Physical Intelligence raised $600 million at a $5.6 billion valuation for software that acts as a universal brain for robots. The hype is real, but so is the gap between lab demos and production reality. We break down what actually works in Physical AI today, the three hard problems nobody's solving yet, and why investors are betting billions on robot brains instead of robot bodies.

Network Services Distribution transformed its IT operations by replacing spreadsheet-based project tracking with an integrated monday.com solution. The result: 300+ hours of administrative work eliminated annually, real-time visibility for leadership, and a scalable foundation that grew with the business.

LLMs aren’t “bad” or “overhyped” – they’re jagged: impressive on benchmarks, brittle in real workflows. This article explains why that gap shows up as real cost in production, and why LLM observability is the foundation for turning capability into predictable throughput. You’ll see how observability, evaluation-driven development, guardrails, RAG, and agentic checkpoints work together to make GenAI reliable, governable, and worth scaling.

Most enterprise AI failures in 2025 had nothing to do with model quality. They failed because the systems didn’t understand context — who the user was, what problem they were solving, and how information related across departments and data silos. Adding more “memory” didn’t fix it. Persistent chat logs and vector databases only stored facts; they didn’t create meaning. The next generation of enterprise AI must treat context as a living system: continuously curated, governed, and shared across every model and agent in the organization. When context becomes a core design principle, AI stops guessing and starts reasoning. It stops recalling text and starts connecting knowledge. That’s when ROI appears — not from bigger models, but from smarter architectures that integrate data, identity, and governance into every answer.

Every interaction in your contact center shapes customer trust. Too often, companies treat it as a cost to cut rather than a strategic driver of loyalty and growth. This article explores how modernization—powered by AI, cloud migration, CRM optimization, and data unification—turns your contact center into a competitive advantage.

AI succeeds when people trust it, understand it, and see it improve their work. This guide outlines Sphere’s approach to enterprise AI adoption—pairing domain leaders with data talent, making systems explain themselves, and focusing on the last mile that differentiates your business. From clear rules to partner-led delivery, learn how to build AI solutions that teams embrace and results that last.

Large language models hold transformative potential for healthcare — from clinical summarization to real-time risk detection — but only if used responsibly. In this guide, we outline a step-by-step roadmap to prepare your healthcare data for LLM use without risking compliance violations. From tackling data silos to securing PHI, and from model fine-tuning to governance best practices, discover how to move from fragmented data to safe, AI-ready infrastructure. Plus, learn how Sphere Data Agent helps organizations deploy LLMs up to 3x faster while staying HIPAA-compliant.

Synthetic data promises better privacy, faster experimentation, and scalable AI training — but only when done right. At Sphere, we’ve seen that the real differentiator isn’t the generation technique itself, but how and where it’s applied. In this article, we unpack what makes synthetic data valuable, when it works best, and what to look for in a partner.

Data is the fuel of modern engineering. Yet many organizations still struggle with silos, outdated files, and fragmented systems that slow down progress and innovation. In this guide, we explore how to streamline engineering data management—from strategy and governance to tools and cloud infrastructure. Whether you're dealing with massive CAD files or real-time IoT streams, this article shows you how to get your data under control and working for you.

Discover how a leading financial services firm leveraged advanced AI-driven proposals and an innovative Smart Wallet application to accelerate client acquisition, enhance stakeholder satisfaction, and secure a distinctive competitive edge.