Decisioning Applications Must Evolve Through Learning

Decisioning processes are based on your organization’s understanding of its unique capabilities and the environment it is operating in.  Decisioning is powered by the knowledge you develop through the experience of your experts and analysis of pre-existing processes and user interaction.    However, the organization and its environment are constantly changing and the best practices and supporting information must evolve to keep up.  Developing an application to automate decisioning processes is only half the battle.  Keeping it up to date and constantly learning from how it is best used is the factor that will actually lead to long-term success and a return on investment.

LogicNets provides a unique visual approach towards capturing and managing data so that the organization can efficiently learn from the experts.  This visual approach is efficient for both creating and, more important, continuously enhancing processes as the experts themselves learn about better ways.  However, the LogicNets system is able to enhance the learning process substantially through machine learning and heuristics.   The system tracks every step of decisioning processes as they are accessed and used by users and external systems in production.  The resulting history can be dynamically analyzed and the system can automatically determine and highlight decisioning processes that are effective versus those that are not.   LogicNets’ predictive diagnostics component allows this machine learning to be automatically integrated into applications to progressively improve guidance.

LogicNets enables a range of machine-assisted learning functions that provide a unique value to the organization because they unite machine learning with human learning.  While applications can use predictive analysis to optimize the decision pathways taken by application users, the platform also empowers the experts to fully understand how their know-how must be adapted over time.