AI Data Management: The Complete Enterprise Strategy Guide

Modern enterprises generate data faster than they can manage it. New data sources are added every day, data volumes continue to grow exponentially and expectations for real-time insights keep rising. Yet many data teams still spend up to 80% of their time preparing data instead of analyzing it. At the same time, executives struggle to trust insights that come from fragmented, inconsistent and poorly governed data.

The promise of AI-driven decision-making remains out of reach, not because AI lacks capability, but because the underlying data management foundation cannot keep pace. AI is only as reliable as the data it is trained on. With 79% of corporate strategists viewing AI as critical to business success, the pressure to get data management right has never been higher.

This challenge is escalating. Organizations now manage petabytes of data spread across cloud platforms, on-premises systems and hundreds of SaaS applications. Data quality issues alone cost enterprises an average of $12.9 million annually. Regulatory requirements such as GDPR, CCPA, HIPAA, SOC 2 and ISO 27001 demand unprecedented levels of visibility, control and auditability.

Meanwhile, many AI and machine learning initiatives fail not due to model limitations, but because the data feeding them is incomplete, inconsistent, or untrustworthy. Manual, rule-based data management processes simply do not scale to meet today's business demands.

This is where artificial intelligence is changing the equation. AI-powered data management applies machine learning, natural language processing and intelligent automation directly to the data management lifecycle itself. Instead of relying on manual effort, AI can automatically discover and classify data, detect and prevent quality issues, enforce governance policies and enable safe self-service access, while continuously learning from how your organization uses data.

This guide explores how AI is transforming data management across the enterprise, from discovery and integration to quality, governance and analytics. You'll learn the core capabilities behind AI data management, practical implementation strategies, and ways to measure ROI, as well as how platforms like Informatica's CLAIRE GPT enable intelligent automation at scale.

Let's start by clarifying what 'AI for data management' really means in practice.

What Is AI for Data Management?

AI for data management refers to applying artificial intelligence directly to the data management lifecycle—how data is discovered, integrated, governed, improved and accessed—rather than using AI only at the analytics or model layer. 

This includes core disciplines such as data integration, data quality, data governance, data cataloging and master data management (MDM), ensuring consistent, trusted views of critical entities like customers, products and suppliers.

Instead of relying on manual processes and static rules, AI-powered data management uses machine learning, natural language processing and intelligent automation to continuously understand data, improve quality, enforce governance and make trusted data accessible at scale. The result is a data foundation that can keep up with business change and support enterprise AI initiatives reliably.

Traditional vs. AI-Powered Data Management

Traditional data management approaches were designed for smaller data volumes, fewer sources and slower change cycles. In practice, this creates persistent friction:

• Manual data discovery and manual data cleansing often take weeks of IT effort to identify, document, catalog new sources, and correct data errors or inconsistencies

• Rule-based data quality checks only catch known, predefined issues

• Static governance policies require frequent manual updates as regulations and usage change

• Data integration depends on custom coding for each new source or schema change, making it difficult to integrate with existing systems and increasing the risk of creating data silos

• Business users rely heavily on IT teams for even simple data requests

AI-powered data management addresses these constraints by embedding intelligence into everyday operations:

• Automated discovery scans and classifies data across cloud, on-premises and SaaS environments in near real time

• Machine learning proactively detects anomalies and emerging data quality issues

• Adaptive governance evolves policies based on data sensitivity, usage patterns and regulatory requirements

• Intelligent AI data integration recommends mappings and transformations automatically

• Natural language interfaces enable governed self-service access for business users

This shift moves data management from reactive and manual to proactive and scalable.

Traditional vs. AI-Powered Data Management 

Core AI Capabilities in Data Management

Modern AI data management platforms combine several complementary capabilities:

• Machine learning algorithms for pattern recognition identify relationships across datasets, automatically detect relationships, detect quality anomalies, predict lineage impacts, and optimize performance based on usage patterns.

• Natural language processing (NLP) enables conversational data discovery, automatically generates business glossary terms, extracts metadata from unstructured data and creates clear, plain-language documentation.

• Generative AI for data management automation leverages both traditional and advanced AI algorithms to automate data cleaning, analysis, generate transformation logic, create data quality rules from examples, produce compliance documentation, and even generate pipeline code from natural language descriptions.

• Predictive analytics forecasts storage and capacity needs, anticipates data quality degradation, identifies potential governance violations and recommends proactive remediation.

Together, these capabilities create intelligent, self-improving data management systems that scale with enterprise complexity and evolving business demands.

Why AI Data Management Matters Now

The Data Management Crisis

The scale challenge

The pressure on data management has reached a breaking point. Today’s enterprises operate at a scale that traditional approaches were never designed to handle. Sixty-four percent of organizations now manage more than one petabyte of data on average, with large portions arriving as unstructured content such as documents, images, emails, logs and video. Manual processes that once worked for gigabytes collapse under petabyte-scale demands, leaving data teams overwhelmed and reactive.

Regulatory pressures

Frameworks such as GDPR, CCPA, HIPAA, SOC 2 and ISO 27001 require comprehensive visibility into where data lives, how it is used, who can access it and how long it is retained. Emerging AI governance regulations further increase scrutiny. Across distributed, hybrid and multi-cloud environments, manual governance simply cannot keep pace with the volume, velocity and complexity of modern data estates.

The AI Adoption Paradox

This contrast between scale and safety creates a growing paradox for AI adoption. While over 80% of enterprises plan to significantly increase investment in generative AI, 57% of CEOs cite data security as a barrier to AI adoption and 45% flag data privacy concerns.

In practice, AI and machine learning projects fail far more often due to poor data quality, inconsistent definitions and weak governance than due to model limitations. Organizations need trustworthy, well-managed data to use AI effectively—yet managing that data at scale now requires AI itself.

How AI Solves Data Management at Scale

Automation of Labor-Intensive Tasks

AI changes data management by removing the manual bottlenecks that limit scale. Tasks such as discovering new data sources, profiling data quality, mapping relationships and documenting lineage, which once consumed weeks of effort, can now be automated and executed continuously. AI accelerates data processes including data retrieval and analytics by streamlining access, discovery, cleaning and analysis of large datasets. This frees data teams to focus on higher-value work instead of operational firefighting.

Proactive vs. Reactive Management

AI shifts data management from reactive to proactive. Instead of responding after issues surface, AI-powered platforms predict problems before they impact the business. Machine learning models detect early signs of data quality degradation, forecast capacity and storage needs, identify potential compliance risks and recommend preventive actions.

Self-Service Enablement

AI also enables user self-service without compromising on consistent controls. Natural language interfaces allow business analysts, data scientists, executives and other personas in the organization to discover and use governed data without waiting on IT. For example, business analysts can discover and access data using conversational queries instead of writing SQL. Data scientists explore datasets without waiting for IT intervention. Executives get instant answers to business questions from trusted, governed data.

Continuous Improvement

AI systems improve over time. By learning from usage patterns, feedback and outcomes, they understand which data sources matter most, which quality rules generate false positives, which governance policies need refinement and which integration patterns work best. Every interaction makes the system smarter and more aligned with business needs. This shift is increasingly described as agentic AI—systems that don't just automate tasks, but take goal-directed actions on your behalf, such as detecting issues, recommending fixes and triggering remediation workflows within defined guardrails. This combination of automation, prediction, self-service and learning makes agentic AI essential, not optional, for modern data management. 

AI Capabilities Across the Data Management Lifecycle

AI delivers real value only when it is applied across the entire data management lifecycle. Isolated automation helps at the margins, but intelligent, scalable data management requires coordinated capabilities that work together from discovery and integration to quality, governance and access. Together, these capabilities form an increasingly agentic data management layer  that can observe conditions, reason across metadata and act proactively across the data lifecycle.

Intelligent Data Discovery and Classification

The challenge

You cannot manage data you do not know exists. In large, distributed environments, shadow data proliferates across cloud platforms, endpoints and SaaS applications. These untracked datasets not only cause missed analytical opportunities, but with many data breaches involving shadow data, they are a significant security and compliance risk.

How AI solves the challenge

AI automates data collection from diverse sources and discovery and classification at scale, improving data accuracy and accessibility.

Automated scanning: Intelligent agents continuously scan cloud storage, network devices and applications to discover and index new data sources in near real time.

Smart classification: Machine learning models classify data based on content, context and usage patterns—accurately identifying PII, PHI, financial data and other sensitive information without manual rules.

Metadata extraction: Natural language processing (NLP) and large language models (LLMs) understand data meaning, relationships and business context to extract structured metadata from unstructured sources.

Relationship mapping: AI identifies connections between disparate datasets, suggesting potential integrations and surfacing hidden relationships.

Industry example: retail banking

At a retail bank, new customer data fields are frequently added to CRM and digital onboarding systems. CLAIRE automatically discovers newly created Salesforce custom objects, identifies fields containing PII such as national IDs and contact details, maps them to existing customer master data and updates the enterprise data catalog. Governance policies are applied immediately, ensuring sensitive data is protected before it is used for analytics or AI models without waiting for manual reviews or documentation.

AI-Powered Data Integration and Transformation

The challenge

Enterprises integrate hundreds of data sources across cloud, on-premises and SaaS environments. Traditional manual integration relies on custom code, consuming engineering capacity and creating fragile pipelines that break as source systems change.

How AI data integration solves the challenge 

• Smart mapping recommendations: AI makes integration adaptive. It analyzes source and target schemas, suggests field mappings based on name similarity, data types and patterns observed in historical integrations.

• Automated transformation logic: Generative AI creates transformation code from natural language descriptions or learns transformations from example data.

• Self-healing pipelines: Machine learning detects schema changes, API updates and data format drift, automatically adapting integration logic to maintain data flows.

• Performance optimization: AI analyzes pipeline execution patterns, optimizes resource allocation and predicts bottlenecks before they impact SLAs.

Industry example: retail / e-commerce

A global retailer integrates data from its e-commerce platform, marketing tools and order management systems. When the e-commerce provider updates its API and changes product and customer schemas, CLAIRE detects the change, recommends updated field mappings, generates revised transformation logic, validates the pipeline against data quality rules and notifies data engineers. This ensures data flows continue uninterrupted, preventing downstream reporting and personalization models from breaking during peak sales periods.

Automated Data Quality and Observability

The challenge

Manual data quality monitoring only catches known issues. As data volumes grow and sources multiply, teams struggle to maintain rules and respond quickly enough to prevent bad data from propagating into analytics and AI models.

How AI solves the challenge

• Anomaly detection:  AI introduces continuous data observability.  Machine learning establishes baselines and automatically detects anomalies such as unusual value distributions, unexpected record volumes, or missing fields. 

• Predictive quality scoring: AI predicts data quality degradation before it impacts analytics or AI models, enabling proactive remediation.

• Intelligent profiling: Automated analysis understands data meaning, identifies quality issues (duplicates, missing values, format inconsistencies) and suggests remediation approaches.

• Self-optimizing rules: Quality rules adapt based on false positive rates, business feedback and changing data patterns

Industry example: Healthcare

A healthcare provider notices declining data quality in patient address and contact information used for appointment reminders and population health analytics. CLAIRE identifies abnormal increases in missing and inconsistent values, traces the issue to a recently updated intake web form and alerts data stewards. It recommends validation rules aligned with clinical workflows, preventing poor-quality data from impacting care coordination and reporting.

Adaptive Data Governance and Compliance

The challenge
Static governance policies cannot keep up with evolving regulations, business needs and distributed data environments. Manual enforcement across thousands of datasets and users is inconsistent and risky.

How AI solves the challenge

• Policy automation: AI operationalizes governance. It translates regulatory requirements and business rules into enforceable data policies, applied consistently across all data assets. Maintaining data integrity is a key aspect of governance and compliance, and AI tools help monitor, enforce, and track data to prevent issues related to data quality and security.

• Intelligent access control: Machine learning recommends appropriate access permissions based on user roles, data sensitivity, usage patterns and compliance requirements

• Automated compliance reporting: AI generates audit trails, lineage documentation and compliance reports automatically, maintaining continuous evidence for regulatory requirements

• Violation prediction: Predictive pattern analysis identifies potential policy violations before they occur, enabling preventive action

Industry example: Cross-border enterprise (GDPR)

A multinational organization receives a GDPR data subject access request, which must be fulfilled within 30 days. CLAIRE automatically locates the individual’s personal data across CRM, marketing platforms, data warehouses and archived systems. It generates the required access report and tracks retention and deletion timelines. Audit-ready lineage and access logs are produced automatically, reducing manual compliance effort and risk across jurisdictions.

Regular audits for bias are essential in AI algorithms to prevent discriminatory outcomes, especially in high-stakes fields.

Self-Service Data Cataloging and Discovery

The challenge

Traditional data catalogs require heavy manual curation and still leave users struggling to find, understand and trust data, forcing ongoing reliance on IT teams for every data request.

How AI solves the challenge

• Automated cataloging: AI scans data sources, extracts metadata, generates business-friendly descriptions and populates the catalog without manual entry.

• Natural language search: Users find data using conversational queries such as "customer revenue by region last quarter", instead of technical database names or SQL.

• Smart recommendations: AI suggests relevant datasets based on user roles, past usage and current analytical context.

• Trust indicators: Machine learning calculates data quality scores, usage frequency and endorsements to surface the most reliable data sources and help users select the right data with confidence.

Industry example: Consumer Marketing

A marketing analyst needs to analyze customer lifetime value by acquisition channel. Using CLAIRE’s natural language search, the analyst finds the approved datasets, sees quality scores and lineage, understands how metrics are calculated and receives auto-generated SQL or Python code. Insights are delivered in minutes instead of days, without submitting tickets or risking use of ungoverned data.

CLAIRE AI-Native Data Management Platform

What is Informatica CLAIRE?

AI data management delivers the greatest impact when intelligence is embedded across the entire data lifecycle, not added as an overlay to disconnected tools. CLAIRE is Informatica’s AI-native data management capability, designed to bring intelligence, automation and context to every aspect of data integration, quality, governance and cataloging. 

CLAIRE Copilot helps achieve more in less time with AI that understands your data context and delivers the right suggestions in your data management workflow, as a data professional would. 

CLAIRE GPT turns every employee into a data expert, making it easy for everyone to work with data using simplified conversational prompts to accomplish complex data management tasks without writing code, all while maintaining human oversight.

CLAIRE Agents independently plan and reason to solve complex data challenges such as data discovery, building pipelines and proactively fixing data quality issues. 

Overall, CLAIRE allows you to take an agentic approach to data management, where AI can observe changes, understand context through unified metadata and take informed actions across integration, quality, governance and access, all within enterprise controls.

Rather than applying AI in isolation, CLAIRE operates as a shared intelligence layer across Informatica Intelligent Data Management Cloud (IDMC), learning continuously from metadata, usage patterns and operational outcomes. Because CLAIRE spans integration, quality, governance, cataloging and master data management, it can reason across master and transactional data—something point solutions cannot do.

This approach enables organizations to scale AI-driven data management confidently, while maintaining trust, compliance and enterprise-grade reliability.

Unified Metadata Intelligence

Most organizations rely on separate tools for data integration, quality, governance and cataloging. Each tool generates its own metadata, creating silos that limit visibility and prevent AI from understanding how data is actually used across the enterprise. CLAIRE addresses this by operating on a unified metadata layer that spans the entire IDMC platform. This unified metadata intelligence is what makes agentic AI data management possible, providing the shared context AI needs to reason across systems and act safely at enterprise scale.

Key Capabilities

Cross-Platform Context: CLAIRE understands relationships between integration pipelines, data quality rules, governance policies and catalog assets, enabling intelligence that point solutions cannot achieve.

Relationship Discovery: AI automatically maps technical metadata (schemas, lineage) to business metadata (glossary terms, ownership, usage), creating comprehensive data context.

Impact Analysis: When schemas change or policies update, CLAIRE predicts downstream impacts across integrations, quality rules and analytics, preventing broken pipelines and compliance violations.

Learning at Scale: Every user interaction, quality rule execution and policy enforcement teaches CLAIRE about organizational patterns, continuously improving recommendations and automation.

For example, when a customer table schema changes, CLAIRE immediately identifies affected integration pipelines, data quality checks, governance policies and downstream analytics. It generates a complete impact assessment and recommends remediation steps in seconds, preventing broken data flows and compliance gaps before they reach production.

Generative AI for Data Management Tasks

CLAIRE GPT extends the data foundation with generative AI that simplifies how people interact with data management systems. This generative AI layer makes sophisticated data operations accessible to non-technical users while accelerating technical user productivity.

Natural Language Data Operations: Business users describe data needs in plain language, for example, "show me customer churn risk factors from the last six months", and CLAIRE generates the appropriate queries, accesses governed data and returns results.

Automated Documentation: CLAIRE eliminates the burden of manual documentation by automatically generating business-friendly descriptions of datasets, transformation logic explanations, data lineage and governance policy descriptions and summaries.

Code Generation: Data engineers can describe integration requirements conversationally and CLAIRE generates the appropriate data pipeline code, transformation logic and quality rules.

Intelligent Recommendations: CLAIRE suggests optimization opportunities, identifies data quality issues, recommends governance policies and predicts resource needs based on observed patterns.

Enterprise-Grade Governance and Scale

AI-driven data management must be built on a foundation of governance and trust. CLAIRE enforces Informatica’s four pillars governance framework across the entire data estate:

• Discover and Classify: Automated discovery of data sources, intelligent classification of sensitive data and continuous monitoring for new data.

• Protect and Comply: Privacy controls, data masking, encryption and policy enforcement aligned with GDPR, CCPA, HIPAA, SOC 2 and ISO 27001.

• Monitor and Assure: Real-time data quality monitoring, pipeline observability, automated anomaly detection and proactive remediation.

• Manage and Optimize: Lifecycle management, automated retention, storage optimization and cost governance.

CLAIRE operates at true enterprise scale, supporting billions of records daily, thousands of concurrent users and consistent governance across hybrid and multi-cloud environments. Backed by Informatica’s 19 consecutive years as a Gartner Leader in Data Integration Tools, CLAIRE delivers the reliability organizations require for mission-critical data operations.

Implementing AI-Powered Data Management

Assessment and Planning 

Before implementing AI-powered data management, perform a current state assessment to evaluate:

Data Maturity: Assess current data management capabilities, existing tools and platforms, data quality baselines, governance maturity and technical team skills.

Business Priorities: Identify high-value use cases where improved data management delivers measurable business impact. For example, regulatory compliance requirements, AI/ML initiatives requiring quality data, operational inefficiencies from poor data access, or business decisions delayed by data preparation.

Technical Environment: Document existing data sources (cloud platforms, on-premises databases, SaaS applications), current integration architecture, data volumes and growth rates and compliance requirements.

Success Metrics: Define measurable outcomes such as time to prepare data for analytics, percentage of data quality issues detected automatically, compliance audit preparation time, business user self-service adoption rate, or cost per data pipeline maintained.

Organizational Readiness: Evaluate change management needs, executive sponsorship, cross-functional collaboration models and training requirements for different user personas.

 Phased Implementation Roadmap

Phase 1 - Foundation (90 days)

• Deploy AI-powered data catalog for critical data sources

• Implement automated data discovery and classification

• Establish baseline data quality metrics and monitoring

• Enable natural language search for business users

• Focus on high-value, low-complexity use cases for quick wins

Phase 2 - Expansion (6 months)

• Scale intelligent integration across enterprise data sources

• Deploy AI-powered data quality across operational systems

• Implement adaptive governance for sensitive data and compliance

• Enable self-service data access for additional user groups

• Measure and communicate ROI from initial deployments

Phase 3 - Optimization (12 months)

• Expand to advanced use cases (predictive quality, self-healing pipelines)

• Integrate AI data management with AI/ML model development

• Optimize based on usage patterns and business feedback

• Extend to additional data domains and business units

• Establish center of excellence for ongoing evolution

Critical Success Factors

• Start with specific, measurable business problems

• Ensure executive sponsorship and cross-functional collaboration

• Provide role-based training for different user personas

• Measure and communicate value continuously

• Iterate based on user feedback and changing business needs

Measuring Success and ROI  

Quantifiable Metrics: Tracking these metrics quarterly helps demonstrate value, identify optimization opportunities and build executive support for continued investment.

Efficiency Gains

• X% reduction in data preparation time for analytics projects

• X% decrease in time to onboard new data sources

• X% reduction in manual data quality checks and remediation

• X% faster resolution of data governance issues

Cost Savings can be achieved by leveraging a data catalog, which helps organizations efficiently find, manage, and govern their data assets.

• X% reduction in data management operational costs

• Decreased storage costs from intelligent lifecycle management

• Reduced compliance violation penalties and audit preparation costs

• Lower total cost of ownership vs. multiple point solutions

Business Impact

• Faster time-to-insight enabling more agile decision-making

• Increased data scientist productivity (more model development, less preparation)

• Higher confidence in data-driven decisions from improved quality

• Accelerated AI/ML initiatives with trusted, accessible data

• Improved regulatory compliance and reduced risk exposure

Adoption Metrics

• Percentage of business users accessing data self-service

• Number of data sources catalogued and governed

• Data quality issue detection rate and resolution speed

• User satisfaction scores with data accessibility

Best Practices for AI Data Management Success

1. Start with Business Outcomes, Not Technology

Avoid adopting AI simply because it is trending or competitors are investing in it. Start by identifying concrete business problems such as decisions delayed by poor data access, compliance risks from ungoverned data, or AI initiatives failing due to data quality issues. Define success in business terms and measure how AI-powered data management improves those outcomes.

2. Establish Data Governance Before Scaling AI

AI amplifies the state of your data foundation. If data is inconsistent, poorly governed, or unreliable, AI will produce inaccurate insights at scale. Implement automated data discovery, classification, governance and quality monitoring early. This ensures AI-powered operations are built on trusted, compliant data from the start.

3. Adopt a Unified Platform Over Point Solutions

Disconnected data management tools for integration, quality, governance and cataloging create metadata silos that limit AI effectiveness. A unified platform with shared metadata intelligence enables deeper automation, better context and more consistent governance than assembling multiple point solutions.

4. Enable Self-Service While Maintaining Governance

Natural language interfaces and AI-driven discovery make data more accessible to business users. Without guardrails, however, self-service can introduce security and compliance risk. Use AI-powered access controls, data masking, lineage and audit trails to enable safe self-service without sacrificing oversight.

5. Measure and Communicate Value Continuously

Track specific, outcome-oriented metrics such as time to prepare data, quality issue detection rates, audit preparation effort and self-service adoption. Leverage advanced analytics and predictive modeling to drive deeper insights and support data-driven decision-making. Communicate progress regularly. Sustained executive support depends on visible ROI, not enthusiasm for AI technology alone.

6. Invest in Change Management and Training

Technology delivers value only when people use it effectively. Provide role-based training for data engineers, analysts, stewards and business users. Identify internal champions who can demonstrate impact and accelerate adoption across teams.

7. Plan for Hybrid and Multi-Cloud Reality

Most enterprises operate across on-premises systems and multiple cloud platforms. Choose AI data management solutions that deliver consistent capabilities across hybrid and multi-cloud environments, rather than optimizing for a single cloud and creating future constraints.

8. Build Feedback Loops for Continuous Improvement

AI systems improve through usage and feedback. Establish processes for users to flag data quality issues, validate recommendations and refine governance policies. These feedback loops allow AI to learn organizational patterns and adapt as business needs evolve.

Conclusion

AI is fundamentally transforming data management from a manual, reactive bottleneck into an intelligent, automated capability that scales with business ambition. Organizations that adopt AI-powered data management gain clear competitive advantages: faster decision-making through improved data access, lower operational costs through automation, stronger governance and compliance and accelerated AI and machine learning initiatives built on trusted data.

The core capabilities outlined in this guide—intelligent data discovery, automated integration, proactive data quality monitoring, adaptive governance and self-service cataloging—directly address the constraints facing modern enterprises. 

Growing data volumes overwhelm manual processes. Regulatory requirements demand unprecedented visibility, control and auditability. Business teams expect faster, more reliable insights. AI-powered data management meets these demands by embedding intelligence across the entire data lifecycle, not just at the analytics layer.

Success depends on a well-defined implementation path. Leading organizations start by assessing data maturity and business priorities, then focus on high-value use cases that deliver measurable ROI. They choose unified platforms over disconnected point solutions, invest in change management and training and measure outcomes continuously to build and sustain executive support.

Platforms such as Informatica’s CLAIRE GPT enable this approach by delivering AI-native data management, where intelligence is built in, not bolted on. Unified metadata intelligence, generative AI capabilities, enterprise-grade governance and proven scalability provide a durable foundation for long-term data and AI success.

Next steps:

1. Assess Your Readiness: Evaluate current data management maturity, identify high-priority business challenges and document technical environment and compliance requirements.

2. Explore CLAIRE GPT: Learn how Informatica's AI-native platform delivers intelligent automation across integration, quality, governance and cataloging.

3. Start Small, Scale Smart: Pilot AI data management with specific use cases, measure impact, communicate wins and expand based on proven value. 

The AI era demands AI-powered data management. Organizations that act now create advantages that compound over time. Explore Informatica’s Intelligent Data Management Cloud to see how CLAIRE GPT transforms enterprise data operations.