Practical thinking for teams building with AI and software.

“Intelligent document processing” gets used as a catch-all term for anything that touches a scanned PDF or a photographed form, which makes it sound like one product when it’s actually three distinct technical problems stacked on top of each other: reading the text, understanding what the text means, and deciding what to do with the…

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Most AI and automation projects don’t fail because the model is wrong. They fail because the new system can’t reliably get data in and out of the systems the business already runs on — the CRM, the ERP, the practice management tool, the internal database nobody has fully documented, the legacy system with an API…

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Voice AI gets pitched as a wholesale replacement for call center staff more often than the technology actually supports. That framing sets it up to disappoint, because most of the value voice agents deliver in production isn’t “replace the team” — it’s absorbing the specific, high-volume, low-complexity portion of call volume that doesn’t need a…

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One of the first decisions a business makes when it decides to invest in AI or automation isn’t which technology to use — it’s how big a commitment to make on the first attempt. Two very different paths get pitched under the same broad heading of “AI project”: a fixed-scope AI sprint that delivers a…

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For twenty years, “getting found online” meant one thing: ranking in Google’s ten blue links. That’s no longer the whole picture. A growing share of the research people used to do by searching and clicking through several results now happens inside an AI answer — a chat interface that reads several sources, synthesizes an answer,…

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A construction or tender team rarely loses a bid because the crew can’t do the work. It loses because the proposal was late, the pricing was inconsistent with what the job actually required, or a scope item buried on page 40 of the tender documents got missed until it was too late to price properly.…

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Customer support is usually the first place a company points AI at, for good reason: ticket volume is high, patterns are repetitive, and the potential time savings are obvious from the first week of data. It’s also one of the highest-risk places to automate badly, because a bad automated decision doesn’t stay internal — it…

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“Should we build this or buy it?” is a question most operations teams eventually ask about some piece of their workflow, and the honest answer is almost never a blanket rule. Advice that says “always buy off-the-shelf, building is too risky” and advice that says “always build custom, off-the-shelf tools don’t fit your business” are…

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Most companies experimenting with AI end up with a drawer full of point solutions: a chatbot plugged into the website, a document processing tool for one back-office task, a workflow automation platform stitching together a handful of app integrations, maybe an agent framework a developer set up for one specific process. Each of these tools…

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