Two products can both advertise AI accounting while changing very different parts of a finance process. One might explain a report; another extracts invoice data; a third coordinates work across systems. Understanding that difference is more useful than counting AI features.
This is an architecture guide from the team building ARTIFI. For named products and official sources, use our software selection guide. The categories below describe roles within a system, not rigid boundaries between vendors. A product may span several of them.
1. An assistant for reading and analysis
An analytical assistant helps users find information, summarize results and explore questions. It can improve access to financial data without becoming the system that records transactions.
The important questions concern evidence and scope. Which records did an answer use? Does it respect the user's entity permissions? Can a reviewer reproduce the calculation? A fluent explanation is not a substitute for a supported result.
This approach may be sufficient when the problem is retrieving or interpreting information. It does not establish that the assistant can execute the process being discussed.
2. Automation inside a specific workflow
A workflow tool can extract invoice fields, suggest coding, route approvals or prepare a reconciliation. Its authority is bounded by the process and the integration supporting it.
Examine how proposals become committed changes. A suggested account is different from a posted journal; an approved invoice is different from a completed bank payment. The interface should make those states clear.
Test ambiguous inputs, duplicates and failed integrations. A routine demonstration should be accompanied by an example where automation stops and the responsible person resolves the issue.
3. An agent coordinating work across systems
A coordinating agent may use several tools to complete a procedure. That raises questions beyond model quality: which system is authoritative, how are partial failures recovered and how is duplicate work prevented?
For example, a collection procedure might read open invoices, prepare a message and record a follow-up. Sending the message requires authorization, and the record of what happened must remain available even if a later step fails.
Design permissions around the work the agent is allowed to perform. A tool connection is not blanket authority to change every record it can reach. Teams also need a way to inspect runs and intervene when the procedure cannot proceed.
4. A finance platform designed for controlled AI operation
In this model, the underlying platform exposes financial records and controlled operations to an AI client. The ledger and approval system remain responsible for enforcing their rules when a proposed action is committed.
ARTIFI's platform overview describes this approach through its ledger, workflows, agents and MCP interface. Its finance-team page explains the intended working experience. Adoption may start alongside an existing system; the exact division of responsibilities must be established for the deployment.
The category itself proves no performance claim. A buyer should still inspect the controls, migration path and actual workflow coverage.
Decide which layer needs to change
If the problem is finding answers in reliable records, start by evaluating access and analysis. If people repeatedly move the same documents through a stable process, a focused workflow may be the appropriate unit of work. If the difficulty lies between several systems, investigate coordination and recovery.
A platform change becomes relevant when the existing records, controls or interfaces prevent the intended operating model. It is a larger decision, with reporting, integration and migration implications. Starting with an ambitious replacement does not eliminate those requirements.
Evaluate evidence at each boundary
Trace one real example from source input to final record. Ask who proposed the action, who approved it, which rules were applied and how the result can be reconciled. Then repeat the exercise with an error and a retry.
Document the limits you find. The useful distinction is what a particular deployment can safely accomplish, not whether its marketing uses the words assistant, agent or AI-native.
Related reading
Updated 25 September 2026 to separate architecture analysis from the product shortlist. Examples describe evaluation criteria and do not imply independently benchmarked outcomes.
Artifi Team
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