In today's ever-changing business environment, companies are leveraging technology to gain a competitive edge and excel in the midst of challenges like inflation, supply chain disruptions, labor market constraints, sustainability demands, and cybersecurity risks. They understand that hybrid cloud and AI stand out as the most revolutionary technologies of our era, capable of tackling current issues and ensuring future resilience. These technologies boost efficiency, cut costs, foster innovation, and drive growth, making them indispensable for forward-looking businesses.

Where AI meets the Hybrid Cloud

The combination of hybrid cloud IT systems with artificial intelligence (AI) and machine learning (ML) capabilities is gaining significant attention in the IT sector. Generally, they offer opportunities for business innovation and expansion. However, integrating these advanced capabilities can prove to be a complex and risky endeavor, marked by uncertainty and challenges.

These days, the most frequent question we encounter pertains to the influence of generative AI on IT operations. We are currently in the initial phases of exploring its potential applications. While the growing interest in AI and ML is sparking new ideas and opportunities, the necessity to manage data, applications, and the core IT infrastructure has led many to seek out best practices, even as they approach these innovations with caution.

The hype surrounding AI has a significant impact on IT in a couple of ways. Organizations must devise strategies for establishing the necessary infrastructure to accommodate AI since traditional computing resources fall short. We're witnessing numerous experiments being conducted across organizations, numbering in the hundreds or even thousands. IT teams are required to swiftly and efficiently support these experiments, posing operational challenges.

Many tech firms are joining efforts to assist IT leaders in devising a strategy and selecting the necessary components for harnessing AI. The ChatGPT launch in late 2022 sparked rapid generative AI innovation, leading to monthly releases and numerous advancements. In the first few months of 2023, the world has witnessed several major events expanded tech applications across businesses (See Table 1).

Our top priority is leveraging AI to better assist our customers. However, maintaining complete control over our data is imperative. IT teams can utilize it to manage their critical data, protect intellectual property, and establish infrastructure that aligns with their security, privacy, and compliance needs.

Is your IT infrastructure ready for AI?

As businesses adopt AI and ML technologies, an increasing number of these capabilities will run on hybrid cloud and hybrid multi-cloud IT systems. According to the 2023 Enterprise Cloud Index, 60% of IT teams surveyed already use multiple types of IT infrastructure, including hybrid cloud (a mix of private and public clouds), multiple public clouds, or a combination of on-premises and hosted data centers. This number is expected to rise to 74% in the near future. Hybrid multi-cloud systems, with their inherent flexibility, scalability, and efficiency, especially when powered by hyperconvergence, are well-suited to meet the demands of AI.

Building the right AI-ready infrastructure can accelerate AI/ML initiatives. However, it must be done while considering sustainability, cost management, security, and other IT governance compliance factors. IT teams remain focused on system efficiency, particularly when evaluating new AI and ML applications.

Some questions that IT leaders might pose include: How many GPUs (graphics processing units) can be accommodated in a rack, and how can they be powered and cooled?

GPUs are crucial components for running AI and ML code for many organizations. GPUs consume significantly more power and generate more heat compared to CPUs. The infrastructure dedicated for AI/ML workloads is often resource-intensive. It requires the deployment of a substantial amount of hardware to enable the systems to learn and perform their tasks effectively.

For example, inputting the entire Internet into ChatGPT requires thousands of data centers. Given that data centers already account for approximately 1.5% of global power consumption, it's reasonable to anticipate that energy usage will emerge as a significant concern for AI-focused companies, particularly those that issue annual Environment, Social, and Governance reports.

The Synergy Between Hybrid Cloud and AI

When Hybrid Cloud and AI are integrated, the synergy is remarkable. Here's how they work together to accelerate digital transformation:

• Scalability: Hybrid Cloud provides the infrastructure needed to support AI and ML workloads, which often require substantial computational power. With the ability to scale resources seamlessly, organizations can leverage AI without worrying about infrastructure limitations.
• Data Management: AI relies heavily on data, and the Hybrid Cloud enables businesses to manage and store vast datasets securely. Whether data is generated on-premises or in the cloud, a well-designed Hybrid Cloud strategy ensures accessibility and data governance.
• Flexibility: Hybrid Cloud allows companies to experiment with AI solutions. They can deploy AI applications in a controlled environment, test their impact, and fine-tune them as needed before scaling them up to the broader organization.
• Cost Efficiency: AI implementations can be resource-intensive. With Hybrid Cloud's Pay-as-you-go model, organizations can optimize costs by only using the necessary resources when running AI workloads, thus preventing unnecessary expenses.
• Security and Compliance: Data security and compliance are paramount, especially when dealing with AI applications. Hybrid Cloud solutions offer a customizable approach to security, allowing businesses to meet industry-specific regulatory requirements.

Hybrid Cloud & AI: Accelerating Digital Transformation

As AI-ready infrastructure options continue to expand, IT leaders must maintain their focus on the integrity and efficiency of their systems. Additionally, they will need to manage expectations at the executive level. Nevertheless, there is a growing enthusiasm for discovering ways in which AI and ML can enhance productivity. This enthusiasm is an integral part of digital transformation, where businesses digitize processes and various aspects of their operations to become more agile, efficient, and high-performing. The digitization of processes can pave the way for experimentation with automation, AI, and ML.

Recent AI innovations are transforming the way enterprises create, implement, and update applications. These new AI tools facilitate automated code translation and alleviate challenges for organizations transitioning to a hybrid cloud model during cloud migration. AI plays a crucial role in cybersecurity by identifying patterns and anomalies in digital transactions and interactions. Many industries rely on AI for data governance and to monitor compliance across their systems. Additionally, AI can be used to write code.

In conclusion, the combination of Hybrid Cloud and AI is driving a new era of digital transformation. Businesses that harness the power of these technologies can stay agile, competitive, and resilient in an ever-changing landscape. By integrating Hybrid Cloud and AI into their strategies, organizations are not only accelerating their digital transformation journeys but also unlocking new possibilities for growth and innovation.