Business intelligence (BI) and artificial intelligence (AI) have some overlaps. However, they’re not necessarily the same.
The former helps organizations visualize analytics to make data-driven decisions, while AI primarily explores the computer’s way of mimicking humans in problem-solving, learning, and judgment.
Despite different end goals, they have overlapping enterprise applications. Some organizations integrate embedded business intelligence software with AI to empower data analysis and aid decision-making.
Let’s take a deep dive into business intelligence and AI to understand similarities and differences and how they could complement each other.
Business intelligence focuses on visualizing data to improve strategic decision-making, while AI performs tasks and makes judgments in human-like ways.
Business intelligence and AI differ in their goals.
Business intelligence assembles chaotic data and interprets it into a coherent, easy-to-digest picture for you. While it gives insights into past and present trends by analyzing data, it might not deliver prescriptive suggestions for future actions.
AI performs tasks, answers questions, and makes judgments in a manner akin to humans. In its infancy, AI may lack the ability to visualize data, but more advanced generative AI like GPT4 can visualize data in charts, graphs, or other illustrations.
Business intelligence makes data analysis much easier but leaves decision-making to humans. And AI enables computers to make autonomous decisions. The two can deliver the best outcome when integrated. BI can provide data analysis, and AI can offer prescriptive suggestions based on the analysis.
We will take a deep dive into how their combination helps enterprises, but let's first look at what BI and AI mean for professionals and executives.
Business intelligence helps professionals make informed business decisions. It aggregates data from internal IT systems and various external sources, runs queries, and visualizes it per need.
The outcomes are presented as a report, facilitating strategic planning and operational decision making. This part is crucial to improve efficiency, enhance competitive advantage, and increase revenue. BI provides the insights needed for such improvements.
Artificial intelligence empowers computers to perform tasks that previously required human intervention. AI is like an umbrella that covers machine learning (ML), deep learning, natural language processing (NLP), and natural language generation (NLG).
There’s a back-and-forth argument on whether true AI or general artificial intelligence exists. But, when professionals use AI, they’re referring to systems that use machine learning. It enables them to improve and perform various tasks like answering questions and analyzing data.
Modern enterprises strive to be data driven. Business intelligence is the vehicle that drives this data toward relevant stakeholders. On the other hand, AI helps to aggregate and process data. Below are some BI and AI use cases and how professionals leverage them to be more data-driven and intelligent in an enterprise setting.
Anyone making sense of data on a spreadsheet has interacted with business intelligence. While other advanced tools exist for visualizing data, spreadsheets remain a popular choice for data visualization. They make the visualization process easier, more effective, and more efficient.
Below are the areas where companies use BI.
AI-powered enterprise applications are generally used for process automation. This includes tasks like updating customer information or outlining standardized contracts and documentation. Additionally, AI is also used for cognitive engagement and insights.
In this context, AI assists in performing back-office or administrative functions, helping professionals focus on more critical tasks.
Furthermore, AI-powered cognitive insights applications can learn and improve as they interact more with users and data. They can predict customer behavior and make suggestions to improve IT security.
Cognitive engagement applications interact directly with customers and employees to offer service and support them in addressing queries.
Below are some examples of AI use cases in enterprises involving heavy machinery.
AI and business intelligence, when integrated, help companies make sense of massive data and get prescriptive details on actions to take. AI completes the process that BI starts. For example, BI analyzes data and creates visualization and reports, and AI then takes over these reports as input to deliver suggestions based on them.
This speeds up decision-making, helping stakeholders arrive at outcomes faster with AI-generated suggestions. They can work on top of these suggestions to ideate a plan of action and put it to execution.
AI systems look at reports granularly, helping human agents translate data into accurate business decisions. With AI, BI tools become more adaptive. They can learn and improve on recommendations delivered and make incremental improvements, making outcomes more precise and helpful.
Below are some expected benefits of using AI in business intelligence.
Professionals might encounter a few challenges when using AI-powered business intelligence.
Embedded business intelligence (BI) software adds analytics to a business application. You can easily add features for self-service analytics to apps using these tools. These tools allow you to put dashboards directly into employees' apps, making data analysis simpler and more accessible.
The leading embedded business intelligence software mentioned below uses AI to supply rich insights to BI users.
To qualify for inclusion in the embedded business intelligence software list, a product must:
*These are the leading embedded business intelligence software from G2’s Winter 2024 Grid® Report. Some reviews might be edited for clarity.
Tableau makes it easy for anyone to analyze data and find insights. With its AI capabilities, it helps users to quickly understand complex data without needing to be data experts. You can use the drag-and-drop features to visualize data in charts and graphs, which makes it easier to spot trends.
“Tableau has truly transformed how we interact with and derive insights from our data, making it an absolute game-changer in the world of analytics. As a devoted user, I can't help but sing praises for this exceptional tool that has consistently exceeded expectations.
Creating visually stunning and impactful dashboards is where Tableau truly shines. The array of visualization options, from dynamic charts to interactive maps, allows us to communicate insights in a way that captivates and resonates with our audience. The visual appeal enhances presentations and facilitates a deeper understanding of complex datasets.”
- Tableau Review, Nitin K.
“Even if the interface is user-friendly, users unfamiliar with data analysis tools may need to invest some time learning how to use advanced features and create sophisticated visualizations. It can call for using different tools or more coding. It turned out to be difficult, but our group succeeded nonetheless.”
- Tableau Review, Kendra J.
Amazon QuickSight simplifies data analysis and allows users to create and publish interactive dashboards easily. QuickSight's AI capabilities let you perform advanced data analysis tasks, such as forecasting and anomaly detection, without requiring users to have deep technical knowledge.
“Amazon QuickSight helps me pull various reports and work with them. It can customize the type of data we want and the formats. We can choose which data to download. Quicksight is easy to use and has a variety of features. It has a pretest which helps us understand how we can use the data as per your requirement.”
- Amazon QuickSight Review, Neha K.
“If data is huge, visual loading takes a lot of time. Also, sometimes, it does not support complicated calculated field creation. Not all visuals are present.”
- Amazon QuickSight Review, Praveen S.
Microsoft Power BI Embedded integrates seamlessly with applications to offer analytics and AI capabilities. With this, you can embed stunning, fully interactive reports and dashboards into applications. This enhances user experience by providing deep insights without switching between apps.
“Power BI embedded is scalable, fast, accurate, flexible, and advancing with new features and capabilities. Anyone can easily see those things happening. The embedding feature is something that we keep using every day in our company. 9/10 days, we use Power BI via web apps or environment, or any arrangement hosted elsewhere, to provide the benefits of Power BI to colleagues who cannot be present or it is difficult for them to work within the original Power BI premises.”
- Microsoft Power BI Embedded Review, Ilias V.
“The complexity of the program. It is not user-friendly. It requires previous training and advanced mathematical knowledge to fully understand each component trend such as baseline multiplier, actual multiplier, and multiplier forecast.”
- Microsoft Power BI Embedded Review, Bonanza Z.
ThoughtSpot stands out for its AI-powered search-driven analytics. Much like a search engine, it allows users to simply type in natural language queries to find data insights for their business data. This means you can ask questions about your data in plain English and get instant answers in easy-to-understand charts and graphs.
“The interface of ThoughtSpot is incredibly user-friendly, especially for people like me who struggle with challenging data. It enables us to create reports and dashboards and handle data without requesting Power BI assistance. The search feature on ThoughtSpot is incredibly quick compared to other search engines. It's like having a data finding quick capability. We can save time searching for data manually.
ThoughtSpot meets our requirements as a new technology. Our analytics tool is created to our specifications, complete with personalized data and integration with the rest of our tech used in the company. We enjoy exchanging data-related ideas, and ThoughtSpot makes it simple.”
- ThoughtSpot Review, Jai K.
“For our use case, key limitations exist, such as a lack of date parameter functionality comparable to other BI tools. Some features are released, but they feel they are not fully fleshed out. Support has overall been pretty responsive. However, some tickets have been open for a long time, necessitating workarounds.”
- ThoughtSpot Review, Katie S.
Sigma makes data analysis as easy as working with a spreadsheet, allowing users to explore, analyze, and visualize data. The platform's AI capabilities enhance decision making by automating data analysis tasks, predicting trends, and providing recommendations.
“Sigma has been such a breeze to implement. We struggled with other BI tools for a year before finding Sigma, and I'm so glad we did. The tool itself is very intuitive to use. The live chat support and "office hours" have made this transition even easier. Cannot speak highly enough of this company and their tool.”
- Sigma Review, Cassie F.
“Date filtering and aggregating can be frustrating at times; if data is not updated for all clients because of upstream delays, we can have checks that break and miss data. I would like more control over this and possibly an alert system when data is updated.”
- Sigma Review, Ben A.
AI and business intelligence are a powerful team. Using them together helps businesses solve their challenges and identify opportunities faster. Machine learning makes BI tools learn, adapt to users, and deliver more relevant, desired, and improved recommendations and suggestions.
Further, NLP allows users to communicate with the tool using plain English, making BI tools accessible to all.
Learn more about self-service BI and understand how it makes data analysis easier.
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