The intellectual capital represented in organizational policies, processes, and business models can entangle managers in a complex web of problems—with resulting inconsistency in decision-making.

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The traditional method of solving difficult problems is the use of experts who analyze facts, apply rules, create solutions, and make decisions. Because experts have limited availability and can only solve one problem at a time, this method of making decisions does not scale when problems are time-consuming, numerous, time-sensitive, complex or difficult.

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To address this “knowledge gap,” experts attempt to transfer knowledge, expertise, and knowhow by training non-experts to make the decisions but usually do not succeed for several reasons. Due to the varying intellectual capacity and analytic ability, knowledge absorption of the non-experts and the level of retention are limited. The knowledge retention is further reduced when the knowledge transferred is not used on a frequent basis. As a result, decisions are often incorrect or inconsistent even when analyzing the same set of facts.

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The best practice for solving complex, routine, and repetitive problems that are too numerous for an expert, is intelligent software created by experts that empowers non-experts to make expert decisions. This genre of software is aptly known as an “expert system” because it models how an expert analyzes and solves a problem.

 

Traditional expert system development methods require a team of programmers to gather knowledge from several experts and develop the software application. This is an inefficient process, referred to in expert system parlance as the “knowledge acquisition bottleneck.”

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The best practice for creating an expert system and avoiding this bottleneck is when an expert uses a software “tool” to directly input knowledge, expertise, and knowhow into software code known as a “knowledge base.”

 

Catalyst is a “no code” software platform that empowers one or more experts, who are not programmers, to quickly create knowledge bases in many domains of knowledge. The expert uses Catalyst to build decision trees and workflows for AI models, including questions, answers, scenarios, rules, decision tables.  Natural Language Processing understands user input (text descriptions) of the desired app's functionality and translates it into actionable steps to help build the AI model. “Explainable AI” functionality allows users and knowledge experts to understand the reasoning behind the app’s recommendations and decisions, fostering trust and transparency.

 

Knowledge bases built with Catalyst can be deployed into production immediately, even after an hour of knowledge input by the expert. InCap applications access Catalyst knowledge bases that analyzes facts provided by the non-expert and data stored within data bases, searches for fact patterns then suggests one or more appropriate solutions to the non-expert. The results may be displayed as text, reports, decisions or other desired output, all of which are correct, complete, consistent, and timely.

 

Reengineering inefficient processes, fine-tuning critical policies, modeling expert decision making, and creating an ideal model are required for the most advantageous application of the knowledge base. InCap’s professionals work closely with an organization’s experts to determine the most efficient methods to distribute and maintain this organizational knowledge base.