Published on September 17, 2025.

The story of NetSuite AI has been unfolding in clear stages.

The first step was what I’d call “AI in NetSuite” - features like generative AI tools that could draft product descriptions, suggest email copy, or help users interact with data in a more natural way. While this is very useful, it is really only scratched the surface of what AI can do when it's applied to an ERP.

The second step came with the introduction of the NetSuite AI Connector. Suddenly, businesses could feed real NetSuite reports - like Income Statements, Balance Sheets, and Trial Balances - into advanced AI models like Claude, and get powerful insights back. But beyond analysis, the Connector can also be used to instruct an AI client to propose or make changes in NetSuite itself. For example, with the right guidance, an AI could draft a journal entry, update a record, or prepare a follow-up email. The limitation? You have to “hold its hand.” These actions require clear instructions, validation, and manual kickoff by a human operator. The Connector extends AI’s reach into NetSuite - but it doesn’t yet run on its own.

Now we’re at the third step: AI Agents. With AI Agents, NetSuite AI moves beyond analysis to action. Agents don’t just highlight anomalies or forecast trends; they take the next step - reconciling accounts, sending notifications, or drafting corrective actions. They turn AI from a reporting assistant into an active digital teammate. This is where businesses running on NetSuite can unlock the greatest ROI yet - because analysis alone doesn’t transform operations. Action does.

What Sets AI Agents Apart

Traditional NetSuite automation—like workflows, scheduled scripts, and similar tools—is rigid. It does exactly what you tell it to do, nothing more. These automations are useful for repetitive tasks, but they’re also brittle and can easily break when business processes or data structures change.

AI Agents are different:
• Autonomy: They don’t require constant oversight.
• Goal Orientation: Agents can be assigned objectives - like reducing DSO or detecting unusual GL postings.
• Awareness: Agents “see” what’s happening across your ERP data and respond dynamically.
• Continuous Learning: Each cycle makes them smarter, which means tomorrow’s reconciliation process runs better than today’s.

Think of them less like a tool and more like a junior analyst who never sleeps, learns quickly, and is focused on a single mission.

NetSuite Use Cases You Can Implement Today

AI-Driven Reconciliation

Agents can continuously monitor bank feeds and GL postings, flag mismatches, and even draft journal entries for review.

Anomaly Detection in Financials

Instead of waiting for month-end surprises, agents can scan trial balances daily, detect unusual variances, and notify controllers instantly.

Supply Chain Monitoring

Agents can track PO fulfillment, vendor performance, and inbound inventory. If a disruption looks likely, the agent raises a flag and proposes mitigation steps.

Customer Service & Collections

Agents can handle tier-1 customer queries in real time, or chase overdue invoices with smart, personalized reminders. Instead of blanket emails, the agent can analyze payment history, detect which customers respond best to different outreach strategies, and craft targeted collection messages.

Package Tracking & Notifications

For logistics-focused businesses, agents can monitor carrier APIs and NetSuite sales orders, then push proactive delivery updates into NetSuite - reducing “Where’s my order?” calls.

Predictive Forecasting

By analyzing past periods, seasonality, and pipeline data, agents can refresh rolling forecasts inside NetSuite without waiting for manual intervention.

How They Fit Into NetSuite’s AI Ecosystem

The good news is that companies running on NetSuite don’t have to wait for some future release to start experimenting with AI Agents - they can build them today.

With tools already at your disposal, like Scheduled Scripts, you can create event-driven automations that act as the backbone for agent behavior. Pair those with external technologies - whether modern LLMs, orchestration layers, or lightweight services - and you can start deploying agents that observe, decide, act, and learn across your ERP data.

Also, keep in mind that you don’t necessarily need a third-party platform to build and run AI Agents. While some providers offer agent frameworks, it’s entirely possible to design and operate agents using NetSuite’s scripting capabilities, SuiteQL, and external APIs. This gives you freedom to decide whether you want packaged solutions or custom, in-house development.

In my own work, I’ve been using Suite.js as the foundation upon which I’m building AI Agents. Suite.js provides a way to integrate NetSuite with external AI services - allowing agents to observe and analyze data, send notifications, update records, and more. It’s proving to be a flexible base that lets me experiment with agents in a real-world NetSuite environment, while still respecting the platform’s guardrails.

Getting Started: A Practical Roadmap

If you’re wondering how to move from theory to practice, the good news is that implementing AI Agents in NetSuite doesn’t have to be overwhelming. By starting small and building iteratively, you can put agents to work in a way that’s low-risk and high-impact.

Step 1: Identify a Pain Point: Month-end reconciliation, collections, vendor monitoring - pick something repetitive but valuable.

Step 2: Build a Pilot Agent: Use NetSuite Scheduled Scripts + external services to train a narrow-scope agent.

Step 3: Validate & Measure: Compare agent results against human teams. Track error rates, cycle times, and satisfaction.

Step 4: Expand Scope: Once proven, add agents for forecasting, procurement, or compliance monitoring.

Step 5: Move Toward Autonomy: Transition from human-in-the-middle to semi-autonomous operation as governance allows.

The Need for Governance

As powerful as AI Agents can be, they also raise important questions: Who approves their actions? How do we prevent “runaway automation”? What audit trail is in place for regulators or auditors?

That’s where governance comes in. Without it, you risk creating shadow processes that nobody fully understands or controls. With it, you gain trust, accountability, and scalability.

As far as governance goes, here are a few things I recommend:
• Human in the Middle (HITM): Keep a human validator between agent actions and final execution, at least in the early stages.
• Trust but Verify: Start agents in “draft” mode where humans approve outputs before anything posts to NetSuite.
• Establish Guardrails: Define thresholds for when an agent should escalate versus act autonomously.
• Maintain Visibility: Ensure every action an agent takes is logged, reviewable, and attributable.
• Plan for Scale: Build governance processes now that will still work when you’ve got dozens of agents across finance, operations, and supply chain.

Handled correctly, governance doesn’t slow AI adoption down - it accelerates it. Because the more confidence leaders have in the guardrails, the faster they’ll allow agents to take on real responsibility.

Wrapping Up

The introduction of AI Agents marks a fundamental shift in how we think about ERP systems. NetSuite workflows and saved searches solved yesterday’s problems. AI Agents are designed for today’s scale, complexity, and speed.

The question is no longer “Should we adopt AI in NetSuite?” It’s “How fast can we responsibly integrate AI Agents into our financial and operational backbone?”

Because the businesses that do will unlock efficiency gains, reduce risk, and create a competitive advantage that others can’t match.

And this is just the start - I’ll be writing more about AI Agents in the weeks ahead, with practical examples and lessons learned as I continue developing them.