For years now, when we talk about Business Intelligence, we think of dashboards full of graphs, KPIs highlighted in red or green, and reports arriving weekly in PDF or Excel format. A solid, technical discipline, often reserved for a few specialists within companies. But today, something is radically changing. And it’s fair to ask a provocative question: is Business Intelligence dead?
The short answer is no. But the full answer is much more interesting. Traditional BI is giving way to a new paradigm, in which artificial intelligence is revolutionizing the way we access and interpret data.
Goodbye dashboard, hello chatbot
Until recently, accessing corporate data meant hiring a data analyst, waiting for them to build a custom dashboard, and learning how to read and interpret it. This process could be lengthy, technical, and not very inclusive. Suffice it to say that, according to Forrester, only about 20% of non-IT professionals have direct and independent access to advanced BI tools (Forrester, 2024), limiting the dissemination of data.
Today, however, we’re approaching a completely different reality: conversational Business Intelligence, accessible via chatbot, where data directly answers the questions we ask. “What is the margin for product X over the last three months?”—and the answer comes in real time, using natural language and a supporting graph, generated on the fly. No complex interfaces, no filters to configure.
This is Self-Service BI 2.0: democratic, instantaneous, intuitive. Conversational AI can significantly accelerate decision-making in key sectors like retail and finance, allowing sales managers to instantly ask: “What were the most profitable products in Southern Italy in Q2, excluding promotions?”
Artificial intelligence as a new data intermediary
At the heart of this revolution is generative AI. Specifically, Large Language Models (LLMs)—like those underlying intelligent chatbots—are now able to query databases, understand context, and build dynamic visualizations based on user requests.
It’s not just a technological shift: it’s a new way of interacting with corporate knowledge, freeing itself from technical constraints and opening itself up to anyone with a question. From the operations manager who wants to know the week’s production bottlenecks to the CFO seeking a comparison between forecasts and actual results: everyone can engage with the data, without intermediaries. According to Gartner, by 2026, it’s expected that over 80% of companies will have used APIs or generative AI models or implemented generative AI-enabled applications in production environments, a huge leap from less than 5% in 2023, a testament to the ongoing acceleration (Gartner, 2023).
And the role of the data analyst?
It’s natural to ask: what will happen to the data analyst if data can be requested directly from a chatbot?
The answer is clear: the role of the data analyst isn’t disappearing, but rather evolving. While their role previously focused on building dashboards and models on demand, in the future it will become one of enabling an AI-ready data ecosystem. New skills will be required: semantic modeling (i.e., creating a representation of data meaning that both humans and machines can understand), prompt engineering, data quality management, and integration with LLM programs. The data analyst of the future will increasingly be an “orchestrator” of data and AI, ensuring not only the accuracy but also the ethics of AI, ensuring that analyses do not generate bias.
Are companies ready?
While it’s true that traditional Business Intelligence is destined to transform, it’s equally true that not all companies are ready to make this leap yet. The technologies already exist, but a clear strategy is needed to adopt them: organizing data, choosing the right AI solutions, training people, and defining new governance policies. Technology alone isn’t enough; a deep data culture is needed that values autonomous exploration and data security, crucial in a context of greater accessibility. Integration with legacy systems and widespread employee training will be significant challenges.
Those who can do this early will have a huge competitive advantage: faster access to data, better decisions, greater autonomy within teams.
Bottom line: BI isn’t dead. It’s being reborn.
No, Business Intelligence isn’t dead. But it’s changing. It’s leaving the technicians’ room and entering the hands of those who make decisions every day. It’s abandoning its “rigid” tools to become flexible, conversational, and natural.
In this new scenario, entrepreneurs and managers must equip themselves: not to abandon BI, but to embrace a new version, enhanced by AI. The challenge today is to adapt. For data analysts, this means enriching their knowledge base with new AI skills. For companies, this means investing in intelligent technologies, but also in data culture, openness to change, and the ability to drive innovation.
Business Intelligence isn’t dead. It’s just entered a new era.
