7 stories
Board week has a familiar rhythm. Finance pulls numbers from three different systems, someone rebuilds the variance table by hand, and the cashflow forecast is already a day old by the time it lands in the deck. The pressure is real.
Introduction Last quarter, a financial services firm I work with made a pricing decision during a meeting, wrote it up in a memo, and walked away confident. A month later, nobody could recall why they'd chosen 14 percent instead of 12. The memo was vague. The reasoning was gone.
Ask an AI a simple question about a supplier, and it can hand back a confident, well-formatted answer that's completely made up, a vendor that was never signed, a contract term that doesn't exist. That's not a quirky bug. In a regulated business, it's a compliance red flag.
A team runs an AI agent on what looks like a clean dataset and asks a simple question: what's the projected Q4 revenue for the East Coast division? The agent returns a number, confident and completely wrong. The real culprit usually isn't the model.
You've felt it. You tell Claude your product roadmap, then jump into ChatGPT to draft a launch email and realize it has no idea what you're talking about. The knowledge you painstakingly built in one tool is completely absent in another.
Your team lives in Slack. Your answers live in a warehouse, a CRM, and a dozen other SaaS tools that don't talk to each other. So people toggle tabs, copy-paste numbers into a channel, and hope the figure they just posted is still current by the time someone reads it.
Introduction You know that feeling of dread when someone asks a question you know is answered in that doc you wrote six months ago? The doc that’s now riddled with stale screenshots and outdated numbers. Traditional wikis rot.
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