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市場調査レポート
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AI (人工知能) のビジネスモデル:永久ライセンス、クラウドベース、オンプレミス、事前構築済みのソリューション、組み込みソリューション、AIソフトウェアのハイブリッド価格モデル

Artificial Intelligence Business Models: Perpetual License, Cloud-Based, On-Premises, Pre-Built Solutions, Embedded Solutions, and Hybrid Pricing Models for AI Software

出版日: | 発行: Omdia | Tractica | ページ情報: 英文 43 Pages; 31 Tables, Charts & Figures | 納期: 即納可能 即納可能とは

価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=105.49円
AI (人工知能) のビジネスモデル:永久ライセンス、クラウドベース、オンプレミス、事前構築済みのソリューション、組み込みソリューション、AIソフトウェアのハイブリッド価格モデル
出版日: 2020年05月05日
発行: Omdia | Tractica
ページ情報: 英文 43 Pages; 31 Tables, Charts & Figures
納期: 即納可能 即納可能とは
担当者のコメント
本レポートは、AIアプリケーションの開発と展開に使用される6つのビジネスモデル(Cloud-based, on-premises, Pre-built solutions, Embedded solutions, ybrid, Perpetual license)について分析しております。Omdia | Tracticaの分析は、AI関連企業へのインタビューから得た洞察に基づいた、信憑性の高いものとなっております。
  • 全表示
  • 概要
  • 図表
  • 目次
概要

プログラミングプラットフォームとツールのほか、クラウドベースのインフラが広く利用できるようになったことで、市場が大きく変化しました。企業は、単一のAI (人工知能) ベンダーに縛られる必要はありません。データサイエンスチームやエンジニアを雇って、AIモデルをゼロから開発、トレーニング、実行することができます。近い将来に向けて、多くのアプローチやベンダーの余地があります。世界のAIソフトウェアの年間収益は、2018年の101億米ドルから、2025年には1,260億米ドルまで増加すると予測されています。

当レポートでは、AI (人工知能) アプリケーションの開発と展開に使用されるさまざまなビジネスモデルについて調査分析し、詳細分析と市場機会について、体系的な情報を提供しています。

目次

エグゼクティブサマリー

市場の問題

  • 市場概要
  • 市場促進要因
  • 市場の障壁
  • 規制、プライバシー、法的問題
  • 販売、マーケティング、フルフィルメント戦略
  • 価格モデル
  • 価格動向

市場参入企業

  • イントロダクション
  • AIチップメーカー
    • 主要企業
    • 使用中のビジネスモデル
    • 主な課題
  • プラットフォーム・インフラプロバイダー
    • 主要企業
    • ベンダーの種類とエンタープライズ開発アプローチ
    • 主な課題
  • カスタムソリューション開発業者
    • 主要企業
    • 使用中のビジネスモデル
    • 主な課題
  • 事前構築済みのアルゴリズムとソリューションプロバイダー
    • 主要企業が使用中のビジネスモデル
    • 主な課題
  • 企業

市場予測

  • 概要
  • 世界の市場予測:業界別
  • 世界のAIソフトウェア収益:業界別
  • 世界のAIソフトウェア収益:ビジネスモデル別
  • 地域別予測
  • 結論と提言
図表

List of Tables

  • AI business model benefits: Enterprises and vendors
  • AI business model limitations: Enterprises and vendors
  • AI business models: Typical enterprises users and typical vendors
  • Annual AI software revenue by industry, world markets: 2018-2025
  • Annual AI software revenue by business model, world markets: 2018-2025
  • Annual AI software revenue by industry, North America: 2018-2025
  • Annual AI software revenue by business model, North America: 2018-2025
  • Annual AI software revenue by industry, Europe: 2018-2025
  • Annual AI software revenue by business model, Europe: 2018-2025
  • Annual AI software revenue by industry, Asia Pacific: 2018-2025
  • Annual AI software revenue by business model, Asia Pacific: 2018-2025
  • Annual AI software revenue by industry, Latin America: 2018-2025
  • Annual AI software revenue by business model, Latin America: 2018-2025
  • Annual AI software revenue by industry, Middle East & Africa: 2018-2025
  • Annual AI software revenue by business model, Middle East & Africa: 2018-2025

List of Figures

  • AI business model benefits: Enterprises and vendors
  • AI business model limitations: Enterprises and vendors
  • AI business models: Typical enterprises users and typical vendors
  • Annual AI software revenue by industry, world markets: 2018-2025
  • Annual AI software revenue by business model, world markets: 2018-2025
  • Annual AI software revenue by industry, North America: 2018-2025
  • Annual AI software revenue by business model, North America: 2018-2025
  • Annual AI software revenue by industry, Europe: 2018-2025
  • Annual AI software revenue by business model, Europe: 2018-2025
  • Annual AI software revenue by industry, Asia Pacific: 2018-2025
  • Annual AI software revenue by business model, Asia Pacific: 2018-2025
  • Annual AI software revenue by industry, Latin America: 2018-2025
  • Annual AI software revenue by business model, Latin America: 2018-2025
  • Annual AI software revenue by industry, Middle East & Africa: 2018-2025
  • Annual AI software revenue by business model, Middle East & Africa: 2018-2025
  • AI software revenue share by business model, world markets: 2025
  • AI software business model revenue shifts, world markets: 2018-2025
  • Annual AI software revenue by industry, world markets: 2018-2025
  • AI software revenue share by industry, world markets: 2018
  • AI software revenue share by industry, world markets: 2025
  • Annual AI software revenue by business model, world markets: 2018-2025
  • Annual AI software revenue by industry, North America: 2018-2025
  • Annual AI software revenue by business model, North America: 2018-2025
  • Annual AI software revenue by industry, Europe: 2018-2025
  • Annual AI software revenue by business model, Europe: 2018-2025
  • Annual AI software revenue by industry, Asia Pacific: 2018-2025
  • Annual AI software revenue by business model, Asia Pacific: 2018-2025
  • Annual AI software revenue by industry, Latin America: 2018-2025
  • Annual AI software revenue by business model, Latin America: 2018-2025
  • Annual AI software revenue by industry, Middle East & Africa: 2018-2025
  • Annual AI software revenue by business model, Middle East & Africa: 2018-2025
目次
Product Code: AIBM-20

The deployment of AI solutions is unlike traditional software, which is largely based around a volume-based sales model. In contrast, AI product capabilities may increase the output of employees, thus reducing the need for additional software license seats. Due to the collaborative and evolving nature of AI, several different business models have emerged. These range from a fully in-house, custom-built approach to a more modular approach using pre-built solutions and tools and a fully outsourced approach solely relying on third-party vendors.

The widespread availability of programming platforms and tools, as well as cloud-based infrastructure, has led to a major shift in the market. Enterprises do not need to lock into a single AI vendor; they can hire data science teams and engineers to develop, train, and run AI models from scratch. Yet, a significant portion of the enterprise market has neither the skill nor the budget to develop AI from scratch. As such, many vendors sell pre-built AI solutions or tools, and consultants and contractors can customize off-the-shelf AI. No single business model is going to be right for all enterprises looking to deploy AI. There will be room for many approaches and vendors-not only today, but for the foreseeable future. Omdia forecasts that annual AI software revenue will increase from $10.1bn worldwide in 2018 to $126.0bn in 2025.

This Omdia report provides a quantitative assessment of the market opportunity for the different business models used to develop and deploy AI applications. The study includes analyses of six business models in use globally and within five global regions and 28 industries. Discussion of strategies used by enterprises and vendors to consume, deliver, and pay for AI software is included. Omdia's analysis is based on insight gathered by speaking with AI enterprises and vendors active in the market.

Key Questions Addressed:

  • What are the key factors affecting the way AI solutions are deployed by enterprises?
  • How are vendors responding in terms of how they market, sell, and deliver their solutions?
  • Which business models are most commonly used by AI vendors?
  • How will AI solutions delivery and pricing models vary among world regions?
  • What are the challenges affecting the delivery of AI solutions?
  • How are regulations, privacy concerns, and data security issues affecting the way AI solutions are delivered and sold?
  • Which specific marketing and sales approaches are being used by vendors to reach and engage with customers?

Who Needs This Report?

  • AI technology companies
  • Software companies
  • Service providers and systems integrators
  • Industry organizations
  • AI consultants
  • Investor community

Table of Contents

Executive summary

  • Introduction
  • Key highlights
  • Market issues and trends
  • Market drivers
  • Market barriers
  • Market forecasts

Market Issues

  • Market overview
  • Market drivers
    • Moving from PoCs to enterprise-wide deployments
    • Allowing customers to better manage and mitigate project risk
    • Allowing customers to limit technology obsolescence
    • Allowing customers to align revenue with enterprise purchasing requirements
    • Creating a long-term client relationship
  • Market barriers
    • Aligning customer demands with the need to generate revenue
    • Attracting top, experienced, and diverse talent to remain innovative
    • Generating long-term, recurring revenue streams
    • Managing intense competition among vendors and Eenterprise AI units
  • Regulatory, privacy, and legal issues
    • Data and user privacy issues
    • Liability concerns
    • Fairness, bias, and anti-discrimination controls
    • Level of automation and human-in-the-loop
    • Tradeoffs between accuracy, privacy, and explainability
  • Sales, marketing and fulfillment strategies
    • The importance of scalability
    • Data is and will remain vitally important to AI development
    • Relationship-based selling is a requirement
    • Demonstrating domain expertise
    • Utilizing blockchain and federated learning to manage data privacy and support ML training
  • Pricing Models
    • Perpetual license
    • Cloud-based
    • On-premises
    • Pre-built solutions
    • Embedded solutions
    • Hybrid solutions
  • Pricing Trends
    • Shifting pricing to incentivize more usage
    • Comparing in-house and outsourced development

Market participants

  • Introduction
  • AI chip makers
    • Key participants
    • Business models in use
    • Major challenges
  • Platform and infrastructure providers
    • Key participants
    • Vendor types and enterprise development approaches
    • Major challenges
  • Custom solution developers
    • Key participants
    • Business models in use
    • Major challenges
  • Pre-built algorithm and solutions providers
    • Key participants Business models in use
    • Major challenges
  • Enterprises

Market forecasts

  • Overview
  • Global market forecasts by industry
  • Global AI software revenue by industry, 2018
  • Global AI software revenue by industry, 2025
  • Global AI software revenue by business model, 2018-25
  • Regional forecasts
  • Conclusions and recommendations
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