Forrester Analysis reveals how Azure helps enterprises scale generative AI securely, overcoming infrastructure and compliance challenges to unlock actual enterprise worth.
Generative AI is a transformative drive, redefining how trendy enterprises function. It has shortly change into central to how companies drive productiveness, innovate, and ship affect. The strain is on: organizations should transfer quick to not solely undertake AI, however to unlock actual worth at scale or threat falling behind.
Reaching enterprise-wide deployment of AI securely and effectively isn’t any simple feat. Generative AI is like rocket gas. It may possibly propel companies to new heights, however solely with the best infrastructure and controls in place. To speed up safely and strategically, enterprises are turning to Microsoft Azure as mission management. Tapping into Azure’s highly effective cloud infrastructure and superior safety options permits groups to successfully construct, deploy, amplify, and see actual outcomes from generative AI.
To know how companies are making ready for AI, we commissioned Forrester Consulting to survey Azure clients. The ensuing 2024 Forrester Complete Financial InfluenceTM examine uncovers the steps companies take to change into AI-ready, the challenges of adopting generative AI within the cloud, and the way Azure’s scalable infrastructure and built-in safety helps companies deploy AI with confidence.
Challenges with scaling generative AI on-premises
Scaling generative AI is like designing transportation techniques for a quickly rising metropolis. Simply as city growth calls for trendy transportation infrastructure to operate effectively, AI leaders perceive that implementing AI in a significant means requires a cloud basis that’s highly effective, versatile, and constructed to deal with future demand. AI leaders acknowledge that the ability and agility of the cloud is required to attain their desired outcomes.
- In actual fact, 72% of surveyed respondents whose group migration to Azure for AI-readiness reported that the migration was obligatory or diminished the boundaries to enabling AI.
- 65% of enterprise leaders agreed that deploying generative AI within the cloud would meet their organizational aims to keep away from restrictions and limitations of on-prem deployments.
Companies that run most or all of their generative AI workloads on-premises face important roadblocks. On-premises techniques, typically missing the agility supplied by the cloud, resemble outdated roadways—liable to congestion, troublesome to keep up, costly to develop, and ill-equipped for at this time’s calls for. Companies making an attempt to scale AI in these environments encounter difficult obstacles—together with infrastructure limitations, a scarcity of specialised expertise, and integration challenges that gradual innovation—which can be irritating to beat. Challenges like restricted community bandwidth and fragmented information environments additional complicate adoption.
Deploying generative AI safely is essential to defending delicate information, sustaining compliance, and mitigating threat. Surveyed decision-makers recognized 4 key areas of considerations:
- Knowledge privateness dangers, particularly with the proliferation of AI-generated content material.
- Lack of know-how relating to generative AI safety finest practices.
- Compliance complexities with evolving laws round AI use and information safety.
- Shadow IT dangers, as customers flip to unauthorized instruments and apps, exposing organizations to vulnerabilities.
To beat these challenges, it’s necessary to accomplice with a cloud platform that gives built-in safety and regulatory compliance. Cloud migration offers the scalable infrastructure, built-in purposes, and AI-ready information basis obligatory for generative AI success. Survey respondents who’ve already transitioned many or all AI workloads to Azure report enhanced international attain, scalability, and suppleness, all main benefits in at this time’s quickly evolving AI panorama.
Why enterprise chooses Azure for AI-readiness
Infrastructure limitations are a barrier to scaling generative AI. On-premises environments typically hinder efficiency, enhance prices, and gradual innovation. In keeping with our survey, 75% of organizations migrating to Azure for AI-readiness reported that the migration was obligatory or it considerably diminished boundaries to generative AI adoption.
Whereas the advantages of deploying generative AI within the cloud are clear, groups nonetheless face hurdles in adopting AI responsibly. Vulnerabilities, restricted experience in AI safety, and information privateness dangers are essentially the most outstanding considerations. Azure addresses these considerations with complete frameworks that safeguard generative AI workloads end-to-end, from improvement to runtime.
Surveyed leaders cited Azure’s colocation technique as a prime purpose for partnering with Azure for deploying generative AI, eliminating information silos and optimizing efficiency. Microsoft Defender for Cloud and Microsoft Sentinel improve safety and make Azure a trusted platform for protected, enterprise-grade generative AI deployment.
4 key differentiators for deploying generative AI with Azure
1. Enterprise-grade safety and compliant options
Safety considerations are a main problem when deploying generative AI within the cloud. Azure protects AI workloads from code to cloud. Azure’s multi-layered method helps trendy organizations meet compliance requirements and minimizes dangers throughout all the AI lifecycle. Key options together with Defender for Cloud, Microsoft Sentinel, Microsoft Azure Key Vault, and infrastructure as a service (IaaS) present end-to-end safety for generative AI workloads, guaranteeing information privateness, improvement lifecycle safety, and risk administration. Backed by Microsoft’s enterprise-grade safety, compliance, and accountable AI commitments, Azure empowers groups to construct AI options that aren’t solely highly effective but additionally moral, clear, and compliant.
2. Scalable cloud infrastructure
Azure’s cloud infrastructure permits companies to keep away from the constraints of legacy environments, enabling them to launch AI tasks effectively and securely. Azure brings a set of superior AI and machine studying instruments to the desk which can be mission vital for generative AI success, enabling organizations to interrupt free from siloed information, outdated safety frameworks, and infrastructure bottlenecks. By deploying generative AI within the cloud, companies can speed up innovation, streamline operations, and construct AI-powered options with confidence.
3. Unified information and AI administration
Efficient AI begins with a stable information basis. Azure’s information integration and administration options—Microsoft Cloth, Azure Synapse Analytics, and Azure Databricks—allow organizations to centralize information, enhance governance, and optimize AI mannequin efficiency. By shifting past the constraints of legacy on-premises environments, companies acquire seamless information entry, higher compliance, and the scalability wanted to drive AI innovation for enterprise. With Azure, organizations can harness high-quality, well-governed information to energy extra correct and dependable AI outcomes.
4. Sooner innovation
By adopting Azure, assets may be redirected from infrastructure upkeep to AI-powered innovation. Azure’s versatile, safe cloud atmosphere allows companies to experiment, adapt, and evangelize AI options with much less threat than conventional on-premises deployments. Surveyed organizations utilizing Azure reported greater than twice the arrogance of their skill to construct and refine AI and machine studying purposes in comparison with these counting on on-premises infrastructure. Key advantages embody larger flexibility, diminished threat when modifying AI options, and the power to reinvest infrastructure assets into AI upskilling and innovation.
The enterprise affect of safe generative AI on Azure
Migrating to Azure for AI deployment enhances efficiency and operational effectivity. Advantages embody:
- Optimized useful resource allocation: Migrating to the cloud frees IT groups from infrastructure administration, permitting them to give attention to strategic initiatives—similar to growing generative AI use instances—that drive significant enterprise affect.
- Accelerated time to worth: Azure AI companies empower information scientists, AI and machine studying engineers, and builders, serving to them to ship high-quality fashions quicker.
- Enhanced safety and compliance: Azure’s built-in safety instruments shield workloads, scale back breach dangers, and meet evolving compliance requirements.
- Larger AI utility efficiency: Deploying generative AI with Azure improves utility efficiency—driving innovation and progress.
Innovation with out compromise
As IT professionals and digital transformation leaders navigate the complexities of AI adoption, Azure stands out as a trusted accomplice for enterprise AI-readiness. With superior infrastructure, protected and accountable AI practices, and built-in safety, Azure affords a safe and scalable basis for constructing and operating generative AI within the cloud. With Azure, organizations can unlock the complete potential of generative AI to drive innovation, speed up progress, and lasting enterprise worth.