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Five Revenue Models That Can Drive Growth For AI Startups


Jason Schoettler is co-founder and managing partner of Calibrate Ventures, a venture capital firm that invests in visionary entrepreneurs.

Artificial intelligence (AI) is one of today’s most hyped technologies. Every startup, it seems, calls itself an “AI company.” And with widespread advances in data capture and manipulation, cloud computing and machine learning becoming low-cost and mainstream, that claim is not far from the truth. Many tech companies today do leverage some sort of AI. However, true AI startups — ones whose products are built on proprietary, computational, deep-tech AI — are just now emerging in larger numbers, finding markets for their technology among a wide range of industries eager to automate their businesses. Startups and larger tech companies bringing AI technologies to market still need to select the right revenue models to drive profits.

As an investor in automation companies for the past two decades, I’ve supported dozens of founders as they built their AI companies from the ground up, helping them hone their go-to-market strategies and business models. AI companies must tackle sales a bit differently than typical enterprise SaaS vendors, but their long-term recurring revenue prospects are excellent if they structure their models right from the get-go. That’s not to say there is one right way to sell AI software; in fact, there are at least five main revenue models that can work for AI companies. Here is an overview of each of these models in hopes these guidelines will be helpful to founders and investors alike.

• SaaS Subscription: This revenue model is the classic used by most enterprise software companies, and it can work very well for AI companies, too. There are, of course, many sub-varieties of the SaaS subscription model: monthly per-seat, monthly all-you-can-eat, annual, etc. For AI companies that offer a specific, vertical application, such as those in HR, healthcare, education, real estate or logistics, this model might make the most sense. Most of these users are accustomed to paying for software via a SaaS subscription model.

• SaaS as a Service: For AI companies that provide a one-time or annual service, this revenue model is rising in prominence. For example, robotics companies that have built sophisticated AI software on the backend often sell their solutions as a service. This could be a robot that weeds farmers’ fields twice annually or one that picks apples in the fall. It could be a robot that measures the floor layout on a building project. SaaS as a Service also works well for AI companies that are software-only but provide similar one-time services. For example, it could be an AI application that is used to train a self-driving robot; once the robot is trained, the service is no longer needed on a regular basis and may only be used once a year to give the robot an update, for example. This model works well if the target customer is accustomed to paying for a service, such as a farm paying x dollars for Y tons of harvested apples.

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• Usage-Based: Similar to SaaS as a Service but a bit more nuanced, this model allows AI companies to charge for their software based on pre-defined usage metrics, such as number of queries, volume of data served, number of models created or other consumption-based measurements. AI companies in retail, financial services, engineering and autonomous vehicle modeling are well-placed to adopt this model successfully.

• Transactional: Though not used all that frequently, transactional models are rising in prominence among AI companies. This revenue framework means an AI company is paid a fee for each transaction that takes place on its platform or each time its software is used to complete a discrete action. AI companies in the HR, retail and insurance sectors are successfully using the transactional model.

• Revenue Share: Few AI companies are using a revenue share model today, but it’s likely to gain traction in the years to come. For AI companies in marketing, advertising or e-commerce, for example, they could earn a percentage of each completed sale that originated on their AI chatbot. A subset of this model is the “cost-savings share” whereby an AI company is compensated when a customer saves money due to its AI platform. In customer service, healthcare or insurance, for instance, if an AI application results in a customer not phoning the call center, choosing a lower-cost plan or opting for an in-network provider, the cost-savings associated with these actions could then be shared with the AI company.

All of these models can be combined in unique ways, with many AI software companies opting to use two or more of them. And, every day, I meet with AI company founders who are thinking outside the box on how to monetize their technologies. Some robotics companies are giving away or renting their hardware at low rates in exchange for commitments to subscribe to their AI software for 12 months or longer. Some are offering free versions of their core software and charging only for enterprise-grade services. If you’re the founder of an AI-driven software company, consider what models you are using to generate revenue from your technology.


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