Most AI vendors develop solutions that target broad use cases with large markets. This is because investors have shown they are only interested in a target market if it is worth several billion dollars. Therefore smaller markets have been excluded and AI vendors build one model and market it to a large set of disparate users. For example, a company selling a vehicle detection system would normally build a single model to detect all types of vehicles across multiple use cases and geographies.
Challenges with Broad Reach AI Solutions
Broad-reach products result in lower model accuracy and erode public trust in AI’s capabilities. They also require that humans remain in the loop for verification, consuming more human resources and increasing the overall cost of the solution for customers.
Why the Investment Community Focuses on Broad-Reach AI
Niche solutions are very costly to produce. In order to make a model for a niche use case, you need data that is very specific to it. And collecting data while addressing all of the relevant regulations and security concerns is a big challenge.
Even if a vendor is able to develop a model for a niche use case, the challenge isn’t over, because an AI model is rarely a standalone solution. For example, in the case of vision AI, critical components include:
- Camera setup and management
- AI Model management and updates.
- Dealing with video storage and data retention policies
- Alerts and notification rules
- Role-based access control for users
The bulk of an AI vendor’s time goes to not just running the AI model but building the rest of the software stack to handle the devices and other services that make a complete solution. Plus, there are compliance and security issues to consider.
How Can AI Vendors Overcome Challenges to Bring Niche Solutions to Market?
- Build a customer council with friendly customers. In order to handle the data-collection challenge, AI vendors should aim to find friendly customers who can help provide some of the necessary data and put the right structure in place for data collection and management.
- Avoid building from scratch. Some vendors decide to build everything themselves using existing software libraries and core infrastructure services from a cloud vendor. This approach provides complete control over the design but can take almost a year to build. The goal should be to build a solution quickly, take it to market for testing and optimize later. Look for platforms that enable quick transition from AI model to full solution.
- Build an AI/ML pipeline. AI vendors can build pipelines that allow them to quickly train models on specific data sets. Design a pipeline so that the data used to build specific models can be easily tracked in order to make it easier to add new data from customers as it is available.
The Bottom Line
There’s a lot of talk about democratizing AI to make it more available to more user organizations. Currently we have many broad-reach AI models on the market for things like human detection and voice recognition. But the models are so generic that they run the risk of being inaccurate. To increase precision and accuracy of AI, and to make it usable to a wider range of organizations, we must enable a long tail of AI models that are designed for niche use cases. Although the current cost of developing such niche models and taking them to market is currently too high, we must find ways to break that barrier.