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.
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