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ML system design interviews test how well you can think beyond models. In these interviews, choosing an algorithm is only one part of the answer. You also need to explain how data is collected, how features are created, how predictions are served, and how the system improves over time. Most real ML systems are built […]
System Design for ML Interviews: 10 Real Problems Walked Through Read More »
If your remit is to help your organisation add AI agents to accelerate its processes, you have to start at the foundation – and that means making your data available for AI consumption. Agentic AI scales on data strength, as Niels Zeilemaker, global CTO at Xebia, explains. “If you don’t think about that, you can
Xebia: On building the data foundation for AI agents – and then accelerating Read More »
You walk into the interview room. The whiteboard displays the following prompt: “A major retailer wants to deploy a GenAI chatbot for customer support. How would you approach this?” You have 35 minutes. Your palms are sweating. Sound familiar? GenAI case studies currently serve as the primary challenge which interviewers use to test candidates in
6 Steps to Crack GenAI Case Study Interviews (With Real Examples) Read More »
Enterprise leaders must progress past generative applications and scale “autonomous intelligence” to capture real P&L margin growth. Generating text or summarising internal communications offers localised productivity improvements, yet these abilities rarely alter the core cost structure of a large organisation. Enterprises are now focused on deploying systems capable of independent execution. Leaders are demanding applications
Deloitte: Scale ‘autonomous intelligence’ for real growth Read More »
Executive Summary. Vasili Triant explains why AI is not replacing enterprise systems but eliminating redundant CRM layers as the stack shifts toward real-time orchestration and unified agent workflows. Enterprise customer experience is entering a structural transition as AI moves from front-end automation to real-time orchestration across systems. The question is no longer whether AI will
Vasili Triant — Why AI Is Replacing CRM Layers, Not Enterprise Systems Read More »
Executive Summary. France Hoang argues that AI in education must evolve from isolated tools into governed, collaborative infrastructure that institutions can oversee, audit, and align with learning outcomes. As AI becomes embedded in higher education, institutions face a fundamental shift from adopting tools to operating AI as core infrastructure. The challenge is no longer access
France Hoang — Building Governable AI Systems for Universities Read More »
Executive Summary. Casey Hite explains how fragmented insurance workflows are becoming the proving ground for AI in healthcare operations, and why real-time validation, disciplined automation, and governance-first design are essential to improving patient access without eroding trust. As healthcare organizations scale, administrative complexity around insurance verification, approvals, and documentation continues to act as a hidden
Casey Hite — Engineering Predictable Access in AI-Driven Healthcare Operations Read More »
Physical AI–the branch of artificial intelligence that controls robots and industrial machinery in the real world–has a hierarchy problem. At the top, OpenAI and Google are scaling multimodal foundation models. In the middle, Nvidia is building the platforms and tools for physical AI development. And then there is a third camp: industrial manufacturers like Hitachi
Hitachi bets on industrial expertise to win the physical AI race Read More »
Executive Summary. Baran Ozkan explains how AI-native systems, false-positive reduction, and workflow clarity are redefining how institutions scale regulated operations without losing audit defensibility. Financial crime compliance is moving from rule-heavy oversight to operational infrastructure. As fintech and banking systems scale in complexity, institutions are being forced to rethink how monitoring, investigations, and audit readiness
Baran Ozkan — Building the Operating System for Financial Crime Compliance Read More »
Data Engineering is not just about moving data from point A to point B. In 2026, data engineers are expected to design scalable, reliable, cost-efficient, and analytics-ready data systems that support real-time decision making, AI workloads, and business intelligence. Modern data engineers work at the intersection of distributed systems, cloud platforms, big data processing, and
30+ Data Engineer Interview Questions and Answers Read More »
