Enterprise AI Business Model

An Enterprise AI business model is a product/service company that leverages its infrastructure, platform, or software to provide applications that enable big data, intelligence, inventory management, and many other use cases to drive digital transformation. Currently, Enterprise AI business models are usually driven by an “as-a-service model” that gives unlimited usage, and in other cases, with a consumption-based model or both (hybrid).

Enterprise AI Business Models Case Studies

Sumo Logic is a cloud-based Continuous Intelligence platform, based on a subscription revenue model, serving primarily customers from IT departments, development, and security, both from small organizations and enterprise, leveraging on self-serving digital channels for new customers’ acquisition, coupled with inside and field sales representative to expand the adoption of the platform.
Palantir is a software company offering intelligence services from governments and institutions to large commercial organizations. The company’s two main platforms Gotham and Foundry, are integrated at the enterprise-level. Its business model follows three phases: Acquire, Expand, and Scale. The company bears the pilot costs in the acquire and expand phases, and it runs at a loss. Where in the scale phase, the customers’ contribution margins become positive.
Unity is a platform for 3D content development, free for companies below $100K in revenues, and subscription-based for companies beyond that. It also makes money on a revenue-share basis with its Operate Solutions helping creators monetize their 2D and 3D content across several platforms. Unity also generates revenues through revenue-share arrangements with strategic partners and within its Asset Store marketplace.
Snowflake is a cloud-based platform whose vision is to enable organizations to have seamless access to explore, share, and unlock data value. With the mission to break down data silos. The company runs through a consumption-based revenue model, enhanced by its professional services. Primarily an enterprise solution, Snowflake leverages on direct sales.
Another example of an Enterprise AI company, C3.ai, which leverages its proprietary technology to build a “model-driven architecture” that offers final customers the ability to use its SaaS products at the enterprise level. In this way, C3.ai can drive faster deployment and testing of enterprise projects for its customers/users, thus preventing the overhead and typical time-consuming execution timelines for enterprise projects.
(Image Source: C3.ai Prospectus)

IaaS vs PaaS vs SaaS As Primary Enablers Of AI Enterprise Business Models

The “as-a-service” models are typical of the second wave of the Web 2.0, built on top of cloud computing. Indeed, these models’ basic premise is to offer a solution to the final customer without having to host it on-premise, with complex implementations and large overhead. Yet while PaaS and IaaS are skewed toward development teams. SaaS has wider applications toward end-users, also in non-technical departments.

Read Next: IaaS vs PaaS vs SaaS

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