I have seen people struggling to develop AI products because the common methodology to digital products development does not work. It requires some tuning. In this article I will explain two ways to prepare yourself for launching successful B2B AI startups .
To develop a traditional software product, interviews with potential users and preparing clickable wire-frames might be sufficient to scope out a desirable product, after which you can jump into writing the code.
But AI systems require domain understanding, code and data. If you have an idea for, lets say, automating the marketing companions or optimizing capacity planning for a logistics network, you need not only the domain understanding but also marketing campaigns data or logistics data to train a model. This kind of data generally is not available publically and very fractured in nature. This is why big clouds did not launch the AI solution as a service yet. For example look at AWS, Azure and GCP’s AI managed services such as chatbots, personalizations, forecasting etc.
So How do you you get the data that is not easily available and problem domain is relatively small and specialized?
For business-facing (B2B) AI products, it’s often difficult to get the data necessary to build a prototype because a lot of highly specialized data is locked up within the companies that produce it. There are a couple of general ways in which AI teams can get around this problem:
1. Get the Data by providing niche consulting & services.
Some AI teams start by doing NRE (non-recurring engineering services, or consulting) work, in which they build highly customized solutions for a handful of enterprises. You may think this approach doesn’t scale, but you can use it to obtain enough data and domain understanding to learn the lessons or train the models needed to build a repeatable business. Given their need for data, AI startups seem to take this path more often than traditional software startups. I have seen this happening especially in large fragmented industries like healthcare.
Continue reading the rest of the article at my personal blog.
