness.
For some, the priority may be reducing technical debt. For others, it may be modernising a critical application, improving assurance, strengthening data foundations or preparing the organisation to adopt AI more broadly. The important thing is to start from the business problem and modernise at a pace that makes sense for your organisation.
Traditional modernisation often relies on manual analysis and delivery, which can be slow, costly and risky. AI-enhanced modernisation uses AI to improve speed, accuracy and consistency, particularly with large, undocumented or ageing systems, while maintaining governance and human oversight throughout.
No. AI is used as an accelerator, not a decision-maker. Datacom's engineers retain responsibility for all architectural, security and delivery decisions. AI supports insight, efficiency and quality, while people remain accountable at every step.
Security and governance are embedded from the outset. Datacom applies proven frameworks and human oversight to ensure AI use aligns with regulatory requirements, data sovereignty expectations and organisational risk appetite.
Yes. Many organisations start with targeted initiatives to reduce technical debt or unlock early wins, then scale over time. Datacom's modular approach supports modernisation at a pace that suits your organisation's priorities and risk profile.
No. Datacom meets organisations where they are, helping assess readiness, strengthen foundations and apply AI safely as part of a broader modernisation journey.
In a 1.5-hour session, a Datacom consultant will work with you to understand your environment, industry context and ambitions, and explore where AI-enhanced modernisation could create the most value and explore practical next steps.
Applying AI successfully requires the right foundations, governance and expertise. Discover how Datacom supports organisations to develop, deploy and scale AI in practical and responsible ways.
