Managing cloud-related issues when implementing AI

Mike Hoy at Pulsant discusses the obstacles that implementing AI will bring and how organisations can manage them 

 

Over the next 12-18 months, businesses across the UK will witness an evolution of AI proof of concepts being implemented into groundbreaking technology as the use of AI continues to grow rapidly across each sector. This progress will also be fueled by AI’s ability to access and utilise a vast reservoir of private data, which is nine times larger than data available on the internet. Overcoming the challenges of accessing this data will be vital for utilising AI’s true potential.

 

However, to be able to harness AI, businesses need to consider the importance of being able to manage it effectively, thus overcoming the obstacles that AI will bring to their organisations while they implement it. This article highlights the importance for businesses to have a resilient and interconnected infrastructure to address the challenges effectively.

 

The indispensable role of data in AI

Being able to access rapid data is the foundation for AI to successful. Without seamless and reliable data being available in a usable format, the foundations of AI development and deployment will collapse.

 

Organisational data is divided across multiple platforms and locations, that transcends through boundaries of prominent ecosystems like AWS and Microsoft. AI applications require a robust and reliable network to ensure consistent latency, performance, and real-time data exchange. Connectivity, therefore, becomes the linchpin for unlocking the value of these disparate data sources.

 

Board members tend to overlook the importance of connectivity, who mistakenly assume that “it will just work”. This oversight can have catastrophic consequences for AI initiatives. Even the most advanced AI applications, equipped with immense computational power, can be crippled by a mere 10-millisecond delay in data retrieval. In 2025, deploying AI without a robust connectivity strategy is not merely a misstep; it’s a strategic failure with severe repercussions.

 

The return of cloud controversy

Connectivity challenges, highlights a critical need for new cloud models to be developed to support the demands of AI.  This has reignited a broader debate about the future of cloud computing.

 

AI Models differ from traditional software applications which makes early cloud infrastructure lack the foundations to handle the immense scale and complexity of AI with these infrastructures having billions of parameters and the dependency to produce a constant flow of real-time data streams. This necessitates a paradigm shift in cloud design and supporting infrastructure to fully unleash the potential of AI.

 

While security, connectivity, and resilience (enabled by geographically distributed networks remain fundamental), the escalating cost of operating in public clouds is forcing organisations to reassess their reliance on providers like AWS and Microsoft. The surge in workload repatriation to private clouds underscores the critical need for standardised data migration processes to ensure a smooth and efficient transition.

 

The role of standards in AI optimisation

The challenge of cloud migration for AI mirrors the complexities of switching bank accounts. Just as banking regulations have streamlined this process, legislative guidance on cloud migration could be a game-changer for organisations. By establishing standardised data movement practices, organisations can more easily adopt hybrid cloud models that are perfectly suited to their AI requirements and broader business objectives.

 

In the face of increasingly distributed AI workloads, a standardised approach is crucial. It will not only accelerate AI adoption and foster best practices but also solidify the position of AI leaders as the market matures.

 

Boosting awareness and collaboration

AI’s growing demands on infrastructure necessitate increased awareness within the tech industry regarding the interplay of connectivity, cloud models, and the broader ecosystem. Successful AI implementation in the real world requires strong collaboration between organisations, suppliers, and partners.

 

In this new era of AI, connectivity and cloud considerations are no longer secondary concerns – they are fundamental to success. By prioritising these factors in planning and execution, businesses can effectively navigate the complexities of 2025 and beyond.

 

Successfully managing cloud-related challenges when implementing AI hinges on a strategic shift in approach, recognising that connectivity, cloud models, and data management are not secondary but foundational elements. Businesses must move beyond the assumption that "it will just work" and actively address the likes of connectivity issues, exploring hybrid cloud models, embrace standardisation and holistic approach that considers both connectivity and cloud considerations being essentials. 

 

If businesses prioritising these factors, then they can effectively navigate the complexities of AI implementation in 2025 and beyond, unlocking the true potential of this transformative technology. Failing to do so risks strategic failure with severe repercussions.

 

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Mike Hoy is Chief Technology Officer at Pulsant’s Manchester Colocation data centre

 

Main image courtesy of iStockPhoto.com and sankai