Instead of "let's try generative AI." Say "let's lift qualified leads volume 15% using 10x more meta ads in Q3 using AI."
Quick Win: Choose one marketing KPI and set a 15–20% improvement target for your AI pilot this quarter.
2. 👥 Create Cross-Functional AI Pods
Pair a marketer + data analyst + someone who understands your tech stack. Context meets execution every sprint.
Why it works: AI needs both domain expertise and technical know-how. Solo efforts fail. The team that got you the Clio Award likely needs AI tech support. (Secret…it's unlikely to be your IT team as most see AI as a tool vs a utility).
3. 📊 Clean Your Data First
AI’s only as smart as your data. Bad data = Bad AI.
Reality Check: Can you with high confidence (bet your car) tell your customer acquisition cost by channel? If not, pause AI as your data needs a health check.
4. 🚀 Run 90-Day Sprints, Not Year-Long Projects
Focus down for 90 days.
Think testing AI natively for ad copy generation for that social channel you hate, meta ads for one product, or customer segmentation not "AI transformation."
👀 Framework to Steal:Team AI Upskilling Sprint- A proven 5-phase approach that takes teams from AI-curious » AI-productive in 12 weeks.
5. 🔄 Embed AI in Daily Workflows
Don't make AI a tool.
YOU WILL FAIL.
I know I tried.
Pick an LLM and connect it inside every workflow.
Even better, go AI native…
‘But Alec, my IT team won’t even let me connect AI to OneDrive…’
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