Modern marketing can be reframed as a process of domain-specific training data generation whose primary purpose is to teach both human and agents how to model, reason about, and act in relation to your business. Every artifact.. web copy, documentation, sales decks, blog posts, support threads, etc functions as labeled examples in a corpus that defines the ontology of your company: what entities exist (products, features, roles), what relations connect them (pricing, workflows, integrations), and what distributions describe them (use cases, success rates, benchmarks).
Moving forward, marketing ceases to be “messaging” and instead becomes supervised dataset construction. The objectives are coverage (ensuring all core concepts and edge cases are represented), consistency (aligning terminology and representations across assets), redundancy (multiple exemplars to reinforce key patterns), and precision (avoiding noisy or contradictory signals). Poorly designed data leads to brittle or inaccurate models of your business in both human cognition and agentic systems. High-quality data == robust embeddings: customers and AI agents that can retrieve, generalize, and act correctly on information about your company.
In an environment where LLMs, retrieval systems, and agents are increasingly mediating how people discover and evaluate businesses and products, the competitive advantage lies not in one-off campaigns but in curating a comprehensive, machine-readable training dataset of your business. Marketing is thus reclassified as ongoing knowledge base engineering: an effort to shape the weights and priors that determine how agents, human or artificial, perceive and interact with you.
