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And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division. source

Two-time CIO 100 winner Ostertag also details Heineken’s unique interpretation of digital transformation, and how it’ll shape the strategy in 2025 and beyond. Watch the full video below for more insights. On a fundamental tech fix: When I joined Heineken in 2019, it was clear that big changes were needed, starting with the positioning and understanding of the tech function at the time. Like in many other companies, it was seen as a cost factor. The technology function was under finance, and we weren’t at the same table with the other key leaders in the company, on a global, regional, or even a local level. And that had an impact because the IT strategy wasn’t fully integrated and connected to the overall enterprise business strategy of the company. Another key reason why a change was needed was we were coming from a very fragmented technology landscape. We had 3,500 business platforms and solutions in the company, 45 ERP solutions globally, and we had no real consistent standardized view on where we were, or tech landscape where we wanted to be. So based on that, a lot of things have happened since 2019. For instance, we have a new corporate function called the digital technology function where we’re now at the table, and we’re represented with leaders like myself and those in the operating companies. The strategy is now fully connected and integrated to the business strategy. On a digital framework: The number-one priority is about people and having the right people with the right capabilities in place. We really put a lot of effort in digital upscaling not only within the digital tech function, but also in the business on all levels, inclusive of senior leaders. And then there’s quite a shift in the company toward hubs, where we leverage the power of them for different purposes. If you continue in this vein, other things we’re doing are digitizing our route to consumer, and creating value for the business by leveraging data and data analytics. And then we’re simplifying and automating our processes with AI. So we’re focusing on establishing what we call a secure digital backbone, which is a full modernization of our technology landscape that’s geared to our focus on our customers and value creation. source

00:00  Hi everybody, welcome to DEMO, the show where companies come in and they show us their latest products and services. Today, I’m joined by Jonathan Van Horn, he is the director of solution engineering at Flexera. But you go by JVH, so I’m going to be calling you JVH for the rest of the show. So welcome to the show. 00:12I appreciate you having me, Keith. 00:14All right, so what are you going to show us today, and can you tell us a little bit about Flexera as well? 00:17Flexera is the market leader in technology value optimization, and we’ve been in this space for over 30 years. We help organizations answer four critical questions about their entire Hybrid IT environment: What do I have? Where am I at risk? What is it costing me? But more importantly, What value am I getting from the technology investments that I’m making? Today I’m going to show you how Flexera customers are answering those four questions, leveraging Flexera One powered by the technology intelligence platform. 00:48Okay, so Flexera One is relatively new, we’re not going back 30 years to discuss that product. So what is this Flexera One? Who is that mainly designed for? Is it the CIO level? Is it someone in IT operations, security teams, network teams, everybody? 01:05We focus on three key pillars within every organization: technology, finance and security. So CIOs, CFOs, chief procurement officers, even CISOs leverage the value and data that Flexera provides to make that data extensible and drive additional value to key business systems like IT service management, the CMDB, IT financial management, even enterprise architecture, applications and SecOps. 01:29And what problem are you solving? This is the big question: why should a viewer care about watching this video — what problem are you helping them solve? And it can be multiple problems, too, right? 01:39Oh, absolutely. Organizations are sitting on a gold mine of data. The challenge becomes making sense of that data, but then also trusting the data to drive critical business decisions. So our customers are excited about our ability to aggregate these disparate data sources, aggregate, cleanse, normalize, contextualize, and even enrich that data to drive this decision making and provide deep insights using actionable technology intelligence. 02:09What would companies be doing if they didn’t have your platform? I’ve seen other point solutions, perhaps, that that address things like Cloud spend and things like that. So there are other finops programs out there, and there are other IT operations type platforms too. So is it that you’re just combining them into one? Or what’s your your big value proposition? 02:31As they say, the modern technology environments are all about convergence today, and even ITAM and FinOps are converging. This is made true by even analysts like Gartner and Forrester, who are saying that it is monumentally critical for organizations to manage this convergence. And it’s even been more proven out by the 2024 State of the Cloud report, where managing software licenses in a cloud environment is the number six initiative across all IT leaders — that’s up three spots and 32% in the last two years alone. So Flexera is not only the market leader here, but we are uniquely positioned to help organizations manage that convergence of ITAM and Finops to drive critical strategic business value. 03:17So let’s get into the demo. 03:18Excellent. So we’re going to start with the problem I talked about earlier, aggregating all of these disparate data sources. This is what Flexera does. This is fundamentally the challenge that organizations have, taking these disparate data sources, aggregating them together, cleansing, normalizing, enriching and contextualizing this data. The only way this is possible is through technology that Flexera provides, called Technopedia. It is the world’s gold standard for IT asset data, and we have dedicated employees who consistently populate this 4,500 times every single day. So what organizations are able to do with that information, very quickly they can identify their entire hybrid IT estate, so understanding what the hardware is, what the software is in their environment, but also contextualizing with, ‘Hey, what is the carbon footprint of the technology investments I’m making? What security advisories are tied to these individual pieces of technology that are potentially opening up my organization for risk?’ We also contextualize and enrich it with currency, information, understanding, end of life, end of support, obsolescence. These are things where the manufacturers are no longer supporting these applications, rendering them vulnerable. The other piece to it is, what do I have and bringing contextualization is we also can then tie the individual technical components to the specific offerings and business services that these companies are providing their customers. So being able to take a look at every business service, seeing the technology that’s supporting it, understanding the vulnerabilities and the contextualization around lifecycle currency is critical to achieving efficiency and effective management of these services. Now that’s the “What do I have? Where am I at risk?” As far as “What is it costing me?”, we provide an aggregated view of every piece of technology spend, everything from on-premise licenses, SaaS and cloud in one standardized view. So you can see exactly where those dollars are going from those investment perspectives. Then we take it even further by understanding the value that I’m getting. So now you can start to see from a FinOps perspective, bringing the cost and the risk aspects to these insights, understanding exactly where my dollars are being spent across the organization, understanding the potential license risk associated with it, and then also driving deeper insights. So I can see here, if I take a look at this new service we just released as an organization here, smart personal assistant, everybody’s trying to incorporate AI into the services that they provide being first to market, I can see that I have some compliance risk. I have spend in the cloud. I have spend that’s in SaaS. I have total technology spend here, and finance is driving a

Organizations look at digital transformation as an opportunity to radically improve operations and increase the value of a product or service to the customer by embedding technology into the decision-making fabric and building automation into its functions. This involves the integration of digital technologies into its planning and operations like adopting cloud computing to sustain and scale infrastructure seamlessly, using AI to improve user experience through natural language communication, enhancing data analytics for data-driven decision making and building closed-loop automated systems using IoT. For the employees, this freed-up human capital helps to invest more time in activities that require human expertise, judgment and creativity, and obtain better work-life harmony.   Leading any major change initiative is a daunting task, particularly the ones in which the result is unclear, the turnaround timelines are vague, and the value erosion rate with time is high. In almost all these transformations, one must prove the justification for change and navigate resistance to it, and go above and beyond to develop the business case. When talking about leading a digital change, the level of all the above is many degrees higher. So the question that plagues any professional entrusted with or motivated to drive a huge change initiative is how to inspire innovation and foster a culture of excellence.   Acknowledging the challenges of digital transformation   Lack of precedence or an absence of suitable benchmarks for results could be limiting factors for a digitalization exercise. Though there are some common goals every organization might want to achieve, there is a unique benefit or advantage each organization will seek to differentiate them from competitors. The unavailability of such a precedent could pose difficulties in allocating resources to the initiative, predicting the outcome of the initiative and stating a timeline upfront for realization of the objectives.   source

Overview How has the advent of robotics driven the vehicle manufacturing arena into high gear? And in a nation with challenges as unique as in India, will self-driving vehicles remain a pipe dream? Anand Deodhar, Group CIO of Force Motors, gives us a technology ‘test drive’, in this episode of CIO Leadership Live. Register Now source

The new RISE with SAP on IBM Power Virtual Server offering, he said, “seems like a practical option for companies already using SAP on IBM. It provides a clear path to the cloud, promising to transition SAP S/4HANA workloads in just 90 days, with potential cost savings of 30% based on IBM’s own experience.” It does, added Kramer, also “include extended support options, helping businesses prepare for the end of SAP support. The familiarity of SAP and IBM Power systems makes the shift less daunting, though adoption will depend on factors such as the company’s current SAP setup, budget, and readiness for cloud migration. Change management and data quality will be key areas to address during the transition.” Bickley said the reference to IBM Consulting as a potential systems integration (SI) partner, “further allows IBM to embed themselves in the SAP partner network as an infrastructure provider. The reality is that IBM’s cloud business never quite took off, so this is really chasing the ‘tag ends.’  Most of this business will flow to Azure or AWS, with the leftovers dropping to Google Cloud Platform (GCP) and now IBM.” source

If you had to name 2023’s single-most impactful and disruptive technology, you’d need just two letters: AI. With the release of OpenAI’s ChatGPT in November 2022, we watched a tsunami of AI news and noise throughout the year. And there’s no sign of things slowing down. Even for technology insiders, the rapid pace of generative AI’s development and adoption across all business sectors was simply astonishing. Many organizations that considered themselves to be forward-thinking in 2022 suddenly found themselves playing catch-up in 2023. But if FOMO was a thing last year, this year we say NOMO, as in no more fear of missing out. Instead, look before you leap. As we move forward into a new year, it’s crucial that we commit to a resolution that will help us create significant value for our shareholders, both now and in the years to come. Let’s promise ourselves that this will be the year that we adopt a pragmatic approach to harnessing the vast potential of AI. To do so, we need to first ask ourselves three key questions: Question #1: How will we use AI to meet our specific business objectives? In this new year, the speed and scale of AI implementation will make the progress made in 2023 look stagnant. By 2025, IDC expects Global 2000 companies to devote more than 40% of their core IT budgets to AI-related activities, with worldwide AI spending predicted to exceed $500 billion by 2027. The question, then, is not whether you will shift toward more AI-influenced operations in 2024 but how — and, more importantly, why. AI promises seemingly limitless possibilities for tech-savvy organizations — everything from sorting and analyzing vast amounts of data to improving customer service and patient care. Looking forward, it’s easy to imagine how AI could alleviate supply chain headaches and even create virtual reality training simulations. Whatever their outward manifestation, though, effective AI activities tend to focus on two underlying objectives: expanding capabilities and eliminating waste. In other words, AI is most often used to increase what one can do and improve how it’s done to free up human and financial resources so that you can deploy them more strategically elsewhere in the organization. As IT leaders start thinking about how to incorporate AI into their organizations, they’ll likely focus on generative AI and other advanced AI capabilities to cut down on costs, especially when it comes to mundane tasks and resource optimization. However, while cost-saving is an important consideration, it shouldn’t be the only one on the board. Organizations without a clear vision of what they want to accomplish in 2024 will find plenty of AI bells and whistles but very little direction. AI is a tool, not a mission statement. Focusing on the general use of AI in your organization is not the same as being strategic in how it’s used. In the same way, it’s no different than what we’ve encountered in the past with other transformative technologies, such as cloud computing. As the number of AI features continues to multiply over the next 12 months, it’s crucial that organizations take precautions now to avoid shiny object syndrome, where the potential of adopting this exciting new technology turns from distraction to detriment. To preserve the integrity of their organizations, leaders must evaluate the strategies they use to prioritize investments so that they can optimize spending in preferred technology areas to reach their business goals. Question #2: How will we make sure that we use AI responsibly? While AI can be a powerful tool for achieving business objectives, it can also be a disastrous liability fraught with risks, another reason why organizations should take a deliberate and pragmatic approach to AI adoption. Many people are concerned about the growth of AI. And, as many organizations discovered in 2023, any perceived misuse of AI will significantly harm brand image, regardless of the initial intention. In the public eye, there is no room for error when it comes to AI use. It’s imperative that you and your business stakeholders carefully and regularly review procedures to ensure the ethical use of AI tools and AI-generated outputs. Even if your organization is not presently active in Europe, the EU’s forthcoming Artificial Intelligence Act should inform your AI-related policy decisions. And with a recent hearing on the oversight of AI in the United States Senate, it’s possible the American government will issue guidance as well. At the very least, you should have clear and detailed safeguards in place to address how your organization plans to handle the following issues: Protecting the privacy of customer data Ensuring that proprietary information is not fed into generative AI models Ensuring that AI models and outputs do not reflect bias or prejudice Maintaining vigilant supervision over all AI-related activities Maintaining clear reporting structures for all employees who use generative AI Demonstrating transparency of AI usage to stakeholders inside and outside of the organization As you might expect, your legal department should be deeply involved in these conversations. Question #3: How will we make sure our employees use AI successfully? Ultimately, AI adoption is not just an IT issue: it is a workforce issue. Are your employees ready? If we’re being honest with ourselves, the answer is probably “not yet.” With any new technology, many companies operate within the “we bought it, so you have to use it” paradigm. This inevitably leads to poor morale and haphazard implementation, which undermine the organization’s goals. ROI quickly becomes DOA. Organizational change often draws out strong emotions from employees. This is especially true when dealing with powerful and disruptive technologies like generative AI. Having conversations with your workforce about AI-related activities before you implement them will go a long way toward calming their fears and making sure you can meet your objectives. Employees need to know: The vision and goals behind your organization’s adoption of AI How AI will augment and enhance the work they do The steps you’re taking to protect them and your customers from AI misuse The steps they should take to report any concerns about specific AI activities How you’ll provide ongoing

Responsibility in action So, what can we do? First, we must recognize that AI doesn’t develop in a vacuum. It’s shaped by people, policies, and cultural norms. To ensure it grows responsibly, we need diverse voices at the table — developers, policymakers, and community leaders who can represent the needs of all users, not just the privileged few. Second, we need stronger governance frameworks that emphasize transparency, fairness, and accountability. This includes mandating bias testing, diversifying datasets, and holding companies accountable for the societal impacts of their technologies. Finally, we need a cultural shift. AI should be seen not just as a technological achievement, but as a societal one. Its development must prioritize equity, inclusion, and responsibility. By doing so, we can harness AI’s potential to bridge gaps and create opportunities, rather than perpetuate harm. source

Artificial intelligence for IT operations (AIOps) solutions help manage the complexity of IT systems and drive outcomes like increasing system reliability and resilience, improving service uptime, and proactively detecting and/or preventing issues from happening in the first place. According to BMC research in partnership with Forbes Insight, more than 80% of IT leaders trust AI output and see a significant role for AI, including but not limited to generative AI outputs. Research respondents believe AI will positively impact IT complexity and improve business outcomes. But many enterprises have yet to start reaping the full benefits that AIOps solutions provide. An increasingly complex technology landscape makes it more difficult to resolve issues. Today, IT encompasses site reliability engineering (SRE), platform engineering, DevOps, and automation teams, and the need to manage services across multi-cloud and hybrid-cloud environments in addition to legacy systems. Because of the adoption of containers, microservices architectures, and CI/CD pipelines, these environments are increasingly complex and noisy. On top of that, IT teams have adopted DevOps, agile and SRE practices that drive much greater frequency of change into IT systems and landscapes. These changes can cause many more unexpected performance and availability issues. At the same time, the scale of observability data generated from multiple tools exceeds human capacity to manage. These challenges drive the need for observability and AIOps. AIOps: Big picture visibility to resolve issues faster AIOps solutions like BMC Helix AIOps are built to ingest large amounts of data and correlate multiple incidents into situations that can be analyzed to reduce time to resolution. When you get deluged by alert storms, you need to understand if and how those alarms are related in order to resolve them. AIOps can take what looks like a storm of alerts hitting the monitoring systems and reduce them down to one or two probable causes. Understanding the root cause of issues is one situational benefit of AIOps. With situational insights, IT operations, SREs, DevOps, and platform engineering teams can reduce time to remediation and quickly restore services with a pre-built set of automations. Many organizations today conflate observability, which is just one important component of AIOps, with a full AIOps deployment. Observability builds on the growth of sophisticated IT monitoring tools, starting with the premise that the operational state of every network node should be understandable from its data outputs. AIOps-powered observability goes beyond monitoring data outputs and provides a complete, up-to-date topology of all business nodes and how incidents on one layer affect other nodes, as well as business outcomes. Observability prepares you to understand incidents and their causes. AIOps provides automatic remediation and insights across the entire change lifecycle, beyond what monitoring or observability alone can do. The journey to AIOps maturity Many IT leaders say their staffing and skills are sufficient to design, deploy, and manage AIOps. Beneath the surface, however, are some crucial gaps. A significant share of organizations say to effectively develop and implement AIOps, they need additional skills, including: 45% AI development 44% security management 42% data engineering 42% AI model training 41% data science AI and data science skills are extremely valuable today. Experience and deliberate cross-functional learning opportunities are needed for people to acquire these skills. IT leaders are looking for good AI content that their employees can reference, plus opportunities for employees to develop AI skills. AIOps unleashes growth and innovation and enables change AIOps helps drive business outcomes by bringing in data-driven business context that drives IT decision making based on what the business needs in real time. In addition to making IT systems more resilient, these operational improvements lower IT costs, enable innovation, and bolster the customer experience. Everyone with a vested interest in their organization’s growth and evolution can appreciate the value of a significant performance benefit and the transformative change of simplifying the complex. Complexity is challenging when you’re trying to make rapid changes to keep up with the needs of the business, but those frequent changes drive risk and incidents. AIOps can show the causal impact of the change in one system, and the learning algorithms of AIOps can help you predict risks of incremental versus major changes. AIOps integrates into existing organizational workflows, enabling IT teams to be more productive. Once IT teams automate repetitive tasks, issue remediation and prevention, IT workers are free to work on more meaningful and strategic initiatives that drive organizational success. Are you ready to transform your IT organization with AIOps? AIOps is more than just the remit of the CIO, and its value needs to be understood in the C-suite and in the boardroom. AI solutions can create a core competitive advantage for the entire organization, with BMC customers having achieved significant results: 100% uptime for their business services 100% visibility across their IT environment 73% reduction in incident volume The financial impact includes more than $1 million in infrastructure cost savings, $2.3 million in reduced tool-sprawl savings, and productivity savings from freeing the time of 96 full-time employees. If you’re ready to start simplifying with AIOps, click here to learn more about BMC AIOps solutions and how to transform your IT landscape. To schedule a consultation with BMC to start transforming your IT organization, click here. source

Despite the huge promise surrounding AI, many organizations are finding their implementations are not delivering as hoped. Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs.[1] The limits of siloed AI implementations According to SS&C Blue Prism, an expert on AI and automation, the chief issue is that enterprises often implement AI in siloes. When AI solutions are designed to address isolated problems without integrating broader organizational data and insights, they miss opportunities to drive transformative outcomes. These narrow approaches also exacerbate data quality issues, as discrepancies in data format, consistency, and storage arise across disconnected teams, reducing the accuracy and reliability of AI outputs. Reliability and security is paramount. Without the necessary guardrails and governance, AI can be harmful. Automation takes care of end-to-end processes while also providing a detailed audit trail. With AI now incorporated into this trail, automation can ensure compliance, trust and accuracy – critical factors in any industry, but especially those working with highly sensitive data. Moreover, siloed initiatives can lead to duplicated efforts, with different departments independently developing overlapping AI capabilities, resulting in wasted time, inflated costs, and diminished efficiency. Taking a holistic approach to enterprise AI However, when AI is implemented effectively it can dramatically enhance productivity and innovation while keeping costs under control. According to PwC, organizations can experience incremental value at scale through AI, with 20% to 30% gains in productivity, speed to market, and revenue, on top of “big leaps” such as new business models.[2]  For SS&C Blue Prism, the key to success in AI lies in deploying the technology holistically across the enterprise and integrating AI technologies alongside comprehensive business automation and orchestration capabilities. SS&C Blue Prism argues that combining AI tools with automation is essential to transforming operations and redefining how work is performed. By leveraging AI technologies such as generative AI, machine learning (ML), natural language processing (NLP), and computer vision in combination with robotic process automation (RPA), process and task mining, low/no-code development, and process orchestration, organizations can create smarter and more efficient workflows. Transforming the enterprise with AI In this approach, businesses are able to move beyond isolated implementations, aligning AI-driven insights and automation capabilities with enterprise-wide objectives. For example, process and task mining can uncover inefficiencies and identify opportunities for optimization, while RPA and low/no-code platforms can empower teams to automate repetitive tasks and develop solutions rapidly. Meanwhile, AI-powered tools like NLP and computer vision can enhance these workflows by enabling greater understanding and interaction with unstructured data. When orchestrated effectively, these technologies drive scalable transformation, allowing businesses to innovate, respond to changing demands, and enhance productivity seamlessly across functions. AI in action The benefits of this approach are clear to see. One insurance company, for instance, automated its mailroom with SS&C Blue Prism, using pre-programmed templates to quickly identify and reorder forms and extract typed and handwritten data, SS&C Blue Prism helped the company replace manual tasks with up to 98% accuracy.[3] Meanwhile, ABANCA, a Spanish retail bank, has used SS&C Blue Prism’s intelligent automation, generative AI, and NLP to automate over a thousand tasks, which improved customer and employee experience and allowed it to respond to customer inquiries 60% faster.[4] On their own AI and GenAI can deliver value. However, it’s only when combined with automation and orchestration that the technologies’ full potential can be unlocked. Clearly, organizations that think strategically about AI and deploy it as an integrated, holistic system stand to unlock the full benefits of the technology, control costs, and outpace their peers. Explore your enterprise AI Process [1] Gartner, Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025, July 2024 https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025 [2] PwC, PwC’s October 2024 Pulse Survey, October 2024, https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html [3] SS&C Blue Prism, Mailroom Automation: AI Replaces Manual Tasks with Up to 98% Accuracy, https://www.blueprism.com/resources/case-studies/digitizing-the-mailroom-ai-system-eliminates-manual-tasks-with-up-to-98-accuracy/ [4] SS&C Blue Prism, IA and Generative AI Help ABANCA Respond 60% Faster to Customer Inquiries, https://www.blueprism.com/resources/case-studies/abanca-customer-experience-cx-generative-ai/ source