探索开源人工智慧的商业策略

本报告调查了开源 AI 的商业策略，并总结了开源 AI 收入预测、开源策略案例研究、开源 AI 策略领导者和建议。

实际好处：

• 确定并制定开源人工智慧的新商业策略
• 从开源人工智慧商业化案例研究中学习最佳实践
• 制定长期的内部、社区、贡献和商业化策略

重要问题的答案：

• 谁是商业上最先进的开源人工智慧工程师？
• 供应商如何最大化其开源投资的价值？
• 对于市场、产品和公司发展而言，最有效的长期开源策略是什么？
• 供应商该如何开始支援开源人工智慧开发？

研究亮点：

• 开源人工智慧策略评估
• 企业开源人工智慧软体支出：传统人工智慧与生成人工智慧
• 开源人工智慧策略案例研究

目录

重要要点

开放人工智慧收入预测：2023-2030

供应商如何制定开源策略

可以透过开源实现的策略

策略个案研究

开源人工智慧策略的领导者

供应商如何利用开源人工智慧脱颖而出

开源人工智慧策略：建议

Actionable Benefits:

• Identify and develop new commercial strategies for open-source Artificial Intelligence (AI) contributions.
• Explore and learn from best practice open-source AI commercial case studies.
• Develop long-tern internal, community, contribution, and commercialization strategies.

Critical Questions Answered:

• Who are the most commercially advanced open-source AI contributors?
• How can vendors maximize the value of open-source investment?
• What will be the most effective long-term open-source strategies for market, product, and company development?
• How should vendors start supporting open-source AI development?

Research Highlights:

• Open-source AI strategy evaluation.
• Enterprise open-source AI software spend: traditional versus Generative Artificial Intelligence (Gen AI).
• Open-source AI strategy case studies.

Who Should Read This?

• AI executives looking to engage with the open-source ecosystem.
• Software strategists looking to translate contributions into monetization.

TABLE OF CONTENTS

Key Takeaways

Open AI Revenue Forecasts 2023 to 2030

How Can Vendors Develop an Open-Source Strategy

What Strategies Can Open Source Enable

Strategy Case Studies

Leading Open-Source AI Strategies

How Can Vendors Build Differentiation in Open-Source AI

Open-Source AI Strategic Recommendations