Synopsis: In this Techstrong AI leadership interview, R Systems CEO Nitesh Bansal explains why attaining and maintaining data competency has now become critical to ensure success in the age of artificial intelligence (AI).
In this Techstrong AI interview, Nitish Bansal, CEO of R Systems, discusses the growing urgency around data competency and lineage in the age of AI. He emphasizes that while AI technologies are rapidly evolving and being widely adopted, many organizations still overlook the foundational role that clean, well-managed data plays in delivering meaningful outcomes. Bansal argues that success with AI doesn’t stem from the model alone—it requires a strong data infrastructure that ensures accuracy, transparency, and consistency. He warns that companies jumping on the AI bandwagon without investing in robust data practices often encounter unexpected results or misaligned outputs.
Bansal explains that as the value of AI applications grows—especially those focused on customer experience and personalization—the complexity of data management increases. Effective AI solutions today may require integration of data from more than eight sources, making observability, bias mitigation, and external data integration vital. He stresses the importance of a “data-first” mindset, continuous cleansing, and feedback loops, noting that AI-generated content and insights must be constantly refined with human oversight. Teams must not only organize around traditional roles like developers and data scientists, but also bring in specialists focused on data quality, testing, and security from the start.
Finally, Bansal addresses the need for structural changes in how IT teams collaborate, incorporating DevOps, security, and quality engineering into every phase of AI development. He highlights growing demand for transparency, with customers and regulators expecting clear data lineage and accountability. The conversation closes with a cautionary note: while many rushed to implement generative AI tools like chatbots, those efforts often lacked proper data curation, leading to flawed outputs. However, Bansal remains optimistic that broader adoption will drive maturity, reinforcing the age-old principle that even in an AI-driven world, “garbage in, garbage out” still holds true.
