New research finds enterprises earn 230% ROI with Neo4j Graph Intelligence Platform

Chief Revenue Officer, Neo4j

May 20, 2026

6 min read

IDC just released a study sponsored by Neo4j on the real-world business value of the Neo4j Graph Intelligence Platform, and the numbers are strong: 230% average return on investment over three years, a $4 million average annual benefit, and payback on that investment in just under eight months. But the customer outcomes are what really stuck with me.

A telecom company cut project timelines from nine months to two. A life sciences company brought LLM hallucination rates down from 20–40% to 2–5%. A manufacturer’s assembly-line robots were waiting up to two minutes per query before Neo4j helped reduce that to real time.

The problems these companies faced all had the same root cause: organizational data that lacked the relational context advanced analytics and autonomous AI systems need. The ability to model relationships is crucial to solving a variety of enterprise use cases, from agentic AI and customer analytics to drug development and supply chain visibility.

Graph intelligence fills this context gap. The Neo4j Graph Intelligence Platform offers easy-to-deploy technologies, including graph databases, analytics, and AI capabilities, that work across any environment and data source. Together, these technologies provide the context you need to uncover insights in your data and deliver more accurate, explainable, and governed AI.

NEO4J OVERALL ROI

• 230% 3-year return on investment

• 7.8-month payback on investment

• $4 million annual benefit

• $116,000 annual benefit per database/model

How nine organizations solved critical data challenges

IDC surveyed nine organizations for the business value study and identified eight use cases. The common theme across those use cases? Disconnected data. To understand how your organization actually works, you need to understand the key connections in the data it generates. Without graph intelligence, the organizations in the study struggled to do that.

Take supply chain management. To understand something as complex as a supply chain, you need to model and analyze connections between many components. Several organizations used the Neo4j Graph Intelligence Platform to represent their supply chain, associated networks, and inventory as connected graphs, and then applied graph algorithms to generate insights into routing, availability, and dependencies.

The IDC business value study includes similar examples in real-time dependency analysis, inventory modeling, medical research, and more.

KEY NEO4J USE CASES

• Real-time dependency and impact analysis

• Clinical, regulatory, and reporting workflows

• Production, asset, and assembly-line tracking

• Supply chain, network, and inventory modeling

• AI, GenAI, and GraphRAG applications

• Customer, user, and interaction analysis

Meeting industry-specific challenges

The difficulty of the data challenges reported varied across industries, but an important finding was that organizations consistently achieved benefits regardless of the volume or complexity of their data. I think the following quotes, from people in manufacturing, telecom, and drug development, illustrate this well:

“Ultimately, the deciding factor for selecting Neo4j was performance, as our plants manage highly complex, tree-like assembly data where robots need real-time access to specific parts.”
“Neo4j’s flexible graph model lets us support changes and new rollouts much faster. We have cut timelines from nine months to two. Performance has also improved dramatically, as fast, index-free reads are critical in telecom.”
“Neo4j is especially strong for complex, highly connected datasets around early drug discovery. We also use graph approaches in global production and supply chains for what-if scenario analysis, improving chatbot efficiency with graph-based methods, and tracking ACP interactions to better understand prescribing behavior and optimize marketing.”

Graphs are built to traverse relationships very quickly—that’s what the quotes here capture. You don’t have to worry about performance bottlenecks when you’re analyzing connected data. And almost every enterprise data problem involves connected data.

The knowledge layer: The data architecture for AI

The organizations in the study used Neo4j to improve LLM reasoning accuracy, deliver more consistent and explainable results, and reduce LLM hallucinations by 44%. The paper notes that Neo4j quickly became a “foundational component for improving the quality, reliability, and scalability of their GenAI applications.”

This reflects an AI-driven shift in data architecture. Think about traditional data architectures. They were built for analytical queries, not AI. They store large volumes of data but struggle to capture relationships between data entities, so AI models don’t get the context they need to return accurate, explainable results.

This is where knowledge graphs and a knowledge layer—key capabilities of the Neo4j Graph Intelligence Platform—come into play. Instead of storing data in rows and columns, knowledge graphs structure data around the relationships between entities. AI models can query those relationships directly, retrieve critical context, and deliver better responses.

The knowledge layer is an architectural framework that defines where and how knowledge graphs fit within your existing data infrastructure and AI applications. It gives AI agents a single place to query for context and relationships, regardless of where the underlying data lives.

IDC’s data on AI outcomes with Neo4j is clear, but I wanted to highlight one customer comment that really captures the impact: “Neo4j has increasingly become a core data backbone for our GenAI initiatives by providing structured and explainable context for models. Using Neo4j has reduced LLM hallucination rates from roughly 20–40% down to about 2–5%.”

AI BENEFITS OF NEO4J

• 43% used a knowledge graph for GenAI

• 44% fewer GenAI model hallucinations

• 31% improvement in overall model performance

The promise of real intelligence

The Neo4j Graph Intelligence Platform is quickly becoming an integral part of enterprise data architectures, and I’m convinced the trend will accelerate from here. Data volumes are only growing, and data is becoming more interconnected. The authors of the research note that “relationship-driven requirements are becoming more pressing across a range of business and operational scenarios.” And they expect many enterprises to keep expanding their analytics and AI initiatives as they look to apply connected data across operational and analytical use cases. I agree with both assessments. We’re already seeing increased demand for “relationship-driven analysis,” as the study puts it.

In this environment, the Neo4j Graph Intelligence Platform becomes the missing architectural piece. With graph database, analytics, and AI capabilities that work across any environment and data source, it provides the context you need to discover hidden insights in your data and develop accurate, explainable, and governed AI systems.

Data architecture is at a turning point. Accumulation and management aren’t the only things teams need to consider. The world now runs on relationships. To reason accurately, and finally deliver on their very real promise, autonomous systems need to understand the context those relationships provide.

IDC White Paper, sponsored by Neo4j, The Business Value of the Neo4j Graph Intelligence Platform, Doc #US54319826, May 2026.