AI’s ability to analyze data and use it to make recommendations makes it a natural fit for business intelligence (BI). At the same time, it can be challenging to understand exactly how AI benefits BI and specific use cases for integrating it into your BI analysis. This is your guide to understanding the impact of AI on BI, and how you can start using it in your business intelligence workflow.
Key Insights
- An AI-powered system can learn on its own, gradually improving its analysis as you feed it more data.
- AI benefits BI in three main areas: descriptive, predictive, and prescriptive analytics.
- You can use AI to reduce risk, boost efficiency, manage your supply chain, better understand customers, and more
- LANSA BI leverages AI by enabling users to make data-powered decisions that drive business improvements
Benefits of AI for Business Intelligence
AI supports your BI system by enabling you to work with more data and enabling more complex, flexible analysis.
Analytics on More Diverse, Larger Data Sets
AI makes it possible to analyze a wider range of data in your BI system. For example, instead of merely analyzing text and .csv files, you can use AI to draw insights from:
- Images
- Videos
- Audio files
- Unstructured data from survey responses
You can also feed data from an IoT system or social media into an AI-powered analysis tool. As a result, your business gets access to more data, which puts you in a better position to make sound decisions.
Fast, Complex Analysis
AI can detect patterns and deliver insights by analyzing large data sets — quickly and accurately. They use parallel processing, which refers to breaking down large workloads into smaller parts and then completing the work on each part in “parallel” or simultaneously. As a result the computational process takes less time than it would for a system using a linear approach on the entire data set.
AI systems use graphics processing units (GPUs) to run complicated calculations faster than normal processors. This means businesses can reap AI-generated insights in real-time by integrating data from multiple business apps.
Adaptive Performance
You don’t have to explicitly train an AI model to improve its performance. It can improve the accuracy of its performance on its own. This makes them capable of delivering more value over time. Therefore, much like a diligent, focused human employee, an AI can get better day by day.
Using Predictive Insights to Power Proactive Decisions
An AI system can analyze historical performance data and juxtapose it with current outcomes. It can then use this information to recommend ways to improve business performance.
For instance, you can use AI to analyze past sales performance data. You can then give it current performance data and a sales goal. The system can then use this data to tell you how likely your company is to achieve its sales goals. It can even recommend changes you can make to achieve your objectives either on time or sooner.
Regardless of whether you choose to use AI to provide advice, the data it produces puts you in a strong position to meet future demands, build solutions that align with customer demands, and more.
How is AI Transforming BI?
While AI has many uses across creative, analytical, and functional domains, it’s transforming BI in three principal ways via descriptive, predictive, and prescriptive analytics. Here’s how each of these work:
Descriptive Analytics
Descriptive analytics enables an AI system to summarize and interpret historical data, identifying trends and patterns. In this way, it describes what has happened.
For example, suppose you work for a manufacturing company. You can paste the contents of an Excel spreadsheet with performance data for a machine on your assembly line into ChatGPT. You can then type in the following prompt: “Please 1. Perform a historical data analysis, and 2. Tell me how this machine has performed over the past year.”
ChatGPT can provide a historical analysis and also describe the machine’s performance using easy-to-understand language.
However, this type of analysis is insufficient for many organizations, especially those that don’t want to have to constantly enter queries into a generative AI model like ChatGPT. For these organizations, a solution like LANSA BI is a better fit. LANSA BI gives you standard reporting features, dashboards, and scorecards, providing assisted insights generation that improves business performance.
Predictive Analytics
Predictive analytics leverages historical data, machine learning, and algorithms to calculate the probability of something happening in the future. For instance, you can use natural language query (NLQ) to ask an AI analytics app, “Which products will generate the most revenue in October 2025?” With guided NLQ, the system provides you with language, such as “products,” “most,” and “revenue,” then aligns them with the categories in the database you’re querying before providing you with the data you need.
Predictive analytics can also be performed by machine learning models that mine data, isolating and then analyzing the information pertinent to your query.
Prescriptive Analytics
Prescriptive analytics describes actions you can take to make a future outcome more likely. For instance, a prescriptive analytics system can tell an IT company which help staff to employ to maximize positive outcomes for employees. You simply feed it data about the customer satisfaction rates associated with each employee, and your AI system can suggest the team that’s most likely to satisfy the most customers in a given time frame.
Depending on the system, it may use simulation algorithms, optimization models, or other tools to provide its recommendations. On your end, you can provide actionable strategic suggestions.
Applications of AI in Business Intelligence
While the number of applications of AI in business intelligence is virtually unlimited, here are some of the more common use cases:
Managing Risk and Opportunity
An AI system can use competitive intelligence to help your organization monitor the risks presented by your competition. For example, it can monitor new product launches, pricing adjustments, customer sentiment toward your competition’s products, and marketing campaigns.
Then, you can use this information to identify opportunities to succeed where your competition may be falling short — or other ways to leverage knowledge of their tactics.
Detecting Inefficiencies and Recommending Solutions
You can use AI to generate digital replicas of physical systems, model their operations, and then use prescriptive analytics to tell you how to boost efficiency.
For instance, an AI system could monitor the energy generated by a solar-powered vehicle’s solar panels in different driving conditions. It can then tell you the optimal size solar panel to use to get the best performance in different regions of the country.
Using Intelligent Process Automation
Intelligent process automation can reduce the number of resources needed to perform repetitive or routine tasks. For instance, using document intelligence, an AI system can pull insights and data from PDFs. You can then use the data it surfaces to automate a data entry process, such as filling out a form or populating a spreadsheet. This can significantly cut down on the time it takes to generate and use these documents in your business intelligence system.
Supply Chain Management
A business intelligence solution enabled with AI can detect outliers and anomalies in your supply chain data. Highlighting this information puts you in a position to identify and rectify problems before they have a significant impact on your shipping and fulfillment costs.
You can also use AI in your supply chain by having the system automatically tell you when inventories are getting too low or to outline the most efficient methods of shipping materials. It can, for example, figure out the most cost-effective shipping channel for goods that have to go from L.A. to New York in a week or less.
Challenges of using AI in BI
Despite the many benefits, using AI in a BI context isn’t without its challenges. For example, you may have to overcome:
- Low data quality. Some AI-powered tools need clean, consistent data to produce actionable results
- Cost. At times, an AI-enabled BI solution may come with a high price tag. It can also be expensive to deploy and maintain over time.
- Expertise. With some AI-powered BI tools, you need significant knowledge to get it to produce the required results.
Key Takeaways
AI and BI make excellent partners because AI has the ability to analyze data, predict what is most likely to happen in the future, and provide advice regarding how to optimize business performance. As such, an AI system can streamline the process of managing risk, making decisions around how to optimize human and other resources, and making tough business calls in real-time.
LANSA BI gives you AI-powered business intelligence using embedded analytics, NLQ, and BI dashboards—all of which combine to help you derive valuable insights. Contact us today to learn more about the potential of LANSA BI in your business intelligence strategy.
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