Data Analytics Trend: AI for BI

February 22, 2022

Image courtesy XPERTI (https://xperti.io/ai-vs-bi/)

A recent trend related to data analytics is using Artificial Intelligence (AI) to generate Business Intelligence (BI). Discussed in two compelling instances, the first advises that AI is collapsing the time between question and answer by way of today’s business users being able to ask complex questions using natural language and receive contextual answers in seconds (Christopher, 2026). The second article continues this thought process on AI’s ability to generate insights, automate data analysis, and predict outcomes — and how it is revolutionizing the way organizations interact with data (Singh Arora, 2025).

Analyzing The Trend

In analyzing this trend and incorporating my own experience, the traditional analytics request processes of submitting tickets, waiting through backlog queues, and reviewing dashboards is inefficient and no longer sustainable (Christopher, 2026). Said processes substantiate inefficiencies — generating data would reflect as such. By incorporating AI into the data analysis workflow, manual workloads can significantly be reduced, helping to speed up the entire process of generating BI. Furthermore, AI is capable of managing complex data sets beyond the capacity of human analysts (Singh Arora, 2025).

There are, however, limitations with implementing AI to generate BI:

AI results are only as good as the user who feeds the data into it.
If AI models are trained on inconsistent, ungoverned, or siloed datasets, the generated insights will be flawed and biased.
Data products act as guardrails for AI in BI. (Singh Arora, 2025).

With that said, utilizing AI is an emerging skill that has been widely adopted. Organizations are investing deeply in AI — 78% of organizations reported using AI in 2024, up from 55% the year before (Stanford University, 2025). In addition, professionals are investing in themselves to learn and adopt AI into their skill set and raise the floor of competency, as indicated by the plethora of Artificial Intelligence courses offered at premium costs (AI Executive Courses | MIT Sloan | MIT Sloan, 2026).

My Position

While I am a regular user of various AI platforms, I am by no means a power-user or an expert. Within my scope of experience with AI, I see both positives and negatives with this trend:

- Positive: Using AI to generate BI can save a tremendous amount of time and generate insights that may not ordinarily have been concluded. AI should be used as a tool in a toolbox, rather than solely relied upon.
- Negative: With the surge of AI usage, regulation has not been able to keep up. Consumers and regulators have concerns towards AI data privacy. Humans can also rely too much on using AI and lose abilities to generate their own taste.

In further analysis, it appears as though the AI bubble is beginning to burst, as indicated by the high volume of layoffs in AI companies. Using AI to generate BI, I believe, is just starting to take off. With this potential bubble bursting, organizational AI licensing/seat costs will decrease, which is when using AI for BI will no longer be a trend, but the norm.