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, inventorymanagement, 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).
An Enterprise AI business model involves offering products or services that utilize infrastructure, platforms, or software to provide applications for various purposes.
OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemium model. Microsoft also commercializes opener products through its commercial partnership.
Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.
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 โas-a-serviceโ 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 for end-users, also in non-technical departments.
One of the first mentions of customer lifetime value was in the 1988 book Database Marketing: Strategy and Implementation written by Robert Shaw and Merlin Stone. Customer lifetime value (CLV) represents the value of a customer to a company over a period of time. It represents a critical business metric, especially for SaaS or recurring revenue-based businesses.
AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.
Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.
The business intelligence models have transitioned to continuous intelligence, where dynamic technology infrastructure is coupled with continuous deployment and delivery to provide continuous intelligence. In short, the software offered in the cloud will integrate with the company’s data, leveraging on AI/ML to provide answers in real-time to current issues the organization might be experiencing.
That is a process that requires a continuous feedback loop to develop a valuable product and build a viable businessmodel. Continuous innovation is a mindset where products and services are designed and delivered to tune them around the customers’ problems and not the technical solution of its founders.
Technological modeling is a discipline to provide the basis for companies to sustain innovation, thus developing incremental products. While also looking at breakthrough innovative products that can pave the way for long-term success. In a sort of Barbell Strategy, technological modeling suggests having a two-sided approach, on the one hand, to keep sustaining continuous innovation as a core part of the businessmodel. On the other hand, it places bets on future developments that have the potential to break through and take a leap forward.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
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