Airo, an innovative Cloud for AI advisory organization, collaborated with a renowned global hospitality group to revolutionize their operations and elevate customer experiences using advanced cloud-based AI technologies. With a footprint spanning 31 brands and over 8,000 properties in more than 130 markets, the hospitality group sought to harness real-time data streaming capabilities to drive enterprise-wide transformation.

A major healthcare provider, based in Illinois, faced significant hurdles in its Azure environment. These challenges were critical in the context of upgrading their cloud environment for a faster adoption of AI technologies:

• Scalability Issues: The Azure Infrastructure-as-a-Service (IaaS) setup struggled with efficient scaling, leading to frequent performance bottlenecks. This inefficiency directly impacted the ability to handle large-scale AI computations and real-time data processing required for implementing AI technologies.
• Cost Concerns: Maintaining high-end services on Azure proved to be financially unsustainable. The escalating costs were a barrier to offering competitive AI solutions.

To overcome these challenges, Airo implemented a multi-faceted cloud optimization strategy tailored to support its AI initiatives:

1. Modernization and Prioritization:
   • Assessment and Planning: Airo conducted a thorough assessment of the existing infrastructure to identify applications that were candidates for modernization. A prioritized list was developed, focusing on those that would benefit most from cloud-native capabilities.
   • Strategic Roadmap: A detailed roadmap was created to guide the migration process, ensuring minimal disruption to ongoing and upcoming AI projects.
2. Serverless and Containerization:
   • Adopting Serverless Architectures: Airo transitioned its applications to serverless architectures where feasible. This shift not only reduced overhead costs but also enhanced the flexibility and scalability of AI workloads.
   • Containerization: For applications that required a more controlled environment, containerization was employed. This approach ensured that applications could run consistently across various computing environments, crucial for reliable AI model deployment and testing.
3. Security Enhancements:
   • Isolation with Azure AD: Using Azure Active Directory (AD) capabilities, Airo created isolated environments for different services. This isolation enhanced security and compliance, a critical aspect when dealing with sensitive AI data and client information.
   • Enhanced Compliance: These security measures ensured that their AI projects met stringent compliance standards, which is particularly important for clients in regulated industries.
4. DevOps Integration:
   • Continuous Deployment Pipelines: Serverless code and applications were deployed through robust Azure DevOps pipelines. This integration facilitated continuous integration and continuous deployment (CI/CD), enabling rapid updates and improvements to AI models and advisory tools.
   • Automated Testing: Automated testing was incorporated into the pipelines to ensure the reliability and performance of AI applications before deployment.
5. Infrastructure as Code (IaC):
   • Standardized Deployments: By using Infrastructure as Code (IaC) methodologies, Airo standardized the creation and management of cloud resources. This standardization reduced human error and ensured consistency across deployments.
   • Scalability and Flexibility: IaC allowed for rapid scaling of resources to meet the dynamic demands of AI advisory services, providing the flexibility to adapt to varying workloads.

The strategic measures implemented by Airo resulted in substantial improvements in both performance and cost-efficiency, directly benefiting its AI advisory services:

• Performance Enhancement: A 45% improvement in application performance was achieved through meticulous tuning and optimization. This enhancement allowed Airo to deliver faster and more reliable AI advisory services.
• Cost Reduction: The annual Total Cost of Ownership (TCO) for Azure consumption was reduced by 27%. This significant cost saving was achieved through effective cloud spend optimization, allowing Airo to offer more competitive pricing for its AI advisory services.
• Automation: The integration of Azure DevOps led to 100% automation of code and infrastructure deployment. This automation not only increased operational efficiency but also ensured that Airo’s AI advisory services could rapidly incorporate the latest advancements and updates.