Most organizations are discovering that AI agents need more than just data—they need the right data. The challenges are familiar: disconnected consumer identities, siloed and underutilized customer data, and inflexible legacy systems.

To drive true personalization and adaptive experiences powered by AI, brands need well-governed, highly connected data environments that support marketing, customer experience, insights, and analytics teams simultaneously. Think about it: the average customer interacts with your brand through 15-20 different channels. Without a unified data foundation, you’re essentially treating one customer as 20 different people.

After working with dozens of enterprise clients on their AI readiness journeys, we’ve identified five critical data capabilities that separate successful agentic AI implementations from those destined to underdeliver.

1. Identity Resolution: Knowing Your Customers

The foundation of exceptional customer experience isn’t personalization—it’s recognition. Before you can personalize, you must first recognize who your customer is across every touchpoint.

Leading companies invest heavily in identity resolution capabilities that connect fragmented customer signals across touchpoints. They’ve moved beyond basic email matching to sophisticated identity graphs that link anonymous behaviors, authenticated interactions, and offline engagements.

What this means in practice: The most successful implementations achieve 85%+ match rates across their ecosystems, enabling them to recognize customers wherever they engage. This requires moving beyond deterministic matching to probabilistic models that can connect the dots between a mobile app session, a customer service call, an email click, and an in-store purchase.

Without robust identity resolution, your AI agents are operating in the dark, making decisions based on fracture customer profiles that result in disjointed, sometimes contradictory experiences.

2. Data Quality as a Living, Breathing Discipline

Best-in-class brands treat data quality not as a one-time cleanup project but as an ongoing operational discipline. They maintain data accuracy rates above 95% through systematic processes, understanding that in a world of real-time AI decisioning, yesterday’s data quality isn’t good enough.

Leading organizations implement:

• Real-time data validation at the point of capture

• Automated quality scoring and remediation workflows

• Continuous enrichment through third-party sources

• Monthly quality metrics are reviewed at the executive level

Think of data quality like physical fitness. You can’t just go to the gym once and expect lasting results. It requires consistent discipline, measurement, and correction.

When AI agents make real-time decisions, they amplify data quality issues. A 5% error rate in your customer data becomes a 5% error rate in every AI-driven interaction.

3. Embrace Data Diversity (Start Small, Grow Deliberately)

Successful companies have abandoned the “golden record” myth. Instead, they’ve embraced a philosophy of data inclusivity, where more signals drive better AI outcomes.

Leading implementations typically integrate 50+ data sources compared to 5-10 for laggards. They ingest structured transactional data, unstructured behavioral signals, third-party enrichment, IoT and sensor data, social sentiment, and partner ecosystem information.

The key insight: Don’t choose which data to use. Build the capability to harmonize and activate any data source that provides customer insight. Your AI agents need diverse inputs to make intelligent decisions.

Start with your most impactful data sources—typically first-party behavioral and transactional data—then systematically expand to incorporate additional signals that improve AI decision-making.

4. Ontology Structure: The Machine-Readable Business Map

This is where most organizations stumble. An ontology structure is a machine-readable map of business concepts and relationships that provides AI agents with the necessary context to operate intelligently. It defines not just what your core entities are—customers, products, transactions—but how they connect and interact within your business. 

Why this matters: AI agents cannot act effectively on raw data alone; they require the ability to interpret meaning, reason across relationships, and apply business logic consistently.

Without a shared ontology, AI agents operate like employees who don’t understand how your business works. They might have access to all the data but lack the business context to make appropriate decisions. The ontology transforms AI agents from simple automation tools into business-aware problem solvers that understand the nuances of your customer relationships, product hierarchies, and business rules.

5. Composable Architecture That Scales Across Every Use Case

The final differentiator is architectural philosophy. Leaders have abandoned monolithic platforms for composable architectures that can simultaneously:

• Process petabytes of unstructured data for AI model training

• Deliver sub-100ms response times for real-time personalization

• Scale horizontally for campaign batch processing

• Integrate seamlessly with existing martech investments

• Adapt quickly to new channels and use cases

The composable approach means:

• Your data lake/warehouse becomes your single source of truth

• CDPs and activation platforms federate data rather than duplicate it

• AI agents can access unified customer profiles across your entire ecosystem

• New capabilities can be added without re-architecting your entire stack

This architecture positions you to adapt as AI capabilities evolve, rather than being locked into a single vendor’s vision of the future.

When we talk about AI-powered agents, "real-time" becomes a common requirement—but real-time means different things to different organizations, and that's perfectly okay. 

For many brands, even starting with a clean, read-only customer profile accessible across systems is a massive leap forward. If your marketing team can query a unified customer view that's refreshed nightly, you're already enabling more intelligent, data-driven decisions than most competitors. This foundation—a single source of truth that's accurate and accessible—is often the unlock that makes AI agents viable in the first place.

From there, brands can progressively enhance their real-time capabilities. You may begin capturing behavioral data with a 15-minute latency, enabling same-session personalization on your website. Then you might advance to true event-streaming architectures that react to customer actions within seconds, powering next-best-action recommendations in mobile apps or contact centers. Eventually, you reach millisecond-level decisioning that enables AI agents to orchestrate experiences across channels in the moment.

The key is to match your real-time capabilities to your business needs and maturity level. A B2B company with long sales cycles might thrive with hourly data refreshes, while a retail brand during Black Friday needs sub-second responsiveness. Don't let perfect real-time become the enemy of good-enough real-time. Start where you can deliver value, measure the impact, and evolve your capabilities as your use cases demand and your infrastructure matures.

Preparing your data for AI-powered agents is a foundational business capability. Organizations that invest in these five areas can enable today’s agentic AI use cases and position themselves to capitalize on whatever comes next in the rapidly evolving AI landscape.

The window of competitive advantage is narrowing. As agentic AI becomes table stakes for customer experience, the differentiator will be how effectively your data foundation enables agents to deliver intelligent, personalized, trustworthy experiences at scale.

Ready to assess your organization’s AI readiness? Contact Merkle’s Customer Data Solutions team to learn how we can help you build the data foundations that power exceptional AI-driven customer experiences.