Avenga Expert Explains How AI Redefines Business Analysis

The rapid rise of generative AI is transforming the daily workflows of business analysts by automating routine tasks like drafting requirements and generating basic prototypes. According to Zuzana Drotárová, who oversees approximately 100 analysts as the head of regional competency at IT services firm Avenga, this shift does not signal the death of the profession. Instead, AI is stripping away the administrative periphery of the role, forcing practitioners to focus on deep diagnostic work and strategic validation.

This transformation aligns with broader corporate trends. A recent Gartner survey revealed that more than half of organizations have already redesigned or redefined job roles because of artificial intelligence, while 78 percent of human resources leaders agree that workflows must adapt to capture the value of AI investments. Rather than replacing humans, AI is pushing analysts into three distinct career trajectories: product ownership with a heavy focus on data, rapid prototyping, and project delivery management.

In these new paths, analysts must leverage data literacy to ensure AI models are answering the correct business questions. Those with technical leanings can use AI to build functional prototypes for immediate customer feedback, though Drotárová cautions that scaling these into secure production systems still requires deep engineering expertise. Meanwhile, the International Institute of Business Analysis 2025 Global State of Business Analysis Report indicates that 74 percent of practitioners view AI as a positive influence on their careers, highlighting that human skills like communication and strategic thinking are more critical than ever.

Ultimately, the value of a business analyst in the AI era lies in verification and judgment. Because AI can generate highly polished but incorrect outputs, human oversight is vital to catch errors before they are implemented. Drotárová notes that the core of the role remains the ability to uncover unstated client needs and trace complex dependencies across systems, qualities that cannot be easily replicated by automated agents.