HCLTech – Canada’s Post

Transcript

By 20-30, AI will transform over half of all jobs, changing 65% of our skill sets. That's a big change. For this session, I am pleased to have Darren Culbreth, who is the director of a Cloud AI lab. Darren is also a coach at the Genii Academy we have at ATL Tech. Hi Darren. Hello. How are you? Let's eat the frog first, OK? How difficult it is to get the talent or develop the skill sets we need for everything AI. You know, I think I've been asked this question at least five times since I've been in this conference today. And you know, it's not as hard as we think. If we think about, you know, we have been working for a while doing the things that we do, even students. AI is just yet another tool set. And so how hard is it to get the talent? I think it's harder to find someone who knows the Pharmaceutical industry in and out. I think it's harder to find someone who knows other skills. But I think, you know, we forget that we are learning machines, right? And so I think getting someone who knows everything about retrieval, augmented generation or rag technology, yeah, that might be hard, but it's not necessarily so hard to. Graduate them to it. And so is it hard to find someone to know that's exact brand new skill set. It's the same of any brand new skill set. But I think the demand right now says we need to instantiate this technology with our clients and what the work that we do. So yeah, it's hard to get that folks with that core experience. But I think if we take that empathetic role and say hey, as leaders or for individuals that we want to be able to move on the line, we have to take. That intellect that people have today and train them on a new tech. So it's hard, but you know, there's a, there is a logical path to get there. And it's not, Oh my goodness, let's make sure they have everything before we hire them. That's really interesting. You spoke about the path and what is the Academy all about? The Academy, it's a pretty ambitious project. So our leader, Alan Flower, he wanted us to create something called a generative AI Academy. And the generative AI Academy has two distinct student sets, one engineers. So think about these engineers who were business analysts or maybe their data, big data analytics engineers. They know core skills like Python And other things, but you know, they don't know the generative AI vernaculars, the language. They haven't tried using generative AI. And so The Academy Is really about. Graduating people to this technology not necessarily only just teaching them the technology O one side is engineering, one side is selling and so from a generative AI Academy we take our our ready experience engineers again they know how to code they know how to run databases they know how to work with clients and do all this other pieces but when we start telling them hey by the way create this vector database and. Using cosine similarity, taking natural language and and be able to get a set of results and able to pass that context to a large language model to do XYZ. They're like, oh, that sounds interesting, How do you do that? And it's like, well, let me show you how. And because they already have started and they understand technology, Yeah, it's not a big lift. You just have to be able to walk them through it. So The Academy Is about graduating people to the level that way they can be practitioners in here. And the cells, individuals have worked on billion dollar projects. They know exactly how to articulate value. Now there's a new value in generative AI and we articulate. With those students, how to message that value and so the Academy again is not starting from scratch. It's taking that that set of individuals that know the craft of technology or sales or business and saying, hey, the new thing is in town. How do we implant that into their normal way to work And now they become part of this crazy explosion of what we call in generative AI. Very interesting you. Talk about value creation. So one end of the spectrum is talent and you know, the skill set development. How does this get into the Genei lab, right and what's our overall approach in, you know, working with clients and what kind of stuff you're doing with them so. I come from a pretty extensive IT background and I've been in many companies that had labs and the labs was, you know, sometimes the thought of like, you know, the magic group that just kind of tinkers around with things. And that's not exactly our approach from the lab perspective. Our labs starts at business problems that need to be solved. And sometimes that might take a little experimentation, but it's all about getting. To the business problem and, and moving the client along via technology and in our case AI or maybe some other cloud native technologies. O we never start with the question, what can generative AI do for you for example? We always start with the question, what are you trying to achieve? What are your metrics? Where are you finding things that are hard, those kinds of things. And what we do in the back end is what we try to understand is OK, there is some technology. That can help. It might be AI, it may not be. But then we will engage in those experimentations for things that our clients want to use, not experiment with, but want to use because why they're tied to core metrics, they're tied to really core business items that are going to drive their their enterprises. And so that is our approach. Our approach is not to go in with our AI tool belt. Our approach is to OK. Let's understand what you're trying to do. We have architects and engineers that understand how to implement AI and different technologies, but it's really about asking them what are they trying to achieve. And so the lapse doesn't via experimentation to get those pieces into production. Very, very interesting. And I think it's not about a single problem statement. It is about, you know, the intersectionality as well on, you know, one how one problem is being applied to. Another area of industry. Yeah, and that story is continuous. And so if we think about, you know, just some of the things in history, I remember the term data warehousing, right, that came out and then, you know, big data came out. I'm just thinking about the data right now. And, and and now, you know, different kinds of no sequel database came out. All we have to do this, but it's all continuous, right? It's just again, right tool for the right job and it's all the story. So every business has a story to tell every and the problems are just stories and we just use the technologies to sort of like enhance the narrative and sort of to make the story real. And so it's a it's a it's always going to be continuous next year. Who knows, we might not even call it dinner today. I we just might call and get stuff done differently. Who knows what it's going to be called. That's great. Thank you, Darren. It was absolutely thank you. Happy to be here.