CIO CIO

Customers are also asking for help with their large language models (LLMs), said Upchurch, noting that SAS can be “a trusted partner in that space.” “We want to make large language models better, or we want them for use embedded in our solutions,” he said. “It’s not exactly where we’ve invested so far, [but] I think that’s going to continue to evolve as people move further away from just an LLM and more into: What’s the real-world applicability for AI and agentic AI, and how do you make it real for your employees?” Upchurch also sees customer appeal in the company’s AI governance initiative, which began in 2021 as a passion project led by Reggie Townsend, now VP of data ethics at SAS. Dr. Goodnight has been “so supportive, and I’ll say gracious, with investment,” Upchurch said, adding that the products SAS has created, such as its newly announced AI Governance Map, are based on customer demand. source

“The UAE-US AI campus offers organizations in the region a unique opportunity to prioritize low-latency AI use cases and accelerate real-time decision-making,” said Eric Samuel, associate director at IDC. The nuclear-solar-gas power mix guarantees always-on, sustainable compute infrastructure, noted Samuel. The new “US-UAE AI Acceleration Partnership” aims to deepen bilateral tech cooperation while addressing US concerns over Chinese influence. The UAE has been making moves in AI for a while, like having an AI Minister since 2017 and their own AI university since 2019. Strategic edge of Gulf AI hub Spanning 10 square miles, Abu Dhabi’s AI campus aims to become the nucleus of GCC-focused innovation across finance, energy, and healthcare, developing regionally tuned large language models and domain-specific breakthroughs. A dedicated science park will be designed to accelerate R&D, while its hybrid energy mix could offer a sustainability edge. source

Matt Egan: I mean, in my experience, it really varies wildly, right? And I’d love to hear from Carl on this, because he is speaking to CIOs day in, day out. But I think, you know, what I’ve seen is it kind of speaks back to that overall core point, which is, is the IT strategy, supporting the business strategy, or is the IT strategy, the business strategy, and therefore, is the CIO a support staff or a leader of change? Right? I’ve seen various support, I think, within it and strategy, most organizations have come to the point of having, like a named individual or team with overall responsibility for AI, maybe projects and solutions, maybe strategy. Most organizations have got lots of technical training within their IT departments. But to your point, Keith, there’s the extra thing of, like, everybody’s got to be involved in this solution. And then I think it goes a little bit further right, because within the IT department, there’s, I guess, the wider issue of what might call AI inside, what drudge work, what toil in all roles can be replaced by agentic or generative AI. And what does that mean in terms of your human workforce? That’s more of a technical thing. A lot of organizations are planning for a world in which, and I know we’ll probably touch on this later, but in which, you know, AI is replacing humans, but humans are then directing the AI. So I think, again, it’s one of my classic answers, Keith, I think, I think I’ve seen both approaches, and I’m not sure which is best. There’s definitely this move where it is in charge of technical skills, for for IT people. But I think a broader level, you know, the more mature organizations understanding that everybody needs to be kind of immersed.   source

CIOs will need to consider how many years of data need to be retained, taking into account statutory requirements for different countries, length of time data needs to be held, and what data is no longer required. “If you don’t spend time doing that, you’re just taking all the poor habits of the legacy systems and you’re not going to get the benefit of the new system,” Bilali says. Clean data helps systems run more efficiently and improves performance. With the prospect of harnessing AI tools in S/4HANA, organizations have an added imperative to integrate clean data, but it’s often fragmented across multiple systems. Organizational data most likely includes a mix of structured data such as financial records as well as unstructured, non-SAP data that all needs to be harmonized to make the most of new tools. “All that data should come together to really build a whole boosted AI platform. So, that’s a key decision for the CIOs,” says Rimini Street’s Mariotto. Managing integration between core ERP and business applications is another major consideration. These connections require extensive coordination with third-party providers, aligning schedules, testing protocols to ensure they’re aligned across different systems and organizational cultures. “You have to get that all coordinated and scheduled, and you have to get a commitment on the other end, that when you’re ready to test, that they’re also ready and available to test,” Bilali says. It’s why Bilali emphasizes spending plenty of time in the planning phase because it can make all the difference to successfully landing the transition to S/4HANA. “It’s like you’re changing the wheels of the plane when you’re at 30,000 feet because the business has to keep running,” he says. source

On flexibility: When you’re doing a transformation, the hardest thing to do is get started. I think because the scale of it looks so enormous, it’s really difficult to know how and where to start. So our biggest challenge was actually breaking it down into bite sizes and figuring it out. Once we started, things began to flow. But another thing that transformations do is you can’t assume what you knew at the start is what you’re going to know at the end. And as we’ve gone down this transformation journey, we’ve learned a lot. For example, we constructed our teams one way at the start of the transformation, and during the process, we reorganized ourselves about four times as we moved through and learned things, and ran into different obstacles and broke through obstacles, refining our scope. As we’ve progressed, we’ve had to be open to change. I think that’s been a big challenge for the team. A technologist likes certainty and what they’re doing. They’re basically engineers at heart, so having to build in a flexible mindset has been challenging, but it’s been good for us. Diversity is essential as well, not just relevant. We have a really diverse team, and when you have that, you get a lot of debate, but debate with the right culture ends up with really amazing outcomes, which enables you to tackle big problems. It’s actually the only way you can do a truly successful transformation. So we have a lot of healthy conflict in our team because of the diversity, but what we don’t have is group think. On staying the course: What often happens in a transformation process, because they tend to go on for a long time, is the business gets bored and they want to get on with doing other things. They want this transformation thing to be over so they can go back to doing the exciting things they want to do, and there’s a real temptation to be distracted to divert into something else. I think that keeping the main thing the main thing has been the other reason why we’ve been able to do this in such a short period of time. So remember the problem you’re trying to solve, be single-minded about it, and choose the simplest path to get there. Don’t try and add everything all at once. There’s plenty of time later to improve it. And once you do that, once you form that path, then you can understand what capabilities you’re going to need, and you can assemble a small group of people highly skilled in those capabilities to form a lead team. And that core team is essential for the success of delivering a transformation. I’d also say that team can’t be people from outside your business because nobody loves your business like people who work in it. That core team is essential, but you’re never going to get enough people for a transformation from your internal team. You need to scale up by bringing in resources you can scale down later. And that’s easy to do once you have that real core team that has ownership, is highly capable, and understands the simple path you’ve laid out. On being digital first: Just about every business is digital first, even if they don’t think they are. Everything involved in every transaction you do in your business is underpinned by some kind of technology. So even if you don’t sell anything online, you’re still a digital business. But what’s changed is the expectation of the experience from both internal customers and those external ones you’re selling products to. They expect digital and in-person experiences to be equally good, and people want to have the option to choose which way to go. For businesses, that’s a big challenge transforming things that you’ve been doing over the phone or through a human into a digital environment. It’s not easy to scale, so that’s where I think the emergence of new technologies like AI is changing the game, and so we’re using it. For instance, we used to do sentiment analysis manually by reviewing call recordings. But now we have continuous AI monitoring of customer sentiment, and it can alert your contact center team leaders when something’s going off track. What that does is it helps us immediately provide a better experience for our customers. Now we know instantly how well we’re doing, and it can also help us pick up on where sentiment might be falling. It might be a particular product design or something they’re finding they can’t do on a website. So this is where AI is accelerating those experiences and helping businesses move faster. We’re very open to adopting that. And I think because we’re a small business and quite agile, it’s probably easier for us to adopt some of those changes and test them. source

AI, she said, is no longer a vision or science experiment, but is delivering real results. According to a release, examples of that include fast-tracking employee onboarding with the help of AI agents, streamlining information retrieval, and enhancing decision making through rapid visual analysis of charts, metrics, orders, and lists; Epicor AI agents then collaborate to suggest parts, suppliers, costs, and delivery schedules. This cuts down communication by up to 40%, the company said. Carbon Cost Rollup, the company said, “establishes ‘Cost Rollup’ methods from standard costing systems. Adapting this concept transforms carbon tracking by treating CO2 emissions as a currency, helping business leaders to calculate their compliance reporting to a high degree of certainty.” Jordan said Epicor understands that “there are long term sustainability initiatives that need to be put in place now for future resilience and creating value. And we have a lot of interest specifically on this carbon costs rollup solution from our international manufacturers, many of them who were here at our customer conference, and very excited about the new release to help them measure and accurately report, as they need to report on Scope One, Two and Three emissions.” source

Like that is very powerful. Like, if you have agents working non stop in that in that regard, yeah, that has a lot of potential. So, and you could probably apply that same, you know, set of use cases to anything, any major function, marketing function, sales functions, Customer Care functions, there are use cases that are out there, everywhere. So I’m, I’m really only because I do a lot of infrastructure operations. Those are the use cases that start to excite me a little bit because, you know, we have incident managers that we have to deploy around the clock on, you know, with page pages at all hours of the night. And if you’ve done Incident Management, it’s not a fun job to be woken up at three in the morning with an incident. The other area I think is going to be really super interesting is around how AI governance and security works, and whether companies are prepared for just how to manage all of that stuff, yeah, especially when you get into autonomous decision making of agents or, you know, you’re you’ve got AI tools that are popping up like mushrooms, you know, at this point in time, how do you govern all of that? You want to give your employees enough to be able to experiment and try things and figure it out, but you can’t. It can’t be the wild wild west either, because there are a lot of these tools can have pretty big impacts to your company if they’re not, you know, handled accordingly. So we’re just wrapping up the first quarter. We’ve just wrapped the first quarter of the year. Just wondering. You know, any emerging trends you feel will dominate tech and how you’re you’re viewing, sort of the next phase of the tech rollout for the remainder of the year. Is there a next phase? I think it’s an evolution. I don’t even know if it’s a Yeah. Just kind of all blends together these days. I don’t know if it’s exactly the next phase, but I do think that there will be a significant emphasis on coding specific to as it relates to the just sheer amount of code that will be deployed into the ecosystem. If you think about it, you know you have agents now or code assist tools that are essentially low code or no code. Some people are calling it vibe coding. Now, if you’ve heard that, that term, but vibe coding, from what I understand, it’s, you know, people that may not be training coding, but they are talking using native language around what they want to design. I want to build a website, and I want a purple background, and like you’re talking about what you’re feeling in your head, and it’s translating it into code, okay? And code is being produced as a result of it, yes. And what that basically means is, if, if, if your CFO can create code, or, you know, anybody with a marketing background can create code. We’re going to see more code produced in the next year than maybe in our entire lifetimes, which is going to be incredible, but also a little bit scary. How do you manage that level of code to make sure that it’s governed the right way? It’s when it breaks. How do you troubleshoot and dag nut and those sit probably using AI as well, but those are the trends that I think are really interesting. Data is probably another big one source

Kari Jones:  Yeah, look, I think lots, plenty. And I was thinking, well, what would what would be the one that I would want to share. And I think there’s still ones that you are work I’m working through. You know that a little bit nearer, but I think the one that I really wanted to share with you today was was really around the opportunity when I was at Air New Zealand, was I got the opportunity to step into the GM, the acting GM role for data and analytics. And great opportunity. I really thought that I had made it, and I got to do the role for nine months, but ultimately, didn’t get the full time role. And was absolutely gutted. It was absolutely it was really wrenching. You feel like you feel a failure, right? You feel like I’ve put all of this effort in. How could people not see how much effort I’ve put in? I’ve, you know, I’ve really pushed hard, I’ve driven for results. I’ve, like, got stuff done and but I didn’t ultimately, ultimately get the role. But and I think for me, I didn’t know what mistake I had made in order to not get that role. And it wasn’t until my new boss, Lionel Lopez, who actually was successful in getting the GM role, when he came in, he was able to provide me with some feedback and feedback that he had gathered bravely and courageously from other leaders around the organisation, and he was kind enough, not bold enough, kind enough okay to sit me down and give me that feedback. And that feedback was blunt and it was hard, and it was a bitter pill to swallow, and it was very much around Kari, your colleagues have said that you are aggressive and bruising to deal with, and and I was like, wow, that hurts. And because I just thought I was getting stuff done, and I just thought that I was really showing the like, the characteristics of what it looks like to be really delivery focused. But what he, you know, explained to me, and what he shared with me, and the journey that I went on as a leader, as a result of that feedback, was really understanding it’s not just about being so single minded. It’s about how you go about doing doing your work. It’s about how you bring other people with you. It’s about how you make people feel as they as they’re working alongside you. And as a result of that, that feedback, I got to do some, you know, a lot of hell, you know, a lot of coaching. I did a the Hogan’s Dark Side assessment, which was just one of the best assessments for me personally. And it really made me realise that my energy, how I bring myself, with this high level of drive, you know, can be perceived by others really differently, and so I have to really watch and think about how I bring that energy, because when it’s overplayed, it can look very different for different people and so, and I think that’s how that was showing up, as you can be results focused, but you have to go at the pace that the organisation wants to go at, and you can’t be so far out in front of everyone else, it’s just becoming really painful and really frustrating for for me and for the teams that you’re leading or the people that you’re working with. So really gave me an opportunity to reflect on, you know, bringing the joy back to work actually doesn’t always have to be super serious all the time. You can, you know, you can, you know, you can have a laugh, you can have fun, and you can still get stuff done. And those two things aren’t, you know, mutually exclusive. They can, they can sit in the same space. So there was, it was, it was like I said. Lionel Lopez, he wasn’t brave, he was kind.   source

맥거크는 또한 각 기술 부채가 단순성, 유연성, 연속성, 보안 및 투명성이라는 5가지 주요 영역에서 운영에 미치는 영향을 고려한다고 밝혔다. 그는 “기술 부채가 이런 운영 원칙 중 어느 하나라도 방해하기 시작하면, 그때는 해결해야 할 수준에 도달한 것으로 보고 있다”라고 말했다. 맥거크와 팀은 영향 수준, 조직에 대한 리스크, 그리고 조직의 전반적인 전략을 고려해 무엇에 주의를 기울여야 하는지 우선순위를 정한다. 그런 다음 결정을 공유해 조직 전체에 해당 주제에 대한 인식을 높이고 있다. 맥거크는 이 모든 과정이 IT 부서의 업무 흐름에 통합돼 있다고 언급했다. 그래야 기술 부채 관리가 일회성 프로젝트가 아니라 지속적인 방식으로 관리될 수 있다는 설명이다. 예를 들어, 스크럼 팀은 새로운 기술 부채를 식별하고 언제 어떻게 해결할지 결정해야 한다. source