Latest news with #ChatGPTs
Business Insider
5 days ago
- Business
- Business Insider
An EY exec tells BI how the consulting firm is helping companies integrate AI this year: 'This idea of up-skilling the entire workforce to use AI, I think it's kind of silly'
The rhetoric around AI in the workplace can be vague: Automation, algorithms, productivity, efficiency, decision-making, up-skilling, the list goes on. Between rapid technological progress and the lag of adoption, there's continued uncertainty about how AI will reshape the future of work. Many employees are anxious about their value, for instance. Executives are at once captivated by the potential for profits and worried about keeping up with their competitors. Investors and company boards are frustrated by the losses they've already incurred from not moving fast enough. Consulting firms are often at the heart of it all. From the outset, at least, they've positioned themselves as the go-to experts to help corporations understand and navigate this latest wave of technology. Yet their work can often be as unclear as the technology itself. To demystify it, Business Insider spoke to EY's new chief technology officer for its Americas Consulting division on what AI really means for workers in 2025. First off, are people losing jobs anytime soon? There have been comments about cataclysmic unemployment rates that are gonna plunge us into the next Great Depression. I mean, I think it's interesting to think about those alternatives. It's just not what I'm seeing. Broadly speaking, what can we expect to see from AI integration in the next year? I think over the next year, you're going to see an increasing uptake in these copilots, these tools like the ChatGPTs and the private and public models, and interjecting some AI capability into existing enterprise applications, and increasing productivity and efficiency. How is EY specifically helping clients integrate AI this year? We're thinking a lot about what we're calling the next generation of enterprise applications — interfaces that present people with what they need based on their role, offer key AI insights, and let them act. The AI agents generate suggestions, and the human validates and approves. We're piloting this now with some major clients, and it's been an incredible success. That's how we're thinking about the convergence of digital and human workforces — not just managing them together, but creating systems where AI augments people in a seamless way. Can you provide an example of these applications in action? If I'm a cruise director on a cruise ship, there are lots of things that impact how my guests enjoy the ship. The makeup of the people on the ship, the weather, what day — if you're on a day at sea, or if you're going to a port — all of that stuff. There's data to be found there on what happens and how the guests behave. I mean like their buying activities, where they like to hang out, those types of things. So, we can harness that information with AI agents to actually understand and predict what's going to happen. We know, for example, that tomorrow's weather is going to be bad, and it's a day at sea. We know historically how all of that affects the movement of people and the consumption of products, whether that be merchandise, food, or beverages. So, we recommend that you take half of the people from this venue and move them to this venue. We recommend moving around products so you don't run out, because we know what demand is going to look like. We recommend redeploying people to do different things in anticipation of this. The AI will turn around and list out and build out that process automatically. The human in the loop says, "Okay, that makes sense," or "I want to change this piece." This is through a very visual, nice interface. They click go, and then there's a chain of orchestration that happens, in which people are notified, leadership is notified, supply chain changes on the ship. What's the value of up-skilling here? How much do employees need to learn about AI? They just know that they have a screen and an application that says, "Here's how much stuff you have now of this," and "Here's how many you have coming inbound," maybe. They don't need to know how the technology works. This idea of up-skilling the entire workforce to use AI — I think it's kind of silly. How are you helping companies think through questions like this? You need to look at the functions — rethink that. That also dovetails into the people part, right? You're not only just giving them technology that's AI-enabled, you're allowing them to start to rethink how they do their job, and how they can be more efficient at the job, and also provide more overall value and capability.
Business Insider
5 days ago
- Business
- Business Insider
An EY exec tells BI how the consulting firm is helping companies integrate AI this year: 'This idea of up-skilling the entire workforce to use AI, I think it's kind of silly"
The rhetoric around AI in the workplace can be vague: Automation, algorithms, productivity, efficiency, decision-making, up-skilling, the list goes on. Between rapid technological progress and the lag of adoption, there's continued uncertainty about how AI will reshape the future of work. Many employees are anxious about their value, for instance. Executives are at once captivated by the potential for profits and worried about keeping up with their competitors. Investors and company boards are frustrated by the losses they've already incurred from not moving fast enough. Consulting firms are often at the heart of it all. From the outset, at least, they've positioned themselves as the go-to experts to help corporations understand and navigate this latest wave of technology. Yet their work can often be as unclear as the technology itself. To demystify it, Business Insider spoke to EY's new chief technology officer for its Americas Consulting division on what AI really means for workers in 2025. First off, are people losing jobs anytime soon? There have been comments about cataclysmic unemployment rates that are gonna plunge us into the next Great Depression. I mean, I think it's interesting to think about those alternatives. It's just not what I'm seeing. Broadly speaking, what can we expect to see from AI integration in the next year? I think over the next year, you're going to see an increasing uptake in these copilots, these tools like the ChatGPTs and the private and public models, and interjecting some AI capability into existing enterprise applications, and increasing productivity and efficiency. How is EY specifically helping clients integrate AI this year? We're thinking a lot about what we're calling the next generation of enterprise applications — interfaces that present people with what they need based on their role, offer key AI insights, and let them act. The AI agents generate suggestions, and the human validates and approves. We're piloting this now with some major clients, and it's been an incredible success. That's how we're thinking about the convergence of digital and human workforces — not just managing them together, but creating systems where AI augments people in a seamless way. Can you provide an example of these applications in action? If I'm a cruise director on a cruise ship, there are lots of things that impact how my guests enjoy the ship. The makeup of the people on the ship, the weather, what day — if you're on a day at sea, or if you're going to a port — all of that stuff. There's data to be found there on what happens and how the guests behave. I mean like their buying activities, where they like to hang out, those types of things. So, we can harness that information with AI agents to actually understand and predict what's going to happen. We know, for example, that tomorrow's weather is going to be bad, and it's a day at sea. We know historically how all of that affects the movement of people and the consumption of products, whether that be merchandise, food, or beverages. So, we recommend that you take half of the people from this venue and move them to this venue. We recommend moving around products so you don't run out, because we know what demand is going to look like. We recommend redeploying people to do different things in anticipation of this. The AI will turn around and list out and build out that process automatically. The human in the loop says, "Okay, that makes sense," or "I want to change this piece." This is through a very visual, nice interface. They click go, and then there's a chain of orchestration that happens, in which people are notified, leadership is notified, supply chain changes on the ship. What's the value of up-skilling here? How much do employees need to learn about AI? They just know that they have a screen and an application that says, "Here's how much stuff you have now of this," and "Here's how many you have coming inbound," maybe. They don't need to know how the technology works. This idea of up-skilling the entire workforce to use AI — I think it's kind of silly. How are you helping companies think through questions like this? You need to look at the functions — rethink that. That also dovetails into the people part, right? You're not only just giving them technology that's AI-enabled, you're allowing them to start to rethink how they do their job, and how they can be more efficient at the job, and also provide more overall value and capability.
Business Times
02-05-2025
- Business
- Business Times
The diverging future of AI
DOES the future belong to a handful of all-powerful, wide-ranging artificial intelligence agents (AI) that navigate the world on our behalf – successors to the ChatGPTs, Claudes and Groks that seek to handle almost any task you throw at them? Or will it be populated by a host of specialised digital aides, each trained to take on a narrow task and invoked only when needed? Some mix of the two seems likely, but the sheer pace of change has left even leaders in the field admitting they have little idea of how things will look a year or two out. For proponents of the 'One AI to rule them all' idea, there have been plenty of encouraging developments. OpenAI, for instance, added a shopping feature to ChatGPT this week that points to how personalised AI agents could reorder the economics of e-commerce. Using a single query to get a chatbot to do your product research and make a buying recommendation threatens to subvert the entire 'funnel' that brands have relied on to steer buyers, putting OpenAI very much at the centre. Advances like these may grab the most attention, but behind the scenes a new generation of more specialised agents is starting to take shape. These promise to be narrowly targeted and – a key consideration – far cheaper, both to build and to run. Meta's LlamaCon developer conference this week provided a glimpse of the state of play. The social networking company has placed its bet on the adaptability of its 'open weights', AI models that have a limited form of an open-source structure. This enables others to use and adapt the models, even if they can't see exactly how they were trained. BT in your inbox Start and end each day with the latest news stories and analyses delivered straight to your inbox. Sign Up Sign Up One sign that Meta has hit a nerve in the wider tech world is the 1.2 billion downloads its 'open' Llama models have had in their first two years. The vast majority of these have involved versions of Llama that other developers have adapted for particular uses and then make available for anyone to download. The techniques for turning these open weights models into useful tools are evolving fast. Distillation, for instance — imbuing small models with some of the intelligence from much larger ones — has become a common technique. Companies with 'closed' models, such as OpenAI, reserve the right to decide how and by whom their models can be distilled. In the open weights world, by comparison, developers are free to adapt models as they want. The interest in creating more specialised models has picked up in recent months as more of the focus of AI development has shifted past the data-intensive – and highly expensive – initial training runs for the biggest models. Instead, much of the special sauce in the latest ones is created in the steps that come next — in 'post-training', which often uses a technique known as reinforcement learning to shape the results, and in the so-called test-time phase used by reasoning models to work through a problem. According to Ali Ghodsi, chief executive of Databricks, one powerful form of post-training that has been catching on involves using a company's proprietary data to shape models in their reinforcement learning phase, making them far more reliable for business use. Speaking at Meta's event, he said this is only possible with open models. Another favourite new trick has been to combine the best parts of different open models. After DeepSeek shocked the AI world with the success of its low-cost R1 reasoning model, for instance, other developers quickly learnt how to copy its reasoning 'traces' – the step-by-step patterns of thought that showed how it worked through a problem – and run these on top of Meta's Llama. These and other techniques promise a tidal wave of smart agents that require less expensive hardware and consume much less power. Among the winners from this will be the companies that have the tools and platforms to create and run this new form of software. For the model builders, meanwhile, it adds to the risk of commoditisation – that cheaper alternatives will undermine their most expensive and advanced models. But the biggest winners of all, as the cost of AI falls, could be the users: companies that have the wherewithal to design and embed specialised agents into their day-to-day work processes. FINANCIAL TIMES
