As data plays an increasingly vital role in business decision-making, the competitiveness and profitability of any enterprise largely depend on its ability to collect, process, and analyze data. Without comprehensive analytics, teams can hardly achieve quality customer service, robust cyber security, and supply chain transparency required to establish a successful business.
Fortunately, the adoption of business intelligence (BI) allows enterprise managers to obtain all the tools they need to turn chaotic data into meaningful business insights. In particular, a team can develop a corporate BI solution on their own or resort to business intelligence services to arrange corporate data flows and transform scattered information into visually compelling reports.
Despite the proven value of business intelligence software, it still lacks one essential capability. While BI solutions can collect and process data, by default, they don’t provide human specialists with any recommendations or actionable steps; thus, team members have to spend time and effort on interpreting the data. As a result, the decision-making process becomes too long and subject to human error. Given that each year, the volume of business data grows drastically, the workload and the chances of making mistakes may only increase.
The good news is that artificial intelligence can help enterprises solve this challenge. Implementing AI into business intelligence, it’s possible to add human-like capabilities to BI software and enable it to make conclusions and offer actionable decisions. Thus, the combination of BI and AI can not only improve data quality but also continuously provide enterprise managers with valuable business insights.
Now let’s discuss how exactly enterprises can apply AI to their business intelligence.
Table of Contents
ML models and algorithms
In short, machine learning technology (a branch of AI) enables software to perform tasks autonomously, making digital solutions capable of human-like intelligence. Also, ML-powered software can recognize even the tiniest connections between data sets and then offer meaningful insights based on the analysis; all this together makes machine learning perfectly applicable to enterprise business intelligence.
As mentioned above, enterprises adopt BI to understand their business data better and turn chaos into insights they can act upon; ML helps make this process faster via workflow automation and optimize data management and decision-making processes across all departments. For instance, using ML-powered business intelligence, sales and marketing teams don’t have to turn to data analysts to gain some insights — the software can interpret data with no outside help.
Intelligent predictive analytics
Enterprises adopt predictive analytics software to review historical data and predict market conditions, customer behavior, sales, and many other parameters. Powered by AI, predictive analytics solutions can make more accurate forecasts. Using such software, teams can automatically receive clear insights even from raw data sets.
Powered by ML, predictive AI models can continually learn and adapt, thereby improving data quality and enabling clearer predictions. As a result, enterprises can significantly improve the effectiveness of marketing campaigns, deliver better customer experiences, continuously track suspicious activity to enhance cybersecurity, and mitigate multiple business risks.
Besides, intelligent predictive analytics can help reduce operating costs and enable better resource and asset management, and here’s how it can work in practice. For instance, there is Black Friday ahead; during these days, ecommerce enterprises will have to allocate additional labor and operational capacity to cope with the growing workload. An AI-powered predictive analytics solution can help teams analyze historical data to automatically determine the amount of workforce an enterprise may require in the coming days.
In recent years, chatbots have evolved significantly. They are no longer dummies who understand only the most straightforward queries. Coupled with AI, conversational bots can understand text, human voice, and images to deliver a better customer experience; what’s more interesting, these capabilities may also be valuable for business analytics.
For example, AI-powered bots can analyze a customer’s previous purchases to suggest more relevant options in a conversation and thus provide an advanced level of personalization; simultaneously, smart bots can gather customer data to help enterprise managers perform market research.
Unlike AI, which is about simulating human intelligence, augmented intelligence aims to improve the capabilities of human specialists; in other words, augmented intelligence doesn’t replace humans but rather makes them smarter and more productive. This way, enterprises can enable both humans and machines to work together and combine their strengths to achieve more remarkable business outcomes.
Here is how it may work in practice. Augmented intelligence solutions can gather unstructured and structured data from multiple sources and present this data in such a way so that employees can get a comprehensive view of each workflow or customer. As a result, team members can be aware of all work processes and understand how these processes can affect customers to foresee all possible opportunities.
Today, data plays a critical role in enterprises across multiple verticals as the ability to make sense of data is a must to remain competitive. For this reason, in recent years, most future-oriented enterprises have been continuously implementing BI solutions. Since BI software allows teams to collect, process, and visualize data, these tools have become essential for making viable business decisions.
The emergence of AI and machine learning technologies makes business intelligence systems smarter and more efficient. Using intelligent predictive analytics, smart business assistants, and augmented intelligence, enterprise managers can gain a 360-degree view of their workflows and customers and discover business growth opportunities.
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