You’re stuck in the same loop. Pipelines break, definitions don’t match, and every quick question turns into a Slack fire drill. Meanwhile, your stakeholders want AI, better forecasts, and faster decisions, but you can’t get the budget for cleanup....
Previously, generative AI was limited to pilot decks, but gradually it has now seeped into daily processes in pharma and life sciences. AI models are now used by medical affairs, clinical and commercial teams for various processes such as scanning literature,...
Classic ML shipped with clean report cards. Accuracy, precision, recall, F1. You could argue about tradeoffs, but at least everyone agreed on what “good” meant. LLM apps don’t give you that comfort. RAG answers can be fluent and wrong. Copilots can...
2026 is a defining year for data, analytics, and AI. While 2025 laid the foundation, this year shifts the focus from experimentation to agentic AI and responsible data governance. Organizations that master what’s new will pull ahead—while others face rising risk,...
A year ago, the loudest AI debates were about model size, benchmark wins, and who trained what first. That still matters, but it’s starting to feel like the easy part. Models move fast now. Open models spread quickly, fine-tunes are routine, and strong capabilities...
For years, attempts to build “emotion-detecting machines” carried a heavy dose of hope, but often underwhelmed. Early emotion-AI systems primarily focused on single signals (like facial expressions or voice tone), and tried to guess a person’s inner emotional state....
