To move fast and deliver value from AI investments, Australian CIOs need to shift from multi-year digital transformation cycles, to a mindset of continuous, governed evolution.
Adam Krebet
Business Architect
Workday
While 52% of Australian organisations have piloted generative AI (GenAI) in their business, there is now widespread recognition that moving beyond chatbots is necessary to empower the workforce with better automation, smarter insights and more time for strategic work.
So where does that leave CIOs? Every CIO in Australia is balancing the potential of AI to transform their business lines and back-office functions, against the anxiety over falling behind competitors. And the chasm between the AI 'haves' and 'have nots' seems to be widening.
The 'haves' have done the foundational work to leverage agentic Al today; the 'have nots' are still struggling to deliver value due to siloed data, a lack of centralised tooling, and the challenges of justifying large investments.
The prevailing wisdom is that the 'have nots' need a massive digital transformation — a two or three-year overhaul — to catch up. However, at the speed that IT departments must deliver value, multi-year transformation cycles are no longer viable.
All of these factors have lowered the entry bar to innovation, which is great, but it has simultaneously increased a dangerous risk: using GenAI and agentic AI as a crutch.
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In the early days of GenAI, it was all about feeding the 'big brain' — bringing data to the LLM so it could learn and advise. But AI agents are fundamentally different, capable of bringing the LLM to data and knowledge that's stored across multiple systems and domains.
It's good news for CIOs, who can now move fast and deliver rapid value, without waiting for a monolithic 'big brain' digital transformation. And with new architecture and low-code frameworks for building agents, these capabilities are more accessible than ever before.
In fact, we no longer need a perfectly cleansed, consolidated data lake to start extracting value. With pre-trained models and techniques like Retrieval-Augmented Generation (RAG), you can quickly test concepts using existing documents and policies.
And because agents from different platforms can effectively communicate with each other via industry-standard protocols, you can also avoid single vendor lock-in and use agents that are already embedded in your enterprise platforms, with significant investment in their functionality, scalability and security baked in.
All of these factors have lowered the entry bar to innovation, which is great, but it has simultaneously increased a dangerous risk: using GenAI and agentic AI as a crutch.
While an organisation with messy, siloed data and poor workflows can simply throw a chatbot at the problem, or rapidly prototype agents to operate across departments and siloed data domains, the truth is that relying on that alone is a dead-end.
Imagine a CFO who still extracts a giant, messy spreadsheet from a legacy system. They can now drop that file into a GenAI tool and get instant insights — a quick win for a single person.
But what has actually been solved? The fundamental business process, the data extraction, the silo, and the lack of clean data remains untouched. This quick fix feels like progress, but it only papers over the cracks of systemic dysfunction.
You need to be constantly asking: do I need to reinvent my business process this quarter? What's new, what's available?
The modern CIO has to be able to 'walk and chew gum at the same time', balancing a two-speed process that respects the need for rapid experimentation, while building a path to cohesion and trust.
This speed is about fast, low-cost experimentation. Encourage your teams to leverage the power of pre-trained models and RAG to quickly test hypotheses and unlock previously hidden insights from unstructured data.
Now, this is a great tool for discovery and quick wins, but should only be used as a temporary AI crutch. This is because RAG helps AI find information, but other methods such as fine-tuning and adapters are needed for it to properly understand the data.
This brings us to Speed 2, the non-negotiable step for long term value and scale. When a 'Speed 1' experiment proves valuable, you must have the discipline to clean your data, establish acceptable patterns and standards, and create a process for productionising your agent lifecycle.
This is where the evolutionary mindset truly kicks in. You need to be constantly asking: do I need to reinvent my business process this quarter? What's new, what's available?
Do you need to settle on a single platform? Not at all, and in fact I would argue that mandating only one stack limits your ability to pursue short-term wins, and puts you firmly into the ‘build’ end of the build-vs-buy spectrum. It may also lock you out of using off-the-shelf capabilities in packages you've invested in.
Mandating one technology for agents is no longer as technically compelling, because agents using LLMs are far more able to adapt to each other than traditional, rigid APIs.
You must stop seeing your role as the manager of a distant, monolithic transformation and become the leader of continuous, governed evolution.
However you decide to construct your technology stack and data landscape, effective governance is crucial for managing the complexities of AI systems and ensuring compliance.
This includes identifying data ownership, establishing rules (such as whether you will use a centralised or federated model for data, like a Federated RAG) and understanding how you will manage fine-tuning and adapters (such as whether you bring data to the model, or fine-tune within the data domain).
From a technical perspective — and recognising that it’s likely you will have agents built in multiple tools — consider your agent registry and agent control plane, as well as CI/CD processes and tools. Each of these aspects must accommodate 'walking and chewing gum at the same time'.
The challenge for Australian CIOs is clear: you must stop seeing your role as the manager of a distant, monolithic transformation and become the leader of continuous, governed evolution that can source innovations from both within IT and across the workforce.
1. Innovate Passively: The fastest way to evolve is to leverage your existing technology investment. Use your core platform for passive innovation — the moment a new Al feature is released, absorb it. This ensures your foundation is always moving forward.
2. Rethink Business Processes: Your job is no longer to wait for a three-year roadmap to tell you how to change. It is to look at your business every quarter and ask: what new capability has my platform delivered that allows me to rethink or reinvent this specific business process?
3. Govern the Gap: Implement the two-speed framework where experimentation is encouraged but production is governed. The goal is to move those experimental 'crutch' solutions into reliable, scalable, and trusted capabilities within your core systems.
If you can master this evolutionary mindset, you will not only catch up to the 'haves', but you will build a resilient, perpetually innovating enterprise that will set you apart. Ultimately, the future belongs not to the companies that complete the biggest transformation, but to those that never stop evolving.
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