Artificial Intelligence (AI) is transforming industries by enhancing innovation and efficiency, enabling businesses to unlock insights, optimise operations, and seize new opportunities. But to fully harness AI’s potential, businesses need more than just sophisticated algorithms and skilled data scientists. A strong, adaptable infrastructure is essential, especially in a multicloud world where data flows across various platforms.
Enterprises today aren’t tied to one IT environment, whether it’s on-premise, edge or on the cloud. They use multiple platforms, cloud providers and environments to optimise costs, ensure redundancy, and meet diverse workload needs. According to Gartner, 70% of workloads will run in a cloud computing environment by 2028. As businesses navigate the multicloud landscape, AI adoption is emerging as a key driver of innovation. Dell’s 2024 Innovation Index reveals that 76% of organisations prioritise AI-driven innovation, with 60% adopting AI in a multicloud setup to enhance flexibility and scalability. However, 57% struggle with integrating AI workloads across cloud platforms, underscoring the need for a seamless infrastructure.
As IT teams consider the best resources for deploying cloud workloads, the choice often comes down to on-premise versus cloud infrastructure via public cloud services. Public cloud resources offer extraordinary scalability and access to next-generation technologies. Private cloud or on-premise infrastructure provides more control, security and visibility. As cloud solutions have proliferated, enterprises have adopted both on-premise and cloud infrastructure to create a multicloud architecture. Yet deploying multiple public clouds and on-premise technology can create a complex management experience that adds cost, risk and administrative burden to the task of running workloads on cloud resources.
To effectively implement AI in a multicloud world, enterprises must focus on four key pillars: Computing Power, Data Management, Storage, and Efficiency. Each of these play a crucial role in supporting AI workloads at scale.
- Scaling Compute Power and Networking for AI Workloads
AI’s potential is fully realised when enterprises have the right computing power and network capabilities to support large-scale data processing. These components serve as the foundation for AI workloads, ensuring models run efficiently and deliver meaningful results.
- Leveraging advanced processing power: AI models, particularly those using machine learning (ML) and deep learning (DL), require extensive computational power. High-speed Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and specialised accelerators are necessary for training AI on large datasets. For example, financial institutions leverage AI-optimised GPUs for real-time fraud detection. Whether through on-premise data centres or cloud-based AI-optimised instances, businesses must ensure they have the right computing resources.
- Ensuring AI connectivity with high-speed networks: AI applications require fast, uninterrupted data transfer. High-bandwidth, low-latency connections between cloud environments ensure smooth AI operations. Businesses should leverage software-defined networking (SDN) and network optimisation tools for seamless connectivity.
- Data Management: Ensuring Seamless AI Data Flow
AI thrives on high-quality, accessible data, but managing data across multiple clouds can be challenging. Without seamless data integration, AI models risk being trained on outdated or incomplete datasets, leading to unreliable insights. Effective data management strategies are key to AI success.
- Unified data governance: With data spread across different clouds, security, compliance, and consistency are critical. Enterprises need strong governance frameworks to ensure regulatory compliance (e.g., GDPR, CCPA) and data security. AI-specific governance policies should address concerns like bias in training datasets and privacy.
- Seamless data integration: AI models pull data from multiple sources, including legacy systems, cloud storage, and real-time streams. Integration tools that ensure seamless interoperability across these sources help businesses consolidate and access data efficiently.
- Real-time data access: Many AI-driven applications, such as fraud detection and predictive maintenance, depend on real-time insights. Enterprises should invest in cloud-native solutions for real-time data ingestion and processing.
- Storage: The Backbone of AI Scalability
AI workloads generate and consume vast amounts of data. Inefficient storage strategies can inflate operational costs as businesses struggle to balance access speed with budget constraints. Therefore, efficient storage management is essential to maintaining performance and controlling costs.
- Tiered storage solutions: Not all data needs instant access. Tiered storage optimises performance and costs by placing frequently accessed data on high-speed storage (like flash) while archiving less critical data in cost-effective solutions like object storage.
- Scalable storage for AI workloads: AI applications generate massive volumes of unstructured data. Distributed storage systems and object storage solutions provide the scalability needed to manage this data efficiently.
- Storage-as-a-service models: With multicloud adoption increasing, more enterprises are embracing storage-as-a-service models. These on-demand solutions reduce capital investment while allowing businesses to scale storage needs as data volumes grow.
- Data lifecycle management: AI models require fresh, relevant data. Automating data archiving, deletion, and migration ensures efficient storage use while maintaining compliance with data retention policies.
- Driving Operational Efficiency and Sustainability
As per IDC, AI data centre energy consumption is forecast to grow at a CAGR of 44.7%, reaching 146.2 Terawatt hours (TWh) by 2027, with AI workloads consuming a growing portion of total data centre electricity use. To address this, it’s crucial to right-size IT solutions to avoid unnecessary computational waste and excessive energy consumption. Implementing energy-efficient hardware configurations and environmentally responsible cooling methods while leveraging management software tools can significantly reduce power usage and extend hardware lifespan. Power management tools that use telemetry provide valuable insights to optimise power and thermal management in real-time, while identifying potential hardware issues early.
To conclude:
The road to enterprise AI success in a multicloud environment is based on infrastructure that prioritises agility, scalability, and efficiency. High-performance computing power, seamless data management, and innovative storage solutions form the foundation for unleashing AI’s potential. By taking a holistic approach, enterprises can unlock AI’s full potential, drive business growth, and confidently navigate the complexities of multicloud environments.
