AI Marketing Operations 101. The course I teach at Google Raichman, condensed.

What marketing ops looks like in the AI era

I designed and taught the first Marketing Operations course at the Google Raichman program. This playbook distills the core framework into something you can apply this week.

The 4 pillars of AI-native marketing ops

Pillar 1: Data architecture

Your AI is only as good as your data. Before deploying any AI tool, ensure: CRM data is clean and normalized, UTM tracking is consistent, lifecycle stages are defined, and attribution is working.

Pillar 2: Workflow automation

Identify every manual, repetitive task in your marketing workflow. Prioritize by frequency and time spent. Automate the top 10 with AI-powered workflows: content drafts, meeting summaries, competitive monitoring, lead enrichment, reporting.

Pillar 3: AI-assisted content

Use AI for first drafts, never for final output. The workflow: AI generates draft, human reviews and adds voice, human approves and publishes. Set up templates for every content type so AI output is consistent.

Pillar 4: Measurement and iteration

Track: time saved per workflow, quality delta (AI draft vs. final), adoption rate across team, error rate (hallucinations, brand violations). Review monthly. Kill what doesn't work.