Revolutionizing Business Intelligence: The Impact of Generative AI
Generative AI has been revolutionizing various fields, with a particularly profound impact on the field of Business Intelligence (BI).
So, what is this Business Intelligence?
Don’t confuse it as a subset of Artificial Intelligence just because it contains the word “intelligence.” By that logic, my refrigerator must be an AI too — it’s got “cooling intelligence” built right in!
BI typically refers to the practices and processes that organizations use to collect, prepare, analyze, and present data and insights to facilitate decision-making. The entire point of business intelligence is to take raw data and convert it into actionable insights. Organizations may use one or multiple BI tools to accomplish this task.
BI can be classified into these three main personas:
Data Engineers: They clean, collect, transform, and prepare data for analysis.
BI Analysts: After the data is ready, they analyze it, create reports and dashboards, and answer business users’ questions. They work closely with business users to meet their needs.
Business Users: They use the reports and dashboards created by BI analysts, mainly consuming the information provided.
Over the last few years, many BI vendors have included no-code, self-serve capabilities that allow line of business users to build reports and dashboards themselves. Despite this innovation, there is an adoption problem. Although 97% of companies are investing over $32 billion in data and AI by 2027, only 35% of line of business users use data and analytics for decision-making.
This 35% adoption rate hasn’t changed in over seven years, mainly due to three reasons.
First, preparing data is difficult, time-consuming, and requires specific skills, causing a big problem.
Second, while some tools allow users to make their own reports and dashboards easily, they still need to understand business concepts, which is hard. Learning how to use these tools takes time, and many users don’t want to learn. They’d rather get insights without having to work with data or make reports.
Third, there’s a space between data and insights. Even if a BI analyst makes a great report or dashboard, users still have to figure out what the data means and what actions to take. This involves lots of manual work, making things less efficient and widening the gap in usage.
However, we are at an inflection point. Thanks to generative AI, we have an opportunity to increase this 35% adoption rate to over 50%. Generative AI can optimize and augment the experiences of the three personas we discussed. For line of business users, generative AI will allow them to interact with their data using natural language. They can ask questions in everyday language, and generative AI will understand the intent, perform the necessary data queries and analysis, and provide answers in a digestible format, such as natural language or visualizations. This reduces reliance on predefined reports and dashboards, shifting analytic power from BI analysts to line of business users.
For BI analysts, generative AI will optimize report authoring by automatically generating code, SQL, reports, dashboards, and visualizations through natural language. As line of business users rely less on BI analysts, these analysts will have more time to focus on higher-value tasks, such as documenting business knowledge into the semantic layer or conducting more complex analyses.
Similarly, for data engineers, generative AI will optimize various data engineering tasks. They can automate code generation, optimize data pipelines, perform automated data profiling, data cleaning, and semantic enrichment.
When business users better serve themselves with insights, it frees up time for data engineers and BI analysts to focus on high-value tasks, creating a virtuous cycle.
Conclusion
Generative AI is poised to revolutionize business intelligence by making data insights more accessible through natural language processing. This shift reduces reliance on specialized roles, allowing professionals to focus on complex tasks and boosting overall efficiency. As generative AI democratizes data analysis, it promises to raise BI adoption from 35% to over 50%, fostering a future where data-driven decision-making becomes standard, enhancing organizational agility and competitiveness.
Reference: IBM Research, AI Academy series “The Impact of Generative AI on Business Intelligence”.
