Today, businesses are entering into a new era ruled by data. AI, specifically, is gradually evolving into a key driver that shapes day-to- day business processes and Business Intelligence decision-making. 

Thanks to advances in cognitive computing and AI, companies can now use sophisticated algorithms to gain insights into consumer behavior, use the real-time insights to identify trends and make informed decisions that give them an edge over their competitors.

BI evolution: from reactive to proactive analytics

The proliferation of new big data sources, including smartphones, tablets and Internet of Things (IoT) devices, means business no longer wish to be weighed down by huge chunks of static reports generated by BI software systems. They need more actionable insights.

This is inspiring a move away from reactive analytics to proactive analytics that offer alerts and real-time insights. These analytics allow the companies to make better use of their operational data while it’s fresh and actionable.

Over the years, BI software has evolved into three essential areas:

  • Descriptive analytics – The most straightforward BI system that summarizes data and informs what happened. It does precisely what the name implies: description. It summarizes raw data and breaks it down into something that can be interpreted by humans. Descriptive analytics enables companies to understand past behaviors and learn how it can influence future outcomes.
  • Predictive analytics – This “predicts” the future. Predictive analytics enables companies to have future insights. Although no statistical algorithm can give 100% prediction, organizations are using these analytics to forecast future events. This system relies on “best guesses” since its foundation is based on probabilities.
  • Prescriptive analytics – A relatively new but robust field that enables users to prescribe various possible actions and advise accordingly towards viable solutions. Prescriptive analytics is all about providing advice. These AI-powered analytics not only predict what will happen but also explain why it will happen.

The enormous progression in analytics and BI tools indicates that businesses are requiring more mature decision-making. Recent business digitization aims at getting to prescriptive level of analytics.

AI is excelling in the business arena

AI has evolved into that “can’t do without” technology in the modern business landscape. Small to large enterprises are leveraging this technology to improve the efficiency of business processes and deliver smarter, more specialized customer experiences. The question is, how is artificial intelligence changing the scenes of today’s business environment?

  • It’s rapidly transforming different industries. AI is quickly changing heavily regulated industries like healthcare, financial services, life sciences and the trading industry. For instance, in medicine, AI is taking the roles of clinical assistant to help physicians make faster and reliable diagnoses. It’s also accelerating the creation and discovery of new drugs and medication.
  • AI is powering modern decision-making. Artificial intelligence is impacting all aspects of modern businesses. Prior to the renaissance of AI, leaders had to depend on incomplete and inconsistent data. Today, AI feeds on big data, chews it and then breaks it down into actionable insights that aid executives in their decision-making processes. For example, a marketing manager must understand their ever-changing customer needs and align products and services to these needs. AI simulation and modeling techniques provide reliable insight into buyer personas. Therefore, these methods are ideal for predicting consumer behavior.
  • AI offers better insights than ever before. AI is, essentially, automation of the maximum sequence of decisions originating from prescriptive analytics. Its intelligence comes from its ability to give real-time feedback data to enhance prescriptive models. This ensures that the next prescribed decision will automatically be better than the previous. This exceptional ability to adapt and learn enables AI to execute actions following automated decisions. As organizations continue generating more data, the analytical might of AI will help power the next phase of decision-making and profitability.

Why do businesses desperately need AI-powered BI systems?

AI-powered BI systems can transform your business data into simple, accurate, real-time narratives and reports. Let’s look at why this is especially important. 

  • Dashboards are not enough. What happens when you have data blasting your BI from different sources? This is where businesses need AI-powered BI tools that help digest all the data and deliver tailor-made insights.
  • Avoid a big data overload. Big data is growing at an unprecedented rate and can easily gag the operations of the business. But, investing in AI-powered business intelligence software can help companies break down data into manageable insights.
  • Get insights in real time. The staggering speed and growth of big data in the market makes it hard to make strategic decisions on time. However, thanks to leaps in AI, Business Intelligence tools offer powerful dashboards that give managers alerts and business insights they need for key decision-making.
  • There’s a shortage of experts. According to McKinsey, there is a shortage of professionals with data analytical skills in the United States. Moreover, there is an acute shortage of about 1.5 million analysts who can make informed decisions based on data. Employing data experts in every department within an organization is very important. However, even if a company can afford to do so, making timely decisions with data requires the right processing software.

AI-powered software has brought tremendous changes in the business world. Although the future remains hazy, companies must remember to embrace AI-based BI tools that will keep them competitive in the tech-powered business landscape.

Like this article? Subscribe to our weekly newsletter to never miss out!

Previous post

AI: Beyond the Hype and Into Reality

Next post

Machine Learning for Connecting Organizations: A Conversation with Roger Gorman