Article By Scott Wiltshire, vice president and general manager, Oracle NetSuite ANZ
Manufacturers across Australia are investing in AI, but its value depends on the quality of the operational data behind it. For example, the National AI Centre found that 43 per cent of Australian SMEs had adopted AI between December 2025 and February 2026, yet confidence and trust remain low.
For manufacturers, that gap often comes down to inventory. When inventory data is accurate and current, businesses can plan with confidence. When it isn’t, missed deliveries, production delays, and unnecessary costs follow.
Rising costs and ongoing supply chain disruption have made inventory discipline even more important. While many manufacturers hold extra stock as a buffer, excess inventory ties up working capital and reduces agility. AI can help manufacturers optimise inventory, but they need to have a clear view of what they hold, where it is, and how demand is changing.
Data and operational discipline drive AI success
Many Australian manufacturers are still working across spreadsheets, manual updates, and disconnected systems spanning finance, procurement, warehousing, and production. As a result, inventory records drift out of sync with purchasing activity, production plans are built on outdated assumptions, and teams spend time reconciling figures that should already align. Too often, inventory issues are only discovered once they begin to affect production schedules or customer commitments.
Connected business systems help create a shared view across purchasing, production, warehousing, and fulfilment. When teams are working from the same picture of inventory, they can identify constraints earlier, make better purchasing decisions, and respond with less guesswork. That improves day-to-day execution, but it also lays the groundwork for more effective AI.
The National AI Centre also found that Australian SMEs are still concentrating AI use in practical, lower-risk areas such as content generation and data analytics, while more complex applications like supply chain optimisation remain largely untapped. For manufacturers, the opportunities are significant. Some of the most valuable AI use cases are in forecasting, supply planning, and inventory management.
AI can help detect changes in demand, flag emerging supply constraints, and identify slow-moving stock earlier. But it cannot compensate for fragmented inventory records, disconnected processes, or inconsistent workflows. That is why the AI conversation in manufacturing should start with operational discipline.
Embedded AI first, with flexibility where needed
Manufacturers will often start with embedded AI, where it can support faster, better decisions in the flow of work. This is a natural starting point because it is in the ERP workflows they already use, drawing on trusted operational data without adding complexity.
Other manufacturers will also want the flexibility to apply frontier models to their data before embedded features become available and connect AI tools to the operational data held in the system.
The reality is that manufacturing organisations are at different stages of AI maturity, so they rarely adopt new capabilities uniformly. That is where standards such as Model Context Protocol (MCP), which the NetSuite AI Connector Service supports, can help by enabling governed access to ERP data through popular AI assistants.
Both approaches create easier access to information while respecting permissions, approval structures, and security policies to help control what the AI models and users can see. These security requirements become more important as more employees begin using AI and significantly help mitigate risk.
Manufacturing has always rewarded accuracy and consistency. The businesses that perform best are usually the ones with business systems that accurately reflect what is happening on the factory floor and out in the market.
Inventory may sit on the balance sheet as a physical asset, but for many Australian manufacturers, it is now something more: a test of operational control and digital readiness. Inventory discipline is a strategic capability, and one of the clearest signs a manufacturer is ready to leverage AI to create better business outcomes.
