Jake O’Gorman on the ever-evolving AI landscape
AI is truly a hot topic right now, as it is an inherently complex and challenging field to navigate. As a tool, it is a powerful and transformative technology, that has the potential to change our lives in many ways.
In an interview with AI Magazine, Jake O’Gorman, Director of Data, Tech and AI Strategy at Corndel, touches upon the future of AI in the working world and how AI can be used to bridge the skills gap. O’Gorman’s previous experience includes six years as a Commercial Director and board member in the AI space, providing enterprise-level AI solutions in coaching, EQ and leadership, and as a facilitator to corporate teams.
What sparked your interest in working in the data and AI industries?
“My work in AI began in 2016, which feels like a lifetime ago in the AI world. I was initially captivated by the applications of sentiment analysis and similar tools, and my interest spread from there. Back then, the most significant barrier was explaining to organisations what AI was and convincing people that it had a future. That seems a strange thought now.”
Do you envision there being a world where AI takes over the roles of the majority of the existing workforce?
“I often find myself thinking back to the turn of the century. We knew new innovations would come along, but we couldn't accurately anticipate how technology would come to dominate our lives.
“Looking to the future, I am convinced AI tools will have a similarly core role in our lives, yet I'm not sure we've got any better at predicting exactly how that will look. In our recent Corndel Better Decisions, Realised Report, we asked 2,000 adults in the first UK if they believe AI could potentially replace any aspect of their role in the future and found that 61% of employees have concerns that this new technology will take at least 25% of their role by 2023, with 39% of UK employees believing that it will take at least 50% of their job in the next ten years.”
Can AI help bridge the skills gap? Or will it mitigate the need for human input altogether?
“A recent PwC survey showed that nearly 40% of CEOs believe that on their current path, their businesses have less than ten years to survive. Organisations are fighting for relevancy, and for many, technology appears to be the answer.
“Therefore, the fact that there is a skills gap around data poses a real issue for organisations' abilities to adapt. Three critical gaps are around data-driven leadership, data literacy, and shortages in technical roles such as data engineering. Whilst efficiencies from new technologies may alleviate some of the long-term effects of a skills shortage, the three areas above will remain.
“Leaders need new skills to navigate the second-order effects of new technologies. Employees increasingly need reskilling around new technologies, and most organisations have woefully underinvested data governance and infrastructure, which directly impacts AI implementation and will remain a roadblock for some time. Investing in workplace training and embedding a culture of continuous learning as we navigate this fast-evolving new world will be critical to success.”
Do you see this change happening in the immediate future or being a more gradual process?
“It might be helpful to distinguish between two terms: AI Development and AI Deployment. Development, such as new LLMs, remains relatively expensive and takes place in distinct steps, such as the move from GPT3.5 to GPT4 or X's new Grok.
“New, larger models will be released as computing costs reduce, yet these will still be relatively infrequent. We may even see a corresponding rise in more specialised, domain-specific models trained on medical data or perhaps therapeutic conversations.
“Deployment, conversely, is about integrating existing developments into products. It's hard to find a SaaS provider who isn't integrating an LLM somewhere in their offering.
“We'll likely see a mix of steady, significant steps in AI development, complemented by a sharp rise in AI deployment, which, although it might slow once some initial hype is over, will continue to rise as computing cost reduces and consumers come to expect AI features as standard.”
What are some of the key factors that influence trust in AI systems?
“Trust is shaped by a combination of factors, each pivotal in determining the technology's impact and acceptance. Initially, much of our experience with AI centred around curation, where content was the means used to capture attention. This stage, while innovative, brought forth challenges related to mental health and data privacy, underscoring the importance of ethical AI use.
“The shift to generative models marked a new era, one which has yet to fully reveal its secondary effects, but it's already reshaping workplace dynamics. In this context, trust in AI hinges on transparency, ethical deployment, and the technology's alignment with organisational values.
“The key to building this trust is data-driven leadership. These leaders foster a culture where AI is not feared but embraced as a tool for innovation and growth. They ensure AI solutions are implemented responsibly and a focus on employee engagement and well-being is maintained.”
When we look to the future, how can we ensure that AI is used in a fair and responsible way?
“As we navigate the rollout of AI within our workplaces, human leadership will play an increasingly important role. Many crucial decisions stand between now and the eventual success of our data and AI strategies.
“Many organisations don't yet have this expertise and so will need to invest in upskilling their managers and leaders in both the technological aspect and the leadership skills critical to an evolving workplace.”
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