Start With Data Hygiene
No AI workflow outperforms the quality of the data behind it. Before layering automation, teams should normalize naming conventions, fix attribution issues, and remove redundant data silos.
Reliable inputs create reliable recommendations.
Prioritize Interoperability
Your stack should allow campaign, CRM, analytics, and content systems to share context cleanly. Fragmented tools create blind spots that AI cannot solve. Integration quality is often a bigger growth lever than adding another platform.
Build Process Before Scale
Define who prompts, who reviews, who approves, and how learning is documented. This operating model is what turns AI experiments into repeatable performance gains.
