A robust, adaptable infrastructure is essential, especially in a multicloud world where data flows across various platforms
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Artificial intelligence (AI) is transforming industries by enhancing innovation and efficiency, enabling businesses to gain deeper insights, optimise operations, and seize new opportunities. But to fully unlock AI’s potential, enterprises need more than just advanced algorithms and skilled data scientists. A robust, adaptable infrastructure is essential, especially in a multicloud world where data flows across various platforms.
Today, enterprises are no longer tied to a single IT environment, whether that’s on-premises, at the edge, or in the cloud. They use multiple platforms, cloud providers, and environments to optimise costs, ensure redundancy, and meet diverse workload requirements. According to Gartner, by 2028, 70% of workloads will run in a cloud computing environment. As businesses navigate the multicloud landscape, the adoption of AI is emerging as a key driver of innovation. Belgium is among the European frontrunners in AI integration. According to Eurostat, nearly 25% of Belgian companies use AI, placing Belgium in the top three in Europe. Large enterprises are leading the way: almost 48% have implemented at least one AI application. Among medium-sized companies, this figure is 24%, and among small businesses, it’s 11%.
These developments underscore the need for a solid IT infrastructure that seamlessly integrates AI into multicloud environments. By investing in innovative technologies and strategic partnerships, Belgian organizations can maximize the benefits of AI and overcome the challenges of cloud integration.
When IT teams consider the best resources for deploying cloud workloads, the choice often comes down to on-premises versus cloud infrastructure via public cloud services. Public cloud resources offer extraordinary scalability and access to next-generation technologies. Private cloud or on-premises infrastructure provides greater control, security, and visibility. As more cloud solutions have become available, enterprises have implemented both on-premises and cloud infrastructure to create a multicloud architecture. Managing the deployment of multiple public clouds and on-premises technologies can be complex, resulting in additional costs, risks, and administrative burdens for running workloads on cloud resources.
To effectively implement AI in a multicloud environment, enterprises should focus on four key pillars: compute power, data management, storage, and efficiency. Each of these pillars plays a crucial role in supporting AI workloads at scale.
- Scalability of compute power and networks for AI workloads
The potential of AI is only fully realised when organisations have the right compute power and network capabilities to support large-scale data processing. These components form the foundation for AI workloads, ensuring models operate efficiently and deliver meaningful results.
• Leveraging advanced processing power: AI models, especially those using machine learning (ML) and deep learning (DL), require extensive compute power. Training AI on large datasets demands fast GPUs, TPUs, and specialised acceleration hardware. For example, financial institutions use AI-optimized GPUs for real-time fraud detection. Whether in local data centers or cloud-based AI-optimized instances, organizations must ensure they have the right compute 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. Enterprises should leverage software-defined networks (SDN) and network optimization tools for seamless connectivity.
- Data management: Ensuring a seamless AI data flow
AI thrives on high-quality, accessible data, but managing data across multiple clouds can be challenging. Without seamless data integration, there’s a risk that AI models are trained on outdated or incomplete datasets, leading to unreliable insights. Effective data management strategies are essential for AI success.
• Unified data management: When data is 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 policies should address issues such as bias in training datasets and privacy concerns.
• Seamless data integration: AI models draw data from multiple sources, including legacy systems, cloud storage, and real-time streams. Integration tools that ensure seamless interoperability between these sources help organizations efficiently consolidate and access data.
• Real-time data access: Many AI-driven applications, such as fraud detection and predictive maintenance, rely 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 drive up operational costs, as organizations struggle to balance access speed with budget constraints. That’s why efficient storage management is essential for maintaining performance and controlling costs.
• Tiered storage solutions: Not all data needs to be immediately accessible. Tiered storage optimises performance and costs by placing frequently used data on fast storage media (such as flash) and archiving less critical data in cost-effective solutions like object storage.
• Scalable storage for AI workloads: AI applications generate massive amounts of unstructured data. Distributed storage systems and object storage solutions provide the scalability needed to manage this data efficiently.
• Storage-as-a-service models: As multicloud adoption increases, more organizations are opting for storage-as-a-service models. These on-demand solutions reduce capital investments and allow businesses to scale their storage needs in line with data growth.
• Data lifecycle management: AI models require fresh, relevant data. Automating the archiving, deletion, and migration of data ensures efficient use of storage space and maintains compliance with data retention guidelines.
- Driving operational efficiency and sustainability
According to IDC, the energy consumption of AI data centers is expected to grow at a compound annual rate of 44.7% to 146.2 terawatt-hours (TWh) by 2027. AI workloads will account for an increasingly large share of total data center electricity use. To address this, it’s crucial to deploy IT solutions at the right scale to avoid unnecessary waste of compute power and excessive energy consumption. By implementing energy-efficient hardware configurations, eco-friendly cooling methods, and management software tools, power consumption can be significantly reduced and hardware lifespan extended. Energy management tools that use telemetry provide valuable insights to optimise energy and thermal management in real time and detect potential hardware issues early.
Conclusion
The path to successful AI for enterprises in a multicloud environment is built on an infrastructure that prioritises flexibility, scalability, and efficiency. Powerful compute capacity, seamless data management, and innovative storage solutions form the foundation for harnessing AI’s potential. Through a holistic approach, organizations can tap into the full potential of AI, drive their growth, and confidently navigate the complexities of multicloud environments.
Tom Van Daele, Field CTO BeLux Dell Technologies/Business AM