Generative AI: from buzzword to boon for businesses

Red Hat Asia Pacific Pty Ltd

By Enzo Compagnoni, Regional Vice President and General Manager, ANZ, Red Hat
Wednesday, 12 June, 2024

Generative AI: from buzzword to boon for businesses

Almost a year ago, the term ‘generative AI’ meant little within most business circles. Today, almost every other organisation is experimenting with some form of generative AI.

Considering that generative AI is predicted by Deloitte to disrupt $600 billion of economic activity in Australia, the growth potential is clear. Not only is generative AI developing at a rapid pace, but its usefulness to business growth, automation and innovation is strengthening day by day.

Generative AI is a powerful tool for organisations that want to create innovative products, optimise processes and gain competitive advantages in rapidly changing markets. Based on advancements in deep learning and neural networks, it goes beyond predictive AI capabilities by not only processing data, but generating new, original content. Generative AI is reshaping human–machine collaboration, inspiring new approaches to problem-solving and helping to deliver business gains across industries.

The time is ripe for forward-thinking business leaders to assess generative AI’s place in their organisation and implement the technologies — from hybrid cloud environments to AI/ML platforms — that are vital to unlocking its full capabilities.

Below are some key business applications of generative AI, along with the technical considerations needed to unleash them.

Taking advantage of generative AI for holistic decision-making

Today’s business leaders have more data at their disposal than ever before to inform their decisions; however, some organisations still struggle to analyse and correlate relevant data for actionable insights. Generative AI can rapidly ingest and synthesise function-specific data from across every enterprise function, along with external data sources to create context-rich business intelligence that previously may have been costly for businesses to obtain.

Anyone who has interacted with ChatGPT might imagine a generative AI program that doesn’t just provide insights into a business problem, but can also determine what’s influencing that problem and why it matters. The quality of insights also improves over time as generative AI consumes new data, identifies new patterns and considers other queries from within the organisation. With the right data, business leaders can obtain up-to-date information, forecasts or simulations to test assumptions and confidently base their decisions on.

Whether you’re using an off-the-shelf AI application or building your own, be mindful that generative AI requires substantial data to learn from. Data storage and scalability can be a concern, necessitating infrastructure that can process terabytes of data. Consider specialised solutions like hybrid clouds configured to run open-source platforms for AI/ML learning models.

Combining the cloud’s flexibility with open-source refinements to AI learning algorithms, businesses can easily scale the foundations of their generative AI program, fine-tune learning models and obtain ever-increasing benefits from their generative AI investments.

Enabling hyper-personalised customer experiences

Generative AI’s ability to synthesise data around certain subjects to create fluid, human-like responses (ie, natural language processing) holds great potential for the personalisation of products and services. For instance, running chatbots or virtual assistants with large language models (LLMs) can result in richer, more contextualised interactions with customers. Generative AI can also be used to analyse user data to create bespoke offerings that cater to the unique preferences of customers.

Some businesses are already using generative AI to this extent. For example, Spotify has experimented with generative AI to create personalised playlists for users based on their music preferences. In the future, leading-edge businesses are likely to offer bespoke AI-driven offerings that adapt to customer preferences on the fly, making hyper-personalisation the order of the day.

Due to the unique requirements of their hyper-personalised offerings or personal data security concerns, businesses need their own AI/ML deployment instance where they can tinker, test and train their generative AI models on customer data. Consider cloud-hosted AI/ML foundation toolkits and easily configured open-source models that can help accelerate the creation and scalability of generative AI applications that can deliver the level of hyper-personalisation that customers expect now and into the future.

Improving risk management

Generative AI has promising applications in the area of risk management. The technology can be used to analyse operational data in real time to find anomalous patterns or issues that could harm business operations. For example, NAB has tapped into generative AI to identify the risks of cybercrime and financial fraud within its banking ecosystem.

Businesses should consider cloud-based AI foundations with a wide array of open-source developer toolkits and configuration options. This facilitates the development of intuitive and accessible AI applications for compliance and risk professionals — applications that readily integrate with compliance and risk frameworks, and can easily scale as the business grows and faces new risks and regulatory challenges.

Unshackling employees for high-value work through automation

It would be remiss of me to not mention the process efficiencies and business automation capabilities offered by generative AI. It’s no secret that today’s organisations must do more with less. In contrast to earlier fears of the technology replacing them, professionals have begun using generative AI to automate mundane, repeatable activities — so they can focus on higher-value work. ANZ’s software engineers have used the rigour of generative AI to test and validate code. St Vincent’s Hospital Sydney is using AI to monitor patients with multiple sclerosis (MS).

Depending on the use case, businesses might utilise a mix of generative AI applications, robotic process automation and function-specific platforms to enable the large-scale automation of business processes. Tapping into a hybrid cloud solution to host these solutions is important for maintaining high levels of performance, stability and scalability as generative AI use accelerates.

There are already solid business applications for generative AI, but as the technology continues to evolve, more will inevitably come into play, shaking up how businesses compete and innovate in their industries in the future. The game-changing capabilities of generative AI have the potential to accelerate early adopters ahead.

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