Start with proprietary data. For a start-up considering taking the long journey to building an enterprise software company, it is imperative to have a relevant and hard to replicate data set. There are several ways start-ups tackle this problem. The first way is to convince a Charter Customer to provide their operational data in exchange for an economic incentive for being the Charter Customer (Tensility’s 2DA Analytics did this to get started). The second way is to devise a strategy to gather and create your own data set. This is particularly useful if the process or behavior you are trying to model is not stored in any enterprise operational systems (Tensility’s Triggr Health is a good example of this path). A third way is to leverage research sponsored projects in exchange for an economic incentive (Tensility’s Health DataLink proved out their product in this fashion). Other less valuable ways to gain access to data, such as procurement of 3rd party data services or gathering publicly available sources, lead to unattractive start-ups because the primary data set can be easily replicated.
Develop models to frame and validate the size of the problem. Leverage open source AI platforms to show your models are valid and provide the proper amount of predictive and/or prescriptive results. Problems requiring ML and Deep Learning platforms can utilize Google’s Tensorflow, Caffe from UC Berkeley, Microsoft’s CNTK, Tencent’s Angel, Baidu’s PaddlePaddle or MXNet by AWS/Baidu/CMU. For Natural Language Processing problems, open source projects like NLTK, TextBlob, Gensim or spaCy are good places to start. The goal of this effort is to develop a set of tested results that can be shown to knowledgeable prospects. This feedback will ensure you are headed in the right direction of solving a high value problem (Tensility’s Genivity did an outstanding job at this).
Design the workflow. A well-designed AI solution will have the ability to modify the current workflow in the enterprise and thereby realize the full potential of providing new capabilities to enterprise workers. Start-ups should map out the current workflow and roles of current users, then creatively and collaboratively move to a re-engineered workflow. The new workflow is likely to start with a “human in the loop” process to help the enterprise understand how and why the AI system works better than status quo.
Use good UI design to help with adoption. There will be some skeptics in the enterprise regarding using an AI solution. Good product design should identify the limitations and opportunities the enterprise has in making decisions currently. The UI design should provide visibility into data integrity, scenarios and reasonability testing by key potential users to improve adoption.
AI start-ups that integrate these four elements into their product design are likely to be more successful and require less risk capital to get started.