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Comparison · updated August 2026

AI-native ERP vs AI features bolted on

Every vendor in this category now has an AI story, and most of them are describing the same thing: a language model with read access to an existing application, summarising and suggesting. That is useful. It is also categorically different from software that can do bounded work on its own authority.

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Evaluating AI claims?

Send the AI section of a vendor proposal. We will tell you what it actually commits to.

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Four questions that separate themWe are also earlyAsk about the authority model
At a glanceChoose AI features ifChoose us ifSwitchingQuestions

At a glance

The distinction that matters.

AI featureserp.io
What it doesAssists a person doing the workDoes bounded work and escalates the rest
AuthorityNone — a person performs every actionGranted per workflow, entity, and threshold
Audit recordThe person’s action is loggedThe agent’s reasoning, policy, and confidence are logged
Failure modeA bad suggestion a person declinesA bounded action that is reversible and logged
MeasurementUsually none publishedAccuracy, escalation, and reversal rates per workflow
CeilingLimited by how fast a person can reviewLimited by the authority you grant
Where it breaksSilently, when suggestions get rubber-stampedVisibly, as a rising escalation rate
Data accessWhatever the application can seeScoped as an actor with its own permissions
GovernanceThe person is the controlThe policy engine is the control
Honest maturityShipping widely todayEarly, including ours

Choose AI features if any of these are true

Assistive AI features are genuinely useful and there are cases where they are all you should want.

  • Your volumes are low. Below a few hundred transactions a month, a person with good suggestions is faster than configuring an authority model.
  • Your processes are unsettled. Automating a process you are still changing produces the wrong answer faster. Suggestions adapt; granted authority does not.
  • Your data quality is poor. An agent with authority on bad inputs escalates constantly, which is worse than a person with suggestions.
  • You have no appetite for governance work. An authority model requires deciding who approves what. If that decision is not going to be made, suggestions are the honest ceiling.
  • You already have a system that works. AI features on an existing platform are incremental and low risk. That is a reasonable place to start.
Four questions to ask any vendor

What can it do without a person clicking approve. What is recorded when it acts. What happens to a release that scores worse on your evaluation set. And what is the published accuracy per workflow, including the bottom quartile of customers. The range of answers to those four is where the category actually separates, and most proposals cannot answer the third.

Choose erp.io if these describe you better

  • Volume is the problem. Thousands of transactions a month where a person reviewing every suggestion is itself the bottleneck.
  • You want the hours back, not the assistance. Suggestions make a person faster. Bounded authority removes the transaction from their queue entirely.
  • You need the automation to be auditable. An agent action with recorded reasoning, policy, and confidence is reviewable. A person rubber-stamping suggestions is not, and looks identical in the log.
  • You want the numbers. Accuracy, escalation rate, and reversal rate per workflow, published including the spread. Ask any vendor for the equivalent.
  • Your processes are stable and documented. Authority can only be granted where the rules are knowable. That is a prerequisite rather than an outcome.

If you are already on AI features

We are early too, and it would be dishonest to present this as a mature category. Our benchmarks show workflows ranging from ninety-four percent straight-through down to sixty-one, and the bottom quartile of our customers sits well below the median. Those numbers are published because the spread is the honest part.

What we would defend is the architecture rather than the current accuracy. An authority model, a policy engine that every actor passes through, and an audit trail that records reasoning are the things that make the accuracy improvable safely. Retrofitting them onto a system where the model shares application credentials is considerably harder than building them first.

If you are evaluating this category, the most useful thing you can do is ask each vendor for the four answers above and compare them side by side. It separates the products faster than any demo.

Questions

Common follow-ups.

Is assistive AI worthless?
No, and this page says so. Below a few hundred transactions a month, or where processes are unsettled, it is the right ceiling.
How do we tell them apart in a demo?
Ask what happens without a person clicking approve. If the answer is nothing, it is assistive, whatever the label says.
Are you mature at this?
No. Our published benchmarks range from ninety-four percent down to sixty-one, and the bottom quartile is materially worse than the median.
Can incumbents retrofit an authority model?
They can and some will. It is harder than building it first because the model typically shares application credentials, which makes the audit assertions weak.
How current is this?
Reviewed quarterly and dated. This is the fastest-moving part of the category and this page decays faster than the others.

Ask the four questions.

What can it do unapproved, what is recorded, what blocks a bad release, and what are the published numbers.

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