AI Strategy
Articles
Articles on AI strategy: use-case selection, pilots that reach production, budgeting, governance and the decisions that make AI projects pay off.
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Most AI projects do not fail on the model. They fail earlier, on the choice of problem, the state of the data or the absence of a baseline that would show whether anything improved. The articles in this category are about those earlier decisions: how to pick use cases that pay off, how to budget and staff a pilot, when to write the stop rule, and how to tell a working proof of concept from a prototype that became production by accident.
The pieces are written for the people who sign off on AI budgets rather than the people who train models. Expect checklists, cost arithmetic and failure patterns from real projects, not tool reviews. Where a topic deserves hands-on help, the article says what that engagement looks like and what it should deliver.
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Ramp-up is the hidden cost of team augmentation
Small teams and the scope they cannot afford
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Three numbers to watch in month one
Two days of work that makes AI changes safe
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When adding engineers makes things worse
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When the internal portal becomes the product
Where AI pilots actually die
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Why a useful AI system has to be allowed to refuse
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AI moved the work, it didn't remove it
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Count connections, not licences
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Design for the compliance boundary first
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Key-person risk is an AI use case
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Team shape decides whether augmentation works
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The bottleneck is the interface, not the data
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The lock-in that survives a data export
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The metric everyone forgets when they add AI to a team
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The migration budget nobody writes down
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The prototype that became production by accident
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The questions your stack cannot answer
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The spreadsheet that quietly became your system of record
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The take-home test is dead. What replaced it.
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The unglamorous prerequisite for your AI product
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The year-three cost of the spreadsheet solution