Australian companies have no ambitions for AI. They have execution problems. The challenge of ensuring teams have access to the right AI tools for their work without adding more risk, cost, and complexity is increasingly a matter of choice.
For many organizations, especially in the mid-market, AI tools are gaining ground faster than the rules around them. The results are familiar: promising pilots, distributed tools, and limited business impact.
Recent research by Deloitte and PwC points to the same challenges. Many organizations in Australia struggle to turn AI into scalable outcomes. Deloitte reports that just 12 per cent of Australian organizations say AI is transforming their business, compared to 25 per cent globally, and just 65 per cent plan to increase investment in AI, compared to 84 per cent globally. PwC similarly found that only around one in five Australian organizations report having the data, governance and technology infrastructure needed to scale AI confidently and securely.
For CFOs, that gap is important. The success of AI in business ultimately depends on the control, productivity, automation, and value it provides. If AI is to forecast, report, make supply chain decisions, or support customer operations, it must be reliable, auditable, based on business context, and connected to workflows.
Fragmentation and missing context
Many companies are adding AI tools across finance, operations, supply chain, and customer service without a unified strategy. This creates integration bottlenecks, data duplication, and inconsistent output, making it difficult for leaders to understand where value is being created, where risk is accumulating, and whether the output can be trusted.
Employee enthusiasm for generative AI is also driving the rise of shadow AI, where employees use unapproved consumer tools to speed up their work. While these consumer tools are useful, they can also expose sensitive data, pose intellectual property risks, and produce output that is difficult to verify.
Additionally, general purpose AI tools are outside of core business systems and removed from the business context of the request. To enhance accuracy, insight, and decision-making, AI must have access to contextual data such as financial structures, approval hierarchies, operational workflows, and business rules. Without that context, even elegant answers can be wrong in ways that are hard to find.
Built-in AI and MCP connectivity
The next stage of AI adoption will require flexibility and provide businesses with different ways to deploy the technology.
AI must be built into core systems to improve productivity, reduce manual labor, and empower teams to act on insights when the situation calls for it. At the same time, organizations should have the flexibility to introduce their own AI models and tools into the business in a controlled manner if they are better suited to a particular use case.
True Protein, one of Australia’s leading health and nutrition brands, provides useful examples of how AI can be implemented in different ways to achieve desired results. By connecting finance, inventory, manufacturing, warehousing, and sales with NetSuite, the company reduced month-end close by 70%, from 10 days to 3 days, while gaining real-time visibility across production and fulfillment, helping teams act faster and reduce errors. True Protein says NetSuite’s AI-powered features also help improve productivity and day-to-day decision-making, demonstrating how AI can add value when embedded within the systems and workflows a business already relies on.
At the same time, the company is experimenting with common large-scale language models that make sense. For example, True Protein uses the NetSuite AI Connector Service with NotebookLM and Gemini to transform NetSuite general ledger data into podcast-style reports to help leaders understand the numbers on the go.
True Protein is a useful example of what controlled choice looks like in practice. Built-in AI that works within core workflows and third-party models that add value are all based on the same business data and controls.
Australian mid-sized companies are often digital-first and ambitious. But we also need to balance innovation and risk, speed and control.
The biggest beneficiaries will be those that use AI to streamline operations, make decisions more timely, and execute more consistently.
