SaaS products built without data and AI offer generalized solutions to their customers. We are in the business of creating AI based business applications and one common theme I have seen across the board is for people to have this notion that AI will solve all their problems instantly. The reality is AI businesses more closely resemble a services business or consultancies because they provide solutions that become tailored to that customer’s specific needs. Like services providers or consultants, an AI product improves as it knows a customer better.
AI businesses are not scalable right out of the gate: AI models take time and require data to train. Moreover, not all AI businesses will scale. Here are the metrics you can use to tell the difference early on.
It is very difficult to build a high-performing MVP version of an AI model without data from customers. In order to demonstrate value right out of the box and be competitive against other vendors, you might automate which processes you can right off the bat using a rules engine, and provide a human operator to perform the rest of the work while simultaneously labeling the collecting data in order to train the AI.
The ratio of human interventions over total automated tasks should be decreasing over time.
Should increase over time. With AI products once the AI’s performance ramps up, it could very quickly exhaust all low-hanging fruit opportunities. If the AI cannot continue to provide value to the customer, the difference in value from one renewal cycle to the next may seem stark to the customer, who may decide to not renew.
AI products incur more significant rev-up costs than a typical SaaS product rollout and may have as much impact on margins as customer acquisition costs (CAC). You should carefully track how much time these rollouts and ramp-ups take, and how much it costs for each new customer. If there are true data network effects, these numbers should decrease over time.
- Accessibility: how easy was it to get?
- Time: how quickly can the data be amassed and used in the model?
- Cost: how much money is needed to acquire and/or label this data?
- Uniqueness: is similar data widely available to others who could then build a model and achieve the same result?
- Dimensionality: how many different attributes are described in a data set?
- Breadth: how widely do the values of attributes vary, such that they may account for edge cases and rare exceptions?
- Perishability: will the data be useful for a long time?
AI models perform better with more data, but that performance may plateau over time. You should take care to track the time and volume of data necessary to achieve an incremental unit of value for your customer, to make sure that the data moat continues to grow.
The higher upfront work necessary to launch an AI business means that most will look more like services businesses. A small subset of AI startups will resemble SaaS businesses from the beginning, before AI is deployed in the product. In order to collect data for their AI models, some businesses first sell SaaS workflow tools and can even achieve meaningful revenue from that workflow tool alone. By SaaS metrics, that company may be blowing the competition out of the water. Without the reinforcement loop generating a compounding volume of data and an increasingly powerful AI over time, however, that company’s product remains vulnerable to copycats and will eventually be commoditized.
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