Navigating AI landscape: Fostering digital agility and resilience

By Adrian Pickering

In an era of rapid technological advancements, organisations are grappling with the challenges of executing AI and machine learning (AI/ML) projects efficiently. As the digital landscape continues to evolve, the need for agility and resilience becomes paramount. Numerous hurdles are faced by organisations, which has necessitated innovative strategies to foster digital agility and resilience through enterprise intelligence.

With the digital economy constantly advancing, agility and flexibility are not just desirable attributes, but are key pillars for organisational success. Navigating the complexities of digital transformation requires a deep understanding of agile principles and the pivotal role of effective data management. Data, often referred to as the currency of the digital economy, plays a key role in enabling organisations to thrive.

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 diverse-owned suppliers and fostering innovation within the global supply chain.

A diverse and inclusive supply chain, including diverse-owned suppliers, enhances resilience and introduces healthy competition, driving innovation. In light of this, Intel’s Smart Capital strategy is a testament to its commitment to long-term growth, aiming to drive development while ensuring flexibility and delivering higher returns on investments.

The successful deployment of AI necessitates organisations to integrate it seamlessly into their core business operations. By prioritising data quality, training employees, and modernising technology infrastructures, businesses can successfully deploy AI. Innovative strategies and collaborations empower enable organisations to adeptly manoeuvre through the intricacies of implementing AI, promoting digital flexibility and robustness in a constantly evolving environment.

The writer is Regional General Manager MENA, Red Hat