OPINION

Artificial Intelligence has, without a doubt, changed the world of data and business analytics, but as the article suggests, there is a real danger in the overreliance and incorrect use of AI. While it is appealing to let an AI agent or a chatbot to query databases and present insights instantly, the truth is that AI without having a strong foundation in data and decision analysis can lead an organization astray. As someone pursuing a master’s degree in Business Analytics and having the opportunity to work with AI in academic and internship projects, such as developing a Retrieval Augmented Generation Model and A price prediction model, I can say that I have seen both the promise and the pitfalls of integrating AI into business intelligence systems.

The article correctly emphasizes the fact that AI hasn’t killed the business intelligence star – it has simply made BI better. This resonated deeply with my experience using AI-centric tools, where the real value not only lies in automation but in augmentation. AI extends itself in processes like data collection and preparation by performing tasks such as automating data cleaning, creating structured data sources, and even creating synthetic datasets. What truly sets us apart is how we leverage, not replace, our analysts and rather empower them to focus more on strategy and insight rather than conducting repetitive tasks.

From an analytics student’s lens, I’ve noticed a growing misconception: the more an AI system includes. The more intelligent it becomes. But in reality, intelligence doesn’t come from algorithms alone; it comes from how we frame questions, prepare data, and interpret insights. The article’s warning about receiving untrustworthy outputs from AI systems is connected to poorly prepared data sources. Without human oversight, contextual understanding, and strict data governance, many organizations will be at risk of receiving inaccuracies in data and their work, which might lead to serious problems down the road in a project.

This integration of AI and BI also requires companies to develop a product mindset, one that views analytics not as a bunch of reports but as a continuous and evolving product that helps in delivering business value. AI- powered BI should be designed around decision makers’ needs, ensuring transparency, ethical use, and sustainable adoption. I believe that future analysts and product leaders must be able to bridge the gap between technical innovation and strategic intent, ensuring that every model or dashboard drives meaningful business outcomes.

Looking ahead, organizations need to cultivate three critical areas:

1.     Human-centered AI literacy – Business users must understand AI outputs well enough to question them.

2.     Ethical and sustainable data use -  As AI models consume more data, we need clear standards to prevent bias and ensure responsible scaling.

3.     Cross-functional collaboration – The future analyst must be able to blend data science, business analytics, and product management skills to translate AI insights into actionable strategy.

AI has not replaced BI; it has redefined it. The next generation of business analysts, like me, stands at the precipice of the integration of artificial intelligence in the workplace. Our role is to ensure that AI doesn’t just make analytics faster, but also smarter, more reliable, and more aligned with long-term business sustainability. The future of analytics isn’t AI vs. BI; it's AI with BI, and the organizations that understand this partnership will lead the way in the future.