Stage 1 of 3: Preparing for AI
Your team isn't AI-ready yet — and that's fixable
Your results reflect a pattern we see in the majority of marketing teams: strong pressure to adopt AI, but real gaps in the foundations required to make it work. This isn't a technology problem — it's a data and strategy problem. And it’s fixable.
What your score tells us
A score in this range typically means one or more of these is true for your team right now:
- Your marketing data is scattered across tools and hard to connect or trust
- There's pressure to use AI but no clear strategy defining where or how
- Team confidence with AI is low — or concentrated in a few individuals
- Privacy and governance around AI use hasn't been formalised
- You're experimenting with AI but not measuring its business impact
None of these is permanent. Every team at the Advance AI stage started here. The difference is they fixed the foundations before the Scaling AI stage — not after.
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 Preparing for AI teams typically have the most urgent gaps, here's where to focus your next 90 days.
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The single most impactful thing you can do right now: identify your 2–3 most important data sources usually paid media, CRM, and web analytics and build one reliable, connected view. You don't need to connect everything at once.
Before you improve quality, document what you have: which metrics do you trust, which do you question, and which do you avoid looking at altogether? That audit is your starting point.
36% of marketers say connecting data is their most important area to improve more than any other single gap.
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You don't need a 20-page document. You need three things:
- One or two specific AI use cases with clear business rationale
- A defined success metric for each use case
- One person accountable for driving them
37% of marketing teams have no AI strategy at all. Having even a basic, documented version puts you ahead of more than a third of your peers.
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39% of marketers cite AI data privacy as a barrier to adoption. The fix isn't complicated - it's formalizing what you probably already know informally: what AI use is permitted, what requires sign-off, and how AI outputs should be validated before acting on them.
A one-page internal policy removes the hesitation that keeps teams stuck in low-stakes AI experimentation indefinitely.
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Low AI confidence is a capability problem, not a technology problem. The fastest fix: run internal sessions on the specific AI tools your team already has access to, create a shared library of approved use cases, and normalise "did we use AI to check this?" as a standard question before campaigns go live.
