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市場調查報告書

AI (人工智慧)的經營模式:永久許可證,雲端基礎,內部部署,事前建立的解決方案,嵌入式解決方案,AI軟體的混合定價模式

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

出版商 Omdia | Tractica 商品編碼 934286
出版日期 內容資訊 英文 43 Pages; 31 Tables, Charts & Figures
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AI (人工智慧)的經營模式:永久許可證,雲端基礎,內部部署,事前建立的解決方案,嵌入式解決方案,AI軟體的混合定價模式 Artificial Intelligence Business Models: Perpetual License, Cloud-Based, On-Premises, Pre-Built Solutions, Embedded Solutions, and Hybrid Pricing Models for AI Software
出版日期: 2020年05月05日內容資訊: 英文 43 Pages; 31 Tables, Charts & Figures
簡介

由於能廣泛利用編程平台與工具,加上雲端基礎的基礎設施,市場大幅變化。企業,不需局限於單一的AI (人工智慧) 供應商。僱用資料科學團隊和工程師,可從零開發,訓練,實行AI模式。不久的將來,有許多方法和供應商的空間。全球AI軟體的年度收益,預計從2018年的101億美元,2025年增加到1,260億美元。

本報告提供AI (人工智慧) 應用程式的開發和展開所使用的各種經營模式調查分析,詳細分析與市場機會相關的系統性資訊。

目錄

摘要整理

市場問題

  • 市場概要
  • 推動市場要素
  • 市場障礙
  • 法規,隱私,法律問題
  • 銷售,行銷,履約策略
  • 定價模式
  • 價格趨勢

市場參與企業

  • 簡介
  • AI晶片廠商
    • 主要企業
    • 使用中的經營模式
    • 主要課題
  • 平台、基礎設施供應商
    • 主要企業
    • 供應商種類與企業開發方法
    • 主要課題
  • 自訂解決方案開發業者
    • 主要企業
    • 使用中的經營模式
    • 主要課題
  • 事前建立的演算法和解決方案供應商
    • 主要企業使用中的經營模式
    • 主要課題
  • 企業

市場預測

  • 概要
  • 全球市場預測:各業界
  • 全球AI軟體收益:各業界
  • 全球AI軟體收益:各商業模式
  • 各地區預測
  • 結論與建議
目錄
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

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