Getting Factual Answers to More Difficult Questions

woman typing a search on her laptop using enterprise and AI search

Every day, businesses and organizations are tasked with making more decisions than any human could ever hope to handle. Often, enterprises need to make complex business decisions with limited information on hand. With the help of AI-based products and AI-driven enterprise search solutions as a critical enabling technology, leaders can make better strategic and informed decisions by gaining insight from a vast amount of data in a short period. With the assistance of custom dashboards and 360-degree views of data, employees with different roles can each have a single view into all the information they need at its most appropriate level. The key message for companies is that making the right decisions and fast decisions are not either-or anymore. In today’s landscape, both are equally critical. Thus getting factual answers to more difficult questions is key.

Getting factual answers

The initiatives businesses undertake and the data required to solve their problems are why holistic 360-degree views of information are becoming necessary for everyday use. All the information needed from various departments has to be available in real-time to make savvy business decisions. The view of the data needs to be all-encompassing so that nothing slips through the cracks. This is necessary because different departments work in silos, and making decisions becomes challenging if the knowledge and insight are incomplete. 

Complex questions like “show me what we know about a customer?” are the challenges businesses are clamoring to solve faster than their competitors. Other problems can also be answered by searching for a full view of a project and its status. Also, essential maintenance notes and other documents can be used to show what an employee needs to know, given their context and activity around part of a product or initiative. The solution to this is holistic views (360-degree views) on digital twins, which assist users in specific situations. Getting factual answers quicker will ultimately provide better decisions.

Even simple use cases, like when product names change, it can be tough to gather past info related to the prior product name. Only after gaining sufficient knowledge about a topic that is complete with old and new versions of product descriptions are we able to apply it to our attempt at solving problems. A good illustration of this is the concept of first-principles. First-principles can be talked about, developed, tested, and iterated upon. They give organizations the ability to solve problems faster by avoiding the question: “What’s wrong?” or “What can we learn from this?” Instead, generating better decisions leads to causing further insights from getting factual answers: “What can I do to solve this for my customers?” “What will this outcome bring to my innovative service?” Organizations that apply first principles thinking with their tendrils find themselves at the forefront of innovating quicker and with greater insight at every level.

Collecting the correct factual information is a critical challenge

Modern, innovative Enterprise Search solutions (e.g., Insight Engines) that are AI-focused are about much more than searching and finding. The newest iterations of search solutions are now focused on collecting and connecting information to answer questions.

Having results and answers easily retrievable via an intelligent search engine can help managers in various situations such as executing the merger or acquiring another entity, identifying and evaluating a new technology investment, evaluating possible subcontractor relationships across functional boundaries, or preparing a response to a changing regulation issue. However, it’s also about taking it one step further and gaining visibility into how all of the data works together to deliver a holistic view of a problem, issue, or situation to complete a task without any vital knowledge or facts getting lost in the shuffle. 

Getting the best out of new digital twin technology

Unlike other technologies, a digital twin can be built for an individual asset, a whole organization, or an entire enterprise.

A digital twin is a duplicate of an actual product or a software model of a system or plant. To share an example of how this works, with AI and machine learning, entire workflows and processes can be simulated in this way, and new insights gained. Unlike other technologies, a digital twin can be built for an individual asset, a whole organization, or an entire enterprise. Complex interrelationships can be quickly identified and clearly shown, changes can be simulated, and possible effects and reactions can be checked in advance. It is critical to measure a twin’s impact in two time periods: before and after the twin is in place. It is best to do this to generate a business case for its continued use. 

A necessary part of every organization

AI-driven enterprise search is a necessary part of a modern enterprise’s infrastructure. Companies that have deployed and implemented these solutions can connect data sources, build and maintain relevant relationships, use pre-built queries, and provide visibility of information in multiple formats into the context of the business and across teams. For most companies, data is siloed and rarely shared with teams who could use it to make informed decisions. In siloed data environments, information flows through various channels, making it harder to find, know, and act on essential insights in the abstract. Enterprise search solutions help organizations assemble and combine diverse data sources to identify meaningful and actionable insights quickly. AI methodologies fused into search solution functionality are the answer to elegantly and efficiently handle the complexities within today’s business challenges. Getting factual answers is a a true necessity.


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