While artificial intelligence (AI) has long been part of the IT lexicon, generative AI (GenAI) is a fast-developing technology that will have a transformative impact on the global workforce.
In a LinkedIn Live event hosted by Dell Technologies, three industry experts discussed the future of GenAI, the potential benefits of this fast-emerging technology, the key capabilities required and the challenges to overcome.
Gen AI, simplified
The panel recognized the excitement surrounding GenAI started with the launch of OpenAI’s ChatGPT in late 2022. Since then, the hype has reached cacophonous levels. Like traditional AI, GenAI involves feeding lots of data into algorithms. However, the outcomes are very different. Instead of using data to make predictions, GenAI models output even more data in the form of text, images or programming code.
EY Americas Emerging Technologies Leader, Matt Barrington, said most professionals today have likely already dabbled in language model capabilities and “had their own ‘aha moment’ where they’ve generated something really powerful.”
This process of content generation has allowed people to connect with GenAI on a human level and has fueled interest in this transformational technology.
The IT industry has talked for decades about the power of harnessing data to create new insight, said Dell Vice President of Corporate Strategy Mindy Cancila: “It’s the capabilities that have recently come to market that have really brought this trend to the forefront.”
Today, ChatGPT and other GenAI technologies make this process manifest and demonstrate how data can help generate fresh insight quickly and effectively.
What are the potential benefits of GenAI?
Barrington said EY is already seeing early-adopter use cases for GenAI, whether it’s creating marketing copy, providing answers to help desk questions or generating new product ideas.
In the longer term, Barrington expects even more: “This is a fundamental change in how companies are going to operate.”
Dell Advisory Systems Engineer and AI Research Scientist Ben Fauber agreed, saying the technology will allow organizations to automate mundane tasks: “We’re automating the boring stuff.”
As generative AI evolves, machines will pick up a larger proportion of day-to-day tasks, freeing employees to focus on higher-value activities. Fauber gave the example of healthcare, suggesting GenAI can quickly summarize patient information, allowing nurses and doctors to spend less time reading notes and more time providing frontline care.
The experts also referred to the multilingual capabilities of GenAI tools as a potential pathway to new business opportunities. Cancila said the technology could mean businesses find it easier to provide their services in different languages and to enter new geographies at much lower cost: “That might mean going into markets you wouldn’t even think of entering.”
Barrington pointed to financial services, suggesting firms will find it easier to take complex data sets, run queries and identify trends that were previously hidden: “You can take full-year financials, run them through these capabilities, and have much more comprehensive analyses.”
What skills will be crucial to GenAI?
Even the most modest market estimates suggest the AI-enabled economy will be worth as much as $10 trillion by the end of the decade, said Barrington. He believes this level of investment will reshape the workforce. The most in-demand professionals will understand how to use AI to augment enterprise activities and will sell their specialist skills as part of a gig economy.
“We’re expecting a whole new era of super-intelligence, where people use AI in combination with their brains to achieve levels of efficacy that we can’t on our own,” said Barrington.
Making the most of GenAI will also necessitate a high degree of technological acumen. However, these technical specialists won’t work on IT infrastructure in isolation.
Fauber said the most highly prized professionals will offer a broad range of skills that blend scientific acumen with creative capability. These specialists will turn business requests for GenAI into minimum viable products: “People don’t care as much about speeds and feeds as they do about outcomes.”
What are the challenges to overcome?
While GenAI brings rewards, it also carries risks. The onus now is on executives to take a top-down approach to technology implementation. Barrington said the primary concern for EY’s clients right now is an issue where generative AIs fake answers to questions, which is often referred to by industry experts as “hallucinations”.
EY is spending a lot of time with its clients to establish what responsible AI looks like, said Barrington: “These tools are somewhat accurate and 100% confident, so you need to govern and implement their use.”
Data is another challenge. Good data provides the fuel for GenAI, but bad data will derail automation efforts. Companies that haven’t already focused on data management techniques will have to play catch up. Innovation leaders are 2.5 times more able to collect, prepare and curate data extremely well, according to the Dell Technologies Innovation Index.
Organizations should also think carefully about computing power and how to support GenAI through an IT infrastructure, whether that’s in the internal data center, on the cloud or via the edge. Executives who want to make the most of GenAI will need to create an ecosystem of trusted technology partners who help them bring in specialist capability as it’s demanded.
Taking a top-down approach
The panelists concluded the discussion by saying now is the time to think about the long-term impact of GenAI. They suggested starting with small experiments that don’t include business-critical data and which prove benefits quickly. These kinds of experiments will help business leaders to scale GenAI when the time is right.
To hear more on the key takeaway from this conversation, check out the panel discussion replay:
This content is part of the GenAI Expert Series that aims to untangle the hype from the reality of GenAI with practical discussions on how to approach its application within your organization. To learn more about how to create a data-driven innovation process, click here.
Lead photo courtesy of Getty Images