As the world heads into 2024 with even more promises of revolutionary AI-driven transformation, enterprises are understandably eager to boost investments into artificial intelligence (AI) technologies. According to the International Data Corp, Asia Pacific’s spending on AI - including software, services and hardware for AI-centric systems - is projected to grow to US$78.4 billion in 2027. Perhaps unsurprisingly, other regional analysis into AI readiness has put Singapore in the number one spot, largely thanks to many AI-related initiatives that have been launched, combined with a conducive policy and business environment.
While economies in Asia Pacific (Apac) have made significant strides in prioritising AI adoption and recognising its potential impact on economic growth, there remains a crucial step towards maximising AI readiness. To fully harness the transformative power of AI, enterprises must acknowledge the necessity of a robust, scalable and hybrid data management platform that will efficiently handle the complexities of advanced AI solutions.
The journey towards AI readiness extends beyond the realm of infrastructure modernisation to encompass the integration of AI-powered solutions within the fabric of data management practices. As enterprises grapple with escalating data volumes and the proliferation of heterogeneous data sources, the imperative to automate and intelligently manage data operations becomes increasingly pronounced.
The data challenge
As enterprises increasingly seek to capture value from AI-enabled insights, a robust ecosystem of data infrastructure, management tools and human resources is critical to keep up with the rapidly growing data needs. Yet, as revealed by the 2023 HPE Data and Infrastructure Management Maturity Survey, many enterprises are struggling in this very regard, with 76% of respondents admitting their current data management capabilities cannot keep up with business data demands.
Traditional methods of data management, such as batch-loading data into warehouses, are proving inadequate for the requirements of real-time insights. Continuing with these methods while attempting to implement AI will only impede organisational progress.
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This is further complicated by the fact that more and more enterprises are embracing hybrid and multi-cloud. With their data spread across multiple environments, it poses a significant challenge for IT teams to manage data efficiently and cost-effectively, leading to inefficiencies, complexities and growing costs.
One of the ways in which enterprises should look to reduce IT complexities to ensure AI readiness is by adopting a cloud operating model across environments, including on-premises infrastructure. This allows enterprises to simplify IT operations and seamlessly manage data and workloads no matter where they live.
Ditch the data silos
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A critical aspect of this model lies in the adoption of a robust hybrid data platform, which strategically distributes data across public and private clouds. It also provides the infrastructure and tools necessary to integrate and access data from disparate sources seamlessly. Without a unified data platform, accessing and integrating data becomes arduous and time-consuming, hindering AI initiatives that rely on diverse datasets for training and analysis.
Moreover, investing in a modern hybrid data platform empowers organisations to harness the full potential of AI, including generative AI and large language models (LLMs).
AI initiatives often require massive amounts of data for training complex models and algorithms. A hybrid data platform offers scalability, allowing enterprises to expand storage and compute resources as needed to accommodate growing datasets and computational demands. Without a scalable data platform, enterprises may struggle to handle the influx of data required for AI applications, limiting their ability to derive meaningful insights and drive innovation.
In addition, AI strengthens this framework by enabling automation of data management and predictive security protocols which helps protect against potential breaches and unauthorised access. These technologies work together to not only provide protection for an organisation’s data but to also prioritise governance and privacy concerns.
Automate data management
However, it's important to approach the deployment of AI and hybrid cloud solutions with caution rather than rushing into deployment for the sake of speed. It’s best to evaluate how these technologies align with long-term organisational needs and leverage their advantages accordingly.
Another best practice for effective data management is by accelerating automation of infrastructure and data management. While AI applications can put a strain on an enterprise’s data infrastructure, AI as a technology also has the power to transform data management.
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AI-powered solutions, including Generative AI (GenAI), can be integrated into IT operations – from data analytics and prediction to monitoring – to simplify and enhance lifecycle management across compute, storage, and networking. In fact, according to IDC, 43% of Apac organisations are currently exploring potential GenAI use cases. GenAI is increasingly be used by enterprises in the region to manage IT operations to reduce potential complexities and growing costs of hybrid and multi-cloud infrastructure.
Building a resilient data management foundation is an ongoing journey. By embracing innovative solutions, fostering a data-driven culture, and adopting a strategic approach, organisations can unlock the transformative potential of AI. A hybrid data platform stands as the cornerstone for successful AI initiatives, offering accessibility, scalability, security, flexibility, and cost optimisation.
Mohan Krishnan is the vice president and general manager of HPE GreenLake Cloud Services for Asia Pacific
