Data Science and Business Intelligence in 2025: How will AI Transform the Data Team?

ation into data teams is reforming traditional roles and responsibilities. The previously clear boundaries between roles such as data scientists, engineers, and business intelligence analysts are becoming increasingly blurred. As noted by Anjali Samani, Senior Director of Data Science at Salesforce, "The lines between roles are becoming more and more blurred." This shift demands a more comprehensive approach, where team members must think beyond their immediate functions and contribute to broader strategic goals. Data scientists are now seen as strategic partners rather than mere insight providers, working closely with product teams to drive innovation. This evolution requires a blend of technical expertise and a deep understanding of business needs, enabling data teams to deliver real-time insights and shape business strategies proactively.

The Appearance of New Roles

The rapid advancement of AI technologies is leading to new roles within the data space, such as AI translators and prompt engineers. These roles focus on closing the gap between AI capabilities and practical business applications. As Anushka Anand, Director of Product Management at Sales Loft, highlighted, "The whole kind of AI translator, prompt engineering... that's a really new one." These roles require a unique set of skills, combining technical proficiency with an understanding of user needs and business objectives. This evolution reflects the growing importance of making AI accessible and usable across various organizational levels, emphasizing the need for data professionals to adapt and embrace these new opportunities.

Encouraging a Culture of Experimentation

Organizations are increasingly recognizing the value of encouraging a culture of experimentation to drive innovation and empower their teams. This involves urging data scientists, engineers, and other team members to explore AI technologies and share their learnings through knowledge-sharing sessions and hackathons. Laura Falker, Senior Director of Go-to-Market Analytics at MongoDB, emphasized the importance of "creating that culture of collaboration" and enabling cross-functional teams to experiment with AI tools. By promoting experimentation and collaboration, organizations can accelerate the adoption of AI, enhance problem-solving capabilities, and drive meaningful business outcomes.

Data Literacy and AI Safety

As AI becomes more common, ensuring data literacy and AI safety across organizations is essential. This involves equipping employees with the necessary skills to use AI tools responsibly and ethically. Anjali Samani highlighted the need for AI safety training, noting that "AI safety training has become a big part of our data literacy initiatives." Ensuring that employees understand the implications of AI use and can handle potential risks is essential for maintaining trust and integrity in data-driven decision-making. Organizations are also focusing on establishing a single source of truth to ensure consistency and alignment across teams, further supporting responsible AI use.