AI was once discussed mainly among major technology companies and data researchers well-versed in its complexities. However, the democratisation of AI has made this transformative technology and its potential accessible to a much wider audience, no longer limited to specific sectors. Today, numerous AI-powered systems enable users to tap into previously unreachable capabilities, regardless of their level of expertise.
The democratisation of AI allows members of different communities to contribute submissions that influence AI governance laws and regulations. In Singapore, the government launched the National AI Strategy 2.0 (NAIS) to advance the field of AI, maximise value creation, and empower individuals, businesses, and communities to use AI with confidence, discernment, and trust. This democratisation not only removes barriers that once made AI seem esoteric, but also reduces costs and supports the development of highly accurate AI models.
The rise in AI-driven activities significantly impacts the data storage industry, especially in terms of data management and analysis, as more systems rely on AI and machine learning. Since AI and machine learning algorithms need to quickly analyse large data sets, effective and secure data storage is critical to the success of AI endeavours.
Limited scalability designed in legacy storage systems
Many companies face data storage issues, as advancements in storage infrastructure have not kept pace with AI innovation. Applications may occasionally fail due to heavy data reliance, or be impacted by disparate cloud solutions that lack integration capabilities in processing data.
For organisations using cloud storage, determining which data to store, how much is needed, and the most appropriate storage methods has proven difficult. They remain unsure of this balance and often default to a “better safe than sorry” approach, storing massive amounts of data in case it might be reused, repurposed, or provide value later. However, this data hoarding impairs operational efficiency, increases the risk of breaches by expanding attack surfaces, and drives up storage and backup costs. A key priority for businesses is now disposing of outdated or unnecessary data stored across different clouds — an administrative and operational burden that could have been avoided.
Developing a robust storage system allows businesses to improve customer experiences by ensuring data is always available and up to date. More importantly, it helps optimise costs and future-proof an organisation’s data infrastructure.
In what ways can improved storage solutions democratise AI?
Improved storage solutions play a key role in the democratisation of AI by providing the infrastructure necessary for learning, experimentation, and implementation at scale. Open-source frameworks, which include extensive documentation and tutorials, become easier to access and manage with better storage systems. This enables both enthusiasts and professionals to engage more deeply with AI development. Furthermore, having a suitable storage model can help reduce the costs of building and deploying AI solutions in the cloud, allowing businesses to use resources such as CPUs, GPUs, and storage space on a pay-as-you-go basis.
AI-as-a-service (AIaaS) providers also benefit from enhanced storage capabilities, which allow them to offer pre-built models for various applications. This simplifies the integration of AI functionalities into projects, removing storage-related constraints and enabling scalability. AIaaS can also assist businesses with compliance by automating the monitoring and analysis of large data sets, helping reduce the risk of penalties or legal issues.
Addressing storage challenges in the face of AI expansion
The rapid growth of AI and machine learning has strained existing data storage infrastructure, prompting the need for solutions capable of handling much larger data volumes. Businesses now require storage systems that can manage both the scale and performance demands of AI applications. For example, advanced storage solutions designed for cloud environments can support business-critical applications by improving performance and resilience while also reducing costs.
Adopting scalable storage solutions, combined with AI-driven automation, can help businesses manage increasing data volumes and optimise performance, which in turn can reduce administrative overhead.
This will benefit organisations and contribute to AI’s broader impact across different sectors and the customers they serve. It not only helps solve complex problems but also drives innovation in various industries.