EA Enterprise AI Finance

Original research | Baseline 2026-07-23

What the first finance AI evidence baseline shows.

A transparent snapshot of the comparison dataset, built to show where public evidence is strong and where a buyer should slow down.

Why read

Use this baseline to see evidence gaps before you trust a shortlist.

The short answer: the dataset is useful for deciding what to investigate next, not for declaring a safe, effective, or universally best product.

For: enterprise finance buyers who need to explain why a product is on, or missing from, a shortlist.

What to do with the numbers

Start with a category, market, workflow, and measurable outcome. Then open the product profiles and ask whether the source supports the exact intended use. A missing independent source is a reason to verify more, not proof that a product fails.

Evidence boundary: the counts below are calculated from the public comparison records at the review date. Read the comparison method before interpreting them.

Evidence baseline

The dataset is useful because its gaps are visible.

This is a descriptive baseline, not a claim about which vendor is best. Source type, scope, independence, market, and workflow boundaries still need buyer verification.

Profiles

30

Six products in each of five enterprise buying categories.

Independent or regulatory

30

Profiles with at least one independent-evidence or regulator source in the current dataset.

Vendor-only

0

Profiles where current public evidence is vendor-provided; these are not upgraded to evidence-backed by default.

Markets

4

US, UK, EU, and Australia notes are kept separate because local duties differ.

Category view

Averages are context, not recommendations.

Average displayed scores include only fully assessed profiles and are rounded for readability. The category pages retain the underlying rationales and evidence gaps.

Baseline counts and average assessed evidence scores as at 2026-07-23
CategoryProfilesAverage assessed scoreBuyer lens
Financial planning and forecasting62.6 / 5CFO, FP&A, finance transformation, and business-unit leadership.
Fraud, AML, and risk operations62.6 / 5Chief risk officer, compliance, AML, fraud, and operations leadership.
Customer service and financial guidance62.6 / 5Chief customer officer, digital, contact centre, product, and compliance leaders.
Credit and lending decisions62.6 / 5Chief credit officer, lending, risk, compliance, and operations leadership.
Finance operations and reporting62.6 / 5Controller, CFO, shared services, audit, and finance transformation leaders.

What this does not show: no score establishes educational impact, safety, legal compliance, current local availability, ROI, or procurement fit. Future snapshots should add dated independent evidence, buyer interview findings, and anonymised pilot learnings when those assets are genuinely available.

Sources and further reading

A practical next step

Turn an evidence gap into a measurable workflow question.

Enterprise AI Group describes a 6–8 week path for a defined business process, with governance, policy management, enterprise security, and Microsoft-tenant deployment considered from the start.

Enterprise AI Group describes a 6–8 week path for a defined workflow. Timing and cost depend on scope, users, integrations, security, governance, and support. These research pages are published by Enterprise AI Group. The implementation links describe optional Enterprise AI Group services; they are not product endorsements or a replacement for local finance diligence.

See the governed platform approach

Do not include personal, confidential, regulated, or other sensitive information in an enquiry.

Keep the useful part

Tell us what you are deciding next.

Send the finance workflow, market, or category you are researching. We will use it to shape the next clear buyer brief.

Useful detail: include the market, workflow, or category behind a finance AI evidence gap.

Request a buyer brief Browse the categories

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