Foundations for the Future - AI | Donna-Marie Patten

Good afternoon, everyone. It's really a pleasure to be here today. My name is Donna-Marie Patten. And yes, it's an honour to talk to such a forward-thinking audience like yourselves today. And really what I share today is from my own personal insights and experience. So, a little bit about myself. I have 22-years-plus in operational and leadership experience within the financial services section. My first 10 years was actually within application management, so supporting front office, middle office, trading platforms. And, when I think about back then, 2002, it's like it was scripting, heavy manual processes, monitoring tools. AI really wasn't that prevalent. And I wish it had been because it would have made my support role a lot more easier than it would be today. But what I would say is, years on from now, as a technical program manager, I am fortunate to run cross-wide impact initiatives across organisations. And that does include AI and how to transform enterprise technology operations. More specifically, use cases to enhance infrastructure stability, resiliency, that might be reducing number of tickets, it may be to do with optimising resource utilisation, reducing recovery time as key examples. So, yeah, real pleasure to be here and share some of my insights. So, let's just set the scene then. We're clearly living in a world where software is rapidly growing. It's evolving, and the pace in which technology is moving is mind blowing, I think we can all agree. And as technology professionals, we are faced with increasing complex, challenging scenarios, multi-cloud environments that we have to essentially cater for. And so, within large scale environments, supporting global operations, right? So, AI really is no longer a buzzword. It's actively transforming the way in which we're doing things, whether that's from knowledge management, the way we automate things, the way we're making decisions, it is changing in what we're fundamentally doing. But in order to unlock AI's full potential, we do need to be strategic in how we go about implementing that, right? And it's about understanding what are the right use cases to introduce and when to leverage AI versus traditional methods that we've all come to know, automation. So, for me, over the next 25 minutes, my aim is to take you through some of my personal insights in how to think about incorporating AI into your organisation, formed by my own experiences, but the intent is really to help you think about how you identify high-value opportunities, and then think about some of those pitfalls and challenges, that you may need to consider when looking to implement within your organisation. So, I wanted to firstly start off with, and I know Kelsey's going to kill me because I just said 'Legacy' here, so maybe I should say Hall of Fame Automation versus AI. I think we are familiar, and we've come to rely on tools like Ansible, Puppet, different scripting languages. They have really been fundamental to our technology operations. They've helped us to automate many manual processes, repetitive, improving efficiency, and ultimately, reducing the chance of human error. For years, I would say that these tools have been the backbone of infrastructure management. They've allowed IT teams to streamline operations, that might be configuring servers, deploying software, monitoring systems without much oversight from humans or intervention. So, the goal has been simple, right? It's to reduce manual work, standardise processes, and make things more predictable. And I can totally, completely relate to that when I think back to my former support days. However, we must be aware of the limitations when it comes to our traditional way that we have been working on, particularly within large distributed organisations, environments that span multiple cloud types, whether that's internal, external cloud, et cetera. And then, risk management and AI reliability. So, AI systems must be thoroughly tested for reliability. Again, I don't think this is actually specific to a highly regulated environment. You want to know that your system's reliable, any system for that matter. And it's important that there's thorough testing, frequent testing that is happening with your systems, and failure to meet operational or safety standards could lead to legal or reputational consequences, which you wouldn't want. So, you want to continuously, continuously assess the risks associated with AI and the decisions that it's making, particularly when decisions impact our customers or folks that decisions are being made about. And let's not forget, I talked about reliability testing, but I think stress testing is also important to do. I'm sure we do that with our normal applications, depending on what industry that you are in, but stress testing is also very, very important. We have data security and protection mentioned here. Where we are dealing with sensitive data, actually, where we're dealing with data, private data, sensitive data, it's crucial that you implement data security to protect data from any threats, that may be cyber threats and breaches. And so, I would say, consider where you can using strong encryption techniques and role-based access control. I have personally seen, I mean, I'm not at liberty to talk about what it is that my current organisation do, again, I'm just talking about personal experience, but it's something that I've seen where there has been a breach or non-compliance. So, it is real. It definitely is real and must be considered. Whilst I didn't put it on here, I did just want to talk about continuous monitoring and oversight. So, ensure that you are continually monitoring and providing oversight on what it is that you're leveraging AI for.