Index › Products › NICE · October 2026 Edition

1 category · Named, not ranked

NICE

7Judge labels

0First choices

1Negative labels

4 / 14Models named it

1Category

October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.8, every buyer segment counted.

Standing

1 label, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. NICE was named 1 time in RPA, where Microsoft Power Automate led with 51%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business22 of 46

under the label threshold

Mid-market37 of 68

under the label threshold

Enterprise11 of 56

under the label threshold

In rpa · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named NICE for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Function	Share	Rank	Negative rate	Labels	Quadrant	Since September 2026
Robotic process automation	IT operations and endpoint	0%	37 of 68	0%	1	under 10 labels · led by Microsoft Power Automate at 51%

Movement

This is the first edition on this tier, so no move can be computed for NICE yet. From the next edition this section shows, per buyer segment and per category, whether its share moved by more than the measured noise floor.

By model

How each model treated NICE across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.

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Model	Mention	Labels
Claude Haiku 4.5	0	0
GPT-5.4 mini	0	0
Gemini 3.5 Flash	0	0
Perplexity Sonar	0	0
Grok 4.1 Fast	0	0
Mistral Small	1	1
DeepSeek V4 Flash	0	0
Llama 4 Maverick	0	0
Qwen 3.7 Flash	0	0
Kimi K2	0	0
GLM 4.7 FlashX	0	0
MiniMax M2.5	0	0
GPT-6 Luna	0	0
Muse Glimmer 30B	0	0

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.

FramingLabels by classFirst choices

Direct0 labelsNone

Paraphrase1 labelNone

Comparative1 labelNone

Budget-constrained1 labelNone

Scale-constrained3 labelsNone

Negative1 labelNone

First choiceAlternativeMentionNegative7 labels in all, every segment counted; 0 of the 0 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

No positive label carried a quote.

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

No model argued against it.

Named alongside

The products named in the same answers as NICE, over the 7 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and NICE was named but was not.

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ProductSame answerTook the first choice insteadHead to head

UiPath4 of 72Not among the top eight

Automation Anywhere4 of 70Not among the top eight

Microsoft Power Automate4 of 70Not among the top eight

Blue Prism3 of 70Not among the top eight

Pega2 of 70Not among the top eight

ChatGPT1 of 71Not among the top eight

GFI MailArchiver1 of 71Not among the top eight

ABBYY FineReader Server1 of 70Not among the top eight

Amazon Textract1 of 70Not among the top eight

Azure AI Document Intelligence1 of 70Not among the top eight

A head-to-head page exists where both products are among a category's top eight. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named NICE. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 2 of the 7 answers that named NICE and are not a share of its labels.

Domains cited

analyticsinsight.net1

automationanywhere.com1

blueprism.com1

chatgpt.com1

cio.economictimes.indiatimes.com1

comm100.com1

demandbase.com1

eesel.ai1

eweek.com1

g2.com1

No domain is on file for NICE, so its own site is not marked.

Pages cited

analyticsinsight.net/amp/story/artificial-intelligence/enterprise-ai-platforms-scaling-bey1

automationanywhere.com/ai1

blueprism.com/products/enterprise1

chatgpt.com/business/enterprise1

cio.economictimes.indiatimes.com/amp/tools/10-best-b2b-intent-data-providers-for-enterpris1

comm100.com/blog/best-enterprise-ai-chatbots1

demandbase.com/blog/best-ai-tools-b2b-marketing1

eesel.ai/blog/chatbot-for-enterprises1

Pages are listed as the models cited them.

Search and answers

Where NICE stands in Google search beside where it stands in the models' answers.

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In search

Google, US estimates

Searches for its name, Google: 165,000 a month (“nice”)
AI search demand for its name, est.: 567,279 a month
Its own site: No site of its own on file, so no site figures

In answers

This edition

Share of first choices

rank 37 of 68 in RPA

Segment leader

51%

Microsoft Power Automate

First choices: 0 across its categories
Named in: 7 answers
Its own site cited: No site on file to match

Search figures are US estimates from DataForSEO, read September 28, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.

Follow NICE

An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.

Already following? Everything you follow, with a stop for each.

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when NICE's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as NICE, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

What a new claim receivesHide what a new claim receives

A new claim receives the current edition's vendor brief for NICE by email, built from the raw record of the edition. It shows:

where NICE is named, by buyer and by framing, and which cells hold its first choices;
the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
its vocabulary against the segment leader's, and the pages the models cited;
who was chosen in the answers that did not name NICE, and every reason the record gives;
a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
one page of published figures cleared to show a buyer.