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Even though business intelligence systems are capable of complex data handling and provide robust security, they require skilled business analysts to make use of them. Using traditional BI, turning raw data into meaningful insights to make data-driven decisions is an extremely complicated process. However, when introducing AI in business intelligence, organizations can derive the same and even better results with less complexity. AI-powered BI tools enable even non-technical users to transform volumes of unstructured data into actionable items that help businesses achieve their goals.
In this article, we’ll explore the limitations of traditional BI, learn what AI in business intelligence is, and explain how it can benefit your bottom line. Additionally, we’ll examine the BI implementation guide and take a look at a case study in which AI played an instrumental role.
While business intelligence platforms have existed for decades, their capabilities have significantly expanded with newer and sharper technologies. When we discuss the concept of artificial intelligence in BI in this article, we juxtapose it with the so-called traditional business intelligence systems. In this part, we’ll focus on some shortcomings that conventional BI solutions have.
In the absence of AI, traditional BI tools utilize historical data to describe what happened in the past. Their main strengths lie in historical data analysis and KPI tracking—in other words, retrospective business insights. While these findings can be useful in strategy meetings, the lack of real-time updates and predictivity that AI and machine learning provide limits the scope of traditional BI software. Especially in fast-paced modern markets, having access to real-time information is key to smart decision making.
With the ever-increasing volumes of big data that companies generate, business intelligence systems are also designed to collect and display various forms of business data. The problem is that traditional BI systems operate with silos, keeping data separated between departments. Although it’s helpful to have an overview of a single department’s metrics, being able to see the bigger picture is essential for executive decision making.
While conventional BI tools can display metrics and charts, emphasize various trends and patterns, and unlock some hidden bottlenecks, they cannot offer any actionable insights for you to follow. Without AI-powered business intelligence, the system will not be able to produce any course of action, simply providing you with problems, not solutions.
So, how can AI solve the aforementioned limitations? In this section, we’ll concentrate on artificial intelligence, discuss what AI in BI means, and discuss the impact of generative AI on business intelligence.
Before we dive into generative AI in business intelligence, let’s begin by examining what artificial intelligence is. AI is a science that deals with intelligent machines, and more specifically, intelligent computer programs. By relying on vast amounts of data, AI models gain the ability to analyze data, perform predictive analytics, recognize patterns, and have human-like interactions. The primary components of AI systems are natural language processing (NLP), machine learning (ML), and deep learning (DL). The definitions of AI/ML/DL often get confused, so here’s a short overview:
NLP enables computers to decode, comprehend, and generate human text and speech.
ML is used to analyze and categorize data and make intelligent predictions based on discovered patterns.
DL is a subset of ML and allows for more accurate insights into data, especially from unstructured data.
Now that we know the basics of AI technology, let’s move to the more pressing issue: What is the role of AI in business intelligence? Using AI-powered systems, business leaders can optimize business processes, improve productivity, and increase revenue. By applying principles of data science, bolstering data analysis, and automating repetitive tasks, companies can leverage insights to enhance decision making, streamline operations, reduce costs, strengthen cybersecurity, and more.
For example, Microsoft Power BI utilizes Azure AI and machine learning services to offer a large array of business intelligence tools. From anomaly detection and sentiment analysis to advanced data analytics and predictive modeling, Microsoft’s clients can forecast market and customer behavior, identify bottlenecks in supply chains, and adjust to changing environments, often exemplified in BI in sectors like retail and logistics.
While the above-mentioned AI models that analyze data and uncover hidden insights are usually referred to as discriminative or analytical, the type of AI that generates content is called generative AI (GenAI). Based on the training data, these AI systems can deliver human-like content that mimics the knowledge base it was trained on. Let’s take a look at the biggest use cases for generative AI in business intelligence:
Custom business intelligence: Organizations can fine-tune existing AI models by feeding them their company, customer, and market data. Using tailored generative AI for insights, they can leverage artificial intelligence to generate new ideas, improve communication and messaging, and identify patterns that were previously beyond their reach.
Content generation: Using generative AI models, organizations can create marketing copy, social media posts, and internal materials considerably faster and more efficiently. These AI tools are also used to make visuals, from complex data visualizations and graphs to simple designs for websites and social media platforms.
Hyper-personalization: GenAI is also a major driver of the hyper-personalization trend, enabling businesses to create tailored emails, posts, and other text-based content.
Among the positive impacts of AI in business intelligence are increased efficiency through task automation, improved agility by responding faster to market changes, enhanced decision making due to data-driven insights, and boosted personalization. However, there are concerns associated with data privacy, biases in training data, and dependency risks.
According to a research paper from Tilburg University, GenAI has the potential to improve various aspects of BI tools. The study mentions that generative AI models drive innovation and decision making, improve documentation and reporting consistency, and enhance data management, streamlining processes and workflows.
At the same time, the study emphasizes that these capabilities come with caveats. For one, the applications of AI in business intelligence are hindered by integration issues and mistakes in outputs. Additionally, they cite challenges in context understanding as another problem, highlighting the importance of human oversight. Data security is another concern that needs to be carefully taken into account.
As a business leader, you’d likely want to learn what benefits you’ll unlock if you implement an enterprise BI platform. In this part, we’ll focus on the benefits of AI in business intelligence for business owners and stakeholders, for companies and employees, and for customers.
Artificial intelligence has the potential to democratize data, allowing non-technical users to access complex data points. On one hand, AI automation can unburden executives from tedious tasks, and on the other, conversational AI assistants can elucidate technical topics in natural language, enabling stakeholders to focus on big-picture strategizing.
Accurate data analysis and forecasting: AI-powered business intelligence leverages natural language processing and machine learning models to deliver in-depth analyses of the company’s data, allowing business leaders to make informed data-driven decisions.
Business agility: AI-powered tools can process real-time data and utilize predictive analytics to enable rapid responses to the changing market environment.
Fast and consistent decision making: On top of delivering real-time insights, AI tools facilitate consistency that is hard to achieve with humans. While people excel at critical thinking and creative approaches, dependable analyses and outputs are something that AI-powered BI systems are designed for. If trained on datasets with a high level of data quality, such solutions can produce reliable recommendations.
Risk management: Business analysts can also harness AI-powered BI to identify potential risks and develop mitigation strategies. For example, artificial intelligence is widely used in finance to detect fraudulent behavior and flag these transactions for further investigation.
Employees of all levels can take advantage of AI-powered BI platforms to generate reports, track metrics, and optimize underperforming processes.
Task automation: From customer service inquiries and administrative tasks to automating BI processes and content generation, AI tools can take over a wide range of mundane tasks that keep your employees from their core duties. Task automation, in turn, also reduces human error, making the business more efficient.
Process optimization: Business users can also rely on AI to analyze company data and identify missed opportunities or suboptimal processes and deliver actionable insights. For example, these intelligent systems can find solutions to reallocate your resources to minimize waste and reduce costs.
Product development: Generative AI is especially excellent at accelerating research and development. This has already been proven in protein development, where AI was instrumental in predicting protein structures.
Finally, customers can also greatly benefit from a broader adoption of AI-powered BI solutions. If you work faster and more efficiently, you’ll be able to offer better services in a shorter timespan.
Enhanced customer experience: AI-driven personalization coupled with better quality of service and faster responsiveness improves the overall customer experience and, subsequently, retention.
Improved customer support: Autonomous agents are employed by businesses to provide 24/7 customer support, offering round-the-clock answers to user queries.
Anticipation of customer needs: AI’s forecasting abilities aid companies in predicting what each customer might be interested in at any particular moment.
How can you use AI in business intelligence, and what does the adoption process look like? In this guide, you’ll learn the basic steps of implementing AI-powered BI in your organization.
The first step in adopting any new technology, especially AI-powered business intelligence, is to evaluate the current state of affairs and define the objectives of the implementation. In other words—what would you like to achieve with AI?
Before integrating AI into your business strategy, consolidate your data sources and prepare the information inside. Without high-quality data, your AI models will fail to produce meaningful and relevant outputs.
There are many examples of BI systems powered by artificial intelligence that you can choose from. From Tableau that excels at data visualization to Power BI for the Microsoft ecosystem, research available tools on the market and select the best one for your objectives.
This step is likely to require technical acumen and domain understanding to ensure that your existing BI systems seamlessly integrate with AI-powered tools. If lacking in-house expertise, consider hiring an external agency to help in this complex process.
Organize a few training sessions to educate the non-technical users on how to work with AI-powered business intelligence tools and extract the best value for their particular job.
After the implementation is complete, monitor the performance of your new systems and make improvements to make sure they’re delivering optimal outcomes.
NIX’s client is a leading US-based cloud service provider looking to scale its business. To facilitate seamless growth, the client decided to optimize its decision making using an AI-powered BI system. The main goal was to retain productivity, agility, and responsiveness while expanding the business.
We started by assembling an expert team to create, operate, and maintain the BI system that is capable of processing vast amounts of business data. Based on the analysis, the AI-powered solution should deliver valuable insights and present them in various formats such as charts, spreadsheets, and dashboards.
Our team considered the client’s requirements and financial possibilities and chose Redash, a cost-effective BI system with extensive data visualization capabilities and broad integration options. The tool is also known for its user-friendly interface, multiple data sources, and high-quality data analytics.
The client remains one of the top web hosting and cloud providers, keeping their competitive edge in the saturated market. Using their AI-powered BI platform, they can now swiftly respond to market changes and fluctuating customer preferences and needs.
If you’re intrigued by the opportunity to expand your business toolkit and harness the power of AI to unlock valuable insights, consider getting in touch with NIX. We’re a software development agency that provides customer AI services to companies across industries and sizes. Our portfolio includes over 3,500 successful projects, and we’d like to help you in your digital transformation journey.
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While traditional business intelligence focuses on descriptive analytics and examines historical data, artificial intelligence is capable of forecasting future trends, detecting patterns and anomalies, and producing actionable items. In other words, conventional BI reports on past events, while AI tools can predict future outcomes and continuously learn to offer new and better insights.
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AI’s exceptional forecasting ability comes from machine learning algorithms that continuously learn from data and refine their predictive power. Unlike traditional solutions, AI can factor in a number of external variables, including economic indicators and customer sentiment, to make precise forecasts. As a result, organizations can make smart decisions about pricing, inventory, research and development, marketing, and much more.
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Yes, AI in BI can leverage customer data to identify purchasing patterns, make personalized suggestions, and even predict future behavior. Using these insights, businesses can pivot their products, communication, designs, and other aspects to make sure their customers are satisfied with their offering.
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Among the biggest concerns associated with integrating AI in BI are biases and hallucinations, data privacy, implementation costs, and technical difficulties. Some risks can be mitigated during training by feeding the AI model with high-quality datasets while discarding incomplete or subpar ones. Additionally, the implementation process requires skillful experts and technical infrastructure, which can add up to a significant investment.
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