How to overcome the data readiness gap for effective AI in marketing

Agentic AI went mainstream in 2025, adding immense pressure on resource-strapped teams to apply, test, and scale new AI workflows.

We’re not far from a world where AI augments every use case we can think of, empowering marketers to deliver deeply personalized experiences at scale — but AI alone won’t guarantee success. Your data and your ability to activate it are your key differentiators.

The Martech for 2026 report examines the rapid shift toward agentic marketing and the many pitfalls teams face in embedding AI to orchestrate campaigns across a growing number of channels. It’s packed with insights on what’s working, what isn’t, and where teams are focusing their attention. 

And guess what? It’s completely free and ungated.

I was honored to join the report authors Scott Brinker of chiefmartec and Frans Riemersma of MartechTribe to discuss my thoughts on the future of martech and what is essential for succeeding with agentic AI. I brought this insight thanks to great conversations I’ve had with marketing leaders at leading enterprise organizations.

To develop a future-ready AI strategy for marketing, let’s explore where teams are in their AI adoption, what technology foundation you need, and other key takeaways from my conversation with Scott and Frans.

The marketing data readiness gap for AI

It’s no surprise that most teams are actively exploring AI solutions. The report found that 90% of organizations are deploying AI agents for marketing and sales, and they all face the same challenge: Data readiness.

AI is only impactful if you provide it with the right data resources, yet teams have historically battled with siloed, inaccurate, and unstructured data. If AI is given incomplete or inaccurate data, then its outputs will likely be flawed or, at worst, actively harm your marketing campaigns. 

Where are you on the data readiness journey?

Marketing leaders are at different levels of the data readiness gap. Many well-intentioned teams rely on tool-specific AI capabilities and expect that tool to be their customer 360, but this approach carries the age-old challenges of data accuracy and consolidation. It also creates an AI ceiling, in which the team’s potential is limited by how quickly individual vendors update their AI capabilities.

If this sounds familiar, you’re likely early on your data readiness journey. But that’s okay — there’s still time to catch up and take advantage of all AI has to offer.

Your AI success requires a solid data foundation combined with intentional workflow changes to help humans thrive with AI agents by their side. A cloud data warehouse is the optimal foundation (we’ll explore why below), and high-performing organizations are using data clouds to fuel AI-driven growth across teams. Other organizations are beginning to implement their data cloud or exploring options as they realize the shortcomings of their current data storage approach. 

Investing in this foundation intentionally will position your team to quickly drive improvements with AI, instead of grappling with outdated technologies or systems that fail to set up AI for its fullest potential. 

We’ve learned a lot about what this foundation looks like from organizations that have actively invested in AI — offering a blueprint to help you evolve your organization’s AI strategy. 

Enabling an agentic marketing organization

Unlike the familiar interface of ChatGPT, enterprise AI isn’t plug-and-play. Teams are actively learning what they need to get started and where AI can drive immediate value with low risk.

The report shares how marketers can accelerate their AI adoption and overcome the hurdles that others have faced: 

Step 1: Centralize data

Your data needs to be connected in a pragmatically accessible way. 

A future-ready martech stack is built on a cloud data warehouse or lakehouse. The data cloud stores all customer data and integrates with channel tools, allowing you to use the tools you love (and easily swap them out when needed, without impacting your active campaigns). 

A cloud data warehouse also solves the number one issue marketers face when integrating AI systems: Poor data quality. Without centralized data, you risk creating audiences with limited data in one tool, instead of building audiences with the source of truth in the data cloud. You also risk using AI to draw insights on incomplete data, limiting its ability to deliver hyper-relevant recommendations. 

The data cloud creates a scalable data foundation that reflects insights from every channel activity — email, social media, in-store, paid advertisements, etc. — so marketers and AI can design engaging journeys tailored to individual customer preferences. 

The report found that only 37.9% of marketers are integrating data directly from their cloud data warehouse or lakehouse to AI agents, meaning this is an essential first step for most teams.  

Step 2: Build a composable martech stack

Composability is key. It’s the architectural decision where you have a source of truth (your data cloud) that powers your AI strategy and your AI application.

Audit your existing martech to ensure it can connect to your data cloud so all campaign insights and customer data feed back into your system. Disconnected tools create data silos, which is a critical risk for your AI strategy.

A data orchestration layer or composable audience and journey builder, like a composable CDP, provides an intuitive interface for marketers to partner with AI, analyze company data, and orchestrate campaigns. 

The layer connects to all channel tools and serves as the hub for accessing data and partnering with AI. It also gives you control over your data models and ensures you’re drawing insights from every customer activity. 

Step 3: Start pilots (and scale them)

Many teams stall their AI progress by spending too much time researching and comparing vendors. 

Because AI is evolving so quickly, you can't afford to spend 18 months selecting a vendor. The capabilities available when you start your search may be vastly different by the time you finish your vendor search. That’s why the traditional RFP mindset doesn’t work in the AI era. 

Instead, pick a starting use case for AI with a very specific, time-bound, and clear outcome you want to drive, and try a pilot or proof-of-concept with a potential vendor instead. Chiefmartec and MartechTribe’s research found that 68.9% of marketers are using agents for content production, 40.8% for audience discovery and segmentation, and 35.9% for competitive analysis — each of these is a fantastic starting point for AI. 

Test different tools with your chosen use case, share learnings across your team, and select a partner that will help you expand to new areas as you strengthen your skills.  

Step 4: Evolve your team structure

AI significantly augments what teams can do and rapidly accelerates campaign cycles. We’re seeing more teams restructure their organization to best leverage AI and reflect the vital importance of first-party data.

We anticipate marketing teams will implement a three-tier structure:

• Marketing C-Suite, which combines marketing and data intelligence to assess the overarching AI strategy and marketing program results.

• Marketing Intelligence Layer, which is an expansion of the marketing operations group that governs the AI systems to ensure quality and enable team members.

• Business units, which orchestrate campaigns across either business unit, product line, or customer journey pods.

This structure evolves marketing operations into a strategic player. They not only own the systems but also consider the efficiency of your data investment, how AI orchestration can power the whole organization, and what outcomes AI can drive.

Our agentic marketing organization ebook explains each of these layers in further detail, offering one idea for how to position your team for lasting success. 

Marketers with strong customer empathy and AI are unstoppable — but they need the right foundation for AI to be an effective partner.

If your organization is challenged in applying AI in meaningful ways, start at your data source. Having a centralized, single source of truth ensures that AI can access your organization’s complete and most accurate data. You can then identify pilot projects to test AI in specific use cases before tackling other workflows.

The Martech for 2026 report offers a wealth of insights as you strengthen your AI strategy. The full report explains how to build the agentic martech stack, how you can drive innovation and value, and stay competitive as more teams adopt AI. 

At GrowthLoop, we’ve long believed in the power of first-party data as your competitive differentiator. Our agentic composable CDP, the Compound Marketing Engine, provides an intuitive interface for marketers to self-serve data and apply AI to optimize your audiences and journeys — and compound growth by scaling campaign experimentation and iteration. 

Learn more about GrowthLoop and schedule a demo today.