The Future of AI is Agentic: Is Your Data Ready?

AI agents are shaping up to be one of the next major developments in enterprise technology. From marketing orchestration and customer experience automation to digital assistants and internal productivity tools, intelligent agents promise to streamline decision-making, operate in real time and learn autonomously as they interact with data, systems and people.

But before these systems can deliver meaningful value to businesses, a foundational question must be addressed: Is your data ready?

The effectiveness of AI agents hinges on the quality, completeness and accessibility of the data they rely on. Without a strong data foundation, agents risk making decisions based on fragmented inputs, leading to flawed outputs, biased recommendations and even compliance issues.

Data Quality is the True AI Bottleneck

Despite advances in machine learning and AI architecture, data quality remains the top operational barrier to AI success. In fact, more than half of organizations cite poor data quality as the main hurdle preventing successful AI adoption. The issue isn’t the intelligence of the agent—it’s the integrity and usability of the data that supports it.

And while AI agents are built to work quickly and autonomously, they’re ultimately slowed down by the same bottlenecks that have plagued data teams for years. Data professionals still spend around 80% of their time cleaning and preparing data, limiting time for innovation and experimentation. That lag is unacceptable in environments where AI agents must continuously learn and respond to dynamic inputs.

Why is Fragmented Data Still So Common?

Organizational sprawl is a big part of the problem. Over time, customer data gets scattered across dozens of platforms—CRMs, eCommerce systems, apps, call centers, analytics tools, loyalty programs and more. Each was built for a specific task, not for interoperability. This results in a disparate, fragmented ecosystem where no single tool has the full picture.

An industry study found that 62% of U.S. retailers have more than 50 systems holding consumer data at any given time. This creates fragmentation that makes it nearly impossible to construct a real-time, end-to-end view of the customer journey. A disjointed landscape forces agents to operate on partial data, undermining their ability to recognize patterns, maintain continuity or apply appropriate personalization strategies.

Data silos also lead to identity fragmentation, which can hinder targeting or customer trust and loyalty. One customer may appear as several different records across multiple databases with slightly different names, emails, device IDs or behaviors. This confuses AI systems, which can’t determine which record is correct, what needs to be consolidated, what the customer wants or even whether different interactions belong to the same individual.

This becomes even more critical under increasingly stringent privacy regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), which mandate clear consent management and transparency into how customer data is used. Unifying customer data isn’t just about better performance—it’s about compliance and trust.

Four Pillars of Data Readiness

Before deploying agents across the enterprise, organizations must first get their data foundation in order. This means prioritizing:

1. Connected data infrastructure: A connected foundation unifies all sources of customer data into a single, cohesive environment. This is the prerequisite for agents to make decisions across the full customer journey, not just within isolated channels.
2. Accurate identity resolution: Identity resolution is the process of stitching together data points across devices, systems and identifiers to form a complete, 360-degree customer profile. This ensures that AI agents recognize users correctly, personalize appropriately and avoid redundancy or mistakes.
3. Real-time availability: Speed matters. In many cases, efficiency is just as important as accuracy. AI agents need access to current, in-the-moment data to make smart and accurate decisions, whether reacting to a customer support issue, adjusting a recommendation or updating a personalization strategy.
4. Compliance-first architecture: As AI agents begin to automate decisions that affect individuals, what they’re offered, how they’re served or how their information is handled, compliance cannot be an afterthought. Enterprises must embed consent tracking, data lineage and role-based access controls into the foundation.

AI Agents Are Changing Identity Resolution

Among the elements of a modern data foundation, identity resolution has historically been one of the most complex and resource-intensive, especially at the enterprise level. AI agents require a consistent, complete view of the customer to operate effectively, but when data is scattered across systems, that clarity breaks down, and the customer journey suffers.

What’s changing now is that AI agents aren’t just dependent on identity resolution; they’re taking it on themselves. Instead of relying on status rules or batch jobs, AI-powered identity resolution agents use machine learning to ingest datasets and unify fragmented records into accurate views of the customer. These agents continuously evaluate signals like divide IDs, transaction patterns and metadata to determine which records belong to a single individual.

The result is a dynamic identity resolution process that delivers:

• Greater accuracy through intelligent pattern recognition
• Real-time updates as new data is ingested and cleaned
• Explainability behind match decisions, enhancing transparency and trust
• Scalability without time-consuming manual tuning or rule management

With AI agents managing identity resolution, businesses can finally eliminate the data gaps and duplication that slow down personalization, orchestration and automation, and the end result is a better customer experience. These agents don’t just clean the data; they build the foundation that makes intelligent customer engagement possible at scale.

From Innovation to Operational Readiness

It’s tempting to rush ahead with AI projects, but skipping over foundational data work is a costly mistake. Instead, organizations should:

• Audit data systems for duplication, fragmentation and latency
• Invest in technologies that unify and contextualize data
• Embed compliance into data operations, not as an afterthought
• Align marketing, data, privacy and AI stakeholders early
• Build human oversight and feedback loops to validate and refine agent performance

AI agents are already changing how businesses operate across industries – from retail to finance. But their success doesn’t depend on flashy interfaces or the latest algorithms. It hinges on the trustworthiness, completeness and timeliness of the data they’re built on. If your data isn’t ready, your agents won’t be either.