AI: Why enterprises should not be putting everything into the cloud

As more businesses turn to AI to drive innovation, many are overlooking the underlying IT infrastructure needed to support their ambitions.

While AI has the capability to transform industries, the belief that AI operates solely or predominantly within cloud environments is a widespread misconception that can prevent organizations from unlocking AI’s full potential. The reality is that hybrid cloud and edge computing, when combined, are critical to unlocking the full potential of AI.

In the GCC, AI could generate as much as $150 billion (9% plus of GCC GDP), with Gen AI accounting for $21 billion to $35 billion. However effective data management and fast processing capabilities are required to unlock its full value.

To meet these demands, AI deployments increasingly rely on hybrid cloud architectures. A hybrid approach allows organizations to effectively manage their data by addressing key challenges related to data governance, as well as latency, and cost management needs.

By embracing a hybrid strategy, organizations can deploy and scale their AI workloads across different environments, be it in the cloud, ‘on-prem’ or at the edge, optimizing management for operational and financial efficiency, thus delivering significant business value.

Although control over data shared via public-facing Gen AI tools has improved, risk of unintentionally exposing sensitive information remains high. By deploying AI workloads in a hybrid cloud environment, user can closely manage data access, significantly reducing the risk of external leaks.

Decide on user-case situations

AI systems frequently process large, complex, and highly sensitive datasets particularly in regulated industries – where strict data control is paramount. In these situations, on-premises or private-cloud deployments support rigorous security protocols, minimize compliance risk, and ensure close adherence to local data-protection laws.

Consider an AI trained privately by a bank to detect fraud using its own transaction data. This secure training environment helps prevent attacks. For instance, it guards against data poisoning, where attackers might taint the training data with bad examples to make the final AI less accurate.

Training privately makes it much harder for attackers to compromise or copy the specialized AI model, protecting the bank's investment and security.

Many AI applications require low-latency processing with minimal delay, especially those required for real-time analytics, robotics, and IoT devices. For e.g., a high-speed manufacturing line using AI-powered cameras for visual quality inspection, defects must be identified and acted upon in milliseconds.

Any significant delay could mean defective products aren't removed, compromising quality and efficiency. By deploying processing power directly on the factory floor, data from cameras and sensors is analysed locally, eliminating network delays.

This enables the immediate, real-time decision-making critical for optimizing production, ensuring product standards, and maintaining safety in fast-paced manufacturing environments.

Take leading global manufacturer Henkel for example, which operates in 124 countries, who increased factory visibility to 93% by leveraging a hybrid cloud strategy. Henkel processes data locally at the edge, sends it to the public cloud to process, enabling them to make real-time operational decisions, minimizing downtime, and optimizes its production lines for enhanced productivity and agility. 

Go for hybrid + edge

This means that businesses running a hybrid cloud strategy with edge solutions can choose where to run AI tasks, based on cost and performance, as well as regulatory needs. While edge computing allows for fast data processing, enabling real-time decision-making, the cloud can be utilized for large-scale data analysis and storage.

This underscores the need for organizations to move beyond a cloud-only approach to fully realize the potential of AI. With a hybrid approach, underpinned by a clearly defined data strategy, and a resilient and future-ready IT infrastructure, businesses can establish a strong foundation for successful AI implementation.

By combining hybrid and edge, businesses can ensure the flexibility, scalability, security measures, and computational power needed to leverage AI for innovation and a competitive edge in the market.