Inside EY’s New Agentic AI Offering: An On-Prem Private Solution Built with Nvidia and Dell

EY.ai enterprise private: A private, on-premises AI platform built for regulated industries in collaboration with Nvidia and Dell

EY has rolled out a comprehensive, on-premises AI platform called EY.ai enterprise private. This new solution is built on EY’s extensive data assets and is designed to address the needs of highly regulated, cybersecurity-conscious sectors. Developed in partnership with leading technology providers Nvidia and Dell, EY.ai enterprise private represents EY’s strategic push to transform its traditional data-driven consulting services into a fully integrated, tailor-made AI capability that can be deployed within client-controlled environments. The overarching aim is to accelerate the consulting engagement by leveraging EY’s data governance capabilities, industry know-how, and advanced AI tooling to drive measurable business outcomes. By delivering a private deployment model, EY seeks to preserve data sovereignty and security while still enabling the speed and sophistication of AI-enabled insights, model training, and automated decision-support across critical business processes. This approach reflects EY’s long-standing commitment to helping clients extract maximum value from their data while meeting stringent regulatory and security requirements.

EY’s private AI platform is designed to integrate seamlessly with clients’ existing data infrastructure, data pipelines, and governance frameworks. The collaboration with Nvidia brings in cutting-edge accelerated computing capabilities, advanced networking, and enterprise-grade AI software blocks that support efficient deployment and scalable operation of AI workloads. Dell Technologies contributes the underlying infrastructure and engineering expertise required to run the platform at scale in on-premises environments. Together, these partners form a validated AI architecture intended to deliver both high performance and robust security, ensuring that private AI deployments can match or exceed the capabilities typically associated with public cloud offerings while staying within the client’s data boundary. This combination positions EY.ai enterprise private as a strategic alternative for organizations that require stringent data control, compliance, and risk management, without sacrificing the benefits of modern AI tooling, automation, or the ability to deploy AI agents across diverse environments.

The strategic motive behind EY’s private AI initiative is to shorten the cycle from data to insight to action within client engagements. EY’s decades of experience in data gathering, analysis, and business transformation provide a strong foundation for a dedicated AI platform that can be rapidly operationalized for specific industries and use cases. By embedding EY’s domain expertise into an on-premises AI stack, EY intends to shorten the path from problem definition to deployed AI solution, enabling faster experimentation, more rigorous validation, and more deterministic outcomes for clients. This is especially relevant in industries where regulatory constraints, data residency requirements, and security concerns make public cloud AI less attractive or even impractical. The goal is not merely to offer an AI tool but to deliver a repeatable, scalable blueprint for AI-enabled consulting that respects client governance, security mandates, and operational realities while unlocking new sources of value from data.

Section overview: Why EY emphasizes a private AI model

• EY.ai enterprise private is positioned as a comprehensive, on-premises AI solution designed to meet the exacting demands of regulated industries. The private deployment model addresses concerns about data sovereignty, regulatory scrutiny, and cyber risk by keeping data within client-controlled boundaries.
• The collaboration with Nvidia and Dell ensures access to world-class hardware, software, and integration expertise. The platform leverages Nvidia’s accelerated computing capabilities and AI software building blocks together with Dell’s robust infrastructure design and support.
• The offering aims to modernize EY’s consulting services by providing a repeatable, scalable, and secure AI foundation that can support a wide range of use cases from data preparation and model training to inference, autonomous decision workflows, and AI-assisted decision support across functions such as risk, compliance, operations, and customer experience.

Section 1 summary: EY’s private AI platform marks a strategic expansion of EY’s data and analytics capabilities into a private, regulated, and scalable AI environment. By combining EY’s domain knowledge with Nvidia’s acceleration and Dell’s infrastructure expertise, the platform aims to deliver enterprise-grade AI that is both powerful and compliant with strict data governance requirements. The private deployment model is designed to give clients the control, visibility, and security they need while enabling the deployment of advanced AI capabilities that can be integrated into existing business processes and systems.

Target markets and data sovereignty: Private AI designed for highly regulated sectors

In designing EY.ai enterprise private, EY focused on organizations operating in highly regulated sectors where data sovereignty and security considerations are paramount. These industries often face strict compliance regimes, complex governance requirements, and significant operational risk if data is mishandled or inappropriately accessed. The private deployment model provides a secure, controllable environment where data remains within the client’s boundaries while still allowing access to advanced AI capabilities. This approach helps ensure that compliance controls, audit trails, and data lineage requirements are preserved, reducing the risk associated with data movement and cloud-based processing.

The private AI platform addresses several core challenges common to regulated industries. First, data gravity: large volumes of sensitive data reside on-site or within tightly controlled data centers, making cloud-based AI solutions less attractive due to latency, bandwidth, or regulatory constraints. EY.ai enterprise private mitigates these concerns by moving computation closer to the data and by enabling governance mechanisms that align with industry-specific requirements. Second, cybersecurity: sensitive data must be protected against a broad range of threats, from insider risk to external breaches. The platform’s architecture emphasizes hardened security controls, encryption, access management, and continuous monitoring to reduce exposure and facilitate rapid incident response. Third, control and compliance: regulated entities require clear data provenance, auditable workflows, and robust change management processes. The private platform is designed to deliver these capabilities through integrated governance, policy enforcement, and traceability across the AI lifecycle.

Industry dynamics and adoption signals

• The regulated sectors most likely to benefit from EY.ai enterprise private include financial services, healthcare, energy and utilities, government and public sector, and critical manufacturing. These industries often require strict data residency, sensitive handling of PII or PHI, and heavy regulatory oversight. By keeping data within the client’s control while enabling AI-driven capabilities, EY aims to deliver both risk mitigation and value creation.
• The platform supports a spectrum of AI use cases that are particularly relevant to regulated sectors. This includes automating routine decision processes with high levels of accuracy, performing complex data analyses with strong lineage, and deploying autonomous or semi-autonomous AI agents in environments where human oversight is still essential for compliance and governance.

The role of technology partners in sector-specific enablement

• Nvidia’s acceleration capabilities, including GPU-accelerated computing and AI software building blocks, provide the performance needed to train, fine-tune, and deploy large AI models within constrained security and latency envelopes. Nvidia’s tools enable efficient model development workflows, scalable inference, and high-throughput data processing that are critical for enterprise-grade AI solutions.
• Dell Technologies contributes the infrastructure backbone and integration expertise required to operationalize private AI at scale. This includes secure, resilient compute, storage, and networking stacks, as well as engineering support to ensure reliable performance in mission-critical environments.
• The combined strengths of EY, Nvidia, and Dell create a cohesive, validated AI architecture designed to meet the unique regulatory, security, and operational demands of target industries, while still enabling rapid deployment and ongoing optimization of AI capabilities.

Section 2 insights: The private deployment model isn’t just about avoiding the public cloud; it’s about delivering controlled, repeatable AI programs that align with regulatory expectations and enterprise risk management. The emphasis on data sovereignty, coupled with high-performance hardware and rigorous governance, makes EY.ai enterprise private a compelling option for organizations seeking to modernize their digital capabilities without compromising safety or compliance.

Technology stack and architecture: Nvidia Blackwell, AI Enterprise, and Dell infrastructure as the backbone

EY’s EY.ai enterprise private platform leverages a robust, integrated technology stack designed to maximize performance, security, and scalability. The architecture centers on Nvidia’s Blackwell accelerated computing technology, complemented by Nvidia AI Enterprise software building blocks, and backed by Dell Technologies’ infrastructure. This combination forms a cohesive, validated AI architecture that supports end-to-end AI workflows—from data ingestion and preprocessing to model training, evaluation, deployment, and ongoing inference in production.

Key architectural components and capabilities

• Accelerated computing: Nvidia Blackwell provides high-performance GPUs and optimized interconnects that accelerate training and inference for large-scale AI models. In enterprise contexts, this acceleration translates into shorter training cycles, faster experimentation, and the ability to deploy more sophisticated models within strict time-to-value constraints.
• AI software building blocks: Nvidia AI Enterprise software components offer a suite of tools designed for enterprise-grade AI development, deployment, and management. These tools help standardize workflows, enable model lifecycle governance, and streamline integration with existing enterprise data platforms.
• On-premises infrastructure: Dell Technologies contributes the physical and virtual infrastructure required to support a secure, scalable on-premises AI environment. This includes compute, storage, networking, and security capabilities designed to operate within a client’s data centers or private facilities, aligned with strict governance requirements.
• Validated AI architecture: The platform is designed as a validated configuration, ensuring compatibility across the stack and reducing the risk associated with custom integrations. A validated architecture supports predictable performance, easier compliance checks, and streamlined support from all three partners.
• Data processing and orchestration: The platform supports scalable data processing for both model training and inference. This includes data ingestion pipelines, data quality controls, feature engineering, and orchestration of AI workloads to optimize throughput and accuracy.
• Flexible execution frameworks: EY.ai enterprise private supports a range of execution paradigms, including autonomous AI operations and dynamic agent-based workflows. It enables the deployment of AI agents that can operate in both cloud-connected and strictly on-prem environments, providing flexibility to match client needs and regulatory constraints.

Use cases and deployment patterns

• Retrieval Augmented Generation (RAG): The platform can combine large language models with an integrated retrieval layer to fetch domain-specific information from trusted sources, enabling more accurate, context-aware responses in enterprise applications such as risk assessment, compliance reporting, and customer service automation.
• On-demand training: Organizations can perform targeted, on-demand model training to adapt AI capabilities to evolving regulatory requirements, new product lines, or changing business processes. This capability supports rapid experimentation while maintaining strict governance and auditability.
• Dynamic AI agents: The platform supports the deployment of adaptive AI agents that can coordinate across systems, fetch data, make decisions, and execute tasks autonomously or with human oversight. These agents can be deployed across hybrid environments, ensuring operational continuity and resilience while respecting data residency constraints.
• Multi-environment execution: The architecture supports running AI workloads in both on-premises environments and cloud-connected scenarios. This flexibility is crucial for regulated industries that may require hybrid configurations to balance compliance, security, latency, and scalability.

Section 3 takeaway: The Nvidia-Dell-EY stack provides a high-performance, secure, and governable foundation for enterprise AI. The combination of Blackwell acceleration, enterprise-grade software, and robust infrastructure gives EY.ai enterprise private the technical depth needed to handle complex, sensitive workloads while remaining adaptable to evolving regulatory and business requirements.

Agentic AI and its strategic role in EY’s roadmap

Agentic AI is a central focus for EY’s future AI strategy within EY.ai enterprise private. It represents a shift toward autonomous systems that can operate in concert with humans to redefine how business processes are performed. EY views agentic AI as a catalyst for a new era in which autonomous capabilities and human expertise converge to unlock higher levels of productivity, innovation, and value creation. The firm emphasizes that successful implementation of agentic AI requires a comprehensive, end-to-end approach that addresses both technical and organizational complexities. This includes building flexible blueprints for integration, governance structures that support safe autonomy, and scalable platforms that can accommodate growth and evolving business needs.

The role of leadership and governance in agentic AI adoption

• EY’s leadership underscores the importance of a thoughtful, well-governed approach to agentic AI. A strategic blueprint for enterprise-scale adoption must balance autonomy with oversight, ensure alignment with regulatory requirements, and provide clear roles for human operators. The blueprint should also address risk management, accountability, and transparency to build trust in autonomous systems.
• The platform’s on-premises nature facilitates rigorous governance, allowing organizations to implement strict access controls, audit trails, and policy enforcement across AI workflows. This governance lens is crucial for regulated sectors where compliance and risk management are tightly interwoven with technology decisions.
• Executing agentic AI at scale requires disciplined change management, including workforce training, process redesign, and clear performance metrics. EY emphasizes that the successful integration of autonomous systems will hinge on organizational readiness, not just technological capabilities.

Section 4 highlights: Agentic AI is positioned as a long-term differentiator for EY’s AI offering. By delivering a private environment that supports autonomous agents with robust governance, EY aims to help clients scale AI more confidently, accelerate decision-making, and drive business value while maintaining strict security and regulatory compliance.

Market outlook and adoption signals: Data quality and unified strategy as accelerants

EY references industry insights indicating that organizations recognize the importance of high-quality data for accelerating AI adoption. In EY’s view, a unified strategic approach that is tailored to sector-specific needs and underpinned by a strong data foundation can unlock substantial value from AI investments. The belief is that when data quality and governance are aligned with business objectives, enterprises are better positioned to deploy AI across functions, achieve faster time-to-value, and realize measurable improvements in efficiency, risk management, and revenue growth.

The role of ecosystem partnerships in accelerating adoption

• The collaboration with Nvidia and Dell Technologies is central to EY’s go-to-market approach for EY.ai enterprise private. The ecosystem approach expands the capabilities and resources available to clients, enabling more comprehensive solutions that can be integrated with existing enterprise systems.
• The partnerships are designed to deliver a coherent, scalable AI factory concept—one that can transform data assets into actionable intelligence, generate insights at speed, and support enterprise-grade AI production. By enabling a controlled, private AI environment with high performance capabilities, EY positions itself as a facilitator of enterprise AI adoption in sectors that require privacy, security, and governance.

The client value proposition in the era of agentic AI

• EY asserts that a private AI platform can serve as a reliable foundation for deploying highly capable AI agents, with the ability to scale across an organization while maintaining governance and security. For clients in regulated industries, the ability to keep sensitive data on-prem while still leveraging AI-driven insights is a compelling combination that supports competitive differentiation.
• The private deployment model is designed to balance innovation with risk management. It enables clients to innovate within predefined policies and controls, reducing the likelihood of data mishandling and regulatory non-compliance while still enabling rapid experimentation and deployment of AI capabilities.

Section 5 synthesis: The market outlook for EY.ai enterprise private envisions a future in which regulated industries increasingly adopt private AI architectures as a standard option alongside traditional cloud-based strategies. The emphasis on data quality, governance, and a cohesive ecosystem of partners positions EY to play a pivotal role in helping organizations realize AI-driven transformation without compromising security, privacy, or compliance.

Implementation considerations: Security, governance, and operational readiness in private AI

Implementing EY.ai enterprise private requires careful attention to security architecture, data governance, regulatory alignment, and operational readiness. The private AI solution is designed to operate within a client-controlled environment where risk management and policy enforcement are central. The following considerations help ensure a successful deployment:

• Security architecture: A defense-in-depth approach with robust access controls, encryption at rest and in transit, and continuous monitoring is essential. The platform should incorporate security best practices tailored to on-premises deployments, including secure software supply chains, vulnerability management, and incident response planning.
• Data governance and provenance: Strong data lineage, quality controls, and auditable data processing are critical for regulated environments. The platform should support reproducible AI workflows, versioned data pipelines, and traceability of model training data, features, and outputs to satisfy regulatory audits and compliance needs.
• Compliance alignment: The platform must align with sector-specific regulatory frameworks, such as data privacy rules, industry-specific guidelines, and any mandated reporting requirements. Policy enforcement, on-chain or centralized audit logs, and documented governance processes help ensure ongoing compliance.
• Operational readiness and change management: Successful adoption requires organizational readiness, including training for data scientists, developers, and business users. It also requires a clear governance model, established escalation paths, and defined performance metrics to monitor AI effectiveness and risk.
• Hybrid and multi-environment flexibility: Although designed for private on-prem deployment, EY.ai enterprise private should accommodate hybrid configurations when clients require limited cloud interaction. This flexibility supports resilience, disaster recovery, and global operations while maintaining data boundaries.

Deployment patterns and practical considerations

• Phased rollout: Begin with a controlled pilot within a single business unit or data domain to validate performance, governance, and user acceptance before expanding to additional areas.
• Model lifecycle management: Establish a repeatable process for model development, validation, deployment, monitoring, and retirement. This should include performance telemetry, bias detection, and drift monitoring to sustain reliability and compliance.
• Interoperability: Ensure seamless integration with existing enterprise data platforms, cybersecurity tools, identity and access management systems, and governance frameworks. Maintain clear data contracts and interface specifications to minimize friction during deployment.
• Training and skills development: Invest in upskilling teams to work effectively with private AI infrastructure, including data engineers, ML engineers, security professionals, and business analysts. Collaboration between EY’s consulting practitioners and client teams can accelerate adoption and value realization.

Section 6 takeaway: The success of EY.ai enterprise private hinges on rigorous security, disciplined data governance, and strong operational readiness. The private model gives clients control and governance while enabling sophisticated AI workloads, but it requires well-defined processes, governance structures, and organizational alignment to unlock its full potential.

Conclusion: EY’s EY.ai enterprise private—A strategic private AI platform for regulated industries and the path to agentic AI-enabled transformation

In summary, EY’s launch of EY.ai enterprise private marks a deliberate strategic shift toward a private, on-premises AI platform designed to serve highly regulated sectors with stringent data sovereignty requirements. By partnering with Nvidia and Dell Technologies, EY is delivering a robust, validated AI architecture that combines accelerated computing, enterprise-grade software, and resilient infrastructure. The private deployment model addresses the core needs of regulated industries, including data security, governance, and regulatory compliance, while still enabling the advanced capabilities that modern AI offers, from efficient model training to scalable inference and the deployment of dynamic AI agents.

The platform’s emphasis on Agentic AI signals EY’s broader vision: a future where autonomous systems augment human decision-making rather than replace it, all within a governance framework that preserves control and accountability. EY’s approach reflects a careful balance between innovation and risk management, ensuring that enterprise AI deployments are effective, explainable, and compliant with industry standards. The EY.ai enterprise private offering aligns with market expectations in sectors where data is highly sensitive and where speed-to-value must be achieved without compromising security.

Looking ahead, EY’s strategy suggests an evolving AI ecosystem in which private AI architectures become a standard option in the enterprise AI landscape, complementing public cloud deployments and hybrid approaches. For organizations that require strict data control and governance, EY.ai enterprise private offers a compelling blueprint: a scalable, adaptable, and secure AI platform that can accelerate transformation while honoring the realities of regulated environments. As client needs continue to grow and regulatory landscapes evolve, the platform is well-positioned to expand its footprint across industries that demand high assurance in data handling, model reliability, and operational resilience. EY’s ongoing collaboration with Nvidia and Dell will likely drive further innovations in hardware acceleration, AI software tooling, and integrated security mechanisms, reinforcing the private AI factory concept as a practical and scalable path to enterprise AI maturity.