The adoption and integration of generative artificial intelligence (GenAI) is revolutionising how businesses operate, offering unprecedented opportunities for growth, efficiency, and innovation. Many forward-facing companies have already incorporated GenAI platforms into everyday processes to deliver cutting-edge automation for their staff or customers.

But, as GenAI and other Large Language Model (LLM) tools grow more ubiquitous and become embedded in everyday applications such as Microsoft 365, businesses are confronted with the pressing question: can their business handle the computing power required to fully leverage these advancements?

GenAI’s influence is poised to be pivotal across a broad range of industries in 2024 and beyond, but sectors that are likely to see the most impact include education, healthcare, smart city, retail and manufacturing.

How GenAI is revolutionising different industries
The automation capabilities of GenAI have the potential to streamline workflows, reduce administration time, and even reduce overhead costs across industries. 

In the healthcare sector, it has the potential to lead to significant innovation and patient care, helping scientists develop new drugs and enabling professionals to interpret data, such as patient’s medical history, faster and more effectively than ever.

Within retail and e-commerce, GenAI can be deployed to facilitate more personalised shopping experiences by analysing purchasing behaviours and offering tailored product recommendations. Customer services is a critical area where cost and time-efficiencies can be driven, particularly when it comes to frequently asked questions. This in turn can  help sales teams focus on what’s really important.

However, some sectors are already navigating the impacts of the technology. Creativity has long been held as a uniquely human trait, however, artists are now finding themselves in unprecedented territory. The likes of Midjourney, DALL.e and Amper – to name but a few - are sparking fierce debates around ownership, credit and financial remuneration, while other parties laud the democratisation of creativity. 

In education, student use of GenAI solutions is centring the conversation around plagiarism, cheating and how it can be a detriment to students’ learning. However, with transparency and guidance, GenAI can play a crucial role in empowering learning, offering students a library of content to delve into, and enabling teachers to build learning materials faster than ever – which some educational institutions and government departments are advocating. 

The value of GenAI lies in what it promises for the future, not just in terms of greater efficiency but also in the possibility that the tools will free people up to redirect time, energy, and effort to more value-adding tasks where humans excel. As a result, the tools have already triggered a transition in which AI is becoming omnipresent, no longer an emerging software segment amidst the technological stack.

Meeting the computing power demands of GenAI tools
While the value and transformation potential of GenAI are real, so too are the technical and implementation challenges that come alongside it.
 
Across all domains, GenAI creates an insatiable thirst for data and computing. To be performant and efficient, this requires heavy-lifting processing capabilities from hardware. It’s this need for next-gen performance (and a host of other relevant benefits) that is driving a wave of specialised hardware.

As the technology improves further, even greater computing power will be required to operate advanced algorithms and models. This will necessitate robust hardware with powerful processors (laptops and computers that have AI-optimised capabilities, for example), in addition to significant network capabilities.

The environmental impact of GenAI is also already significant and will likely grow alongside businesses’ ambitions. The datasets used to train all AI are increasingly large and take a large amount of energy to run, an MIT Technology Review report even found that one AI model can emit nearly five times the lifetime emissions of an average car. Estimating carbon footprints of GenAI models, examining how and where data is stored, and increasing transparency and measurement of energy consumption is all key to helping to understand this issue.
 
Considering the breakneck speed at which the technology has evolved to date, businesses may need to plan how they scale their infrastructure to address this challenge sooner rather than later.

Are businesses currently doing enough?
At present, businesses are exploring and evaluating how GenAI tools can be leveraged and integrated. Mapping out how they will address the potential risks when it comes to privacy, security and intellectual property is the first critical step to take.

Until GenAI is adopted wholesale, it may be some time yet before businesses seriously consider investment in future-proofing infrastructure. However, now is the time for organisations to begin planning how they will navigate the inevitable advancement of GenAI tools and their applications in the coming years.

Another aspect of this challenge is the shortage of skilled professionals capable of managing GenAI-based systems. It will become increasingly important to upskill and reskill the existing workforce to handle the technological complexities. Likewise, businesses must implement company-wide guidelines and best practices to ensure that GenAI usage remains ethical, legal and in line with business objectives and outputs.

Moving forward, organisations must begin to project how GenAI will scale in their respective industry and integrate with existing IT systems (especially in terms of technology standards). It is also vital to establish a governance mechanism to tackle concerns related to privacy, manipulation, biases, security and transparency.

Businesses should also consider auditing their existing connectivity solutions and exploring what hardware upgrades would better facilitate a GenAI-friendly business infrastructure. This includes investing in reliable and modern networking hardware, including routers and switches, in order to deliver the high-speed, low-latency network required for high-performance GenAI models, especially those involving real-time processing.

What’s more, onboarding hardware which is scalable, secure and can easily accommodate large datasets, more complex models, and increased network traffic, is vital for building a business which is ready to adapt to the fast developments in GenAI.

Ultimately, while the biggest burden of computing power and tech infrastructure will, of course, fall on the organisations developing and training the technology, the impact on general users will be felt sooner than we may think.