Data infrastructure modernization for AI at scale

Data infrastructure modernization for AI is the key to realizing enterprise AI at scale. Despite model readiness and strong teams, most AI projects never reach production. The biggest barrier isn't algorithms or talent—it's a lack of unified, ready data. As organizations run hundreds of systems with sensitive data in varying formats and across regions, AI stalls because data can't support scalable, compliant innovation.

This guide explains why investing in data systems is critical, how fragmented data ecosystems block AI transformations, and how Transcend enables secure, scalable, and compliant AI operations.

Why AI initiatives demand a modernized data ecosystem

Success with AI starts with data. Machine learning needs data from your whole stack, but static permissions can't keep pace. Adoption rates are high—78% of enterprises have adopted AI as of 2025—but 70–85% of these AI projects still fail. The disconnect is almost always about infrastructure, not algorithms.

Legacy tools fall short for today's demands, especially as most organizations maintain multiple disparate data storage systems. These come with different governance models, permission structures, and data formats. Shadow IT and data sprawl mean you can't get a real-time, accurate view of personal data across your enterprise.

Without unified visibility, cross-functional teams spend cycles chasing down records, not enabling strategy. Modern AI infrastructure requires a single source of truth for data. You need a real-time view and automated compliance to build trust with stakeholders and regulators alike.

• Real-time data classification and discovery enable compliance with privacy and AI regulations
• Manual survey methods and legacy permissions open the organization to compliance gaps and incomplete datasets
• Engineering teams spend more time maintaining brittle plumbing than developing features

If your storage isn't fully optimized for AI, you're facing a systemic infrastructure challenge. 84% of organizations say their storage doesn't meet AI needs—this is a foundational gap, not an isolated issue.

Data infrastructure modernization for AI: Key drivers and benefits

Three forces drive modernization: increased performance needs, growing regulatory complexity, and the need to reduce engineering overhead.

• Performance and scalability: AI workloads differ from traditional analytics, requiring real-time inference, continuous model training, and multi-modal data processing.
• Compliance: AI amplifies data governance challenges. With GDPR, HIPAA, CCPA, and emerging laws, legacy tools can't keep up. Most large enterprises hold years of distributed consent and preference data across dozens or even hundreds of systems.
• Engineering efficiency: Custom scripts and manual workflows don't scale past pilot phases. Modern platforms reduce engineering effort, shortening time-to-insight so teams focus on high-value work.

The measurable benefits include 427% annual ROI and 11-month payback periods. However, the true value lies in building AI products that deploy successfully with clean training data, real-time permissions, and compliance that's provable and automated.

Challenges in transforming legacy data systems

Legacy environments cause specific AI blockers. Data silos top the list—84.3% of organizations cite silos as a challenge. Siloed data results in a 31.2% decrease in operational efficiency. When records are split between Salesforce, Snowflake, consent tools, and S3, you lose visibility and strategy turns to chaos.

Disparate permissions cause confusion. Most enterprise AI projects stagnate because teams lack clarity about which data they're allowed to use. Without extending permission enforcement and lineage into AI pipelines, models risk training on incomplete or noncompliant data, triggering rollbacks and new regulatory exposure.

Manual data mapping is tedious and error-prone, with 92.4% of enterprises reporting significant standardization challenges. Isolated, point-in-time snapshots lead to uncertainty about personal data and risks.

Manual privacy operations remain common—spreadsheet workflows can't scale or safeguard the business. Inadequate privacy systems become liabilities, not safeguards. This overburdens engineering teams and forces compliance into reactive postures, stalling AI in legal review.

Harnessing Transcend's data compliance layer

Transcend delivers a data compliance layer to centralize and enforce permissions across your stack. This isn't another workflow tool—it's infrastructure that operates directly on your data systems.

• Automated discovery and classification: Transcend's Structured Discovery classifies data down to the column level Unstructured Discovery governs sensitive information in PDFs, text logs, and more.
• System Discovery highlights where personal data, vendor governance, and AI systems live, yielding full visibility.
• Data Inventory presents a comprehensive source of truth across systems, while Data Lineage tracks personal data flow for greater confidence.
• Preference Management captures, stores, and enforces preferences for user data across the stack, including consent and AI usage controls like Do Not Train. When a user opts out, Transcend applies that change system-wide—analytics, live models, and more.

The result is clean, compliant data for AI. By embedding permissions in your training sets, you build innovative tools with confidence. Rather than writing custom scripts for each tool, you install a single compliance layer and gain real-time, transparent oversight into data usage and purposes.

Transcend as a catalyst for data infrastructure modernization for AI

Transcend powers the world's largest companies with real-time data permissioning and modern privacy infrastructure. It supports enterprise teams activating data, personalizing customer experiences, and scaling AI with confidence—especially for complex, multi-brand, multi-region operations where manual governance can't keep up.

Transcend enforces permissions at scale, supporting more than 340 million user records for Fortune 500 companies in finance, telecom, healthcare, and retail. Changes in user preferences sync automatically, and new models inherit permissions from day one.

• Hundreds of integrations: Transcend's integration ecosystem features more than 220 API connectors across databases, CDPs, warehouses, SaaS, and internal systems. All integrations are built and managed in-house, so there's no engineering maintenance burden.
• AI-specific controls like Do Not Train are standard. Transcend ensures opt-out data stays out of model pipelines, with deletion requests enforced wherever data resides.
• Developer-friendly APIs and programmatic workflows accelerate implementation, so teams start working with governed, AI-ready data in weeks.

Organizations using Transcend see outcomes: AI products that ship and scale, models relying only on permissioned data, and governance that's invisible but functional. Embedded governance lets companies move up to three times faster and deliver successful outcomes 60% more often.

Future-proofing AI initiatives with Transcend

AI regulations and use cases will continually evolve. Your infrastructure must adapt without constant re-engineering. Transcend's architecture is designed for change and resilience.

• Continuous discovery and monitoring: Real-time classification keeps pace with your ecosystem, flagging new sensitive information before it enters pipelines.
• Automatic scaling: As you add systems, brands, or regions, Transcend automatically discovers and classifies data. No need for manual surveys.
• Audit trails are always-on: Every access event—who, when, why—is logged, reducing audit fatigue and giving leadership real confidence.
• Enterprise scalability: The platform scales with your roadmap. Adding models, new data sources, or new markets? Permissions propagate automatically, so you don't have to rebuild logic each time.

Transcend transforms governance from a roadblock into a strategic accelerator. CIOs gain control and visibility to scale AI securely, while engineering stays focused on shipping products, not maintaining pipelines.

Accelerate your enterprise's AI potential

Data infrastructure modernization for AI isn't just about technology upgrades—it's about systematically removing blockers and ensuring your organization can ship and scale AI products. The challenge isn't the models; it's governing and orchestrating user data with precision.

When you build on a unified compliance layer—where every dataset, every system, and every pipeline reflects real-time user choices—you create the conditions for scalable, responsible AI. Models train on high-quality, permissioned data. Compliance works in the background, quietly minimizing risk and strengthening enterprise resilience.

Managing permissions isn't just a legal checkbox—it decides whether AI unlocks growth or creates risk. With Transcend, compliance is a catalyst for growth, and trust is built into every stage of innovation.

The way forward: unified visibility, real-time enforcement, and automated governance across the data ecosystem. That's how you turn stalled pilots into a durable AI capability for your enterprise. Contact us to see how Transcend can power your data infrastructure modernization.