The Judgment That Left With the Employee
When someone configures an AI agent and then leaves the company, the agent keeps running on that person's logic. The problem is that no one else knows that logic ever existed.
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AI in practice
Ideas, guides and behind-the-scenes on how companies are using artificial intelligence across sales, marketing, support and management — without the hype.
Strategy for AI-First Companies
The Agent Found the Niche. The Company Stopped Asking Questions.
When an AI agent identifies the most profitable segment in your funnel, the danger isn't concentrating resources there — it's that growth in that niche starts disguising the silent disappearance of everyone else.
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July 2026 6
The Agent Is the Process. No One Documented It.
When automation replaced the informal knowledge of people who already left, the company loses its recovery layer. A minimum protocol for capturing what there's still time to save.
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OpenAI Courts the White House, Anthropic Enters Drug Discovery, and Alibaba Pulls the Plug on Claude Code
Three moves this week that say more about where enterprise AI is actually heading than any product announcement: a political hedge, a science bet, and a corporate security call.
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What Your AI Agent Chose Not to See
Agents optimized for a single metric make silent choices about what to discard. The problem never shows up on the dashboard — it shows up months later, when the damage is already done.
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Claude Science, AI agents as coworkers, and the data problem no one wants to talk about
Anthropic bets on scientific research as its next major frontier, a sharp MIT piece reframes what AI agents actually are inside organizations, and agriculture shows why bad data kills good AI before it starts.
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June 2026 9
The Agent Optimized the Funnel. Nobody Logged the Hypothesis.
When an AI agent takes over funnel optimization, it makes decisions that embed implicit assumptions about your business. The problem is that none of those assumptions are ever written down.
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The Agent Learned. The Salespeople Forgot.
When a company captures its top performers' patterns to train a sales AI agent, it creates a quiet problem: the knowledge freezes, and no one notices until something needs to be fixed and no one knows how anymore.
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You Picked the Vendor for Price. Now They Set the Limits.
Most companies choose their LLM provider based on cost per token. The problem shows up 18 months later, when switching models costs more than staying locked in.
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The exception the agent ignored was your best customer
AI agents follow patterns with precision. The problem is that your most valuable cases rarely fit any pattern — and the agent can't tell the difference.
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The Agent Answered Right. The Question Was Wrong.
Most AI audits measure whether the agent delivered what was asked. Nobody audits whether what was asked still makes sense.
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The Report Was Right. The Decision Was Wrong.
The AI delivered an accurate analysis and the company still made the wrong call. Understanding why that happens — and how to separate tool failure from judgment failure — is what sets learning organizations apart from those stuck in circles.
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The end of the prompt: why AI's next skill will be writing loops
The first skill of the AI era was learning to ask. The next one isn't writing better prompts: it's writing loops — systems that execute, test, fix, and only stop when they meet the criteria.
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You Automated the Output. The Input Is Still a Guess.
Most companies apply AI at the bottom of the funnel while ICP definition and value hypotheses remain untested assumptions. When the premise is wrong, automation just accelerates the mistake.
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AI Security Cracks, Google Reinvents Search, and the Price of Coding Agents
This week's most important AI moves for business leaders: a real attack on Meta's support agent, OpenAI's new security layer, Google's biggest search redesign in 25 years, and the cost debate around AI coding tools.
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