For many finance teams, turning data into insight begins the same way: Run a report in NetSuite, export it to Excel, rework the data, and use another tool to analyze the results or prepare a presentation. Despite modern functionality, critical finance workflows often still rely on manual steps outside the system.
At the same time, artificial intelligence (AI) is rapidly changing how organizations analyze data and make decisions. Finance teams are experimenting with tools like ChatGPT and Claude to summarize reports, identify trends, and accelerate analysis. But these tools operate separately from the systems where their most important business data lives.
As Dan Cooper, Solution Architect at Sikich, explained during our recent webinar on AI and NetSuite, “Everyone is talking about AI and how they can utilize it, but most struggle with one key challenge: “How do you connect AI to a system that actually runs the business?”
The next phase of AI adoption is about connecting AI to ERP systems through a secure integration layer so it can analyze data, support workflows, and help organizations make faster, more informed decisions.
Why AI needs to connect to ERP systems
NetSuite, like other ERP platforms, captures the operational and financial data that underpins daily decision-making, from financial statements and transactions to inventory, purchasing activity, and customer information.
When AI tools cannot access this data, they operate in a silo. They may generate insights, but those insights are limited, disconnected from the organization’s full, real-time business picture.
The true opportunity lies in connecting AI to NetSuite. Only then can organizations use AI to analyze and act on the data that drives their business.
The challenge: AI tools often sit outside core business systems
For all the momentum with AI, most organizations encounter the same friction point early on: Their AI tools and their business systems don’t talk. Again, the typical workflow looks something like this:
- Run a report in the ERP
- Export it to Excel
- Rework the data into a usable format
- Bring it into a separate AI tool for analysis
Any changes or recommendations that result must then be manually translated back into the system. It works, but it’s slow, it introduces risk, and by the time the analysis is done, the underlying data may have already moved.
Teams end up spending more time managing data handoffs than acting on insights. Errors creep in at every export and re-import, and the promise of AI-powered decision-making stays just out of reach.
ERP data as the foundation for AI
The reason this gap matters comes down to what lives inside an ERP system. NetSuite contains some of the most important datasets in any organization:
- Financial reports and income statements
- Transactions and journal entries
- Vendor and purchasing information
- Customer and revenue data
- Operational and inventory metrics
This data forms the foundation for financial reporting, operational planning, and strategic decision-making.
When AI operates outside that system, it’s working with a snapshot: a frozen, partial view of the business. The insights it generates may be directionally useful, but they aren’t grounded in current operational reality. For finance teams responsible for planning, reporting, and performance management, that gap is significant.
When AI is connected to NetSuite, trends are drawn from actual transactions, not last week’s export. Anomalies surface in context, not in isolation. And the AI is working from the same data that everyone else in the organization relies on.
The modern ERP + AI stack
As businesses integrate AI into their ERP environments, a new technology architecture is emerging. The modern stack typically includes three key layers.
1. AI interface layer
At the top of the stack are AI tools, which allow users to interact with data using natural language. Instead of navigating complex reports or building queries, a finance leader can ask what’s driving the variance in Q3 operating expenses or request a summary of accounts payable aging. The AI handles the interpretation and surfaces the answer.
2. Integration or connector layer
Between the AI interface and the ERP sits the integration layer to securely connect NetSuite ERP data and processes to AI platforms like Claude and ChatGPT. This layer doesn’t bypass NetSuite’s existing controls; it works within them. Key capabilities include secure connections between NetSuite and AI platforms, controlled data access, real-time interaction with financial and operational information, and full governance and auditability of system activity.
3. ERP layer
At the foundation is NetSuite itself, storing the financial, operational, and transactional data that powers the business. By connecting AI tools to this data source, organizations can ensure that analysis is always based on accurate, current information.
What happens when AI connects to NetSuite
Once AI tools are connected to NetSuite, their capabilities extend well beyond generating basic summaries. Rather than pulling reports and analyzing them offline, teams can engage with their data conversationally, asking follow-up questions, drilling into specific time periods or business units, and requesting analysis that would otherwise take hours to produce manually.
For example, a simple prompt can trigger an analysis of a NetSuite income statement, surfacing revenue drivers, cost patterns, and items that warrant further review.
AI can also help automate certain accounting workflows: reviewing unapproved vendor bills and generating accrual journal entries based on predefined instructions, without requiring anyone to export data or build entries in a spreadsheet manually.
Security and governance considerations
For many organizations, security, and governance are the first questions that come up when introducing AI into financial systems. A well-designed integration addresses both without requiring a separate control framework.
The same roles and permissions that you’re used to in NetSuite are what the tool uses when it’s interacting with your system. In other words, AI tools operate within the same governance framework as human users. For example:
- AI can only access the data a user is authorized to see.
- System permissions determine what actions can be taken.
- Changes made through the connector are logged in system records.
- User activity remains fully traceable.
- Secure authentication methods such as OAuth are used to establish connections between systems.
In short, the governance framework already built into NetSuite carries forward. The AI works within those boundaries, not around them.
What finance teams should consider next
For organizations ready to move from experimentation to integration, getting started doesn’t require a large-scale transformation.
Identify high-friction workflows, the ones where someone is regularly exporting data from NetSuite to analyze it elsewhere, and evaluate whether a connected AI workflow could eliminate that effort. From there, it’s a matter of finding a secure way to connect AI tools to the ERP and piloting with a focused use case.
The goal isn’t to automate everything at once. It’s to free finance teams from the work of preparing data so they can spend more time on the analysis and decisions that move the business forward.
Sikich works with organizations to implement and optimize NetSuite, including building AI integrations that connect ERP data to the tools your teams are already using.
If you’re ready to explore what that looks like for your organization, let’s start the conversation.
This publication contains general information only and Sikich is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or any other professional advice or services. This publication is not a substitute for such professional advice or services, nor should you use it as a basis for any decision, action or omission that may affect you or your business. Before making any decision, taking any action or omitting an action that may affect you or your business, you should consult a qualified professional advisor. In addition, this publication may contain certain content generated by an artificial intelligence (AI) language model. You acknowledge that Sikich shall not be responsible for any loss sustained by you or any person who relies on this publication.
