Businesses in Singapore are operating in a more unpredictable environment. Economic uncertainty, supply chain disruption, geopolitical tensions, and rapidly shifting customer expectations have become constant features of the landscape. At the same time, a new generation of AI is arriving that can do more than answer questions. Agentic AI can plan, reason, and take action on behalf of a business. <\/p>\n\n
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In one widely cited forecast, Gartner predicts<\/a> that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025.<\/p>\n\n Singapore is well placed to capitalise on this shift. Worker-level AI readiness is among the highest in the world, the government has committed more than S$1 billion to public AI research through 2030, and enterprise adoption is climbing. For business leaders in Singapore, agentic AI raises a practical question. How can these systems help organisations respond faster, operate more efficiently, and make better decisions when conditions keep changing? <\/p>\n\n <\/p>\n\n The pressures facing businesses are real and measurable, and Singapore\u2019s open, trade-dependent economy is especially exposed to them.<\/p>\n\n In mid-2025, the OECD downgraded its forecast<\/a> for global economic growth to 2.9% for both 2025 and 2026, citing rising trade barriers and geopolitical tension.<\/p>\n\n Supply chain research<\/a> indicates that 94% of companies have experienced a revenue impact from disruptions, while only 6% report full visibility across their supply chains. In the same vein, Deloitte has found<\/a> that 71% of chief executives plan to alter their supply chains over the next three to five years, partly in response to trade uncertainty. <\/p>\n\n In this setting, the ability to see what is happening and act on it quickly has become a core competitive capability.<\/p>\n\n <\/p>\n\n Generative AI creates content in response to a prompt. Agentic AI goes a step further. An AI agent can pursue a goal, break it into steps, draw on data and tools, and carry out tasks with limited human supervision. <\/p>\n\n <\/p>\n\n In a business context, that might mean an agent that monitors cash flow and flags emerging risks, reconciles transactions and resolves exceptions, prepares a draft forecast, or manages parts of an order-to-cash process. The promise is faster decisions, greater operational efficiency, and quicker responses to change. <\/p>\n\n <\/p>\n\n Adoption is accelerating quickly, and Singapore is moving ahead of many of its peers.<\/p>\n\n <\/p>\n\n An IDC study commissioned by Microsoft<\/a> found enterprise uptake of agentic AI in Singapore at 37%, broadly in line with global peers and ahead of the Asia-Pacific average. Local enterprise AI adoption has also climbed steeply, rising to 23.5% in 2025 from just 4.3% in 2023, according to figures shared at ATxEnterprise 2026<\/a>. <\/p>\n\n This momentum sits within a wider global surge.<\/p>\n\n <\/p>\n\n Gartner\u2019s 2026 research<\/a> found that only 17% of organisations had deployed AI agents, while more than 60% expected to do so within two years, the most aggressive adoption curve among the emerging technologies it measured. Investment is following that intent, with IDC projecting<\/a> that worldwide AI spending will reach US$1.3 trillion by 2029, growing at a compound annual rate of around 32% as agentic applications expand. <\/p>\n\n <\/p>\n\n The excitement around agentic AI comes with a caution. An agent is only as capable as the systems and data it can reach. When operational data is scattered across disconnected platforms, an agent has an incomplete view, and its actions carry more risk. <\/p>\n\n <\/p>\n\n The scale of that risk is already evident, with Gartner expecting<\/a> more than 40% of agentic AI projects to be cancelled by the end of 2027 due to escalating costs, unclear value, and weak risk controls.<\/p>\n\n <\/p>\n\n This is where many agentic AI ambitions meet reality. To act safely and usefully, agents need connected systems, trustworthy data, and clear governance. A unified operational core gives them that foundation, which is exactly what many Singapore firms are still building. <\/p>\n\n <\/p>\n\n A modern cloud ERP brings finance, operations, inventory, and customer data into a single system with a shared data model. This gives both people and AI agents a single, current source of truth. <\/p>\n\n <\/p>\n\n At SuiteWorld 2025, Oracle NetSuite introduced new SuiteCloud capabilities<\/a> that let organisations build custom AI agents, connect external AI assistants, and orchestrate AI-driven workflows across the platform.<\/p>\n\n <\/p>\n\n Through frameworks such as SuiteAgents, businesses can embed agents directly into NetSuite workflows, where they draw on the same governed data that runs the business. NetSuite\u2019s real-time dashboards and analytics give leaders the visibility to act with confidence, which matters most when conditions are changing quickly. <\/p>\n\n <\/p>\n\n Few businesses run on a single system. Most operate an ecosystem of applications, and agentic AI has to work across that whole estate to be useful. This is the role of orchestration. <\/p>\n\n <\/p>\n\n Workato was named a Leader and furthest in vision in the 2026 Gartner Magic Quadrant for iPaaS<\/a>, and now positions itself around agentic orchestration, connecting AI agents to a company\u2019s data, applications, and existing workflows.<\/p>\n\n <\/p>\n\n Orchestration platforms coordinate how agents interact with systems and with each other, while keeping humans in control through approvals, audit trails, and governance. This allows agents to execute real business processes safely across the organisation. <\/p>\n\n <\/p>\n\n Agentic AI is only valuable when the data it acts on can be trusted. In finance, that means accurate records, controlled close processes, and consistent reconciliations. <\/p>\n\n Netgain provides NetSuite-native accounting software<\/a> that automates lease accounting, reconciliations, and close management, giving finance teams greater accuracy and control.<\/p>\n\n <\/p>\n\n With a stronger accounting foundation, the insights and actions that agents produce rest on data that finance leaders can stand behind. This combination of automation and control makes agentic AI dependable in a financial setting. <\/p>\n\n <\/p>\n\n When agentic AI sits on a connected, well-governed core, the benefits become tangible:<\/p>\n\n <\/p>\n\nA More Volatile Operating Environment<\/strong><\/h2>\n\n
What Agentic AI Means for Business<\/strong><\/h2>\n\n
Singapore Is Moving from Experimentation to Production<\/strong><\/h2>\n\n
Agentic AI Needs a Connected Operational Core<\/strong><\/h2>\n\n
AI-Ready Cloud ERP as the Foundation<\/strong><\/h2>\n\n
Orchestrating Agents Across the Business<\/strong><\/h2>\n\n
Trusted Financial Data Underpins It All<\/strong><\/h2>\n\n
Where the Value Shows Up<\/strong><\/h2>\n\n
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