Despite the excitement surrounding Generative AI, most industry experts have yet to deal with a big query: Is there an infrastructural platform that may support this technology long-term, and if that’s the case, will it’s sufficiently sustainable to support the novel innovations Generative AI guarantees?

Generative AI tools have already built quite a popularity, with their ability to jot down well-synthesized text at the clicking of a button – tasks which may otherwise require hours, days, weeks, or months to finish manually.

That’s all well and good, but absent the right infrastructure, these tools simply don’t have the scalability to really change the world. Soon to exceed $76 billion, Generative-AIs astronomical operating costs are a testament to this fact already, but there are additional aspects at play.

Enterprises have to deal with creating and connecting the fitting tools to leverage it sustainably and must put money into a centralized data infrastructure that makes all relevant data seamlessly accessible to their LLM without dedicated pipelines. With strategic implementation of the right tools, they’ll give you the chance to deliver the business value they seek despite the capability limitations data centers currently impose – only then will the AI revolution truly advance.

A Familiar Pattern

In line with a brand new report from Capgemini Research Institute, 74% of executives consider the advantages of generative AI outweigh its concerns. Such a consensus has already prompted high adoption rates amongst enterprises – about 70% of Asia-Pacific organizations have either expressed their intentions to speculate in these technologies or have begun exploring practical use cases.

However the world has been down this road before. Take the web, for instance, which regularly attracted increasingly more attention before surpassing expectations via a myriad of remarkable applications. But despite its impressive capabilities, it only really took off once its applications began to deliver to businesses at scale.

Looking beyond ChatGPT

AI is falling into an identical cycle. Businesses have rapidly bought into the technology, with an estimated 93% of enterprises already engaged in several AI/ML in-use case studies. But whatever the high adoption rate, many enterprises still struggle with deployment – a telltale sign of incompatible data infrastructure.

With the right infrastructure, corporations can look beyond the surface level of Generative AI’s tantalizing capabilities and leverage its true potential to rework their business landscapes.

Indeed, Generative-AI might help write a temporary quickly and, normally, quite effectively, but its potential goes far beyond that. From potential drug discovery to healthcare treatments to produce chain optimization, none of those breakthroughs are possible if the information centers that support and drive AI applications aren’t robust enough to administer their workloads.

Overcoming the Barrier to Scalability

Generative AI has yet to actually deliver significant value to businesses since it lacks scalability. That is attributable to the undeniable fact that data centers have capability limitations – their infrastructure was not originally made to support the huge exploration, orchestration, and model tuning that Large Language Models (LLMs) require so as to run multiple training cycles efficiently.

Reaping value from Generative AI subsequently relies on how well a business leverages its own data, which could be improved through developing a sturdy data architecture. This could be achieved by connecting structured and unstructured data sources to LLMs or by increasing the throughput of existing hardware.

It is important that corporations trying to train their LLM on organizational data can first consolidate that data in a unified manner. Otherwise, data left in a siloed structure will likely generate bias within the LLM’s learning powers.

A Support System

Generative AI didn’t appear out of thin air – it has been within the works for quite a while, and its usage and potential will only grow within the many years to come back. But for now, its business applications are hitting a wall which will not be scalable.

The fact is that these various tools are only as strong as the information processing infrastructure that supports them. It’s subsequently critical that business leaders leverage platforms that may process the petabytes of knowledge these tools have to tangibly deliver on the numerous value they promise.