Mike Hoy, Chief Technology Officer, Pulsant, on the importance of ‘harnessing’ AI.
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 transcend 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.
In 2025, deploying AI without a robust connectivity strategy is not merely a misstep; it’s a strategic failure with severe repercussions.
Connectivity challenges highlight 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.
Legislative guidance on cloud migration could be a game-changer for organisations, establishing standardised data movement practices allowing organisations to more easily adopt hybrid cloud models 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.
AI’s growing demands on infrastructure necessitate increased awareness within the tech industry regarding the interplay of connectivity, cloud models and the broader ecosystem.
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.
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 are 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.
