The Rise of AI in Enterprise Computing
According to a recent survey by PWC, AI could add as much as $15 trillion to the global economy by 2030. Even today, artificial intelligence (AI) & machine learning (ML) are being used for a wide variety of tasks like creating large language models, voice recognition, translation mechanisms, and even writing code. It has become a transformative tool that can reshape organizations and industries, and we expect it to be as prevalent in business as Word documents, Internet browsers, and mobile applications in the near future.
Companies that act immediately and begin developing AI-centric strategies have the opportunity to create a competitive advantage. Consequently, failing to adopt and leverage these technologies to their full potential could make you fall behind the pecking order when it comes to your industry.
Organizations are taking notice.
According to IDC’s Future Enterprise Resiliency & Spending Survey, 41% of organizations are investing significantly in generative AI with an established spending plan, while another 37% are in the midst of initial model testing & proof of concept projects.
The challenge lies in the fact that AI is advancing rapidly, with datasets expanding in size, models increasing in complexity, and demand for real-time insights becoming crucial for business survival. Large-scale AI-based computation requires intensive computing power & massive volumes of training data. For example, many companies rely on graphics processing units (GPUs) to run AI & ML code, which are extremely power-hungry and heat-generating when compared to CPUs. Through instances like this, it has become increasingly clear that traditional systems do not have the capabilities to optimize your AI & ML initiatives.
Understanding Hybrid Multicloud for AI
Cloud has become the obvious answer for enterprise AI needs, because of benefits like on-demand resource provisioning, pay-as-you-use pricing, and ability to account for AI scalability. According to the survey by IDC, 70% of enterprises will form strategic ties to cloud providers for Gen AI platforms, development tools, and infrastructure by the end of next year. However, one must be cognizant of the evolution of the cloud.
What exactly is hybrid multicloud? Think of it as your enterprise’s digital ecosystem rather than a single destination. It’s an integrated environment that combines:
- Public cloud platforms for scalable, customer-facing applications
- Private cloud infrastructure for sensitive workloads
- On-premises systems for legacy applications and data-sensitive operations
We often think of the cloud as a single location, namely the public cloud, and to be fair, the main 3 public cloud platforms have made significant advancements when it comes to accounting for AI:
- Google Cloud Platform has started making industry-specific AI models, like ultrasound image interpretation for healthcare.
- Azure’s new Gen AI solutions integrate seamlessly with their Office suite and entire workplace ecosystem, making it easier for organizations that already use their products.
- AWS provides various pre-trained models for generative tasks like image, text, and music creation.
IBM recently deemed these two technologies – AI & hybrid cloud – to be the most transformative in the present-day IT space, and the beauty of combining these two in your organizational initiatives is the complementary nature they both show towards each other. Hybrid multicloud enables you to run your AI & ML applications on a variety of infrastructures based on suitability and maintain their integrity to evolve with changing business needs and regulations. On the other hand, AI can simplify and fortify various components of your hybrid multicloud strategy, like automating routine tasks when it comes to your cybersecurity strategy, speeding your application development time to market and even helping your organization seamlessly with its cloud migration.
However, AI and ML initiatives cannot follow a ‘one cloud fits all’ approach when it comes to the public cloud, for a variety of reasons:
- The sensitivity of business data in industries like healthcare, defense, and finance makes using the public cloud a security risk.
- The multi-client nature of the public cloud can lead to performance issues due to the competition for resources. Its remote nature could lead to network latency, slowing down of application response times, and possible outages that can have significant negative effects on your business.
- As more and more data gets created outside your traditional data centers, significant costs can arise from transmission and storage in the public cloud. It is more cost-effective to run workloads on a dedicated infrastructure built for its specific requirements.
These challenges have led many to adopt a cloud-smart strategy that chooses the ideal environment for each AI-based application, whether it’s public clouds, private clouds, or your existing on-premises infrastructure. This makes hybrid multicloud the ideal option for your AI & ML initiatives. Hybrid multicloud is transformational in that it doesn’t treat the cloud as a single location, but as an extensive ecosystem where organizations have the flexibility of choosing what makes sense for individual tasks, projects, or components. Hybrid cloud strategies have recently gained immense popularity in India.
According to the 2024 Enterprise Cloud Index (ECI) Survey by Nutanix, hybrid multicloud is the dominant deployment model among Indian enterprises, with 44% of Indian companies using it – a figure that exceeds all other ECI countries surveyed.
Building Your AI-Ready Hybrid Infrastructure Strategy
So how does your organization create a strategy that optimizes both AI and hybrid multicloud benefits? We believe your strategy should have these key components:
- Creating A Coherent Data Strategy: For your AI & ML strategies to be considered successful, it is imperative to build a data foundation that can store, manage, and process data for your AI applications. This means creating an inventory for every location where your data is stored.
- Establishing A Data Pipeline: The life cycle of AI-specific data involves multiple, exhaustive stages, from ingestion to preparation, training, tuning, and inference. Creating a data pipeline that covers all of this will require commitment from various stakeholders in your organization.
- Comprehensive Data Governance: This strategy involves data classification where you categorize data effectively based on parameters like sensitivity, value, and regulatory needs. It also involves data optimization, where you ensure your AI models receive valid, contextual data by eliminating duplicate, old, or irrelevant data that can distort results.
- Enhanced Portability Through Containers: For your AI initiatives to not face any roadblocks in the entire journey, it is advisable to use containers. These are packaged environments that allow AI-enabled software to move smoothly across all stages, from development to testing to production, without the need to rewrite code.
- Achieving Interoperability Through Open-Source Platforms: To avoid vendor lock-in and data silos from having to deal with so many platforms, it is recommended to use open-source platforms as the backbone of your hybrid multicloud strategy. This allows you to seamlessly orchestrate tasks across different types of cloud & IT infrastructures. Kubernetes is a popular open-source platform used to manage workloads & services in containers so that consistent deployment of applications can be maintained.
- Automation-Led Hybrid Cloud Cybersecurity: Putting your entire cloud cybersecurity processes in the hands of IT teams can get unwieldy and lead to fatigue. Therefore, the best solution is to integrate AI in all the routine tasks involved in your cybersecurity posture, so that your security teams can continue to work on more high-level organizational initiatives. AI can scour through thousands of infrastructures within seconds, highlighting incidents or situations that human professionals may miss.
- Unified Data Management: For your initiatives to have real success, complete visibility is a must. Cloud management platforms help integrate complex multicloud systems into a single IT control environment that has end-to-end coverage & capabilities. This allows you to seamlessly consume, orchestrate, and govern AI-led applications across multiple components & systems.
- A Trusted Team with Expertise: AI & hybrid cloud are two relatively new technologies currently facing a skills shortage. There are not many professionals out there specializing in one, let alone both. For your strategy to be successful, it must be managed by professionals who know how to combine the two capabilities seamlessly.
Following this framework will help you create a complementary hybrid multicloud for AI, leveraging its game-changing benefits like flexibility, scalability, efficiency, and hyper-convergence while having full control over your data. This will help transform your company into a truly data-driven organization that stays ahead of the curve compared to your competitors.
While the widespread adoption of AI & ML is breathing new life into new use cases and possibilities, it must be done with an eye on sustainability, cost management, security, and other compliances. Given the current global shortage of AI expertise and the complexity of building AI-ready infrastructure, partnering with experienced technology consultants like iValue Group can provide strategic advantages. Our team of specialists can help develop a comprehensive roadmap for your AI and machine learning initiatives, ensuring efficient implementation and sustainable growth while minimizing the challenges of in-house capability building.
FAQ Section
Q: How does hybrid multicloud benefit AI implementations?
A: It provides flexibility, enhanced security, cost optimization, and improved performance for AI workloads through distributed computing resources.
Q: What are the key considerations for AI data management in a hybrid environment?
A: Organizations must focus on data governance, security, accessibility, and efficient movement across different cloud environments.
Q: How can enterprises ensure security in a hybrid multicloud AI environment?
A: Implement comprehensive security frameworks, use AI-powered threat detection, and maintain strict compliance monitoring across all environments.
