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.

AI products and services require a deep understanding of the business domain. For example, for people working in finance and marketing domains, it's essential for them to understand those fields well. To be able to build a smart marketing app, you need to be right marketing expert who has a deep understanding of the marketing tools and techniques even though he/she may not know much about AI. The goal of AI is to take existing things and make them better and first you have to be good at the domain itself.

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. For example, if an enterprise is involved in creating a good chatbot, it would need customer support executives who can train the AI model on the various facets of customer interactions. In the bare minimum, it is required that business executives have a minimum conceptual understanding of AI to understand where they can add value. 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.

One of the leading challenges that data scientists face is understanding the features to be built into machine learning models. So, if businesses have to develop a model which is an AI model, data scientists will need to understand the various business drivers and data elements need to be accommodated. Typically, it is the subject matter experts who have this deep understanding, not data scientists. 

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. 

There is a process in the model development life cycle where data scientists have to validate and train the model. There again, the subject matter expert in the domain can provide with the right types of data. Finally, you may have to use the tools that data scientists use to see if the model works for a given business problem. Again, here, domain experts come into the picture who understand the result when a specific set of inputs are given. 

Outlook

Non-technical employees like domain experts and project managers can, therefore, be translators who can bridge the gap. There are multiple entry points into an AI project for such non-technical people. Combining a basic understanding of AI systems with domain expertise, and you may be in good shape to handle the challenges of reskilling that dawn upon us. One crucial thing for non-technical employees is to take some basic training in artificial intelligence and make use of great courses out there which can help them take the first step towards AI from a business perspective.

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