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What Makes Data “AI-Ready”? A Practical Checklist for Enterprise Leaders
Every enterprise AI initiative starts with the same quiet assumption: that the data feeding the model is good enough to trust. That assumption is where most projects fail. Not because the AI models are wrong. Not because the use cases ...
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Building an AI Governance Framework: Roles, Policies, and Controls for the Enterprise
A few years ago, "AI governance" mostly meant a slide in a board deck and maybe a one-page ethics statement nobody read twice. That's not where most enterprises are anymore. AI is approving loans, screening resumes, drafting customer correspondence, and ...
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Beyond RHITL: It’s Time to Name the Loop After the Expert
Executive Summary (TL;DR) RHITL established that the right human must be in the loop. This blog takes the next step: naming the loop after the expert. “Human in the Loop” is engineer-speak. “Clinician in the Loop” is the language of ...
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Switch To Production: Building An AI-Ready Data Foundation
As organizations embrace enterprise AI, a fundamental question rumbles beneath the excitement: How do we govern enterprise AI and switch pilot projects into production? The answer depends less on AI’s unseen algorithms and far more on the data that supports ...
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Your AI Is Only as Good as Your Data
My CEO, Sai Gundavelli, shared a recent Gartner report on the top pressures CIOs face right now. One section captured the AI production problem more clearly than anything I’ve read this year. The numbers laid out the AI investment paradox ...
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The AI Reliability Gap
The AI Readiness Problem No One Can Ignore AI is moving from experiment to enterprise standard at remarkable speed. Teams are rolling out copilots, conversational interfaces, and agentic workflows with real ambition, but many organizations are discovering that deployment speed ...
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Enterprise Data Preservation: Turning Application Retirement into Strategic Advantage
Executive Summary Enterprise data preservation has moved far beyond cost savings. Three forces—cloud migration, surging M&A activity, and AI’s demand for governed data—have made it a strategic imperative. This post examines how application retirement now includes cybersecurity urgency, how M&A ...
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Remember, Backup is not Archiving!
I’ve been in the Information Lifecycle Management space since my days with Princeton Softech, and then IBM Optim. Way back then there was a misunderstanding about Backups really being a proxy for Archives. They aren’t. I’ve recently gotten some feedback ...
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The Great AI Splinter: Why Sovereign Stacks are Replacing Global Platforms
Gartner® just released a prediction (https://www.gartner.com/en/newsroom/press-releases/2026-01-29-gartner-predicts-35-percent-of-countries-will-be-locked-into-region-specific-ai-platforms-by-2027). “By 2027, 35% of countries will be locked into region-specific AI platforms using proprietary contextual data. Gartner also predicts that platform lock-in will rise from 5% to 35% by 2027.” This is not about ...
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How to Choose the Right Data Masking Solution – and What’s Next for the Industry
Data breaches aren’t slowing down—and neither are the demands for tighter data protection. With rising cyberattacks, data privacy regulations tightening globally, and non-production environments becoming a major source of breaches, data masking has shifted from a best practice to a ...
