表紙:AIビジネスパフォーマンス指標分析2Q21
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AIビジネスパフォーマンス指標分析2Q21

AI Business Performance Metrics Analysis 2Q21

出版日: | 発行: OMDIA | ページ情報: 英文 | 納期: 即日から翌営業日

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AIビジネスパフォーマンス指標分析2Q21
出版日: 2021年07月27日
発行: OMDIA
ページ情報: 英文
納期: 即日から翌営業日
  • 全表示
  • 概要
  • 目次
概要
当レポートでは、AIビジネスパフォーマンス指標について分析し、AIのパフォーマンスを測定するケーススタディで引用されている値(コスト削減率、収益増加率、精度率など)を分析し、データベースの概要と業界動向などを提供しています。

目次

  • AIビジネスパフォーマンス指標データベースの概要
  • 上位の指標
  • 水平展開
  • 業界
  • メタテクノロジー
  • 付録

図、表、ダウンロードのリスト

目次

Report Summary:

The dataset should be considered an anecdotal sampling and is not exhaustive. The purpose is to consolidate this type of data into an easily referenced format and within an analytical context. Most data is self-reported, and Omdia has not validated these metrics directly with the vendors or sources. These can be largely described as metrics of successful AI cases, as most failures go unreported.

Because of the anecdotal nature of this file, with limited records, the best use of this data is as a finding aid.

Pivot tables and charts, shown on the following slides, have been created to help users frame and manipulate the dataset. However, the data cannot be compared when drilled down into a high level of detail (e.g., metric by horizontal by industry) as the record counts will be small. Even some of the horizontal applications and the industries with fewer than 15 records and should be considered highly anecdotal.

Summary tables show values cited in the case studies that measure AI performance (e.g., percentage reduction in cost, percentage increase in revenue, or percentage accuracy). While these have been averaged in some tables for illustration, be careful in drawing conclusions from comparing or combining values, as the use cases and the things being measured in each record may be unique to the vendor, client, or project.

Key messages

  • The dataset should be considered an anecdotal sampling and is not exhaustive. The purpose is to consolidate this type of data into an easily referenced format and within an analytical context. Most data is self-reported, and Omdia has not validated these metrics directly with the vendors or sources. These can be largely described as metrics of successful AI cases, as most failures go unreported.
  • Because of the anecdotal nature of this file, with limited records, the best use of this data is as a finding aid.
  • Pivot tables and charts, shown on the following slides, have been created to help users frame and manipulate the dataset. However, the data cannot be compared when drilled down into a high level of detail (e.g., metric by horizontal by industry) as the record counts will be small. Even some of the horizontal applications and the industries with fewer than 15 records and should be considered highly anecdotal.
  • Summary tables show values cited in the case studies that measure AI performance (e.g., percentage reduction in cost, percentage increase in revenue, or percentage accuracy). While these have been averaged in some tables for illustration, be careful in drawing conclusions from comparing or combining values, as the use cases and the things being measured in each record may be unique to the vendor, client, or project.

Table of contents

  • AI business performance metrics database overview
  • Top metrics
  • Horizontal applications
  • Industry
  • Meta technologies
  • Appendix

List of figures, tables and downloads

  • Figure 1: Frequently cited metrics and average uniform value
  • Figure 2: Metrics by horizontal applications (top 7)
  • Figure 3: Customer experience
  • Figure 4: Chatbots & VDAs
  • Figure 5: Process optimization
  • Figure 6: Metrics by industry (top 7)
  • Figure 7: Retail
  • Figure 8: Financial services
  • Figure 9: Business services
  • Figure 10: Metrics by meta technology
  • Figure 11: Analytics
  • Figure 12: Language
  • Figure 13: Vision & analytics