The explosion in popularity of generative artificial intelligence – known colloquially as “GenAI” – is largely due to its democratisation. Anyone can use one of several available online tools to “generate” anything from a humorous poem to a factually correct essay.
But the business use cases for GenAI go far beyond the banal and organisations are already beginning to embed the technology into its operations. Not only is it helping them to automate and enhance their existing processes, they are completely reimagining these processes using a capability that has only become widely accessible in the last couple of years.
Tim Morthorst, director of AI & Automation at EY Ireland, says there is “real excitement” about what AI can do for organisations and its value as a transformative technology in the coming years. “This is a technology discussion that has vaulted from the server room to the boardroom,” he says.
And businesses are happy to invest: EY’s 2023 survey of 1,200 chief executives from around the world found that almost all – 99 per cent – plan to invest in GenAI, with 70 per cent saying they want to act quickly to avoid being edged out by competitors. “Organisations see it as a value accelerator, boosting productivity and efficiency, supporting research and new product development, automating repetitive tasks, reducing silos across an organisation, ultimately enabling them to better serve their customers,” Morthurst says.
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“There is a huge appetite to realise the benefits that GenAI, and indeed more conventional machine learning, can deliver,” says Owen Lewis, head of AI and management consulting at KPMG in Ireland. “The prize is there for those organisations that can set up their data and technology foundations, governance and processes, and customer propositions to realise the AI advantage.
Looking ahead, Lewis says, organisations will continue to develop meaningful use cases, pivoting their talent to be skilled and fit for purpose to operate within the AI-powered economy, and organisations moving from experimenting with AI to having it as a fundamental capability within their businesses.
Colin Melody, Deloitte’s director of artificial intelligence and data, agrees, noting that organisations are rapidly accelerating their AI adoption, moving from proof-of-concept stages to the implementation and scaling of GenAI capabilities. This includes integrating it into their employees’ toolset and embedding it across all enterprise applications. “The general-purpose nature of GenAI capabilities, in particular large language models (LLM), is what differentiates it from traditional AI and also allows for a wide and varied set of applicable use cases. As a result, an increasing number of companies now use AI assistant systems to help employees in various ways, including drafting emails, summarising and providing insights into large documents, as well as conducting analysis and offering recommendations,” he explains.
“Similarly, this is resulting in GenAI capabilities being integrated into systems to support a variety of enterprise activities, such as AI assistants for customer communications, cybersecurity tools, predictive maintenance systems, and more.”
Melody points out, however, that there are varied and mixed levels of adoption across organisations, in particular depending on the industry, as more heavily regulated industries will lag others. A common reason for lack of adoption, he says, is that there is still a limited understanding about what GenAI can do, how it works and how it can be effectively deployed, managed and maintained within the organisation in a secure and trustworthy way. Those who have embraced it, however, are reaping the rewards, he says; Deloitte’s State of Gen AI Quarter 2 report showed more than 18 per cent of respondents said their organisations’ generative AI initiatives were already delivering expected benefits to a “large” or “very large” extent. “This proportion increases to 36 per cent depending on the type of benefit being pursued,” notes Melody.
As time goes on, Morthorst says, the focus will shift from performing existing functions more efficiently to rethinking these functions from the ground up with GenAI. For example, EY is working with hospitals to analyse massive quantities of patient data to identify those at greatest health risk, to aid in future healthcare planning. “Life sciences companies are already benefiting from GenAI use in the manufacturing of life-saving drugs and improving product yields, while GenAI will help make supply chains more predictive and dynamic in anticipating and responding to supply chain disruptions,” he adds.
Many of the organisations EY works with are already actively investing in AI or assessing their AI maturity to identify where the opportunities are for their businesses, Morthorst adds. Becoming popular is the augmentation of internal and external conversations with smart GenAI chatbots, such as deploying these smart chatbots to deal with incoming chat and phone conversations in a contact centre. It also allows for “hyper personalisation” of products and contents across both physical and online services/products; for example, rewriting websites in real-time based on information available about the specific user looking at the webpage.
But for businesses seeking to maximise the impact of GenAI, knowing when and where to deploy the disruptive technology is key, warns Melody. “It’s important to note that use cases where GenAI provides support at each step in a given process are increasingly powerful as they maximise the use of GenAI capability and realise value throughout the process,” he says. “What remains essential is understanding how to use these tools effectively and what controls and structures need to be in place to ensure they are trustworthy and secure. Experience and human judgement are also key to the process to assess the output of these systems.”
Morthorst says a key factor in the adoption of GenAI over the next five years will be the rising maturity of “multi-modality”. “Organisations are beginning to grasp the idea of textual GenAI at this stage, but still don’t have a great understanding of how image- and sound-based GenAI solutions can bring value in their organisations,” he explains.
“Additionally, widespread invisible deployments of GenAI solutions in organisations will start happening – i.e. software being procured by organisations will start containing GenAI-led features without users necessarily knowing that they are GenAI-based. This is what will drive the highest level of adoption of the actual technology and this both poses a massive opportunity for the adoption of the technology while causing potential challenges.”