There is a tremendous value that domain experts can add to AI systems, in their interactions with data scientists.

Most artificial intelligence projects are in very early stages across various industries, and people are trying to figure out how they can best derive value from the technology. One strategy is to buy AI-powered versions of existing apps, and the other is to start building domain-specific models which have AI technologies unique to the business offering. Here, without any technical expertise, there is a tremendous value that domain experts can add to AI systems, in their interactions with data scientists.

Ultimately, AI is only going to model the intelligence of domain experts in different business functions and therefore finding subject matter experts whether, in finance, marketing or sales is critical for AI to bring the desired innovation. With such extensive use cases, AI systems will need billions of quantifiable parameters from domain experts.

Bridging The Gap Between Data Scientists And Desired Business Outcomes

We need subject matter experts to know what goals a business is shooting for, what data to potentially add and provide a feedback loop. A data scientist can only have an intuition about whether the model is working and whether the users are satisfied. Domain experts can only know whether the application of AI has improved a business function or not.

Understanding what it is that influences the decision-making process in the business domain, it will be best left to domain experts, and data scientists will need to interface with continuously to improve models. Subject experts understand the decision features, the decision influence and the business characteristics and translate it to data scientists.