Algorithms
The set of rules a system follows to turn data into a decision — and why those rules are never as neutral as they seem.
An algorithm is a defined set of steps or rules that a system follows to produce an output from an input. In business AI, algorithms determine how systems rank leads, score risk, route requests, set prices, recommend content, or flag exceptions. They encode choices: what to optimize, what data to use, what constraints to apply, what to ignore. Those choices are made by people — and they have consequences for the customers, employees, or processes the algorithm acts on.
Algorithmic outputs carry an aura of objectivity that manual decisions don't, which makes them harder to question and easier to over-trust. A ranking algorithm that prioritizes revenue can disadvantage small customers; one that optimizes efficiency can erode safety margins. The outputs feel neutral because a computer produced them. Executives who ask what the algorithm is actually optimizing — not just what it was designed to optimize — tend to find the gap between the two is where the real risk lives.
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Machine Learning
AI that learns patterns from data rather than following fixed rules — which means its behavior is only as good as the data it learned from.
FoundationsModel
The learned component at the core of an AI system — what turns inputs into predictions, decisions, or generated content.
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Teaching a model to sort things into categories — and learning why the wrong kind of wrong can be more costly than no AI at all.
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Algorithms
The set of rules a system follows to turn data into a decision — and why those rules are never as neutral as they seem.
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