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3/19/24, 1:58 PM Navigating AI landscape: Fostering digital agility and resilience - Middle East News 247
However, several challenges stand in the way of seamless AI/ML project execution. A significant obstacle is
the shortage of talent with key skills, making it challenging to find and retain qualified professionals.
Additionally, the lack of self-service access to AI/ML tools and infrastructure impedes the workflow of data

scientists and developers. Operationalising AI projects further adds to the challenge, with slow, manual and
siloed operations hindering the swift execution of AI lifecycle. Acknowledging these challenges is crucial, and
constant vigilance is necessary to mitigate these setbacks to achieve success in AI/ML initiatives.

Companies can adopt strategic approaches to leverage AI/ML, such as enabling scale for AI-enabled

applications. This involves providing a consistent cloud application platform across multiple private and
public clouds, facilitating building, training and deployment of AI-enabled applications. Moreover,
collaborations with the ISV & SI community to offer AI tools, models and services accelerates the

development and deployment of AI solutions. Also, the adoption of containers in AI workloads is gaining
prominence, with 94 per cent of AI adopters leveraging or planning the utilisation of containers in the coming
year. This further underscores the increasing significance of containerisation in developing and enhancing AI
workloads.

The hype surrounding AI/ML has sparked interest in new use cases and possibilities. However, effective
governance of data, applications and IT systems remains a key priority. According to the 2023 Enterprise

Cloud Index, organisations are leveraging multiple types of IT infrastructure, indicating a shift toward diverse
and hybrid environments. Building the optimal AI-ready infrastructure has the potential to expedite AI/ML
initiatives, but companies should prioritise sustainability, cost management, security and other IT governance

compliance aspects.

The integration of AI into the open hybrid cloud is a significant step forward. To drive this transformation,
open-source artificial intelligence and machine learning (AI/ML) platforms like Red Hat OpenShift AI offers a

unified platform for data scientists and developers to design, train, serve, monitor and manage the life cycle
of AI/ML models and applications across diverse environments. The platform aims at meeting the demands
of foundation models and ensures consistency in production deployment and monitoring capabilities. It
further ensures that customers can build and deploy intelligent applications seamlessly.

For instance, tech giants like Intel play a significant role in the AI ecosystem, showcasing their investments in
semiconductor manufacturing to strengthen supply chain resiliency. Intel’s strategic investments in various

fields aim to cultivate robust epicentres of technology and thriving ecosystems for semiconductor
manufacturing.

Furthermore, Intel’s AI Everywhere Portfolio extends from the data centre and cloud to the client and edge.
Its strategic investments in semiconductor manufacturing contribute to supply chain resiliency, supporting

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