Stage 2 of 3: Scaling AI stage
You’ve built the foundations — now it’s time to scale AI
Your results show a team that has made real progress: data is reasonably connected, AI tools are in use, and there's awareness of where you need to get to. The challenge now is structural — going from isolated experiments to consistent, strategic AI-driven performance.
What your score tells us
A score in this range typically reflects a team that has cleared the basics but hasn't yet made AI a strategic driver of performance.
Here's what we usually see at Scaling AI stage:
- AI is being used for efficiency and automation — not for analytics, attribution, or activation
- Data is mostly connected but the process isn't fully automated or trusted at every level
- Some team members are confident with AI; others aren't — capability is uneven
- Use cases are defined informally but success metrics and ownership are partly unclear
- ROI of AI initiatives is hard to demonstrate to leadership consistently
The teams that break through from Scaling AI stage to Advance AI stage share one trait: they stopped experimenting across everything and went deep on one high-value use case with proper measurement from day one.
You're not alone, here's what the data says
80% of marketing teams feel pressure from leadership to adopt AI. But only 6% have fully embedded it in their workflows.
- 37% of marketers lack a clear AI strategy or vision from leadership.
- 39% have concerns about AI data privacy — but most don't have formal policies in place.
- 66% of marketers report only moderate trust in AI outputs.
Your personalized action plan
Based on where Scaling AI teams typically have the most urgent gaps, here's where to focus your next 90 days.
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Stop spreading AI across everything. Choose the single use case with the clearest business value and run a structured 60-day pilot:
- Define the hypothesis before you start
- Set success metrics on day one — not at the end
- Assign a named owner who is accountable for the result
Budget allocation optimisation, predictive audience scoring, and cross-channel attribution are the three use cases with the strongest track record at Scaling AI stage.
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Uneven AI capabilities create internal friction and slow adoption. Audit where the gaps are across your team and fill them deliberately:
- Targeted training on the specific tools you already use (not generic AI literacy)
- A shared, living document of approved use cases your team can reference
- A norm of critically reviewing AI outputs before acting — not just accepting them
The goal isn't for everyone to use AI identically. It's the removal of capability variance that makes AI adoption inconsistent.
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If you can't demonstrate AI's impact clearly, it stays experimental. Before scaling any AI use case, define:
- What metric this AI use case should move
- By how much, over what timeframe
- How you'll isolate AI's contribution from other factors
Marketing Mix Modelling is the #1 planned measurement investment for 2026 (40% of marketers). If you're not already using it, evaluate whether it's the right tool for your current use cases.
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You can build audience segments — but can you act on them automatically? 44% of marketers can segment vs. just 24% who personalize at scale.
Start with the simplest, highest-ROI activation use case: excluding existing customers from acquisition campaigns. It requires your CRM data to be connected to your paid media platform — which is a realistic goal for a Scaling AI stage team — and delivers immediate, measurable reduction in wasted spend.
