As more companies turn to AI to drive innovation, many overlook the underlying IT infrastructure needed to support their ambitions.
While AI has the ability to transform industries, the belief that AI is operated only or primarily within a cloud environment is a widespread misconception that prevents organizations from unlocking the possibilities of AI completely. In reality, hybrid cloud and edge computing combine to unlock the full potential of AI.
At GCC, AI could generate $150 billion (9% plus GCC GDP), while GEN AI accounts for between $21 billion and $35 billion. However, to unlock the maximum value, effective data management and fast processing capabilities are required.
To meet these requirements, AI deployments are increasingly relying on hybrid cloud architectures. A hybrid approach allows organizations to manage their data effectively by addressing key challenges related to their data governance, latency and cost management needs.
By adopting a hybrid strategy, organizations deploy and scale AI workloads across a variety of environments, either on the cloud, on the “on-prem” or at the edge, optimizing management for operational and financial efficiency, thus providing critical business value.
While control over data shared via Public Facing Gen AI tools has been improved, the risk of unintentionally revealing sensitive information remains high. Deploying AI workloads in a hybrid cloud environment allows users to closely manage data access and significantly reduce the risk of external leaks.
Determine the status of the user case
AI systems often work with large, complex and highly sensitive data sets, especially in regulated industries. In these situations, on-premises or private cloud deployments support strict security protocols, minimizing compliance risks and ensuring close compliance with local data protection laws.
Consider AI that is personally trained by the bank to use your own transactional data to detect fraud. This safe training environment helps prevent attacks. For example, an attacker could contaminate training data with a bad example to reduce the accuracy of the final AI.
Personal training makes it much more difficult for attackers to compromise or copy specialized AI models and protect bank investment and security.
Many AI applications require low latency processing that minimizes the latency required, especially for real-time analytics, robotics and IoT devices. For example, high-speed production lines using AI-driven cameras for visual quality inspections require defects to be identified and acted in milliseconds.
Serious delays can mean that defective products will not be removed and will result in a loss of quality and efficiency. Deploying the processing power directly on the factory floors allows data from cameras and sensors to be analyzed locally, eliminating network latency.
This makes immediate, real-time decisions important to optimize production, ensure product standards and maintain safety in a fast-paced manufacturing environment.
For example, take a look at Henkel, a global manufacturer operating in 124 countries. By leveraging the hybrid cloud strategy, we have increased our factory visibility to 93%. Henkel processes data locally at the edge, send it to the public cloud for processing, making real-time operational decisions, minimise downtime, optimize production lines for increased productivity and agility.
Move to Hybrid + Edge
This means that businesses using Edge solutions to implement hybrid cloud strategies can choose where to perform AI tasks based on cost, performance and regulatory needs. Edge computing allows faster data processing and real-time decision-making, while the cloud can be used for large-scale data analytics and storage.
This underscores the need for organizations to move beyond the cloud-only approach to fully realize the possibilities of AI. A hybrid approach supported by well-defined data strategies and a resilient, future-ready IT infrastructure allows businesses to establish a strong foundation for successful AI implementations.
By combining hybrids and edges, businesses have the flexibility, scalability, security measures and computing power needed to leverage AI for innovation and market competitiveness.
