Systems that perform tasks associated with human intelligence, including understanding language, recognizing patterns, making decisions, and generating content. For agencies, AI is no longer an abstract technology category; it is the substrate beneath a growing share of the tools that run creative production, media planning, analytics, and client reporting.
Also known as AI, machine intelligence
Artificial intelligence is a broad field of computer science concerned with building systems that can perform tasks that would normally require human intelligence. The category includes everything from narrow rule-based systems to modern machine learning models. What unifies them is the goal: making machines that can perceive, reason, and act in ways that approximate or extend human cognitive capability.
Modern AI, especially since the mid-2010s, is dominated by machine learning approaches in which systems learn patterns from large datasets rather than following explicit, hand-coded rules. The current wave is largely driven by deep learning, a subset of machine learning using layered neural networks. Generative AI is the most commercially visible branch of this wave, producing text, images, audio, and video on demand.
AI is not a single technology. It is a family of approaches with different strengths, failure modes, and costs. Understanding which type of AI a given tool uses, and what that type can and cannot do, is prerequisite knowledge for any agency making adoption decisions.
Every industry is affected by AI, but agencies are affected in a structurally different way. Most industries use AI to automate back-office processes. Agencies use AI to produce, or assist in producing, the deliverables clients pay for. That is a different risk profile, a different ethical surface, and a different conversation with clients.
Creative production. AI tools are now embedded in the workflows that produce copy, design, video, and media assets. Agencies that have not mapped where AI is and is not being used in their production process cannot accurately represent that to clients, and increasingly clients are asking.
Client advisory. Clients look to their agencies for guidance on AI as a business and marketing capability. An agency whose leadership cannot distinguish between narrow AI, large language models, and AI-powered automation platforms is poorly positioned to advise on any of them. The knowledge gap is a competitive disadvantage.
Accountability and disclosure. As AI governance standards mature, the expectation that agencies disclose AI use in deliverables is growing. Knowing what AI actually is, at a working level, is the prerequisite for building any coherent disclosure policy.
A brand agency onboards a new media client and runs an AI audit as part of discovery. They catalog every tool in the production stack that uses AI in some form: the copy assistant used by the content team, the image platform used by design, the media optimization tool used by planning, and the analytics dashboard used by account. The audit produces a simple map: what AI is in use, what it touches, and what human review steps exist at each handoff.
That map becomes the basis for the agency’s disclosure conversation with the client and for internal quality standards. It also surfaces two tools nobody had a clear owner for. Getting AI literacy across the team is not just about understanding the concept. It is about making the invisible infrastructure of the studio visible.
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.