Tom HundleyPart 3 of 3
🤖 Ghostwritten by Claude · Curated by Tom Hundley
This article was written by Claude and curated for publication by Tom Hundley.
69% of CMOs are reworking their data strategies right now. Thats not a coincidence—its a recognition that the data marketing has collected for years is about to become either a competitive weapon or an expensive liability.
Yet heres the paradox: only 7% of marketers strongly agree that AI has improved their performance. Marketing has more AI tools available than ever before, but the tools arent delivering.
The problem isnt AI. Its the data feeding it.
Third-party data is becoming more expensive and less reliable. Privacy regulations are proliferating. Browser restrictions are tightening. The era of cheap, abundant third-party data is ending.
This isnt just a compliance issue—its a strategic inflection point. The CMOs who own rich, clean, unified first-party data will be able to power AI personalization, predictive analytics, and customer intelligence at scale. The CMOs who dont will be buying increasingly expensive, decreasingly accurate third-party signals while their competitors build direct relationships.
Your first-party data assets—CRM records, email engagement, website behavior, purchase history, support interactions—are the fuel for AI-driven marketing. But only if that data is:
Unified. Does your CRM know what your website knows? Can your email platform see support history? AI cant synthesize customer intelligence if customer data is scattered across disconnected systems.
Clean. Duplicate records, inconsistent formatting, outdated information—these arent just operational nuisances. Theyre the reason AI recommendations feel generic rather than personal.
Consented. AI amplifies privacy risk. If your data collection practices are ambiguous, AI-powered personalization becomes a compliance liability. The time to audit consent is before you train models on the data.
Marketings relationship with data is changing. 64% of CMOs are now held accountable for company profitability—not just awareness metrics or lead generation. That accountability demands the ability to connect marketing activities to business outcomes, which requires data infrastructure that most marketing organizations dont yet have.
Consider what AI-powered marketing actually requires:
Personalization at scale needs a unified customer view. If your AI personalization is just pulling the customers first name from a database, youre not competing with organizations that can dynamically adjust content based on purchase history, browsing behavior, and predicted intent.
Predictive analytics need historical data with business outcomes attached. If you cant trace which marketing touches contributed to which conversions, your AI has nothing to learn from.
Marketing mix optimization needs clean attribution data across channels. If your channel data lives in platform-specific silos with incompatible metrics, AI cant tell you where to allocate budget.
The 57% of organizations with data that isnt AI-ready? Many of them have marketing departments that collected the data but never prepared it for analytical use.
Before your organization invests in AI-powered marketing tools, answer these questions honestly:
Not can we manually pull reports from each system—unified. Can a single query return a customers complete interaction history? If not, your first AI priority is a customer data platform or data warehouse that consolidates marketing data.
AI content generation and recommendation engines need to understand what content you have and how its categorized. If your content library is a folder structure that only makes sense to the person who created it, AI tools cant effectively match content to customer contexts.
Vanity metrics dont train useful AI models. If your analytics track impressions and clicks but not downstream business outcomes, AI optimization will optimize for the wrong things.
AI tools amplify human judgment—they dont replace it. A marketer who doesnt understand how an AI recommendation engine works cant effectively supervise it, evaluate its outputs, or improve its performance over time.
Heres what too many CMOs miss: your data is enterprise data. The customer records in your CRM, the behavioral data from your website, the engagement data from your campaigns—this isnt just marketing data. Its organizational data that AI initiatives across the company might need.
The CMO-CTO partnership on data architecture is no longer optional. Your CTO is building the infrastructure for AI readiness (as covered in Part 2). If marketing data isnt part of that conversation, youll end up with one of two problems:
Marketing data isolated from enterprise AI. Your CTO builds beautiful data infrastructure, but your customer data isnt in it. AI initiatives that need customer context cant access it.
Marketing blocked by IT requirements. Your CTO establishes data governance standards, but marketing systems werent designed to meet them. Youre locked out of enterprise AI capabilities until you retrofit compliance.
Neither outcome serves your interests. Get your data into the enterprise AI conversation now.
Marketing technology stacks have grown into complex ecosystems—CRM, marketing automation, analytics, CDP, attribution, personalization, each with its own data model and integration challenges.
The CTOs job is building infrastructure that connects systems. Your job is ensuring marketing systems are connectable—that they expose APIs, follow data standards, and dont create proprietary data prisons.
When evaluating new marketing technology, ask: How does this connect to our enterprise data architecture? If the answer is well figure that out later, youre building another silo.
What does AI readiness look like for marketing specifically?
Inventory your customer data. Where does it live? Whats the quality? Whats connected? Whats consented?
This audit will reveal uncomfortable truths: duplicate records, inconsistent fields, data that shouldnt have been collected, integrations that exist on paper but dont actually work. Good. Now you know what needs to be fixed.
Based on the audit, develop a plan for customer data unification. This might mean a CDP, a data warehouse, or simply connecting existing systems more effectively. The goal: a single, queryable view of customer interactions across channels.
With unified data in progress, identify the marketing AI use cases that will deliver the most value. Personalization? Predictive lead scoring? Content optimization? Budget allocation?
Prioritize based on data readiness, not ambition. The use case that requires the cleanest data and most complex integration is not where you start.
By Q4, you should be piloting at least one AI-powered marketing capability with real success metrics. Not were experimenting with ChatGPT—a systematic pilot with defined outcomes, measurement frameworks, and learning goals.
Marketing sits on more customer data than any other function. The CMO who transforms that data from scattered touchpoints into unified customer intelligence becomes a strategic asset to the entire organization.
The AI readiness conversation is happening at the board level. Your CEO is being asked about AI strategy. Your CTO is building the technical foundation. The question is whether marketing will be ready to leverage that foundation—or whether youll be the bottleneck.
The 12-24 month timeline for enterprise AI deployment starts with the data work that marketing should have been doing all along. The CMOs who recognize this and act now will define what AI-powered marketing looks like. The ones who wait will be implementing someone elses vision.
This is Part 3 of The AI Roadmap Imperative series. Part 1: A CEOs Guide to Strategic Readiness covers why AI must be a board-level priority. Part 2: A CTOs Guide to Technical Readiness addresses the infrastructure and data architecture decisions that determine whether AI initiatives succeed or fail.
For a complete implementation framework, see our Enterprise AI Implementation Roadmap and Building an AI-Ready Organization.
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