Ready for What? AI Readiness in Marketing Today

AI is already embedded in many of the marketing tools we use daily—the real question is whether your data and your team are ready to use it well.

Jasmine Libert

Senior Vice President, Data Solutions

"I like to think about this stuff in my free time. "

AI is already embedded in many of the marketing tools we use daily—the real question is whether your data and your team are ready to use it well.

AI Is Already in Your Marketing Stack

In a recent LinkedIn discussion about AI readiness in marketing, someone asked, “Ready for what?” It’s a fair question. Many marketers talk about “getting ready for AI” as if it’s some far-off future event. The reality is that AI is already here, working behind the scenes in the marketing tools you use daily. Rather than asking when AI will arrive, we should ask whether we’re taking full advantage of the AI that’s already built into our stack. Here are just a few examples of Google-powered marketing tools leveraging AI right now:

Google Analytics (GA): GA uses Google’s advanced machine learning under the hood. It can automatically detect anomalies in your data (like unusual traffic spikes or drops) and generate insights. More impressively, GA offers predictive metrics and audiences – AI models segment users based on their likelihood to perform certain actions (purchase, churn, etc.) and can even forecast future behaviour. For example, predictive audiences allow you to target users likely to convert, which early adopters have seen increase ROAS by up to 30% when integrated with Google Ads.
Campaign Manager 360 (CM360): As part of Google Marketing Platform, CM360 uses AI for advanced attribution modeling. Its data-driven attribution option employs machine learning to assign credit to marketing touchpoints along the customer journey. This model looks at historical conversion paths and dynamically learns which ads or channels have the most impact. Brands switching to this model have reported more accurate ROI insights and smarter reallocation of media budgets—sometimes shifting spend mid-campaign to outperform original plans by 10–15%.
Google Ads: If you’ve used Google Ads lately, you’ve probably noticed AI at work. Features like Smart Bidding, responsive search ads, and automated targeting are all powered by Google’s AI. For example, Smart Bidding uses machine learning to set bids optimally for each auction, and responsive ads automatically test combinations of headlines and images to find what works. Smart Bidding alone has been shown to improve conversions by 20% on average, while reducing cost-per-acquisition. And it’s not just optimizing spend—it’s learning from every single conversion signal, in real time.
BigQuery + BigQuery ML: Google’s cloud data warehouse might not sound like a marketing tool, but it’s a powerhouse for marketers with lots of first-party data. BigQuery enables you to build and run machine learning models right where your data lives (via BigQuery ML). Recently, Google introduced an AI assistant (Gemini) within BigQuery’s interface to help with data prep – it can suggest how to cleanse or transform your data intelligently. This means even data wrangling is getting an AI boost. For marketers, BigQuery and its AI capabilities are key to analyzing large datasets (like your GA exports or CRM data) and uncovering deeper insights. One global brand using BigQuery ML to segment customers and personalize offers saw a 21% increase in email engagement and 18% lift in retention.
Google’s Gemini AI: Speaking of Gemini, Google’s gen-AI chatbot is now weaving its way into marketing tools. For instance, Looker Studio Pro (Google’s BI/dashboard tool) recently integrated Gemini as an AI assistant. It can suggest calculated fields in your reports, help manipulate data, and even generate slide presentations from your dashboards. And of course, on the consumer side, Google’s Gemini is behind new AI-driven search experiences.

Why This Matters Now

This isn’t a futuristic “what if” scenario—it’s what’s powering your marketing stack today. The marketers getting the most value out of AI aren’t necessarily the ones building custom LLMs. They’re the ones feeding good data into tools that already know what to do with it.

So “AI readiness” isn’t a matter of if you’ll need it. It’s whether you’re giving these tools what they need to perform—and whether your team is equipped to use the results.

High-Quality Data: The Core of AI Readiness

If AI is the engine, data is the fuel – and you don’t want to run a Ferrari on low-octane gas or, worse, garbage. Every AI-driven feature we just mentioned, from GA’s predictive insights to Google Ads’ bidding algorithms, depends on your data. In fact, Google’s own advanced attribution models require large amounts of high-quality data to function properly. This is why “AI readiness” in marketing starts with your data foundation, particularly your first-party data.

We’ve all heard the saying “garbage in, garbage out,” and that’s especially true with data and AI. Whether it’s segmenting customers or forecasting trends, AI tools need clean, accurate, and reliable data to work their magic. Feed an algorithm flawed or incomplete data, and it won’t miraculously fix those errors – it will amplify them. In practical terms, if your conversion tracking is broken or your analytics implementation is sloppy, AI (the AI that is already running in the background of many of your tools) will steer your marketing in the wrong direction.

First-party data is especially crucial. With third-party cookies crumbling, your own customer data (from your website, app, CRM, etc.) is now the primary fuel for these AI systems. Being “AI-ready” means you’ve invested in capturing and unifying that data:

Is your GA set up correctly (all key events and conversions tracked, and linked to the relevant Google marketing platforms)?
Are you piping your analytics data into BigQuery or a data lake where you can enrich it with other sources (like CRM or point-of-sale data)?
Have you implemented server-side tagging or Conversion API solutions to retain data quality in a privacy-compliant way?
Do you have processes to audit and clean your data regularly?

These are the less glamorous but absolutely essential parts of AI readiness. Napkyn’s approach to AI projects has been to make data quality a strategic priority - think first-party data integration, rigorous data audits, and processes to catch errors before they snowball. The payoff: with clean, complete data, AI stops being a buzzword and becomes a business-changing asset. Marketers who have invested in good data infrastructure will find that the AI features in Google’s tools perform far better. High-quality data is the price of entry for effective AI.

Educate and Empower Your Team

There’s another side to the AI readiness coin beyond the technical data work: your team. Tools alone won’t get you very far if the people using them aren’t prepared. AI promises big wins, but your team’s skills and adaptability play a crucial role in unlocking its potential.

In the rush to adopt the latest AI-powered tools, organizations sometimes overlook the need to train the marketers, analysts, and developers working with those tools. Think of it this way: you now have platforms that can auto-generate insights, adjust bids, and predict customer behaviour. But do your marketers and analysts know how to interpret an AI-generated insight from GA? Do they trust it, verify it, and incorporate it into strategy? Does your advertising team understand how Google’s AI is making decisions in campaigns, so they can feed it the right goals and constraints?

For any AI-driven initiative to succeed, employees must be ready to work with new tools, adapt to fresh methods, and embrace different ways of thinking. This might mean upskilling team members on data science basics or simply training them to use new features (like GA’s predictive audiences or Looker Studio’s Gemini assistant). It certainly means fostering a culture where human expertise works with AI, not against it. Your team should feel comfortable asking questions like, “Why did the algorithm bid up on these keywords?” or “What is this predictive metric telling us about user behaviour?” and then digging into the data for answers.

Leadership has a major role here. Marketing leaders need to champion continuous learning. Make time for your team to explore new AI features rolling out in tools. Encourage experimentation. Share success stories internally when automation frees up time or improves results. The goal is an educated, AI-savvy marketing team that can leverage these tools strategically – and also know their limits. The human touch (for creativity, empathy, and contextual judgment) is still critical. AI can surface patterns in data or automate optimizations, but it’s your team’s insight and strategic thinking that turns those outputs into meaningful campaigns.

Being ready for AI means having people who know what to do with it. Even the most advanced AI won’t deliver results if your team isn’t prepared to use it.

So… Ready for What?

Ready to stop waiting for AI to “arrive.” It’s here.

Ready to stop treating GA’s predictions like magic and start treating them like strategy tools.

Ready to invest in the kind of data infrastructure that makes AI features accurate instead of misleading.

Ready to train your team to work with AI—not around it.

And if you’re not quite there yet, that’s okay—but it’s time to get moving.

Need Help Getting AI-Ready?

At Napkyn, we help marketing teams get the most out of the tools they already use—by cleaning up tracking, improving data quality, and training teams on how to interpret and act on AI-powered insights.

From server-side tagging to BigQuery pipelines to GA audits and training, we can help you get your data in order and your team ready to win.

Because “AI readiness” isn’t a buzzword—it’s a business advantage. And we’re ready to help you get there.

Let’s talk.

P.S. This is just the warm-up.

The AI built into GA, Google Ads and BigQuery? It’s table stakes now. These tools are the foundation—and if you're using them well, you're already ahead of the curve. But the real opportunity lies beyond.

Once your data is clean, your tracking is reliable, and your team is trained to spot and act on the right insights—you're in a position to do much more. We're talking about custom LLMs trained on your own content, hyper-personalized marketing powered by real-time data, advanced forecasting models, and AI copilots built specifically for your business workflows.

But here’s the truth: you don’t get to skip the fundamentals.

The marketers who will win this next phase of AI are the ones who invested early in their data infrastructure and team readiness. If that sounds like where you're headed—or where you want to be—Napkyn’s here to guide you through it.

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