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Stay on top of LLMs, Generative AI, and what’s trending in the industry. Explore actionable strategies for adopting AI in complex enterprise environments.
Recent blogs
Generative AI Strategy: A Complete Guide for Businesses
Key Takeaways The $10 Million Paradox: Why can two companies invest the same amount in the same AI technology, yet...
Why Businesses Need an AI Strategy
Key Takeaways The Billion-Dollar Paradox: A costly paradox is crippling enterprise AI. While 83% of executives label it a top...
AI POC (Proof of Concept): Benefits, Importance & Steps to Build One
Key Takeaways The stark reality: 95% of enterprise AI pilots fail to reach production, yet the organizations that succeed see...
BROWSE ALL BLOGS
Why Payers Need Enterprise AI-First Infrastructure
Key Takeaways Enterprise AI-infused systems often fail in production because inference is treated as a sidecar, not a core capability....
Why Off-the-Shelf AI Fails Healthcare?
Key Takeaways Off-the-shelf LLMs create hidden costs and risk. Custom models align better with payer workflows. You gain auditability, prompt...
How to Navigate GenAI Fog: A Strategic Map for Payers
In the past six months, I’ve been in more than a dozen boardrooms, mostly as the person they call when...
Why Payers Are Replacing Proprietary LLMs
Key Takeaways Proprietary APIs create hidden risks in healthcare workflows Open-source LLMs now match proprietary performance in key tasks Full...
Why MLOps Breaks Down in Enterprise AI for Payer Orgs
Key Takeaways MLOps assumptions fail under LLM complexity in payer workflows Prompt behavior, not just model performance, must be operationalized...
Why Hospitals Still Struggle with Real-Time Data
Key Takeaways Most hospitals can’t act on data fast enough to support timely decisions. Real-time failure leads to clinical risk,...
Why Healthcare Interoperability Still Fails
Key Takeaways Poor interoperability costs U.S. healthcare $30B annually Most health systems still operate on fragmented legacy systems FHIR adoption...
How to Build Scalable AI Teams in Health
Key Takeaways 70% of leaders cite talent as the top blocker to enterprise AI adoption. The gap is engineers, translators,...
How to Earn Clinician Trust in Enterprise AI
Key Takeaways 96% of clinicians see potential but trust still lags. Only 26% of U.S. providers trust Enterprise AI today....
How Predictive Tools Ease Nurse Burnout
Key Takeaways Nurses lose up to 35% of their shift to documentation. Documentation, staffing, and med workflows are major burnout...
Why Enterprise AI Is Key to Clinician Retention in Healthcare
Key Takeaways Burnout is a workflow failure, not a wellness issue. Administrative drag and decision fatigue stem from data system...
Why Every AI Demo Looks Amazing and Every AI Deployment Falls Apart
TL;DR Enterprise AI isn’t underdelivering because the models are bad. It’s underdelivering because leaders are building LLM initiatives on top...