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Solving Business Intelligence Problems with AI


AI is already having a significant impact for the U.S. government, including defense and intelligence community use cases, and is also providing game-changing capabilities for global enterprises in a range of industries, including financial services, life sciences, and technology.

In a presentation at Data Summit 2019,  titled “Solving Business Problems in Government, Financial Services,” Amy Guarino, COO, Kyndi, offered real-world examples of how AI is driving measurable benefits in a range of industry sectors, and discussed the importance of explainable AI to regulated industries like financial services and healthcare, where being able to justify the reasoning behind algorithmic decisions is essential.

In government, AI is being used by defense specialists to determine which academic papers on new technologies are pertinent so they can spend less time reviewing papers that are not relevant and deliver results faster. In life sciences, it is helping to uncover hidden relationships and insights in the forest of big data, and in financial services it is helping organizations improve customer service, improve fraud detection, and speed up processes such as mortgage and credit card applications.

AI has reached a turning point, where companies are not considering whether to use AI, but when, said Guarino.  

They are doing it for competitive reasons and because they want to be positioned well competitively for the future, and increasingly, as time goes on and AI becomes more accepted, there will be gaps between the have’s and the have-not’s in the future.

In addition, unstructured data is a growing problem, creating workflow inefficiencies costing enterprises $3 trillion, said Guarino.

Guarino suggested that organizations just getting started with AI, spend time defining a problem where technology can help:
Think about where AI can be injected to make decisions faster, what you could do if you had more time, more people, if your best expert on the problem, or were able to do something faster so it was closer to real time.

Those are examples of questions you want to ask in order to determine how to get started, Guarino advised.

Kyndi's technology uses a combination of natural language processing, machine learing, and knowledge graphs which means that results are explainable because you can go back tgo the provenance of data, or root information, Guarino said.

Many presenters are making their slide decks available on the Data Summit 2019 website at www.dbta.com/DataSummit/2019/Presentations.aspx.


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