In the complex alphabet soup of business acronyms, the popularity of artificial intelligence (AI) has added a new term for business leaders to learn. Just one letter difference, business intelligence (BI) is another term often thrown around, and increasingly intermixed with discussions of AI. What is the difference between AI and BI? 

AI and BI: Unpacking the Differences

Fundamentally, AI involves creating systems that can perform tasks that would traditionally require human intelligence. These tasks can include reasoning, learning from past experiences, making sense of complex data, and much more. AI is about using machines to execute these tasks independently and more efficiently. 

Compare that definition to Business Intelligence. BI refers to the strategies and technologies that enterprises use for data analysis of business information. BI uses statistical and algorithmic analysis to provide historical, current, and predictive views of business operations, usually by accessing a data warehouse.

Combining AI and BI

The confusion between the terms likely comes from the increasing use of AI in BI systems. While BI systems are already designed to handle big data, AI brings a layer of technology that enhances the analysis process, almost acting as a built-in data analyst on top of BI tools. AI can sift through massive datasets faster and with greater accuracy than humans can. This synergy not only saves time but also helps decision-makers get insights faster at a lower cost.

There are many advantages to integrating AI into BI. The tools can perform data analysis and generate insights in real-time. AI is also far more capable of using and creating powerful predictive algorithms that give businesses the power to forecast future trends more precisely. 

The Impact of AI on Business Intelligence

With the proliferation of AI across industries and through technologies, AI is increasingly powering business intelligence tools and providing significant benefits. By supercharging the data analysis and visualization of data, AI makes BI software not only more powerful but also user-friendly.

AI technologies like machine learning and deep learning are able to analyze and learn from data at a scale impossible for humans. This provides not only increased accuracy in data analysis and insights but also opens up a deeper and more granular understanding of the data. Businesses can detect subtle patterns and correlations that go unnoticed in traditional BI systems.

In addition to providing more accurate and granular data analysis, AI tools are able to use advanced algorithmic analysis techniques, like predictive analytics and prescriptive analytics. Previously, only an advanced data analyst or data scientist would have the training to run these analyses, but AI gives entry-level staff that power. These methods can not only anticipate future trends and behaviors but can help businesses make decisions to lead to improved outcomes.

With the more accurate analyses using a much larger suite of analytical tools, AI in BI tools provides businesses with the instant insights that help decision-makers take immediate action. Getting real-time answers enables businesses to make quicker decisions, stay agile, and maintain their competitive edge.

Key Features of AI for Business Intelligence

AI enriches business intelligence systems with a range of sophisticated capabilities that transform raw data into actionable business insights. Here’s a look at some of the key AI features that are setting new standards for BI:

• Generative AI: The technology has entered the public conversation with chat tools Gen AI tools like ChatGPT and image tools Gen AI tools like DALL-E. This category of AI uses advanced models to generate new data instances from scratch by mimicking human writing, speaking, or image generation. 
• Natural Language Processing (NLP): By understanding how humans actually speak, NLP enables BI software to ingest raw instructions and transform them into the necessary queries or code. This gives any user the power to use this software to its full capability. 
• Real-Time Data Analysis and Reporting: Processing and analyzing vast quantities of data as they are stored and recorded is a 24/7, 365 task. AI components of BI software can immediately take new data and provide real-time reporting for decision makers to have the most current information.
• Customized and User-Specific Reports: Getting the right data in front of the right people is a key objective of Business Intelligence. By customizing dashboards using AI creation tools, reports can be tailored for specific users and functionalities. 
• Integration into Existing Applications and BI Workflows: AI tools are also increasingly being added to existing BI workflows and applications, so they can enhance pre-existing processes and be embedded where users already go to get answers. 
• Forecasting: AI has the ability to greatly improve forecasting accuracy and take many factors into account when forecasting compared to traditional methods
• Insights and anomaly detection: Building AI capabilities into BI software allows the software to detect insights and anomalies not easily identified by the user and expose them through visualizations, alerting, and written language

Top Considerations for Integrating AI into Business Intelligence Tools

As businesses consider integrating AI into their BI systems, several key considerations must be addressed to ensure a seamless and effective fusion of the two technologies:

• Data Privacy And Security: Not all AI tools are created equal when it comes to data privacy and security. Businesses must know what privacy laws they adhere to, like GDPR and HIPAA, and make sure their AI tools have robust controls so their data does not leave the BI system.
• Integration With Databases: Analyses are only as good as the data they are based on. When using AI in Business Intelligence, organizations must ensure that the underlying databases integrate seamlessly with the BI tool.
• Ensuring Data Quality: The principle of "garbage in, garbage out" highlights the necessity for businesses to engage in rigorous data cleanings and preparation to supply their AI tools with reliable data. Poor initial data will build a rocky foundation for all future analyses.
• Embedded Analytics Solutions: Putting BI tools where users actually will use them is vital. Consider embedded analytics solutions, like Explo, that bring real-time dashboards and reports into SaaS platforms.

The Next Steps

Once you understand the difference between BI and AI and see how AI can greatly enhance your Business Intelligence software, you need to choose a platform that leverages AI correctly and easily. Consider Explo AI, with its easy NLP generation of reports and dashboards. 

Read Explo’s blog to learn more about Embedded Analytics and compare BI options.

Interested? Sign up for a demo to see how Explo AI can help your business maintain its competitive advantage.