Forrester Research shows how Azure helps enterprises scale generative AI securely, overcoming infrastructure and compliance challenges to unlock real business value.
Generative AI is a transformative force, redefining how modern enterprises operate. It has quickly become central to how businesses drive productivity, innovate, and deliver impact. The pressure is on: organizations must move fast to not only adopt AI, but to unlock real value at scale or risk falling behind.
Achieving enterprise-wide deployment of AI securely and efficiently is no easy feat. Generative AI is like rocket fuel. It can propel businesses to new heights, but only with the right infrastructure and controls in place. To accelerate safely and strategically, enterprises are turning to Microsoft Azure as mission control. Tapping into Azure’s powerful cloud infrastructure and advanced security solutions allows teams to effectively build, deploy, amplify, and see real results from generative AI.
To understand how businesses are preparing for AI, we commissioned Forrester Consulting to survey Azure customers. The resulting 2024 Forrester Total Economic ImpactTM study uncovers the steps businesses take to become AI-ready, the challenges of adopting generative AI in the cloud, and how Azure’s scalable infrastructure and built-in security helps businesses deploy AI with confidence.
Challenges with scaling generative AI on-premises
Scaling generative AI is like designing transportation systems for a rapidly growing city. Just as urban expansion demands modern transportation infrastructure to function efficiently, AI leaders understand that implementing AI in a meaningful way requires a cloud foundation that is powerful, flexible, and built to handle future demand. AI leaders recognize that the power and agility of the cloud is needed to achieve their desired outcomes.
- In fact, 72% of surveyed respondents whose organization migration to Azure for AI-readiness reported that the migration was necessary or reduced the barriers to enabling AI.
- 65% of business leaders agreed that deploying generative AI in the cloud would meet their organizational objectives to avoid restrictions and limitations of on-prem deployments.
Businesses that run most or all of their generative AI workloads on-premises face significant roadblocks. On-premises systems, often lacking the agility offered by the cloud, resemble outdated roadways—prone to congestion, difficult to maintain, expensive to expand, and ill-equipped for today’s demands. Businesses attempting to scale AI in these environments encounter complicated obstacles—including infrastructure limitations, a shortage of specialized talent, and integration challenges that slow innovation—that are frustrating to overcome. Challenges like limited network bandwidth and fragmented data environments further complicate adoption.
Deploying generative AI safely is crucial to protecting sensitive data, maintaining compliance, and mitigating risk. Surveyed decision-makers identified four key areas of concerns:
- Data privacy risks, especially with the proliferation of AI-generated content.
- Lack of expertise regarding generative AI security best practices.
- Compliance complexities with evolving regulations around AI use and data protection.
- Shadow IT risks, as users turn to unauthorized tools and apps, exposing organizations to vulnerabilities.
To overcome these challenges, it’s important to partner with a cloud platform that provides built-in security and regulatory compliance. Cloud migration provides the scalable infrastructure, integrated applications, and AI-ready data foundation necessary for generative AI success. Survey respondents who have already transitioned many or all AI workloads to Azure report enhanced global reach, scalability, and flexibility, all major advantages in today’s rapidly evolving AI landscape.
Why enterprise chooses Azure for AI-readiness
Infrastructure limitations are a barrier to scaling generative AI. On-premises environments often hinder performance, increase costs, and slow innovation. According to our survey, 75% of organizations migrating to Azure for AI-readiness reported that the migration was necessary or it significantly reduced barriers to generative AI adoption.
While the benefits of deploying generative AI in the cloud are clear, teams still face hurdles in adopting AI responsibly. Vulnerabilities, limited expertise in AI security, and data privacy risks are the most prominent concerns. Azure addresses these concerns with comprehensive frameworks that safeguard generative AI workloads end-to-end, from development to runtime.
Surveyed leaders cited Azure’s colocation strategy as a top reason for partnering with Azure for deploying generative AI, eliminating data silos and optimizing performance. Microsoft Defender for Cloud and Microsoft Sentinel enhance protection and make Azure a trusted platform for safe, enterprise-grade generative AI deployment.
4 key differentiators for deploying generative AI with Azure
1. Enterprise-grade security and compliant solutions
Security concerns are a primary challenge when deploying generative AI in the cloud. Azure protects AI workloads from code to cloud. Azure’s multi-layered approach helps modern organizations meet compliance standards and minimizes risks across the entire AI lifecycle. Key solutions including Defender for Cloud, Microsoft Sentinel, Microsoft Azure Key Vault, and infrastructure as a service (IaaS) provide end-to-end protection for generative AI workloads, ensuring data privacy, development lifecycle protection, and threat management. Backed by Microsoft’s enterprise-grade security, compliance, and responsible AI commitments, Azure empowers teams to build AI solutions that are not only powerful but also ethical, transparent, and compliant.
2. Scalable cloud infrastructure
Azure’s cloud infrastructure allows businesses to avoid the constraints of legacy environments, enabling them to launch AI projects efficiently and securely. Azure brings a suite of advanced AI and machine learning tools to the table that are mission critical for generative AI success, enabling organizations to break free from siloed data, outdated security frameworks, and infrastructure bottlenecks. By deploying generative AI in the cloud, businesses can accelerate innovation, streamline operations, and build AI-powered solutions with confidence.
3. Unified data and AI management
Effective AI starts with a solid data foundation. Azure’s data integration and management solutions—Microsoft Fabric, Azure Synapse Analytics, and Azure Databricks—enable organizations to centralize data, improve governance, and optimize AI model performance. By moving beyond the limitations of legacy on-premises environments, businesses gain seamless data access, better compliance, and the scalability needed to drive AI innovation for enterprise. With Azure, organizations can harness high-quality, well-governed data to power more accurate and reliable AI outcomes.
4. Faster innovation
By adopting Azure, resources can be redirected from infrastructure maintenance to AI-powered innovation. Azure’s flexible, secure cloud environment enables businesses to experiment, adapt, and evangelize AI solutions with less risk than traditional on-premises deployments. Surveyed organizations using Azure reported more than twice the confidence in their ability to build and refine AI and machine learning applications compared to those relying on on-premises infrastructure. Key benefits include greater flexibility, reduced risk when modifying AI solutions, and the ability to reinvest infrastructure resources into AI upskilling and innovation.
The business impact of secure generative AI on Azure
Migrating to Azure for AI deployment enhances performance and operational efficiency. Benefits include:
- Optimized resource allocation: Migrating to the cloud frees IT teams from infrastructure management, allowing them to focus on strategic initiatives—such as developing generative AI use cases—that drive meaningful business impact.
- Accelerated time to value: Azure AI services empower data scientists, AI and machine learning engineers, and developers, helping them to deliver high-quality models faster.
- Enhanced security and compliance: Azure’s integrated security tools protect workloads, reduce breach risks, and meet evolving compliance standards.
- Higher AI application performance: Deploying generative AI with Azure improves application performance—driving innovation and growth.
Innovation without compromise
As IT professionals and digital transformation leaders navigate the complexities of AI adoption, Azure stands out as a trusted partner for enterprise AI-readiness. With advanced infrastructure, safe and responsible AI practices, and built-in security, Azure offers a secure and scalable foundation for building and running generative AI in the cloud. With Azure, organizations can unlock the full potential of generative AI to drive innovation, accelerate growth, and lasting business value.
