Scaling AI in Regulated Industries
The AI cost challenge in regulated sectors is a pressing issue for many organizations. Enterprise-wide AI can be expensive, and costs can escalate quickly due to infrastructure inadequacies. Many enterprises struggle with infrastructure that isn’t optimized for AI workloads, leading to deployment delays, budget overruns, and compliance risks.
Addressing the Challenges with EY.ai
EY.ai enterprise private offers solutions to these challenges by delivering:
- Cost savings of up to 40% achievable for the right workloads through private AI deployment, which also helps in reducing risk.
- Simplified deployment with fully integrated, ready-to-use infrastructure that minimizes implementation time.
- Pre-built sector solutions that accelerate business impact while ensuring sensitive data remains on-premises.
This approach makes AI adoption more cost-effective while maintaining the governance required in regulated industries.
The Importance of Deployment Models
From insights gained through working with clients in compliance-driven sectors, several common barriers to AI adoption have been identified:
- High infrastructure costs
- Data quality and management issues
- Privacy and intellectual property concerns
- Integration challenges
- Shortage of AI development talent
These hurdles are particularly steep in compliance-driven industries where data cannot leave secure environments, and real-time performance is critical.
Private and Hybrid AI Deployment as a Practical Solution
Some organizations are addressing these issues by shifting to private or hybrid AI models. These deployment strategies allow companies to retain control over their data, optimize infrastructure for performance, and meet compliance needs more easily.
For instance, a model where private AI inferencing is utilized has shown significant cost benefits compared to public cloud or application programming interface-based alternatives.
Sector Illustration: Financial Services
Consider a global financial institution that aims to modernize its risk and compliance functions using large language models (LLMs). Due to data privacy laws in various key markets, the movement of sensitive transactions and customer data outside sovereign environments is prohibited.
In such scenarios, a private AI deployment model that supports in-country data processing and inference can enable institutions to:
- Meet compliance requirements without compromising performance.
- Achieve projected cost savings over time compared to public cloud setups.
- Accelerate deployment timelines using validated infrastructure and pre-built frameworks.
- Enhance auditability and governance for regulators and internal risk teams.
- Improve resilience with infrastructure tuned to meet business-critical latency demands.
This example illustrates how financial services organizations can responsibly scale AI while addressing compliance, cost, and performance challenges.
Considerations for Choosing a Deployment Model
Organizations contemplating their AI deployment strategy should take into account:
- Where their most sensitive data resides
- Latency and performance requirements
- Total cost of ownership across options
- Regulatory and audit obligations
Key questions to explore include:
- Can we meet our governance and compliance obligations with our current architecture?
- How might we improve performance by processing data closer to its source?
- What would a phased hybrid strategy look like, and where would it begin?
- How will our deployment model impact vendor lock-in, transparency, and long-term flexibility?
A Grounded Path to AI at Scale
For compliance-driven enterprises, achieving success with AI relies on aligning deployment strategies with regulatory and operational realities. As cloud costs rise and governance needs intensify, hybrid and on-premises AI models are emerging as flexible alternatives to cloud solutions, enabling enterprises to choose the right fit for each workload. Real-world examples indicate that with an appropriate 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, like life sciences and health care, face similar challenges. For instance, clinical trial data is highly sensitive and often cannot be transferred across borders, making public cloud deployments infeasible. Hybrid models enable organizations to keep protected health information within sovereign environments while leveraging modern processing capabilities.
In the energy sector, where latency is critical, AI models used for monitoring equipment or predicting 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 solutions can continue to manage less time-sensitive workloads.
Maximizing Existing Investments
Many compliance-driven enterprises have already established robust data centers or private cloud infrastructure. Rather than incurring high costs associated with public cloud migration, some organizations are opting 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 expenditures, resulting in a more cost-effective and sustainable approach to AI growth.
The Role of Alliances in Strategic AI Deployment
Successfully scaling AI in regulated industries necessitates a solution that fulfills regulatory expectations without hindering innovation. By combining sector-aligned use cases, integrated infrastructure, and simplified rollout models, partnerships can facilitate accelerated AI adoption while mitigating risk, complexity, and cost. This ecosystem approach allows enterprises to deploy AI where needed—on-premises, in the cloud, or at the edge—without overhauling existing systems.
Looking Ahead: Future-Ready AI Infrastructure
As AI capabilities continue to evolve, especially with the rise of agentic systems, the infrastructure supporting these capabilities must be equally adaptable. Future-ready platforms should support pre-built sector use cases, governance frameworks, and composable architectures that empower organizations to scale confidently—on their own terms.
