Why marketing needs a decision infrastructure for AI | MarTech

"Programming languages are structured systems. They have syntax, grammar, modularity, version control, testing protocols and shared conventions that engineers learn early and reinforce daily. Tasks can be decomposed, interfaces are defined and dependencies are explicit. When an AI model is trained on code, it operates within an environment that already has deeply standardized patterns and well-understood constraints."

"Marketing is different. It's often described as both art and science, but in practice it operates on partially documented logic, data and that leader with a strong opinion. Taste, timing, perception, risk tolerance and lived experience all influence decisions. The rationale for why a campaign pivoted midstream, why a claim was softened or why an audience was excluded often lies in five-minute Slack exchanges, verbal reviews or the instincts of experienced leaders."

"If AI can excel where structure already exists, the opportunity in marketing is to build the missing structure around decision-making. Not to remove creativity or judgment, but to make the logic behind those judgments captured and actionable."

AI excels in code generation because programming languages possess deeply standardized patterns, explicit dependencies, and well-understood constraints that create machine-readable infrastructure. Marketing operates differently, relying on partially documented logic, subjective taste, timing, and experienced leaders' instincts. Marketing decisions often emerge from informal communications and nuanced judgment rather than formal structures. Unlike engineering teams with shared, modular decision languages, marketing organizations lack consistent terminology and formalized processes. The opportunity lies in building structured decision infrastructure for marketing through context graphs, which would capture the logic behind creative and strategic judgments without eliminating human creativity or expertise.

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