Humans compensate for bad data. Agents can’t. Here’s what changes. The Uncomfortable Number A March 2026 study by Harvard Business Review Analytic Services and Cloudera surveyed 230+ enterprise data leaders with a simple question: is your data ready for AI? Only 7% said yes. Only 7%. Completely ready. The other 93% are not — and are running AI experiments on a foundation that was never designed for what they’re asking it to do. ...
Here’s a pattern I keep seeing in teams building with LLMs. An engineering team adopts AI. They pick a frontier model — Opus, GPT-5.4, whatever’s top of the leaderboard. It works. They ship. Every task runs through it. JSON extraction. Summarization. Classification. Multi-step reasoning. All of it. The problem wasn’t a wrong choice. It was no choice at all. If you’ve been in cloud long enough, you recognize this. It’s 2012 again. Running an m5.16xlarge for a cron job. The bill arrives. You feel it. ...
The Age-Old Problem For most of human history, dreaming was easy. Execution was not. People have always carried brilliant ideas inside them—stories, products, systems, businesses, and art. But between imagination and reality stood massive friction: time, money, skills, teams, and permission. So, most dreams stayed exactly where they were born. Unexpressed. Unbuilt. Unshared. AI changes that. It is fundamentally collapsing the distance between thinking and doing. The Shift We’re Underestimating We often talk about AI in terms of mere productivity: Faster design mockups. Faster code. Faster analysis. ...