Is your business intelligible enough for AI to connect the dots across customers, transactions, policies, supply chains, systems and decisions. In this conversation, Persistent and Neo4j explore why the real battleground for enterprise AI is shifting from intelligent models to intelligible enterprises.

Watch the podcast to understand how graph intelligence can help enterprises reduce hallucinations, uncover hidden relationships, make faster decisions and turn fragmented data estates into an AI-ready intelligence layer.

When More Data Stops Being the Answer

For years, enterprise AI programs have pursued scale, adding more data, larger models, broader experimentation and faster pilots. But scale without context creates a new paradox. The same AI that can summarize millions of documents may still fail to understand how a supplier disruption affects customer commitments, why a transaction is suspicious or which service action matters most to a specific customer. The gap is no longer computational power. It is enterprise context.

Featured Speakers

Ish Thukral, Vice President and General Manager, Asia Pacific and Japan at Neo4j, explains how connected data can give AI the business context it needs to reduce hallucinations, improve model performance and support mission-critical use cases across fraud detection, anti-money laundering, government investigations, supply chain decision-making and customer experience.

Darshan Watve brings the practitioner’s lens to the conversation, centers his perspective on the operational reality of AI: how organizations move from promise to production, from pilots to measurable outcomes and from isolated systems to connected business value.

Intelligent AI is Only as Useful as the Enterprise It Can Understand

The discussion challenges a comfortable assumption that enterprise AI maturity is primarily a function of model sophistication. The sharper truth is that AI becomes valuable only when it understands the relationships that define a business, connecting customers to accounts, transactions to devices, suppliers to plants, assets to ownership structures, policies to actions and decisions to outcomes. Without that context, AI can retrieve information. With it, AI can reason over connected knowledge.

Six Moats Explored:

1. Context as the new AI control plane: The biggest enterprise AI risk is not that models lack intelligence. It is that they lack the business context to apply intelligence correctly. Graph intelligence creates a connected layer across enterprise systems, helping AI understand why information matters, how entities relate and what downstream impact a decision can have.
2. Trust as a measurable outcome, not a governance slogan: The conversation points to a hard metric behind trust: Neo4j-sponsored IDC research reported a 44% reduction in GenAI hallucinations and a 31% improvement in overall model performance for organizations using the Neo4j Graph Intelligence Platform. Trust, in this framing, is engineered through traceable relationships, explainable context and connected knowledge.
3. Relationship intelligence as the fraud and AML moat: Fraud rarely announces itself as a single suspicious event. It hides in weak signals, such as reused addresses, connected devices, common beneficiaries, shared IPs, circular payments and ownership trails. Graph intelligence changes the investigative question from “Does this transaction look abnormal?” to “What does this transaction connect to?” That shift is the moat.
4. Real-time decisioning as the operational moat: In volatile environments, a wrong AI-generated insight can be more expensive than no insight at all. Taking the instance of a manufacturing firm, the discussion shows why enterprises need AI that can connect supply constraints, oil prices, logistics, demand signals and customer commitments in real time. The advantage is not automation for its own sake. It is the ability to make better trade-offs when every variable is moving.
5. Customer context as the experience moat: The discussion extends graph intelligence beyond risk and compliance into growth. In retail, connected identity resolution can link a card swipe, abandoned cart, service ticket, app journey, household behavior and aisle-level promotion into one real-time customer understanding. This turns Customer 360 from a static data ambition into an active decisioning engine.
6. Implementation depth as the services moat: The final moat is execution. Neo4j brings the graph intelligence platform. Persistent brings the engineering transformation, domain knowledge and implementation rigor to move connected intelligence from demo to production. The value is not in replacing enterprise investments. It is in adding an intelligence layer that makes those investments work together.

Join the conversation. Contact us at podcasts@persistent.com.

Speakers 

Ish Thukral, Vice President and General Manager, Asia Pacific and Japan at Neo4j

Darshan Watve, Senior Vice President, India Sales, Persistent Systems