What is AI-Native ERP?

The clients Raiz works with typically fall into two categories:

1. Those who hear “AI-native” and say, “This is what I’ve been looking for.”
2. Those who hear “AI-native” and say, “I’m not sure about that.”

This article is for the latter, the skeptics.

The question we hear more than any other is, “What is AI-native ERP?” The goal of this article is to define, explain, and make the case for AI-native ERP.

A brief history… 

There have been three waves of ERP:

• First wave (1970s - 2000s): On-premise ERPs. Custom, rigid, expensive, slow to implement, and dependent on heavy IT and consulting.
• Second wave (2000s - 2020s): Cloud ERPs. Cheaper infrastructure and better usability - but same batch processing model underneath.
• Third wave (2020s+): AI-native ERPs. Rebuilding the ledger and workflows around automation and agents - adding context and action on top of the system of record.

So, what is AI-native ERP? An AI-native ERP has three defining characteristics:

1 - Built in the ChatGPT Era

Was the product built pre November 2022 (the ChatGPT moment)? If so, it’s not an AI-native ERP despite what their marketing materials tell you. It has technical debt that is hard to overcome. AI is a bolt on, not native.

Imagine you could build NetSuite today from scratch. How might you reimagine it using all the AI infrastructure and tools available? That’s what ERPs like Rillet are doing. The infrastructure is a real-time architecture, enabling a continuous close. Not batch processes like legacy systems. 

Not all automation is AI. Some of the best stuff is just good deterministic software. Good software can automate things like a revenue recognition or prepaid expense schedule - boring but powerful.

2 - LLMs & Agents Native to the Platform

The natural next question is, can you interact with your financial data through natural language natively in the product? Or simpler, can you chat with your data?

For a Large Language Model (LLM) to query the GL, the ERP must have a core product architecture that includes clean GL data. LLMs and Agents love good, clean data.

AI-native ERPs allow you to query your GL with prompts like:

• “Can you estimate last month’s accruals?”
• “List my top 10 customers by revenue over the last 12 months”
• “Perform a quarter over quarter flux analysis for the last two quarters”

If the LLM/chatbot in your ERP only allows you to query help documentation, it’s not an AI-native ERP.

You might be thinking: "I can just connect Claude with my existing ERP.” It’s possible, but there are four key differences:

• Security: your financial data leaves your environment and - depending on your LLM licensing - may train someone else's model. 
• Capability: external AI is not specialized for accounting data and lacks context - it doesn't persistently know your chart of accounts, fiscal calendar, or entity structure. 
• Model lock-in: you're committing to whichever provider you picked, in a market that seems to reset every quarter. 
• Audit: when external AI suggests an entry, you do the booking - there may be no audit trail of agent activity.

AI-native ERPs solve all four. Your data stays in your environment, agents are trained on accounting workflows, you can swap between OpenAI, Anthropic, Google, or xAI as the market evolves, and every agent action lives in the same audit trail as a human action. AI inside the ledger, not on top of it.

Let's talk about AI agents. Does your ERP have agents that do work a person used to do?

Some legacy ERPs have AI functions that can auto-detect errors or match fields. AI-native ERPs have agents that do accounting grunt work. And importantly, they surface outputs to a human for review and approval. 

Examples: 

• An accruals agent that can estimate and book accruals 
• A reports agent who can analyze financial data and produce reports
• An accounts receivable agent that can send invoices and chase collections

What about audit and controls? AI-native ERPs are built for it. Every agent action is logged, every output is explainable and traceable to its inputs, and every booking goes through human approval. The audit trail an auditor needs is built in.

Agents are like digital staff accountants that get work done.

3 - Open Integrations (Native, API, MCP)

AI is only as useful as the data it can reach, which is why integrations make or break an AI-native ERP.

Integrations in ERP have three key layers:

Native - integrations built and maintained by the ERP natively

AI-native ERPs have custom, purpose-built integrations with other tools (e.g. CRM, payments, banking, AP/expense, payroll, etc.). The integrations are core to the product architecture. Clean data flows into the general ledger in a standardized format. That means AI can operate inside the ledger rather than sitting on top of it.

Most legacy ERPs treat integrations as a bolt-on problem. You end up with messy CSVs, manual imports, and data that's inconsistent or incomplete. AI layered on top of that garbage data produces garbage outputs.

API - enables integrations built and maintained by 3rd party products or partners

For 3rd party integrations, the quality and openness of the API matters. AI-native ERPs expose more of the system through the API. Objects are readable and writable. They also follow standards like OpenAPI for usability and security. This supports a healthy ecosystem of 3rd party integrations.

This is table stakes for modern software but something legacy ERPs often get wrong. Most legacy ERP APIs are a total headache - convoluted data flows and fragile middleware. Plus, you get charged the more you use it, discouraging integration.

And because AI-native ERPs use modern standards and AI-assisted integration tooling, the cost of adding and maintaining new integrations stays low as your stack grows. The integration layer doesn't become a tax that scales with your tool count.

MCP - AI agent access, ability to connect tools like Claude and ChatGPT

The MCP is the newer, more interesting piece. Think of it like a universal adapter for AI. Most legacy ERPs struggle with MCP because of old architecture and limited APIs.

In an AI-native ERP, you can connect Claude to query your GL, pull reports, and interact with accounting data.

These three layers set the ceiling on what AI can do for your finance team. A low ceiling is hard to lift after the fact.

Let's test these characteristics on two legacy leaders and an AI-native challenger:

Why does it matter?

For finance teams, AI-native ERP delivers two common business outcomes:

1. Close the books fast - clients who used to take 15 days now close in two or three. Some hit day one. Zero day close is the aspiration.
2. Scale leaner - Windsurf grew to $100m in ARR with a finance team of two (one finance, one accounting).

Is AI-native ERP right for you?

To find out, you’ll need to schedule a call with us… kidding.

AI-native ERP is not for you if…

• You're too early for ERP. Under $5M revenue, you don't need this complexity yet. QuickBooks is fine until you outgrow it - meaning you need multi-entity consolidation, advanced revenue recognition, audit-ready controls, or you can't trust the data because spreadsheets are multiplying.
• You just implemented a new ERP. If you spent 6-12 months and six figures standing up NetSuite, switching now isn't honest math - even if you'd be better off in five years. We'll be here when the calculus changes.
• You want a 1:1 modern NetSuite replacement. AI-native ERPs are a different model. If you want every legacy workflow and batch process preserved, you'll be frustrated.
• You're in a complex industry like manufacturing or construction. AI-native ERPs aren't built for deep vertical workflows yet.

There is also a common concern about vendor risk. Switching ERPs is one of the riskiest, most time-consuming projects a finance team takes on. Legacy vendors have been around for 25+ years with a proven track record. AI-native ERPs are newer companies. 

Consider: The best AI-native ERP products are well capitalized, with strong customer demand and revenue growth. Oracle recently announced cuts of up to 30,000 to free cash for AI data centers. The cuts hit applications and SaaS operations - exactly the teams that maintain NetSuite. So legacy isn't risk-free either.

What happens for the CFO who picks an AI-native ERP? You become a future-proof finance leader.

No one makes an executive career by posting and fixing journal entries every month. They do it by closing the books faster, scaling leaner teams, and putting real-time numbers in front of the CEO. That’s hard to do with a 6-figure legacy ERP or QuickBooks.

Ask yourself…

Do you work for the product or does it work for you?

Too many accountants and finance professionals in the world are getting their life snatched away every day from logging into archaic systems, dealing with clunky and rigid interfaces, and chugging away entering data into boxes with fonts that look like 1999. There has to be a better way.

Thankfully, AI-native ERPs work for accountants and finance professionals, not against them. They can do their best work and soar in their career.

The ROI on an ERP should be reduced labor hours, not just a better tool.