Operational Database Management Systems
Q1. Your research found that 57% of enterprises prioritize storage performance to avoid AI bottlenecks — actually ahead of compute and GPU availability. That surprises a lot of people, since the AI conversation is dominated...
Q1. You’ve spent over 15 years working across AI and multilingual technology, often joining organizations before the direction was fully clear. In your experience, what is the most common mistake teams make when they...
Q1. Enterprise ERP modernization has been on the agenda for decades, yet many organizations still struggle with legacy complexity. What are the most common reasons large enterprises find themselves stuck — and what are...
Q1. Reva, you’ve spent two decades evaluating automated technologies in high-stakes settings — from your work as a forensic scientist at the U.S. Secret Service to leading NIST’s AI risk management efforts. How has your...
Q1. Imran, one of the most compelling promises of Semantic RAG with Progressive Graphs is improved accuracy and reduced risk in AI systems. Can you walk us through how this approach delivers on these...
Q1. The scorecard makes it clear that 2025 still exposed fundamental weaknesses in how organizations approach data resilience, from untested recovery plans to unverified AI data lineage. When you talk to IT leaders about these...
Interview by Ramesh Chitor Q1 : Enterprise HR and legal teams are under pressure to adopt AI for talent while avoiding the pitfalls now in the spotlight with examples like Eightfold.AI. Can you give us some...
Q1. How does the shift toward no-code RAG platforms change the accessibility landscape for organizations implementing generative AI, and what are the potential trade-offs between ease of use and customization flexibility that enterprises should...
Q1. Most executives understand they need better data and better AI models, but the movement of data isn’t typically on their radar. When you talk to boards and C-suites about why milliseconds matter to...
Q1. How is Python evolving its infrastructure and tooling to support the explosive growth of AI and machine learning workloads, particularly around vector operations and large-scale data processing? For AI and ML workloads I see...