Practical thinking for teams building with AI and software.

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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Professional services firms — consulting shops, accounting practices, engineering and architecture firms, marketing and creative agencies — sell expertise and time, which makes “automate the business” sound like a threat to the entire revenue model. That reaction is understandable but usually misplaced. The billable work itself, the actual advice, analysis, and judgment a client is…

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Hospitality and travel companies run on something automation is naturally bad at: warmth under pressure. A guest checking in after a delayed flight, a family calling about a specific dietary need, a business traveler trying to rebook a suite five minutes before a big meeting — these moments define brand loyalty more than almost anything…

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“Should we build this with AI or just write regular software?” comes up on almost every project scoping call now, usually because AI has become the default first idea rather than one option among several. That’s a mistake in both directions: traditional software gets dismissed as old-fashioned for problems it actually solves better, and AI…

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An AI agent that takes actions — sending an email, updating a record, issuing a refund, calling an API — is a fundamentally different kind of system to operate than one that just answers questions. A chatbot that gives a wrong answer is a bad experience. An agent that takes a wrong action has done…

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E-commerce has no shortage of AI pitches: chatbots that promise to replace the support team, recommendation engines that promise to double conversion, generative tools that promise to write every product description overnight. Some of that moves revenue. Most of it doesn’t, or moves so little that the cost of running it eats the gain. The…

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“When will this pay for itself?” is the question every AI automation project eventually has to answer, and it’s usually asked before the project starts, when nobody actually knows yet. Vendor demos don’t help: a working pilot makes it look like value should start the day the system goes live. In practice, ROI on an…

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Real estate operations run on volume: leads coming in from a dozen channels, purchase agreements and disclosures stacking up on every deal, showings and closings that all need to be scheduled around other people’s calendars. None of this work is intellectually hard on its own — reading a disclosure form, logging a lead, checking that…

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Predictive maintenance is the AI use case every manufacturing vendor pitches first, and for good reason — unplanned downtime is expensive, the failure modes are physical and measurable, and “we told you the bearing would fail three weeks before it did” is a genuinely compelling story. It’s also the use case most likely to be…

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