AI doesn’t fail because it’s not powerful enough—it fails because it doesn’t know who or what it’s dealing with. When AI initiatives go off course, we often blame the model, the algorithm, or the team behind it. But in reality, the most common failure point is far more fundamental: the AI is only as good as the entities it understands. And without a trusted system for identifying and linking those entities across your data, everything built on top starts to unravel.
Entity Resolution (ER) isn’t just a behind-the-scenes function—it’s the silent force that determines whether AI outputs are reliable, relevant, and real-world ready. Without it, even the most advanced systems can’t see straight.
When AI Doesn’t Know Who It’s Talking About
AI models depend on patterns. If the underlying data confuses the core entities they need to understand—be it people, organizations, locations, products, or even events—those patterns get scrambled. And it doesn’t take much. Consider:
- A customer appears as “J. Smith” in CRM, “Jon A. Smith” in marketing automation, and “Jonathan Smith” in billing.
- A product is logged under slightly different names across inventory and ecommerce platforms.
- A fraud ring (an entity in itself) uses subtle name variations to evade detection across systems.
- Geographic locations might be inconsistently recorded as “New York, NY” or “NYC” across different datasets, preventing a unified view of regional trends.
Even transactional events can be duplicated or miscategorized if the associated entities are not properly resolved. To a human, these connections are obvious. To an AI trained on messy, disconnected data? They look like entirely different entities—leading to poor predictions, skewed insights, and missed opportunities.
Generative AI: When Errors Multiply
Generative AI raises the stakes even further. These models aren’t just analyzing data—they’re producing content and decisions based on what they’ve learned.
If the training data contains unresolved entities, GenAI models will have an increased tendency to:
- Reference the wrong person, account, or object.
- Generate hallucinated associations (e.g., mixing up product attributes or customer histories).
- Deliver outputs that are inaccurate, biased, or misleading.
According to Gartner, at least 30% of generative AI (GenAI) projects will be abandoned after proof of concept by the end of 2025, primarily due to poor data quality, inadequate risk controls, and unclear business value1.
False Precision: The Illusion of Intelligence
One of the most dangerous outcomes of AI without entity resolution is the appearance of success—what we might call false precision.
The model is generating predictions. It’s optimizing campaigns. It’s surfacing anomalies. The dashboards look healthy. But under the surface, it’s operating on fragmented, duplicated, or misidentified entities.
So, while precision might appear high—meaning the model correctly identifies positive results—it’s misleading, because the model’s understanding of the ground truth is broken. Worse still, recall suffers because valid patterns across unresolved entities are never seen or learned.
The result?
- Marketing models make confident but incorrect recommendations based on incomplete customer profiles.
- Risk engines flag irrelevant anomalies, while real threats remain hidden.
- Customer service bots deliver responses that sound right—but reference the wrong account or product.
This isn’t a failure of math. It’s a failure of context. And that failure grows exponentially in any system attempting personalization, prediction, or autonomous response.
The Hidden Cost: Wasted AI Spend and Lost Trust
Gartner reports that poor data quality—including unresolved entities—costs organizations $12.9 million per year on average2. But the real cost of running AI without Entity Resolution is strategic: diminished trust.
Executives stop trusting the output. Customers disengage from poorly personalized experiences. Teams pull back from automation initiatives. And AI becomes a burden instead of a multiplier.
When ER Is Embedded, Everything Works Better
Organizations that integrate entity resolution into their data and AI pipelines see compounding benefits:
- Models train faster and require less tuning, since the data is coherent.
- Insights are more precise, enabling better segmentation and targeting.
- Fraud detection shifts from reactive to proactive, linking subtle behavioral signals and obscure connections across seemingly unrelated variations. Instead of merely identifying fraud after it occurs, ER enables AI to spot emerging patterns and potential threats before significant damage is done, by creating a complete picture of individuals and their activities.
- Customer experiences feel smarter, because they’re powered by a unified understanding.
These gains are backed by research. According to Forrester, organizations that centralize identity and entity resolution as part of a data fabric architecture are 2.8x more likely to report improved AI performance and trust3
Looking Ahead
As we enter the age of intelligent agents and generative systems, one truth is becoming increasingly clear: AI can’t afford to guess who it’s talking about.
Entity resolution ensures every insight, prediction, or recommendation is rooted in clarity. Without it, AI isn’t artificial intelligence—it’s artificial confusion.
In the next installment of this series, we’ll explore how traditional entity resolution techniques fall short under pressure—and why solving today’s challenges requires a new, AI-first approach to resolution.
Gurpinder Dhillon
Head of Data Partner Strategy & Ecosystem
Gurpinder Dhillon has over 20 years of experience in data management, AI enablement, and partner ecosystem development across global markets. Gurpinder is also a published author and frequent keynote speaker on AI ethics, master data strategy, and the evolving role of data in business innovation. He currently leads the strategic direction and execution of the Senzing data partner ecosystem.
