How compliance-driven enterprises are scaling AI on their terms

The AI cost challenge in regulated sectors

Enterprise-wide AI can be expensive — and costs can balloon quickly. Many organizations struggle with infrastructure that isn’t built for AI workloads, leading to deployment delays, overruns and compliance risk.

EY.ai enterprise private helps address these challenges by delivering:

• Cost savings of up to 40% which can be achieved for the right workloads through private AI deployment while reducing risk
• Simplified deployment with fully integrated, ready-to-use infrastructure that reduces time to implementation
• Pre-built sector solutions help accelerate business impact while keeping sensitive data on-premises

This makes AI adoption more cost-effective while maintaining the governance that regulated industries require.

Why deployment models matter more than ever

From our work with clients across compliance-driven sectors, EY teams see five common barriers to AI adoption:

1. High infrastructure costs
2. Data quality and management issues
3. Privacy and IP concerns 
4. Integration challenges 
5. Shortage of AI development talent 

These hurdles are especially steep in compliance-driven industries where data cannot leave secure environments and real-time performance is critical.

We also see a growing trend toward diversification of deployment strategies. Many enterprises are moving some AI workloads away from single-cloud setups to private or hybrid environments. This shift reflects the need to balance cost, performance and compliance while maintaining flexibility to adapt to changing regulations.

A practical solution: private and hybrid AI deployment

Some organizations are now solving these problems by shifting to private or hybrid AI models. These deployment approaches allow companies to retain governance over their data, optimize infrastructure for performance and meet compliance needs more easily.

In one economic model, private AI inferencing delivered significant cost benefits over public cloud or application programming interface-based alternatives (Enterprise Strategy Group, April 2025).

Sector illustration: financial services

Consider a global financial institution aiming to modernize its risk and compliance functions using large language models (LLMs). Data privacy laws in several key markets prevented the movement of sensitive transactions and customer data outside sovereign environments.

In scenarios like this, a private AI deployment model — designed to support in-country data processing and inference — can help institutions:

• Meet compliance requirements without compromising performance.
• Achieve projected cost savings over time, compared with a comparable public cloud setup.
• Accelerate deployment timelines using validated infrastructure and pre-built frameworks.
• Enhance auditability and governance, giving regulators and internal risk teams stronger oversight.
• Improve resilience, with infrastructure tuned to business-critical latency demands.

This example shows how financial services organizations can scale AI responsibly while addressing the compliance, cost and performance challenges they face most acutely.

What to consider when choosing a deployment model

Organizations weighing their AI deployment strategy should consider:

• Where their most sensitive data resides
• Latency and performance requirements
• Total cost of ownership across options
• Regulatory and audit obligations

They should also ask:

• Can we meet our governance and compliance obligations with our current architecture?
• How might we improve performance by processing data closer to where it resides?
• What would a phased hybrid strategy look like — and where would it begin?
• How will our deployment model affect vendor lock-in, transparency and long-term flexibility?

A more grounded path to AI at scale

For compliance-driven enterprises, success with AI depends on aligning deployment strategy with regulatory and operational realities. As cloud costs rise and governance needs intensify, hybrid and on-premises AI models are emerging as flexible options alongside cloud — helping enterprises choose the right fit for each workload. Real-world examples show that with the right deployment approach, AI can deliver value — securely, efficiently and at scale.

Broadening the business case for hybrid AI deployment

The financial services example highlights just one scenario where hybrid or private AI offers tangible benefits. Other sectors with similar requirements — such as life sciences, health care and government — face parallel challenges.

In life sciences, clinical trial data is highly sensitive and often cannot cross borders, making public cloud deployments infeasible. Hybrid models allow organizations to keep protected health information within sovereign environments while still taking advantage of modern processing power capabilities.

In the energy sector, latency is critical. AI models used to monitor equipment, detect safety risks or predict outages need to process data in near-real time. On-premises infrastructure — tailored to the physical realities of a facility — can support this responsiveness, while cloud can continue to support workloads that are less time-sensitive. By minimizing the physical distance between data generation and model inference, energy companies can improve both speed and reliability.

Making the most of existing investments

Many compliance-driven enterprises already have robust data centers or private cloud infrastructure in place. Rather than investing heavily in public cloud migration, some are choosing to modernize these assets and integrate them into their AI workflows. With the right architecture, these organizations can extend the value of legacy systems while minimizing new capital expenditure. The result is a more cost-effective, sustainable approach to AI growth.

The role of alliances in strategic AI deployment

Scaling AI in regulated industries demands a solution that meets regulatory expectations without slowing innovation. By combining sector-aligned use cases, integrated infrastructure and simplified rollout models, working with alliances can help accelerate AI adoption while reducing risk, complexity and cost. This ecosystem approach enables enterprises to deploy AI where it’s needed — on-premises, in the cloud or at the edge — without reworking existing systems.

Looking ahead: future-ready AI infrastructure

As AI capabilities continue to evolve — particularly with the rise of agentic systems — the infrastructure underpinning those capabilities must be equally adaptable. Future-ready platforms must support pre-built sector use cases, governance frameworks and composable architectures that allow organizations to scale with confidence — on their terms.