Agentic AI is coming faster than you think - are your systems ready?
First it was AI. Then copilots. Now it's agentic AI - systems that not only suggest what to do but go ahead and do it. Each leap has brought more autonomy, less tolerance for messy data and a bigger blast radius for bad decisions. To get value, you need more than enthusiasm. That's because agents only deliver when they run inside disciplined processes with guardrails you can defend to a CFO or an auditor. That means picking narrow, high-frequency workflows, wiring them to trustworthy data and tracking outcomes with the same rigour you'd apply to a product release.
What exactly is agentic AI?
But first, a refresher on agentic AI. While a copilot suggests and you decide, an agent sets a goal, breaks it into tasks, calls the right tools or systems and loops back to check if the outcome worked. And it doesn't live in a chat window either. It sits across multiple applications, orchestrating work that humans used to stitch together.
Think of it more as a junior colleague with plenty of stamina but not so much intuition. Most importantly, it needs structure - clean data, clear definitions of "done" and explicit permissions. Without those, it could run confidently in the wrong direction.
Where AI agents make sense first
The most practical use cases for agentic AI sit in repetitive, rules-based processes where accuracy and speed matter. In planning, agents can draft rolling scenarios, prepopulate variance notes and nudge owners for inputs. In reconciliation, they can propose matches, surface anomalies and attach supporting evidence. Inventory management benefits when agents flag low-stock risks, recommend purchase orders and prepare supplier communications. And for operational support tasks, AI agents can help with triaging tickets, preparing refunds and assembling dispute packs. These are all workflows with clear inputs, high frequency and objective outcomes - exactly the type of conditions where agents can prove their value quickly.
Laying the groundwork for AI agents
Agentic AI only performs well when the basics are solid. It depends on stable identifiers so customers, suppliers and items are consistent across systems. It needs clear permissions, with least-privilege roles and approval gates that stop accidental overreach. It runs best when integrations are durable and event driven. And it must be auditable, with logs that show who triggered what, when and with which data. Add to that fresh master data - customers, items, exchange rates and cost centres that stay accurate - and the environment is ready for agents to operate safely.
Why ERP matters for agentic AI
Agents are only as effective as the systems they connect into. An ERP like NetSuite provides exactly the kind of foundation they need – clean master data, role-based access, embedded workflows and a single point of truth across finance, inventory and operations. That structure turns what could be a chaotic automation experiment into something far more auditable and safe.
It's unsurprising that native capabilities in NetSuite are already pointing toward an agentic future. Features like Text Enhance generate context-aware descriptions and notes directly inside workflows, while the Enterprise Performance Management suite offers AI-assisted planning, forecasting and reconciliation. These tools reduce manual effort but, more importantly, they run within governed processes that already have approval hierarchies and audit trails. That powerful combination - automation plus accountability - is what makes them usable at scale.
The wider ERP ecosystem builds on this base. Third-party solutions bring specialised agents for areas such as tax compliance, supply chain planning and warehouse management. When integrated properly, they consume the same master data and respect the same permissions that the ERP enforces. This ensures an AI-driven planning agent or reconciliation agent doesn't become a rogue process but a controlled extension of the ERP core.
Put simply, NetSuite acts as the control tower. Whether the intelligence is native or external, the agent is still working against trusted ledgers, consistent identifiers and role-based guardrails. That's why the first step in making agentic AI work isn't switching on a model - it's making sure your ERP is ready to serve as the system of record the agent relies on.
The market is moving, are your systems ready?
Across the ERP landscape, new use cases are surfacing almost weekly with vendors already piloting agent functionality in areas like finance, supply chain and order management. These are early moves, but they clearly signal where the market is heading, and fast. NetSuite, in particular, is opening up space for experimentation with capabilities such as the AI Connector, which allows partners to safely link external agents into the ERP core. That means businesses will have the option to let agents act directly within governed processes - provided their systems are ready.
These are exciting times for ERP, and the groundwork you lay today will decide how quickly you can benefit when agents move from pilots to production.
You can read more about the NetSuite AI Connector and what it unlocks in Annexa's blog:
Bringing your own AI to NetSuite - meet the new NetSuite AI Connector
