Why It Matters
NetSuite’s announcement is further evidence of IBRS’s predictions that more than 85 per cent of agentic AI will be embedded as a standard capability within core enterprise systems, not an optional bolt-on.
CIOs and ICT executives more broadly are quickly realising that ready-made AI solutions embedded in existing SaaS platforms over custom, do-it-yourself initiatives are a less risky, often less costly approach to AI. Waiting for a core platform vendor to deploy the AI agents needed directly into the platform (which may take some years to mature) looks increasingly to be a better strategy than building agentic services today. However, the announcement masks several critical challenges that Australian and New Zealand executives must address before realising value.
The embedded AI imperative is real. By 2026–2027, GenAI orchestration is expected to become a core component of enterprise resource planning (ERP) systems. NetSuite’s approach, which layers agentic workflows into finance, operations, and customer engagement, is the expected trajectory across the entire business software industry. Competitors such as Salesforce, Workday, TechnologyOne and even smaller players such as Zoho are pursuing similar strategies. The evolution of core business platforms is becoming table stakes rather than a source of competitive differentiation.
For organisations with existing NetSuite deployments, the value proposition centres on operational acceleration: automating repetitive financial tasks, improving project revenue recognition, and streamlining inventory visibility without requiring significant custom development.
The MCP standard offers genuine flexibility, but creates new risks. The adoption of model context protocol (MCP) represents a meaningful step toward reducing vendor lock-in by enabling NetSuite to interoperate with third-party AI assistants. This ‘bring-your-own-assistant’ model addresses a legitimate market concern: organisations want choice in AI tools without abandoning their core ERP investment.
However, IBRS cautions that MCP remains an immature standard and the shift from proprietary APIs to agent-based communication introduces new governance complexity. Every action an agent takes on live business data must be logged, auditable, and subject to role-based access controls equivalent to those applied to human users.
Implementation is not primarily a technology problem; it is a change management challenge. The real challenge lies in workforce adaptation. Finance teams transitioning from manual journal entry to validating AI-proposed transactions require retraining in digital literacy, risk awareness, and the limitations of AI outputs. Operations teams must learn to interpret AI-generated narratives and act on insights rather than executing predefined workflows.
Who’s Impacted?
- Chief Financial Officers and Finance Teams: Direct beneficiaries of enhancements to revenue recognition, project profitability, and automated reconciliation (particularly with Amazon Seller Central and Shopify). However, they must champion data quality initiatives and governance frameworks to prevent hallucinations that undermine financial reporting.
- IT Architects: Responsible for evaluating MCP integration points, establishing security protocols for agent-based workflows, and ensuring auditability and compliance. CIOs must also assess the ‘AI kill chain’, understanding how new dependencies affect risk and resilience.
- Chief Operations Officers and Supply Chain Managers: Will leverage warehouse management enhancements and AI-powered inventory narratives to improve visibility and reduce manual exception handling. Must ensure that inventory data is of sufficient quality to support trustworthy AI outputs.
- eCommerce and Digital Transformation Leaders: Will benefit from bidirectional synchronisation and automated Loop returns management, reducing manual reconciliation. Must plan integration testing and change management for warehouse and fulfilment teams.
Next Steps
- Evaluate MCP integration points and multi-vendor strategy. If adopting the NetSuite AI Connector Service, define which external AI assistants will be permitted, what data they can access, and what actions they can take on behalf of users. Document vendor dependencies and assess the risk of ‘soft lock-in’ as vendors add proprietary extensions to the MCP standard.
- Establish a pilot programme with measurable outcomes. Rather than deploying agentic workflows across the entire suite, begin with a specific operational challenge (e.g., automated inventory anomaly detection or project revenue recalculation). Define success metrics, track cost and time investment, and assess user adoption before scaling to other functions.
