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Rapid GenAI application adoption drives new era of application infrastructure modernisation

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Key Findings of the 2023 ECI Survey

The 2023 ECI survey reveals several key findings that highlight the evolving landscape of enterprise cloud adoption. Some of the most notable takeaways include:

  • Cloud adoption is on the rise: The survey found that 83% of respondents have adopted cloud services, with 44% using public cloud services and 39% using private cloud services. GenAI adoption is increasing: The survey revealed that 71% of respondents are investing in GenAI, with 45% planning to increase their investment in the next 12 months. Security and compliance are top concerns: The survey found that 85% of respondents consider security and compliance to be a top priority when adopting cloud services. * Cost savings are a major driver: The survey revealed that 75% of respondents believe that cloud adoption will result in cost savings, with 55% expecting to save more than $1 million per year.

    The adoption of cloud-native applications is expected to continue growing, driven by the increasing demand for scalability, flexibility, and cost-effectiveness.

    The Rise of Cloud-Native Applications

    The shift towards cloud-native applications is a significant trend in the IT industry, driven by the need for organisations to adapt to changing business requirements and technological advancements. Cloud-native applications are designed to take advantage of the scalability, flexibility, and cost-effectiveness of cloud computing, allowing organisations to deploy and manage applications more efficiently.

    Benefits of Cloud-Native Applications

  • Scalability: Cloud-native applications can scale up or down to meet changing business needs, without the need for expensive hardware upgrades. Flexibility: Cloud-native applications can be easily deployed on different cloud providers, allowing organisations to choose the best fit for their needs. Cost-effectiveness: Cloud-native applications can reduce costs by eliminating the need for expensive hardware and software maintenance. ### The Impact of GenAI on Cloud-Native Applications*

    • The Impact of GenAI on Cloud-Native Applications

    The adoption of GenAI (General Artificial Intelligence) is expected to further accelerate the growth of cloud-native applications. GenAI is a type of AI that can learn and improve over time, making it an ideal fit for cloud-native applications. The benefits of GenAI include:

  • Improved decision-making: GenAI can provide organisations with data-driven insights and recommendations, enabling them to make more informed decisions. Enhanced automation: GenAI can automate repetitive tasks, freeing up human resources for more strategic and creative work.

    The Benefits of GenAI Adoption

    GenAI solutions have the potential to revolutionize the way organisations operate, leading to significant improvements in productivity, automation, and efficiency. By leveraging artificial intelligence and machine learning, GenAI solutions can automate routine tasks, freeing up human resources for more strategic and creative work. This can lead to increased productivity, improved accuracy, and enhanced decision-making capabilities. Key benefits of GenAI adoption include:

  • Improved accuracy and reduced errors
  • Enhanced decision-making capabilities
  • Increased productivity and efficiency
  • Automation of routine tasks
  • Improved customer experience

    • The Challenges of GenAI Adoption

    While GenAI solutions offer numerous benefits, their adoption also poses significant challenges. One of the primary concerns is the potential impact on data security and privacy. As GenAI solutions rely on vast amounts of data, there is a risk of data breaches and unauthorized access.

    Scaling GenAI Workloads: Challenges and Opportunities

    Scaling GenAI workloads from development to production is a complex task that poses significant challenges for organizations. The vast majority of respondents (98%) face difficulties in this area, indicating that it is a widespread concern. In this article, we will delve into the challenges and opportunities that come with scaling GenAI workloads, and explore the necessary investments required to overcome these hurdles.

    Challenges in Scaling GenAI Workloads

  • Lack of standardization: GenAI workloads are often custom-built and tailored to specific use cases, making it difficult to standardize and replicate them across different environments. Inadequate infrastructure: The current IT infrastructure is often inadequate to support the demands of GenAI workloads, leading to performance issues and scalability problems. Insufficient training: Many organizations lack the necessary training and expertise to effectively deploy and manage GenAI workloads, leading to a lack of confidence and trust in the technology. ### IT Infrastructure as a Key Investment Area**

    • IT Infrastructure as a Key Investment Area

    IT infrastructure is identified as the number one area of investment needed to support GenAI workloads. This is because GenAI workloads require significant computational resources, storage, and networking capabilities to function efficiently. Investing in IT infrastructure can help organizations overcome the challenges of scaling GenAI workloads and ensure that they can support the growing demands of this technology.

    The Need for IT Training

    Fifty-two percent of respondents say that their organization needs to invest in IT training to support the deployment and management of GenAI workloads. This is a critical area of investment, as IT training can help organizations develop the necessary skills and expertise to effectively work with GenAI technology.

    Cloud computing adoption is widespread, with 71% of organizations already on board.

    The study found that 71% of respondents reported that their organizations have adopted cloud computing, with 45% of respondents stating that they have moved to a multi-cloud strategy.

    The State of Global Enterprise Cloud Deployments

    The study, which was conducted by Vanson Bourne, a UK-based research firm, surveyed 1,500 IT and DevOps/platform engineering decision-makers around the world.

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