The buzz around artificial intelligence (AI) and its advanced counterpart, generative AI (GenAI), has gained significant momentum in recent times. After the once-dominant machine learning and deep learning tools, these latest technologies are now in the limelight for their transformative potential across businesses of all scales.

In light of these new developments, we had industry experts from Telstra and Digital Realty come together for an insightful webinar, unraveling strategies to craft future-ready GenAI data infrastructure. The session addressed the formidable challenge IT leaders face today: building a robust data framework tailored to meet the AI demands of today while remaining nimble enough to adapt to the AI needs of tomorrow and, ultimately, driving the business goals.

Watch the on-demand webinar now for full insights.

This blog explores the key webinar takeaways in two parts:

1. How AI transformed the data economy

The success of AI hinges on effective utilization of data, and the Global Data Insights Survey from 7,295 C-level executives confirms this trend. The respondents highlight data-driven strategies as pivotal for enhancing customer experiences and driving innovation, marking a shift towards a data-centric economy.

According to the report by World Economic Forum, this seismic shift towards a data-driven digital economy is projected to unlock over $100 trillion of untapped opportunities across sectors such as oil and gas, healthcare, banking, retail, and insurance by 2025.

The potential benefits of AI adoption are immense. For instance, AI use cases in financial services include fraud prevention, transaction integrity, and augmentation of existing functions like customer service and product automation, to name a few.

These large language models and finely tuned chatbots have catalysed an unprecedented growth in data driven digital interactions, catapulting industries into the hyperconnected AI era. Almost every industry today is looking to streamline processes and enhance efficiency with AI’s functionality.

2. Key considerations for IT leaders for future-proofing GenAI data infrastructure

Business outcomes continue to remain central to any enterprise's AI strategy. Hence, IT leaders must confront the operational challenges to craft a future-ready infrastructure capable of harnessing GenAI investment opportunities.

1. Data gravity challenges: AI algorithms require enormous amounts of data to train their models for making accurate predictions. As data grows in size and complexity, it creates data clusters posing challenge for storage, processing, and real-time analysis leading to Data Gravity. Overcoming these challenges requires new infrastructure architecture to facilitate data exchange and platform growth.

2. AI ready Hybrid infrastructure: The high computing workloads of AI deployment drives the need for Hybrid IT infrastructure to reduce network latency, minimize data transfer costs, and enhance performance and user experiences. GenAI workloads vary in computational needs, making a hybrid multi-cloud approach essential. Hybrid IT enables seamless resource allocation between on-premises infrastructure and cloud-based services, offering scalability and flexibility based on workload demands.

3. Sustainability: Although AI has the potential to help businesses, its deployment causes significant strain on the environment due to high energy consumption and heat generation. This calls for unique cooling needs like Digital Realty's Air-Assisted Liquid Cooling (AALC) technology, which reduces water usage, and improves energy efficiency, ensuring a more sustainable approach to AI implementation.

In what feels like the tip of the iceberg, there is much potential of GenAI yet to be discovered. Enterprises, having witnessed the limitations of generic AI models, are increasingly drawn to Private AI solutions now. When enterprises use Public AI, sensitive company data becomes part of shared models, compromising security and data ownership.

In contrast, Private AI, trained on proprietary information and compliant with regulatory standards, offers privacy and data custody. With private AI, companies can develop and customize models tailored to their unique business requirements optimizing investment opportunities. For example, a Private GPT trained on medical literature will prove to be of immense value to the healthcare industry.

Adapting to this versatile nature of GenAI mandates a thoughtful approach that extends beyond simply upgrading technology. It calls for an industry-wide reconsideration of infrastructure strategy that right-fits, right-sizes, and right-locates the technology investments to reach business goals.

Watch the full webinar as Digital Realty’s Patrick Lastennet joins Telstra’s Data Center Lead, Truman Crotty to discuss actionable insights to navigate the complexities of adopting and optimizing GenAI data infrastructure.

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