EXECUTIVE INSIGHT time constraints . The result is you have moved your technical debt from the data centre into the cloud , increasing your overall cost long term and delaying the inevitable modernisation activity .
The total cost of ownership in the cloud is also often not fully understood until it is too late and the service has been migrated to the cloud . At this point , there is often activity undertaken to optimise the costs . A better approach is to activate FinOps early in the migration process .
Other challenges include ensuring data security and compliance . Moving sensitive data to the cloud can expose it to potential breaches , so companies need robust security measures and must adhere to regulations like GDPR . Another challenge is managing costs . While cloud services can be cost-effective , unexpected expenses can arise if usage isn ’ t carefully monitored and optimised . Additionally , downtime and service disruptions during migration can impact business operations , making it crucial to have a solid migration plan in place . Companies also face the challenge of integrating legacy systems with new cloud-based solutions , which can be complex and time-consuming .
A growing concern is the shortage of cloud skills . Many organisations struggle to find skilled professionals who can effectively manage and optimise cloud environments . This skill gap can slow down migration efforts and lead to inefficiencies . Alongside this , there ’ s adoption resistance from employees who may be hesitant to embrace new technologies and processes .
Change management strategies , including training and clear communication , are vital to overcoming this resistance .
Q . WHAT ARE SOME OF YOUR PREDICTIONS FOR THE FUTURE OF CLOUD TECHNOLOGY IN THE NEAR FUTURE ? In the coming year and beyond , cloud technology is set to evolve in several exciting ways . One significant development will be the rise of AI as a Service ( AIaaS ). As more businesses look to harness the power of AI , AIaaS will continue to become increasingly popular . This model enables organisations to access advanced AI tools and platforms via the cloud , eliminating the need for substantial in-house expertise or infrastructure . Consequently , AI will become more accessible , allowing companies of all sizes to utilise machine learning , natural language processing and other AI technologies to enhance their operations and customer experiences .
Another key trend is the growth of Edge AI computing . This involves integrating AI with edge computing , where data processing happens closer to the source , such as on IoT devices or local servers , rather than in centralised data centres . As the demand for real-time processing and low-latency applications increases , Edge AI computing will become more prominent . By enabling AI-driven insights and decision-making at the edge , this approach will boost the efficiency and responsiveness of various sectors , from manufacturing to healthcare .
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