In engineering businesses where something is manufactured or assembled, specifications tell suppliers qualities and characteristics of the material they or customers require. Specifications could be based on physics (temperature ranges) or business objectives (material preferences that allow them to achieve cost efficiencies at scale). Specifications are also one way for businesses to make data-driven process improvements, like optimizing supply chains.

Businesses often ask:

• “Given our set of specifications, can we reduce or combine them and still meet our customers’ engineering needs?”

• “Can we do so while optimizing our supply chain by saving time?”

• “Can we simultaneously minimize the diversity of activities we might need to support in the future?”

This example represents an important use case I’ve encountered in many places where artificial intelligence can provide quantifiable value. Experts typically capture their knowledge and reasoning about complex knowledge like specifications in unstructured text (comment fields attached to documents, manually written reports) to draw upon later. However, standard search technologies and data-mining solutions tend to fail when required to retrieve that knowledge. Search technologies struggle to account for relevant factors that are most important for specific events, like which factors are relevant in different engineering specifications.

To allow AI to help experts make better decisions and answer critical questions about engineering specifications, the AI solution first needs to learn how to leverage the knowledge coming from the expert. And that knowledge is usually not just book knowledge — it is heuristic and experienced-based knowledge gained from many years on the job. Below are three ways using expert knowledge in our AI system helped answer questions and optimize the business:

1. Expert Knowledge Allows AI To Convert Text Into Useable Data Points

AI applications are challenging to build because businesses and users rely heavily on experience-based knowledge obtained through years of solving similar challenges in different situations. AI applications must achieve something functionally similar, even if they do so in a different way. Experience-based knowledge is similar to our visual system’s ability to identify and understand complex patterns in pictures: We quickly identify concepts and patterns in a picture and, through our prior experiences of similar patterns, infer what might have led to the image and simultaneously predict what might happen next to fully understand the picture.

Our brain can look across experience-based knowledge captured in text in a similar way to associate prior experiences, identify the most important factors in the current situation and suggest the best solutions to problems. However, with expertise captured in text in a business, we first must identify meaningful concepts, relationships and patterns from the text and documentation. Natural language processing is an AI technique that experts can train to recognize from documents important entities, events and relationships between those events and entities. By allowing the expert to label important entities and their relationships, NLP technology helps turn knowledge from these documents into something computers can process: useable data points that represent experience-based knowledge.