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Overview In this fascinating conversation, Harvinder Singh Banga, CIO of CJ Darcl Logistics talks how emerging technology like Generative AI and sound cybersecurity principles are being leveraged to bring their core philosophy of ‘Humanizing Efficiency’ to life. Catch the whole conversation, in this episode of CIO Leadership Live. Register Now source

To this end, the Microsoft exec referred to the company’s announced plan to invest more than $35 billion in 14 countries within three years “to build trusted and secure AI and cloud datacenter infrastructure.” According to Smith, Microsoft’s global infrastructure now reaches 40 countries, including the global south, “including in the Global South, where China has frequently focused so many of its Belt and Road investments.” To build on this, Smith is calling for more political support, writing, “the most important U.S. public policy priority should be to ensure that the U.S. private sector can continue to advance with the wind at its back.” The United States can’t afford to “slow its own private sector with heavy-handed regulations,” Smith adds, calling for a “pragmatic export control policy.” After all, the aim is to “expand rapidly and provide a reliable source of supply to the many countries that are American allies and friends.”  Europe between IT dependence and the desire for sovereignty Whether these allies and friends will join in the AI ​​game outlined by Smith is questionable, however, despite Microsoft publicly announcing billions in investments in European infrastructure last year, including €3.2 billion in Germany. With the AI ​​Act, the EU has passed a set of rules that prescribes clear guidelines for the use of AI in Europe. AI lobbyists are currently haggling over the final wording to pull the teeth out of the regulation in the interests of their own business.  source

00:00 100:00:03,966 –> 00:00:06,000Welcome to CIO Leadership Live.200:00:06,000 –> 00:00:10,133I’m Lee Rennick, executive directorof CIO communities for cio.com,300:00:10,366 –> 00:00:13,233and I’m very excited and honored todayto welcome Christine400:00:13,233 –> 00:00:16,500Bongard,CEO, co-founder of the WIT Network.500:00:16,800 –> 00:00:17,800Christine.600:00:17,800 –> 00:00:20,500Please introduce yourselfand welcome to the show.700:00:20,500 –> 00:00:20,933Hi there.800:00:20,933 –> 00:00:22,833Lee, thank you so much for having me ontoday.900:00:22,833 –> 00:00:26,500Yes. Christine Bongard,CEO and co-founder of the WIT network.1000:00:27,266 –> 00:00:30,000I have been a woman in technologymy whole career.1100:00:30,000 –> 00:00:33,933I had the fortunate, fortunate experienceof being part of a Techstars1200:00:34,033 –> 00:00:38,866up in the New York metro areaand, had the opportunity to grow business1300:00:38,866 –> 00:00:42,300from start and learn all about everything1400:00:42,333 –> 00:00:45,600technology, how to manage teams,how to run a business.1500:00:45,600 –> 00:00:49,100It was just such an incredible opportunityfor me1600:00:49,400 –> 00:00:52,500and, thatthat led me to being a woman in tech.1700:00:52,833 –> 00:00:57,733And I got to build a great network,over all of that time and,1800:00:58,933 –> 00:00:59,733have the fortunate1900:00:59,733 –> 00:01:03,600experience to then take my, careeras an entrepreneur2000:01:03,600 –> 00:01:08,100and in the tech industry and builda global network for women in tech.2100:01:08,466 –> 00:01:10,366as my my second part of my career.2200:01:10,366 –> 00:01:11,700So loving it.2300:01:11,700 –> 00:01:14,366Wow. Wow, that sounds really amazing.2400:01:14,366 –> 00:01:16,000And we’ll dive into thata little bit more.2500:01:16,000 –> 00:01:18,000And, you know, I’ma member of the Witt network.2600:01:18,000 –> 00:01:21,166Our organization is, I’m so proud that2700:01:21,166 –> 00:01:24,166we have that connectionwith this organization.2800:01:24,166 –> 00:01:26,433and all thatyou do to ensure that women have a safe2900:01:26,433 –> 00:01:29,433place,a place to network and learn and share.3000:01:29,433 –> 00:01:32,133And, so I’m looking forwardto having this interview today.3100:01:32,133 –> 00:01:35,200So I really do appreciate youjoining us today.3200:01:35,200 –> 00:01:36,266Christine, thanks so much.3300:01:36,266 –> 00:01:39,800We have created this seriesto support diversity in technology3400:01:40,000 –> 00:01:43,000and really listen to womenworking in this space.3500:01:43,066 –> 00:01:45,900and how, you know, they’re buildingand supporting out,3600:01:45,900 –> 00:01:48,833other women in, in, in our industry.3700:01:48,833 –> 00:01:51,833And so the first questionand I ask everyone this question,3800:01:52,233 –> 00:01:55,566could you please tell us a little bitabout your own career path and maybe3900:01:55,566 –> 00:01:59,166provide some insights or tips on that rolepath, especially as a women woman?4000:01:59,166 –> 00:02:02,133Excuse me, are there any lessons sharedthat you could, you know,4100:02:02,133 –> 00:02:03,900any lessons learned that you could share?4200:02:03,900 –> 00:02:05,500So a couple of things.4300:02:05,500 –> 00:02:05,966You know, as4400:02:05,966 –> 00:02:10,466I mentioned, just a great opportunityto come up in tech throughout my career.4500:02:10,466 –> 00:02:13,866And I will say thatjust about the whole way through,4600:02:14,300 –> 00:02:18,500I was, consistently one of the only womenin the room, whether we were4700:02:18,500 –> 00:02:23,000with a customer, a partner,at a conference, at networking events,4800:02:23,933 –> 00:02:26,933you know, leads to feeling isolated.4900:02:27,066 –> 00:02:31,733and, you know, some of those eventswere golf events and whiskey tastings5000:02:31,733 –> 00:02:36,666and cigar, events, and those were thingsI really couldn’t relate to.5100:02:36,666 –> 00:02:39,300And, you know,it was hard to break into conversations5200:02:39,300 –> 00:02:42,166with people and I thought,where were the women?5300:02:42,166 –> 00:02:43,200You know, we have to5400:02:43,200 –> 00:02:46,766the technology industry is suchan incredible place to be.5500:02:47,166 –> 00:02:51,066And, you know, just felt like,how do we bring more women into this5600:02:51,066 –> 00:02:54,766and, and make, you know,bring gender parity to these events?5700:02:55,066 –> 00:02:59,000so that you mentioned the word safe,you know, so that that women feel5800:02:59,000 –> 00:03:03,500more comfortable, you know, being in,being in the room and being present.5900:03:03,500 –> 00:03:07,666So, you know,I really took those experiences that6000:03:07,666 –> 00:03:11,566that fueled, my need to, to want to bea part of starting this network.6100:03:11,966 –> 00:03:14,533But you asked about, you know,what are some lessons learned?6200:03:14,533 –> 00:03:16,366And I would say, you know, given6300:03:16,366 –> 00:03:20,166that we haven’t seen a ton of changein the numbers for gender parity,6400:03:20,433 –> 00:03:24,566in this industry,you have to really be bold and go for it.6500:03:24,566 –> 00:03:29,133And, you know, I think, you know,sitting back and kind of waiting for6600:03:29,366 –> 00:03:33,266career paths to materializein front of you or for your manager6700:03:33,533 –> 00:03:38,666to take the time to help you build it out,or for even HR teams to, to do that.6800:03:38,666 –> 00:03:39,966We’re we’re in a,6900:03:39,966 –> 00:03:43,133we’re in a place now in the worldwhere they just don’t have the time7000:03:43,133 –> 00:03:44,233to do that stuff. Right.7100:03:44,233 –> 00:03:49,233So I think we have to really inspire womento get clear on their vision.7200:03:49,300 –> 00:03:50,300Where do they want to go?7300:03:50,300 –> 00:03:54,333What skills do they want to build,and then look at the networks around them,7400:03:54,333 –> 00:03:58,800look at, you know, look at wherethey can take advantage of learning.7500:03:59,133 –> 00:04:02,433You know, we’ve got lots of stuffon LinkedIn learning, and there’s lots of,7600:04:02,633 –> 00:04:07,300technology companies and organizationslike the Witt network that provide7700:04:07,500 –> 00:04:11,466skills training and different,you know, educational opportunities.7800:04:11,466 –> 00:04:14,566And so I just really encourage womento be bold.7900:04:14,566 –> 00:04:15,400Go for it.8000:04:15,400 –> 00:04:18,400You know, get out there and and capture,8100:04:18,533 –> 00:04:21,733you know, the skills that they needto help accelerate their careers.8200:04:22,500 –> 00:04:24,066Yeah, I think that’s great advice.8300:04:24,066 –> 00:04:27,600I was just speaking to a friendand colleague about just this very thing.8400:04:27,866 –> 00:04:31,666I have my own personal like networkingbank book that I have on It’s8500:04:31,700 –> 00:04:35,100a rainbow and it’s on my desk,and it’s like I look at it and when I8600:04:35,100 –> 00:04:39,266need those moments to kind of plan and,you know, speak to others.8700:04:39,266 –> 00:04:42,833And I have a business coach I work with,you know, that’s super important.8800:04:42,833 –> 00:04:46,800And I didn’t really learn this until, likenow in life or like, you know, when I,8900:04:47,066 –> 00:04:50,666when I, started working with organizationslike the IT network.9000:04:50,666 –> 00:04:52,933So, it’s really impactful to me.9100:04:52,933 –> 00:04:56,400So maybe we could, we couldthe segue as well into the next question.9200:04:56,700 –> 00:04:59,933could you please tell our audiencea little bit about the Witt network,9300:05:00,333 –> 00:05:01,566you know, what is it?9400:05:01,566 –> 00:05:02,700How is it formed?9500:05:02,700 –> 00:05:06,300And, you know, we talked about thisa little bit, but why it’s important right9600:05:06,300 –> 00:05:09,300now, especially for women in tech,to have organizations like.9700:05:09,600 –> 00:05:12,700Oh, gosh, I could talk about this for,for

The mobility industry is pioneering the use of data spaces   The mobility industry is at the forefront of the data spaces initiative. Almost 180 players are currently involved in its data space Catena-X – major manufacturers as well as suppliers, mobility providers and organizations such as ADAC. Their stated goal: a common data infrastructure for the entire supply chains of the industry.   Raw material suppliers, component suppliers, carmakers, users and recyclers are also networking via Catena-X to better trace sources of error. So far, companies have only documented information on processes and components for which they are responsible internally. In the future, end-to-end data chains will show exactly who has installed which materials and components or which software has been used.   A second shared data space for the industry is the aforementioned MDS, which is funded by the Austrian Federal Ministry for Digital and Transport (BMDV). Around 150 companies are currently active there – mobility service providers, municipalities, public transport companies, companies from the energy sector, mobile network operators and research institutions. The data offered by MDS includes information on traffic and traffic flow, construction sites and road conditions, parking and public transport, car and bike sharing and much more.   source

In this episode, we speak with Paul Potgieter, Director of Technology, Product, and Platforms at NEOM, who brings a wealth of experience as both a technologist and a leader in the Middle East’s business and tech sectors. With over a decade of shaping the region’s technology and business landscape, Paul shares what drew him to Saudi Arabia’s ambitious GIGA Projects, particularly NEOM. He delves into his personal philosophy, Vision 2023, and the crucial role technology plays in NEOM’s development, as well as the benefits of sustainability and innovation for the region’s future. Register Now source

Instead of selling off assets, some organizations are using private equity funding to increase investments in AI, adds Claire Milligan, CEO of cloud cost optimization firm Aimably. In some cases, companies are turning to “creative” funding options, including private credit and so-called continuation funds to pay for AI projects that have not yet generated revenue growth, she says. “In private markets, investments in AI projects are seductive to equity investors as a justification for cash infusion to their holdings without the need to sell off assets,” Milligan says. “By capitalizing these projects as research and development, investors open the door to new sources of funding for existing portfolio companies, such as private credit or a transition to new fund vintages, without requiring investors to demonstrate value creation success to public markets.” The danger to investors and companies, however, is a continued lack of revenue from many AI projects, she adds. “Reinvestment activities into existing portfolio companies are necessary as these companies’ revenue performance has been lacking,” Milligan says. “If portfolio companies cannot reduce these projects to revenue engines, more recent fund vintages have a strong potential to deliver poor returns.” source

CIOs have a tough balance to strike: On one hand, they’re tasked with maintaining a large number of applications – research from Salesforce shows that in 2023 organizations were using 1,061 different applications – in varying stages of age, all the while maintaining interoperability and security and reducing overall spend. On the other hand, they must look to the future state of the business with an eye toward innovation and investment in new technologies like artificial intelligence (AI). While savvy CIOs bring both business and technology acumen to the table, the most successful follow a business-driven IT roadmap, not one handed to them by their ERP vendor. Especially when it comes to AI. AI requires a shift in mindset Being in control of your IT roadmap is a key tenet of what Gartner calls composable ERP, an approach of “innovating around the edges” which often requires a mindset shift away from monolithic systems and instead toward assembling a mix of people, vendors, solutions, and technologies to drive business outcomes. And nothing necessitates this shift more than AI. AI is a generation-defining paradigm shift in the way the world works and lives. The technology has made tidal waves in society, as more than 180 million ChatGPT users tap the fastest growing app for everything from writing term papers to debugging code. And, as explained in Rethinking ERP Reimplementation in the Age of AI, AI is causing significant impact on enterprises worldwide. While vendors wield the promise of AI as a forcing function for reimplementation, customers who comply with vendor-dictated AI roadmaps likely face four significant challenges: Challenge 1: Roadmap limitations & delays How do SAP and Oracle stack up in terms of AI features and functions? In this nascent field, do they have the right technologists, engineers, and product developers to support continuing growth? Are they on the bleeding edge of this technology or are they simply following the pack? While they certainly could become powerful AI players, successful organizations need flexibility and should be able to select from AI industry leaders for technologies—beyond their ERP ecosystems—that meet business needs today, adopt technology from industry AI leaders that can easily plug into multiple databases across your entire enterprise. Why limit your enterprise’s innovative potential to the speed of a big ERP vendor? Will Henshall, a writer for Time magazine, reports that AI progress over the past 10 years has been nothing short of staggering. His article notes that over the past decade, AI’s performance has exceeded that of humans when it comes to speech recognition, image recognition, reading comprehension, language understanding, and common-sense completion. With such rapid development underway, your enterprise must have the flexibility to choose the right AI vendor to deliver the right AI solution at the right time in order to drive the best business outcomes. And while SAP and Oracle could emerge as major AI players, there’s a lot of greenfield out there. Your organization must direct a business-driven IT roadmap to stay ahead of the curve. Challenge 2: Leaving on-premises data behind For AI algorithms to be successful, they need a massive amount of historical data to draw from. As Gene Marks, a contributor to Forbes wrote, “For AI to do its job it needs to use data.” Remember the “garbage in, garbage out” adage: The more clean data available to an AI algorithm, the more predictive and fine-tuned the results will be. Henshall’s article in Time echoes the importance of data for training AI: More than half of the AI models Henshall analyzed since 2020 have training sets of 100 million or more data points. “In general, a larger number of data points means that AI systems have more information with which to build an accurate model of the relationship between the variables in the data, which improves performance,” he writes. With the high price of cloud storage, customers reimplementing on the vendor’s SaaS cloud might not take all their on-premises historical data with them. We often see organizations migrating only a few years’ worth of data, potentially leaving 10 or more years of data behind—the very data that’s the lifeblood of AI. There is no denying the fact that with more historical, clean data, the more accurate predictive analytics and data correlation can be. The value of the ERP in AI is the data that it contains, and that already exists today within the on-premises systems. It’s best to ingest the relevant, clean, and accurate data from ERP and other systems into a centralized external AI model for best results. Challenge 3: ERP vendors’ AI setups only look at data in the system Vendor-embedded AI typically can only work with ERP data. But there are many data stores across an organization that are independent of the ERP system that should be included in any enterprise AI implementation. So, leaving AI to a single monolithic ERP vendor makes little sense. The good news is that there’s a better way. You can adopt technology from industry AI leaders today that can easily plug into multiple databases across your entire enterprise This flexibility speaks to the power of having a composable ERP, especially one with a robust data orchestration layer. Making your data accessible across your organization will not only benefit your employees but also unlock new potential for more powerful AI algorithm use inside your organization. Challenge 4: Loss of license ownership risks cost increases & shrinkflation In addition to leaving your customizations and data behind, reimplementing on-premises ERP functionally to the subscription cloud could mean leaving your leverage of software license perpetual entitlement behind, which can lead to out-of-control costs and shrinkflation. According to recent financial estimates from Deloitte, many companies that have moved to cloud have incurred complex software licensing issues and costs that can reach as much as 24 percent of total information enterprise technology spend. Even after initial TCO analysis, “many organizations still encounter a cost explosion when the actual migration begins, in part because they were unaware of the licensing requirements for cloud, which can include licensing transfer, purchasing, and visibility issues,” Deloitte says. Turns out shrinkflation—the