A defined sequence of steps or rules used to solve a problem or produce an output, where AI algorithms go further by learning patterns from data rather than following only explicit instructions. For agencies, “the algorithm” is not an abstraction: it is the mechanism behind every ad auction, every content feed ranking, and every automated optimization their campaigns depend on.
Also known as AI algorithm, computational algorithm
An algorithm is a finite set of instructions that takes an input and produces an output. Sorting a list alphabetically is an algorithm. Calculating the shortest route between two addresses is an algorithm. In traditional computing, the logic is explicit: the programmer defines every rule. In machine learning, a different kind of algorithm is used: one that adjusts its own parameters based on data until it produces useful outputs without the programmer specifying every rule.
The word is used loosely in media and marketing to describe any automated system that makes decisions, especially platform decisions about what content to show, what ads to serve, and how to rank results. That usage is not wrong, but it flattens a meaningful distinction: some “algorithms” are rule-based systems with explicit logic, and others are learned models whose decision logic is not fully interpretable even by the people who built them.
For agencies, both types matter. Rule-based algorithms govern platform policies and ad auction mechanics. Learned models govern content distribution, audience targeting, and optimization signals. Knowing which kind you are dealing with changes how you interpret its outputs and how you try to influence it.
Agencies live inside platform algorithms. Ad auctions, organic reach, content recommendation, search ranking: all of it runs on algorithmic logic that the agency cannot fully inspect but absolutely needs to understand in order to serve clients well. Algorithmic fluency is a professional requirement, not a technical bonus.
Platform algorithms change without notice. A distribution algorithm update can cut organic reach by half or double the cost-per-click on a campaign overnight. Agencies that understand what platforms optimize for are faster to adapt because they understand the mechanism, not just the outcome. Those that treat algorithms as black boxes keep getting surprised.
Creative decisions are increasingly algorithmic inputs. On most platforms, the creative asset is part of the signal the algorithm uses to decide how to distribute content. High-engagement creative gets more distribution; low-engagement creative gets penalized. Understanding this reframes what “good creative” means in a performance context.
Algorithmic bias is a client risk. When a client’s campaign automation makes decisions based on learned patterns in data, those patterns may encode historical biases. Agencies that help clients deploy algorithmic tools share some accountability for what those tools optimize. Understanding algorithm types is the first step toward responsible deployment.
A social media team notices that a client’s organic content is reaching a fraction of its typical audience after a platform update. Rather than treating the drop as random, the strategist maps what changed in the platform’s stated ranking signals, tests three content format hypotheses over two weeks, and identifies that the algorithm is now weighting save rate more heavily than comment rate. The creative brief for the following month reflects that finding: content designed to be saved, not just reacted to. The algorithm did not change the creative strategy. It informed it. That is the practical relationship between algorithmic understanding and good agency work.
The generative AI foundations module of the workshop covers how today’s models work, what they can and can’t do, and how to choose between them.