Artificial Intelligence Business Models

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

Report Details

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Pages: 43
Tables, Charts,
     & Figures:
31
Publication Date: 2Q 2020
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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 enterprisewide 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 enterprise 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 Charts, Figures, and Tables

Figures
  • 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 industry, Latin America: 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
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