"AI decisioning is more closely aligned with how human decision-making goes. It uses real-time data and patterns to determine the best action - instead of legacy if/then logic that requires you to pre-map every route. It's about patterns and trends - and giving AI the tools it needs to make the decisions you've decided it can make on your behalf."
"It's the evolution of traditional enterprise decisioning. AI learns from structured and unstructured data - patterns, interactions, trends - to adapt decisions over time. The live poll during the session showed a split audience: some were "doing it well," others were "working on data hygiene," and a sizable group was "not sure where to begin." That spread framed the rest of the hour."
"If AI is so smart, why does hygiene matter? Because inference fills gaps - and sometimes invents them. Robbert offered a crisp answer: "Data hygiene is step one. You don't want AI making assumptions - especially in decisioning." She uses the Six Cs data-quality framework to audit inputs: Clean (free of errors). Complete (no missing info). Comprehensive (actually covers the question you're asking). Calculable (structured so business users can work with it)."
AI decisioning moves brands beyond brittle if/then logic by using real-time data, patterns, and both structured and unstructured inputs to adapt decisions over time. Effective AI decisioning mirrors human decision-making and relies on pattern recognition and trend analysis to automate allowed decisions. Reliable outputs depend on rigorous data hygiene because inference can fill or invent gaps. The Six Cs framework—Clean, Complete, Comprehensive, Calculable—provides an audit for data quality. Teams must set guardrails and build roadmaps that prove ROI before scaling decisioning into critical marketing processes.
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