A Best Practice Approach to Insight Engines: 5 Levels of Insight Engine Maturity

Enterprise search projects start with intentions to provide ‘Google for our organization’ but too often fail to deliver on that promise. In our experience, these projects fail due to a lack of sustained effort and governance. The commercialization of next-generation search technologies allows you to fulfill this promise if you take a systematic approach to implementation.

Embracing the Era of Insights for Search

We are witnessing a paradigm shift in technology where it is possible to deliver unique search experiences to every user at every touchpoint. Driving this shift is a collection of technologies that industry analysts refer to as Insight Engines. Insight Engines, as defined by Gartner, serve to “augment search technology with artificial intelligence to deliver insights—in context and using various modalities—derived from the full range of enterprise content and data.” They embed AI technologies within traditional search infrastructures. This embedded AI makes them aware of the context from which a user is searching while understanding where users (should) want to go.

Insight Engines are a collection of component technologies rolled into platforms, rather than a singular technology. Subcomponent technologies to an Insight Engine can include Natural Language Processing, Machine Learning, and Artificial Intelligence. These technologies are game-changing, but their impact is realized when implemented through a systematic approach that is focused on how they can improve a user’s search experience. When focused on a user, their journey, and the fact that searching is not what they intended to do when visiting your website or using your application, you deliver better outcomes. Better outcomes for users drive improvements to all other business metrics.