Table of Contents
Table of Contents
Table Of Contents
Table Of Contents
Get the master blueprint to build your agentic AI data foundation.
Enterprise AI agents are transforming customer experience, supply chain operations and regulatory compliance. But most organizations are making the same architectural mistake: building a separate data foundation for each.
That approach feels logical. It's also what's keeping AI stuck in pilot.
47% of companies have already adopted agentic AI and 86% are increasing data management investments to support it. Yet the obstacle that consistently stalls the leap from pilot to production isn't the models or the agents themselves. It's the data underneath them.
When customer records sit in CRM, product data in ERP, supplier data in procurement and compliance data in governance tools, AI agents operate with partial context. They recommend products customers already own. They miss supply chain disruptions they should have predicted. They generate regulatory reports from incomplete lineage. The root cause is always the same: fragmented data across disconnected systems.
For a comprehensive blueprint on preparing your enterprise data pipelines for autonomous AI workloads, read our foundational AI Data Management Guide.
How to Solve Data Fragmentation for Enterprise AI
Building AI-ready data for agents in customer experience, supply chain and compliance isn't three separate challenges, it's just one fragmented data infrastructure. The instinct is to solve each function in isolation: a customer data platform for marketing, a visibility tool for operations, a governance layer for compliance. This approach seems logical, but it is ultimately counterproductive as it creates exactly what slows AI agents down: more fragmentation, more silos, more incomplete context.
The difference between organizations stuck in pilot mode and those scaling AI enterprise-wide isn't better algorithms or bigger budgets. It's a fundamentally different approach to the data foundation.
One Data Foundation for Multiple Business Outcomes
Data leaders aren't building separate foundations for customer experience, supply chain and compliance. They're building one unified, intelligent platform that delivers trusted, governed, real-time data across all domains simultaneously, letting AI agents make faster, smarter decisions across every business function.
61% of data leaders report that higher quality, more complete data is what actually makes it easier to transition AI pilots into production. — CDO Magazine
Break Customer Data Silos for Real-Time CX
Customers expect personalized experiences across every touchpoint. AI agents can deliver "show me you know me" moments at scale, from targeted marketing and personalized sales to seamless commerce to proactive service, but only when they can access a unified view of customer identity, preferences, purchase history and consent.
The Challenge of Siloed Customer Data
Customer data is fragmented across CRM, e-commerce, marketing automation, customer service and loyalty systems. Without a complete customer view, AI agents recommend products based on incomplete profiles, send offers to wrong identities or violate consent preferences, often breaking trust instead of building it.
In a survey of over 300 business and IT professionals, only 27% rate their customer data as "excellent" even as AI applications depend on that data to succeed. — Informatica & Bodine & Co., From Data Silos to AI-Enabled Customer Engagement
The Reality for a Modern CX AI Agent
To deliver personalized customer experiences, an AI agent requires real-time access to every customer interaction, purchase and support ticket. However, most agents are fed data from disconnected systems with conflicting customer IDs and outdated product profiles. Instead of building value, the agent becomes a liability, recommending items customers already own and violating privacy preferences simply because the underlying data foundation is broken. A unified data platform fixes this instantly.
AI-Driven Data Management for Customer Experience Agents
See customers and products clearly: IDMC uses a single AI-powered platform to connect, map and continuously improve data quality across all customer touchpoints and product systems. The platform automates integration, cleansing, data reconciliation and enrichment to create trusted, unified 360-degree customer profiles and product views that AI agents can act on with confidence. Here’s how:
Deliver real-time data: Cloud Data Integration and App Integration capabilities synchronize commerce details instantly across hybrid and multi-cloud environments.
Keep your data trusted: Informatica’s CLAIRE® AI engine automates matching, deduplication and data enrichment across domains without requiring manual IT intervention.
Enable confident data access: Advanced metadata-driven access controls enforce GDPR and CCPA privacy standards automatically, building long-term customer trust.
Democratize business insights: Teams access verified customer profiles through the self-service Cloud Data Marketplace while maintaining strict enterprise data governance.
Customer Case Study: Holiday Inn Club Vacations (HICV) Drives Greater Personalization
The Challenge: Siloed Data Infrastructure and Communication Risks
HICV's legacy data management infrastructure was siloed, making it difficult to get a common view of the customer across the technology stack. Data quality issues had to be manually identified and resolved across different systems to avoid the risk of misguided customer communications. The legacy on-premises master data management (MDM) solution was unable to ingest data or move it at the needed frequency and pace.
The Transformation: Unifying Seven Systems into a Single Cloud Platform
With IDMC, HICV transformed customer data management by unifying data from seven systems into a single cloud platform. It unified customer data with Informatica's MDM, Data Governance and Data Quality solutions, making trusted member profiles readily available for downstream applications and services while mitigating policy and compliance risks.
Cloud transformation results:
- Cloud migration executed in under four months by consolidating siloed data structures for seamless personalization
- 7+ legacy systems unified into a single cloud platform
- 350,000+ unified member profiles trusted 360-degree guest views established
Over 90% of supply chain leaders plan to use AI for demand forecasting in the next two years and over 75% see potential for autonomous AI agents to handle tasks like reordering and shipment rerouting. — ABI Research
The Challenge of Disconnected Supplier Data
Supply chain data is siloed across procurement, logistics and manufacturing systems, leading to critical business failures. Without a unified data foundation, AI agents operate with blind spots, resulting in:
Reactive Firefighting: The inability to proactively identify disruptions leads to costly operational downtime and emergency shipping costs.
Limited Agility: Slow, manual processes for supplier onboarding and product introductions delay time-to-market and hinder the ability to adapt to market shifts.
Flawed Forecasting: Inaccurate data feeds unreliable AI models, causing stock-outs that damage revenue and excess inventory that ties up capital.
Hidden Compliance Risk: Failing to track supplier certifications or material origins exposes the business to regulatory fines and damages brand reputation.
The Reality for a Supply Chain AI Agent
To successfully predict disruptions and automatically optimize logistics, an AI agent requires end-to-end traceability from raw material origin to final delivery. Instead, typical agents receive supplier data that is three weeks out of date and product components scattered across disconnected ERP systems. Consequently, they cannot foresee port congestion or cascading supplier bankruptcies, leaving organizations stuck reacting to crises too late. A unified foundation shifts operations from reactive firefighting to predictive forecasting.
AI-Driven Data Management for Supply Chain Agents
IDMC provides the trusted data foundation to transform the supply chain from a reactive function to an intelligent, predictive network. Here’s how:
Create end-to-end visibility: Multidomain MDM creates a single source of truth across suppliers, locations and materials, giving AI agents complete network context.
Enable proactive decisions: Advanced data engineering streams clean, reliable data into AI models in real time to simulate disruption impacts and predict demands.
Build a compliant supply chain: Integrated Data Governance and Catalog tracking delivers immutable data lineage, making automated agent re-routing decisions fully transparent and auditable.
Case Study: Psycho Bunny Gains Real-Time Supply Insight
The Challenge: Inventory Visibility Gaps and Split Shipments
A lack of visibility and insight into inventory levels resulted in high numbers of short and split shipments, disrupting Psycho Bunny’s customer experience. Their goal was to improve inventory accuracy, support high processing speeds across warehouses and stores, support omnichannel business needs and reduce short shipments to near zero across all locations, in a short time window before the next shopping season.
The Transformation: Implementing Real-Time AI-Powered Data Integration
Enabling seamless cloud data integration, eliminating manual work and providing a tier one architecture was paramount to supporting rapid growth and enhancing business agility at Psycho Bunny. Implementing the AI-powered data management platform from Informatica in under six months provided a variety of features such as business data integration, application integration and API management.
Psycho Bunny supply chain impact:
- 99% inventory accuracy achieved in under six months
- 93% reduction in short shipments across warehouses and retail stores
- 50%+ reduction in costly, split shipments
- 30% increase in incremental revenue recorded by year's end
Build Compliance and Trust into AI Guardrails
AI can transform regulatory compliance from a cost center into a competitive advantage, automating policy enforcement, ensuring data lineage and enabling confident innovation, which in turn drives broader AI adoption with confidence.
Over 85% of companies are continuing to increase data management investments for 2026. The most common drivers: improving data privacy and security (43%) and improving data and AI governance (41%). — Informatica CDO Insights Survey 2026
The Challenge of Evolving Privacy Regulations
Regulatory requirements are complex and constantly evolving across privacy (GDPR, CCPA, HIPAA), financial and risk reporting (CCAR, BCBS 239) and ESG standards. Without complete data lineage and automated governance, AI agents risk automating inaccurate regulatory reports or even compliance violations, especially when working with sensitive customer, financial and operational data.
The Reality for a Compliance AI Agent
To automate regulatory compliance and audit readiness, an AI agent requires complete data lineage tracking every source, transformation and AI decision. Instead, most operate with undocumented data pipelines and privacy rules that change weekly while data maps remain months old. Without unified data, agents cannot locate scattered profiles during a GDPR deletion request, creating massive regulatory risk. A unified foundation builds compliance directly into your guardrails so teams can safely innovate faster.
AI-Driven Data Management for Compliance Agents
Informatica IDMC provides the comprehensive, automated framework needed to eliminate data silos and enforce continuous policy governance. Here’s how:
Ensure data consistency everywhere: IDMC integrates, consolidates and standardizes data across regulatory domains. This unified approach maintains the consistency and accuracy crucial for compliance reporting and audit readiness.
Establish a master source of truth: Multidomain MDM automates reconciliation and cleansing, building accurate master records AI agents use to generate regulatory reports.
Create reports with confidence: High data quality is essential for accurate regulatory reporting. IDMC’s robust AI-powered data quality features cleanse, validate and enrich data across customer, risk, financial and ESG reporting requirements, ensuring AI agents work with reliable information.
Always be audit ready: Metadata management tracks end-to-end lineage, mapping every single AI decision back to its original data transformation.
Build compliance into every pipeline: Cloud Data Governance & Catalog unifies automated data discovery, classification and profiling to optimize compliance management.
Enforce advanced access controls: Privacy controls actively protect sensitive fields, enforcing GDPR, CCPA and HIPAA compliance mandates enterprise-wide.
Automate proactive responses: CLAIRE analytics identify hidden anomalies and compliance gaps before they become violations.
Case Study: Gras Savoye Centralizes Compliance Data
The Challenge: Complex M&A Silos and Tight KYC Deadlines
After years of acquiring smaller companies, Gras Savoye had complex and siloed data and lacked central data governance. The company was under pressure to meet tight deadlines for complying with Know Your Customer (KYC) regulations, centralize data to better assess the risk of financing fraudulent and illegal activities and set the stage for a digital transformation by becoming more data-driven.
The Transformation: Creating a Trusted Third-Party Master Data Solution
With Informatica’s MDM solution, they created a trusted, comprehensive view of third parties across the organization to support Know Your Customer (KYC) processes and provided the compliance team with a single source of truth for KYC status.
The Outcome: Meeting Strict Compliance Requirements with Automated Quality Checks
Within months of deployment, Gras Savoye met KYC compliance requirements to verify the legitimacy of third parties. Integrated with Informatica Data Quality, a connection with Thomson Reuters keeps data accurate. This single record eliminates cross-divisional discrepancies, allowing the team to confidently feed centralized, automatically verified data to downstream applications.
Why Informatica IDMC Is Built for the Agentic AI Enterprise
Cloud-Native and AI-Powered Architecture
Informatica’s Intelligent Data Management Cloud (IDMC) delivers the comprehensive, AI-powered platform that unifies what most organizations are stitching together from multiple vendors:
Cloud Data and App Integration for real-time connectivity across any system
AI-Powered Data Quality for automated cleansing and enrichment via CLAIRE
Cloud Data Governance & Catalog for metadata management and complete lineage
Multidomain Master Data Management for Customer 360, Product 360, Supplier 360
Data Access Management for data privacy regulatory compliance
Cloud Data Marketplace for democratized, self-service data access
Built cloud-native and AI-powered at its core, IDMC scales elastically across multi-cloud environments while reducing deployment time and cost.
A Unified Platform Built for Humans and AI Agents
Business users get self-service access to trusted data through the Cloud Data Marketplace
Data engineers automate pipelines with low-code/no-code interfaces
Data stewards manage governance policies with AI-powered assistance
AI agents get trusted, governed context for automated decision-making
Future-Ready Data Foundations for Business Growth
By delivering the high-quality, connected data that AI requires, IDMC helps organizations scale innovation across customer experience, supply chain and compliance initiatives. This trusted foundation accelerates time-to-value for new initiatives, from product launches to market expansion to M&A.
The Agentic Data Readiness Framework
Evaluating your technology options requires shifting focus from basic features to cross-functional capabilities. Use our Agentic Data Readiness framework to determine if a platform can support a true enterprise-wide agentic AI strategy rather than just adding more isolated data tools:
1. Platform comprehensiveness
An effective agentic data foundation relies on a single, unified platform that shares a metadata layer and has AI-powered data quality built into every core capability.
2. Embedded AI and automation
The platform must feature an embedded AI engine that automatically discovers sensitive data, resolves quality issues and handles deduplication with clear explainability.
3. Cloud-native scalability
A microservices-based architecture must support all major cloud ecosystems, scale resource optimization automatically and offer flexible, consumption-based pricing.
4. Automated governance and compliance
The system must automatically track end-to-end data lineage, apply advanced role-based access controls and enforce regulatory policies like GDPR, CCPA and HIPAA.
5. Proven enterprise scale
Technology providers must demonstrate measurable business outcomes, such as reduced vendor onboarding times, higher data productivity and lower IT overhead.
6. Business model alignment
Pricing models must feature flexible processing units and built-in cost optimization so organizations can scale their AI strategies without constant contract renegotiations.
Next Steps for Your Agentic AI Data Foundation
To successfully scale agentic AI, your critical choice isn't deciding which bottleneck to tackle first. The real decision is architectural. Will you build three separate data foundations or one unified platform that solves all three?
Check out this complete guide to powering enterprise agentic AI across CX, supply chain and compliance.
Agentic AI Data Foundation FAQs
Data fragmentation across disconnected systems remains the primary obstacle preventing organizations from scaling AI agents. When critical customer, product and supply chain records reside in isolated silos, AI agents operate with incomplete context. This limitation leads to inaccurate decisions, missed business opportunities and heightened operational risks.
A unified data foundation connects and cleanses customer interactions across CRM, marketing and service systems in real time. By providing AI agents with access to a single, trusted 360-degree customer profile, organizations can reliably deliver highly personalized, real-time experiences at scale without violating privacy preferences.
AI agents require real-time, end-to-end visibility across suppliers, locations and inventory to automate decisions like shipment rerouting. When fed outdated or disconnected data, supply chain agents cannot predict logistical bottlenecks. A unified master data platform shifts operations from reactive firefighting to proactive, predictive forecasting.
Enterprises maintain regulatory compliance by using automated metadata management to map end-to-end data lineage for every AI-driven decision. This comprehensive approach enforces advanced access controls and privacy standards like GDPR and CCPA directly within data pipelines, ensuring that automated reporting is consistently accurate and fully auditable.
Informatica’s Intelligent Data Management Cloud (IDMC) provides a unified, cloud-native platform that combines data integration, AI-powered quality, multidomain master data management (MDM) and governance. By delivering a single source of trusted, real-time data, it empowers both human teams and autonomous AI agents to make fast, accurate decisions.
