Ideas, guides and behind-the-scenes on how companies are using artificial intelligence across sales, marketing, support and management — without the hype.
When an AI agent replaces human interaction in sales or support, efficiency goes up. But something disappears quietly: the unstructured information that only existed in that conversation.
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.
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.
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.
Most AI audits measure whether the agent delivered what was asked. Nobody audits whether what was asked still makes sense.
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.
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.
ChatGPT is recommending your company to your next client. The problem is it's recommending the version from two years ago.
1,200 customers at risk. 200 prioritized by the system. Eighteen months later, the team could no longer read the other 1,000 - and those were precisely the ones keeping the critical cases from surfacing.
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