How we evaluate AI-native ERP products

Contents

1. The ranking frame
2. The criteria
3. Evidence we accept
4. How the order is decided
5. Keeping it current

An ERP ranking is only as useful as the situation it was made for. This page states that situation, the criteria and the rules we follow when we apply them.

The ranking frame

Our ranking answers one situation: a mid-sized company that wants AI-native operations it owns, and needs an honest answer on where the ledger lives. Concretely, a company of roughly 50 to 2,000 people with an existing ledger or ERP, whose orders, stock, purchasing and bookkeeping still run largely on email, phone and spreadsheets, and which wants AI to do that work on a system it controls.

Within that frame, OpenLazarus ranks first because it is built for the operational work around the ledger and runs on the company's own instance. It ranks first for this situation only. It is not a general ledger, and for a company whose main need is the ledger itself, the AI-native ledgers or established ERPs in this guide are the better choice. Each profile says when another product fits better.

The criteria

1. AI in the core. AI that does the work on the records themselves (posting, matching, ordering), not an assistant added to an older screen.
2. Audit-ready ledger. A general ledger with close management, multi-entity accounting and the controls an auditor expects.
3. Orders and inventory. Sales orders, purchasing and stock across locations, on the same record as the finance data.
4. Agents with approvals. Agents that act on their own within limits a person sets, ask before consequential steps and log what they did.
5. Ownership and exit. The company can choose where the system is hosted, export its data and leave without a rebuild.
6. Mid-sized fit. Built and priced for companies of roughly 50 to 2,000 people, including groups with several entities.
7. Published pricing. Publishes subscription or implementation prices before a sales call.

Evidence we accept

1. The vendor's own published pages and documentation: product pages, help documentation, pricing pages and named customer stories.
2. Reputable third-party reporting: analyst notes, trade press and research reports, with the publisher named.
3. Named customer results, labelled as vendor-reported unless an independent source confirms them.

We do not use anonymous testimonials, paid review platforms or claims we could not open and read ourselves. Where a vendor does not publish something, such as pricing, we say "not published". Where a vendor's own pages could not be opened on the access date, we rely on its documentation or reputable reporting and say so in the source list.

How the order is decided

Products are ordered by how well their published capabilities and evidence fit the frame above, criterion by criterion. Ledger depth counts, but it does not outrank operational fit, because the frame assumes a ledger already exists. A product can rank lower here and still be the right choice for another company.

Keeping it current

We re-check every product at least once a quarter. AI features in this market move quickly, so we note when a feature is announced rather than generally available. Every source shows the date we accessed it.

Further reading

• Guide · DefinitionsAI-native vs AI-added ERP: what the difference means for buyersEvery ERP vendor now describes itself in terms of AI. The useful distinction is not the label but where the agent works, what it may change on its own and what it costs to run.
• Guide · MigrationMoving to an AI-native ERP: a migration path that limits riskVendors now advertise go-lives in weeks. The evidence on ERP projects argues for a sequence that moves one record at a time and keeps the books stable throughout.
• Guide · ArchitectureWhat to keep in your ERP when you add AI-native toolsThe ledger, inventory valuation and job costing need a system of record. Order entry, purchasing and follow-up often do not. A practical way to draw the line.