AI everywhere has the potential to improve operations, enhance customer engagements, and speed the time to insight. Learn how AI embedded into business processes enables the greatest benefits and how to identify and overcome the challenges.
To make faster, smarter decisions, businesses are adopting increasingly sophisticated analytics methods. The transition that is occurring reflects a shift from reporting historical data to making predictions with artificial intelligence (AI). Beyond basic reporting and dynamic dashboards that present business intelligence information, telling companies what has happened or is happening, companies are now using predictive analytics and prescriptive techniques based on AI and machine learning (ML) models that do much more.
The transition from descriptive to predictive analytics and AI is being driven by a need to be less reactive and more proactive. Analytics based on AI and machine learning models simply provide better insights. They build on the benefits of predictive analytics by complementing human decision-making. Often, such analysis is used to recommend one or more courses of action and show the likely outcome of each decision.
For example, a financial services company might use predictive analytics to identify what percentage of customers is most likely to fall behind on loan payments. In contrast, an AI-based analytics approach might provide guidance as to which debt relief approach to use on a specific customer or set of customers.
The real benefits come when such predictive analytics and prescriptive capabilities are embedded into business processes and are used to provide continuous intelligence (CI), allowing for decisions to be based on events that are happening in the moment as well as historical data, enabling actions to be taken in milliseconds to minutes.
CI offers a way to extend advanced analytics applications into the realm of decision support and decision automation. By processing event-based and streaming data, businesses can understand what’s happening now and react rapidly. Running prescriptive analytics, ML, and AI algorithms against streaming data can derive actionable information. That information can then be used by systems to decide what to do next and enable the systems to take actions automatically.
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