Page 31 - Red Hat PR REPORT - MARCH 2024

3/19/24, 1:56 PM Navigating AI landscape: Fostering digital agility and resilience – UAE DIGITAL NEWS

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

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