november 14, 2025
AI and BI: differences and combined use
Business intelligence (BI) and artificial intelligence (AI) are powerful tools that help companies transform raw data into valuable insights and actions. In today’s world, where data volumes are growing exponentially, the ability to efficiently process, analyse, and use this information is critical for competitiveness.
Goals of artificial intelligence (AI) and business intelligence (BI)
Business intelligence (BI) and artificial intelligence (AI) are becoming increasingly important tools for modern companies. In the era of big data, the ability to extract valuable insights and use them for informed decision-making is vital.
Business intelligence tools allow companies to collect, analyse, and visualise data, helping them better understand operations, customers, and markets. Artificial intelligence complements analytics by offering more advanced capabilities for automation, learning, and prediction based on data.
It is important to understand that BI and AI are not merely sets of technologies but comprehensive approaches requiring strategic thinking and expertise. Proper integration enables companies to identify opportunities, optimise processes, and enhance overall efficiency.
Artificial intelligence explores the use of computer systems to simulate various aspects of human intelligence, such as problem-solving, learning, and reasoning. Businesses see enormous potential in AI for speech recognition, decision-making, and many other applications.
Business intelligence refers to the use of various technologies and tools to collect and analyse data. The use of BI allows companies to make decisions nearly five times faster. BI tools can turn large volumes of data into a cohesive picture but are not designed to prescribe how these data should be used in the decision-making process.
Companies such as Microsoft, Oracle, and Tableau have developed BI tools for a wide range of business functions, including HR, sales, and marketing. Daily monitoring and the use of data to create spreadsheets, performance indicators, dashboards, charts, and other useful visualisations enable companies to organise data and make complex decisions much more easily. Over the past three years, BI adoption has increased by nearly 50%.
One of the main goals of artificial intelligence is modelling human intelligence. By simulating human behaviour and thought processes, AI programmes can learn and make rational decisions.
Technology professionals developing and applying AI systems are trying to determine whether machines can learn and adapt.
Exploring these questions can bring significant benefits to companies willing to invest and experiment. The use of AI-based applications such as chatbots can improve efficiency and profitability.
Unlike BI, which simplifies data analysis but leaves decision-making to humans, AI can enable computers to make business decisions independently. For instance, chatbots can respond to customer queries without human involvement.
BI versus AI: examples of use
Anyone who has used Microsoft Excel or another spreadsheet application has encountered BI. Spreadsheets allow companies to organise, analyse, and visualise data much more efficiently.
Companies interact with customers through multiple interfaces, including email, chatbots, and social media. BI tools enable the collection of customer data from these disparate sources and present it in a unified format. By gathering and synthesising data from multiple touchpoints, companies can gain a deeper understanding of who their customers are and how to serve them better.
Companies also use business intelligence to improve operational efficiency. BI tools make it possible to monitor key performance indicators in real time, allowing problems to be identified and resolved much faster than otherwise. BI includes spreadsheets, data visualisation tools, data warehouses, and reporting applications.
There is a wide range of corporate AI applications — from improving medical diagnostics to designing more efficient energy grids and gaining deeper insights into retail customer behaviour.
Corporate AI applications typically fall into one or more of the following categories:
- Process automation
- Cognitive insight
- Cognitive engagement
Process automation is the least glamorous but most common and often the most valuable form of corporate AI application. Such applications can automatically update customer records, process standard communications, and provide recommendations for routine contracts and documentation. As noted by the Harvard Business Review, these applications often yield a high return on investment.
Cognitive insight applications — what Harvard Business Review calls “analytics on steroids” — are more sophisticated than process automation tools. They can learn and improve over time by interacting with users and data. These applications can predict customer behaviour, offer improved cybersecurity solutions, and design personalised advertising.
Cognitive engagement applications interact directly with employees and customers. These include chatbots that can offer medical advice, answer internal queries, provide customer service, and more.
Does business intelligence need artificial intelligence
BI and AI are distinct but complementary concepts. The “intelligence” in AI refers to machine intelligence, whereas in BI it refers to smarter business decision-making enabled by data analysis and visualisation.
BI helps companies bring order to the vast amounts of data they collect. However, visualisations and dashboards are not always enough. By combining AI and BI, companies can synthesise large volumes of data into coherent action plans.
AI allows BI tools to derive clear and actionable insights from the analysed data. An AI-based system can clarify the significance of each data point and help determine how the data can translate into real business decisions.
Many technology companies — from established giants to startups — are seeking to take advantage of this approach. IBM’s research division, for example, aims to “reimagine enterprise architecture and transform business processes by combining artificial intelligence algorithms, distributed systems, human–computer interaction, and software engineering.”
AI can also lead to the development of smarter and more adaptive BI tools. As these tools collect more data, interact with users, and learn from outcomes, they can identify which types of recommendations are most useful and adjust accordingly.
Ultimately, it is artificial intelligence, not programmers, that will drive incremental improvements taking BI tools to a new level.
It seems likely that the future of BI will, to some degree, depend on AI. Although AI and BI differ substantially, together they form a powerful tandem.
Conclusion
Business intelligence and artificial intelligence are rapidly evolving fields that are critical to the success of modern companies. As data volumes grow and technologies become more complex, the ability to manage information flows effectively, extract valuable insights, and use them for decision-making becomes a key competitive advantage.
Companies that successfully implement advanced analytical solutions with AI elements will be able to make better-informed decisions, optimise processes, anticipate customer needs, and stay ahead of competitors.
Integrating business intelligence with artificial intelligence and machine learning technologies will open new horizons for growth and innovation.
Ultimately, the combined use of BI and AI is not just about implementing tools but represents a holistic approach to data management and decision-making.
