A computational model loosely inspired by how biological neurons connect and signal, organized in layers that learn to recognize patterns by adjusting connection weights through repeated exposure to training data. Almost every AI tool an agency uses today runs on some form of neural network at its core.
Also known as ANN, neural network, deep network
An artificial neural network (ANN) is a mathematical system made up of layers of interconnected nodes (neurons). Input data passes through these layers, each of which transforms the data in some way. The network learns by adjusting the strength of connections between nodes based on how wrong its outputs are, a process called backpropagation. Over many training cycles on large datasets, the network gets better at producing correct outputs for the task it was trained on.
Deep learning refers to neural networks with many layers. The depth is what allows the network to learn increasingly abstract representations of data: early layers might detect edges in an image; later layers detect shapes; still later ones detect objects. This hierarchy of representations is what makes deep networks powerful for complex tasks like image recognition, language generation, and translation.
The large language models behind today’s text tools, the image models behind image generation platforms, and the recommendation systems behind every major media platform are all neural networks of different architectures and scales.
You do not need to know how to build a neural network to use one effectively. But understanding roughly how they work changes how you evaluate their outputs, how you write briefs for AI-assisted work, and how you answer client questions about what the tools are actually doing.
Output quality and its limits. Neural networks learn from training data. If the training data is biased, narrow, or outdated, the outputs will reflect that, regardless of how sophisticated the architecture. An agency that understands this can make better decisions about which tools to use for which tasks and can set more accurate quality expectations with clients.
Explaining AI behavior to clients. When a generative tool produces unexpected output, clients want to know why. “The model made a mistake” is less useful than “the model was trained primarily on data that skews toward X, which is why it defaulted to Y.” The neural network framing gives account teams vocabulary for honest, grounded explanations.
Vendor evaluation. AI tool vendors often describe their products in terms of model architecture. Knowing that a tool uses a transformer-based network versus a convolutional one, and understanding broadly what that means for the task at hand, is useful when comparing options for specific production needs.
A content team is using a text generation tool and noticing that outputs in a particular category, say financial services copy, consistently feel generic and overly formal. They escalate to the strategy director. She knows that the model was trained on a broad corpus weighted toward formal institutional text, and that the tool does not have built-in exposure to the client’s specific tone guidelines. The solution is not to ask the model harder. It is to provide more precise context in the prompt or to evaluate whether a fine-tuning approach is worth the investment for this client volume.
That is a practical neural network insight: understanding that the model’s outputs are shaped by its training distribution helps the team intervene at the right point rather than fighting the output indefinitely.
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.