CIO CIO

“The software marketplace is being flooded with new solutions, and the main players are doing lots of releases and updates. That can overwhelm their clients,” Montgomery says. “There are more tools on the market, there’s more marketing of those tools, so we’re seeing higher rates of replacements.” Montgomery says CIOs and business colleagues sometimes think the solutions they have in place are falling behind market innovations and, as a result, their business will fall behind, too. That may be the case, but they may just be falling for marketing hype, she says. Montgomery also cites the fast pace of executive turnover as contributing to the increasingly short shelf life of IT solutions. She says new CIOs and new business execs often seek to replace some of the existing solutions they inherit in their new roles with ones they prefer or used in their prior jobs. source

With 71% of organizations actively deploying artificial intelligence (AI) at scale, the companies experiencing the most dramatic success share a common characteristic: They’ve made the most significant investments in IT modernization and are undertaking multiple AI projects simultaneously. The emergence of this critical divide in the enterprise AI landscape is one of the key findings from a recent Foundry survey of more than 250 senior IT leaders representing multi-billion-dollar companies across the U.S., EMEA, and APAC about IT modernization and AI. The survey paints a clear picture of AI’s current impact on enterprise operations. More than half (56%) of the surveyed organizations reported revenue growth directly attributable to AI initiatives, and 54% are seeing increased staff productivity. Another 51% reported enhanced customer engagement. But the degree of success varies significantly, depending on an enterprise’s underlying infrastructure investments. Given the positive results that respondents reported, it’s little wonder that spending on AI is exploding. Worldwide generative AI (genAI) spending alone is expected to reach $644 billion in 2025, a 76.4% increase over 2024, according to Gartner.1 What’s more, nearly half of the organizations had dedicated budgets for AI projects in 2024, up from 26% the year before, according to the Foundry AI Priorities Study 2025.2 Big bets pay off The most compelling insight from the survey centers on what researchers classified as “heavy investors” in IT modernization: enterprises that have undertaken four or more significant modernization efforts. These organizations consistently outperformed their peers across every critical business metric. Heavy investors achieved improved IT efficiency rates of 89%, compared to just 61% for all others. Similarly, these organizations reported faster AI adoption of 85%, versus 60% for their less modernized counterparts, and 98% of the heavy investors experienced increased innovation, compared to 75% of other organizations. The most significant disparity appeared in accelerating time-to-market capabilities, where heavy investors achieved success rates of 87%, compared to just 32% for all others. This dramatic differential underscores how IT modernization extends far beyond technical improvements to deliver core business advantages. The modernization advantage extends directly to AI implementation capabilities. Heavy investors demonstrated significantly higher confidence in their infrastructure’s ability to support AI applications, with 48% expressing strong confidence, compared to 33% among other organizations. This confidence translates into more aggressive AI deployment strategies, with 72% of the heavy investors actively modifying AI applications in production, compared to 41% of other organizations. Building on a foundation of innovation The survey also reveals that successful organizations are incorporating AI into critical aspects of the business, building on prior innovations such as cloud and DevOps. Over the past five years, leading enterprises have prioritized developer experience enhancements in digital transformation, with 71% investing in automation to improve developer productivity. This focus on developer empowerment reflects a recognition that people remain central to successful technology deployment, even as AI automates many routine tasks. Platform standardization emerged as another critical investment area, with 66% of the surveyed organizations working to gain visibility across diverse environments. This effort addresses one of the most persistent challenges in enterprise IT: managing complexity across hybrid and multicloud environments. Platform-as-a-service (PaaS) adoption followed closely, with 58% of the organizations pursuing PaaS strategies to streamline development processes. Infrastructure abstraction represents a more sophisticated modernization approach, with 42% of the organizations working to reduce complexity by abstracting underlying infrastructure concerns from development teams. Nearly a third (32%) have undertaken the significant effort of refactoring applications into microservices architectures. Platforms are imperative The survey findings also highlight the growing importance of platform engineering teams and dedicated AI platforms in successful enterprise AI strategies, with 53% of the survey respondents describing such teams as “very important” to accelerating AI implementation. Similarly, nearly half (48%) of the respondents identified structured AI platforms as “essential” to their operations, and an additional 34% described such platforms as “important.” This recognition has translated into concrete investment decisions, with 70% of the organizations either purchasing or building platforms specifically designed for AI application delivery. “You have to look at what you’re trying to do,” said a VP of IT at a U.S. retail giant. “If you have an organization that’s using more modernized applications, then a platform is better, because you’re already in that ecosystem and you can build out using the technologies that you already have in place.” The platform approach addresses several of the most significant barriers to AI deployment. Complexity topped the list of obstacles, at 49%, followed by security and compliance concerns and model costs, each cited by 44% of the respondents. Dedicated AI-native platforms can systematically address all three challenges through standardized deployment patterns, built-in security controls, and optimized resource utilization. A migration is on to private-cloud PaaS Enterprises are moving away from self-managed on-premises platforms. Currently 42% of custom applications run this way, but 76% of the surveyed organizations plan to migrate these applications within the next 12 to 24 months. The largest segment, representing 44% of the planned migrations, will move to private-cloud PaaS environments. The drivers behind this migration reflect core enterprise concerns about security, cost, and performance. Security considerations motivate 58% of the planned migrations, demonstrating that data protection remains top of mind even as organizations seek to leverage cloud capabilities. Cost savings drive 40% of migration decisions, and concerns about scalability, flexibility, performance, and latency each influence 28% of the organizations. This migration pattern suggests that enterprises are seeking to balance the benefits of cloud-native architectures with the control and security of private environments. Private-cloud PaaS solutions offer the standardization and automation benefits of public-cloud platforms while maintaining the governance and compliance capabilities enterprises require. Building AI-native organizations The survey results show that successful AI adoption requires more than technology investments — it also demands organizational transformation toward AI-native operating models. This transformation builds on established patterns — including cloud-native architectures, microservices designs, and DevOps practices — but extends these concepts to encompass AI-specific requirements. Success requires substantial up-front investment in IT modernization, with particular emphasis on developer experience improvements, platform standardization, and

Keith: Let’s check out the demo. What do you have for us with Omni? Steve: This is Omni’s initial screen. It may look like most Kubernetes cluster managers, but I’m going to show how easy it is to deploy Kubernetes on bare metal from scratch. You download installation media customized for your environment — AWS, Google Cloud, Akamai, bare metal, VMware, even Raspberry Pi. Just pick your platform and enable system extensions — for example, if you want GPU drivers, just check a box. Want secure boot? Done. When you download the image, it’s preconfigured with kernel parameters that create an encrypted tunnel back to Omni. Boot from that image (ISO, AMI, etc.), and once the machine boots, it appears in Omni as “available.” Now I can create a new cluster. Let’s say I want this machine to be a control plane node. Omni has intelligence built-in. If you try to create a single-node cluster, it will ask: “Do you want to override the default and allow workloads on the control plane node?” If I try to set up two control plane nodes, it warns: “That’s a bad idea. You need three nodes for quorum with etcd.” So I’ll go with three control plane nodes and one worker, all on AWS. That’s it. I click “Create Cluster.” Omni sends commands through the encrypted tunnels, each machine pulls down the OS and Kubernetes version, and the control planes and workers configure themselves accordingly. source

This creates interesting boardroom dynamics. When a 35-year-old VP of Operations presents AI-generated supply chain optimization recommendations that conflict with the 55-year-old CFO’s financial projections, the debate isn’t just about data; it’s about fundamentally different approaches to decision-making authority. The future of executive decision-making The traditional CFO dashboard with its monthly financial reports, quarterly forecasts and annual budget cycles represented an era when information scarcity made centralized data interpretation both necessary and valuable. I found that operational AI has created information abundance. The challenge is no longer accessing data or generating insights; it’s synthesizing multiple streams of algorithmic intelligence into coherent strategic decisions. Just as I concluded in my ERP paradox analysis, the challenge isn’t that CFOs are ineffective leaders; their expertise in financial governance remains indispensable. Rather, it’s that their natural focus on control and compliance can inadvertently constrain the operational agility that AI-driven businesses require. For operational AI to unlock its full potential across the enterprise, organizations need the same collaborative shift I advocated for ERP implementations: empowering neutral leaders, whether CAIOs, CTOs or cross-functional intelligence teams guide AI adoption while ensuring CFOs maintain their essential stewardship role. However, the CFOs who will thrive in this environment won’t be those who resist operational AI or attempt to maintain traditional gatekeeping authority. There’ll be those who embrace their evolution from data interpreters to intelligence orchestrators, helping their organizations navigate the complexity of multiple AI systems while ensuring that algorithmic insights support sustainable financial performance. Emerging companies that are built with AI capabilities from the ground up often have fundamentally different organizational structures, with chief AI officers or VP-level roles focused on algorithmic decision-making that report directly to the CEO, bypassing traditional financial hierarchies entirely. The dashboard paradigm is giving way to something more dynamic: real-time, multi-dimensional intelligence that flows through organizations in ways that would have been impossible just a decade ago. The critical question remains the same: Will your company double down on entrenched control mechanisms, or will it courageously build a forward-looking culture where every function is empowered with real-time, AI-generated insights? The executives who understand this shift and adapt their leadership accordingly will define the next era of corporate decision-making. This article is published as part of the Foundry Expert Contributor Network. Want to join? source

In today’s experience-driven economy, customer experience (CX) is no longer a support function; it’s a core driver of competitive advantage. Businesses with exceptional CX across every touchpoint satisfy their customers and turn them into brand advocates, often outperforming competitors who might have larger marketing budgets. Positive words of mouth amplify growth significantly, while a single negative experience can unravel trust and loyalty at scale. “As expectations rise and markets saturate, the real battleground isn’t price or product — it’s customer experience.” Customers in the AI era are well-informed, highly demanding, have low patience, and often vocal about their experiences. According to the IDC Digital executive sentiment survey, 55% of organisations in the Middle East, Türkiye, and Africa (META) consider customer behaviour data as critical for their data architecture. The roadblocks to CX: why many strategies fall short While the importance of CX is widely acknowledged, business leaders should understand that they cannot consistently serve all types of customers, all the time. Choosing the right customers is essential. Focusing on those who align with your value proposition enables better experiences and long-term growth. Serving the wrong customers can strain resources and harm brand equity, often at a greater cost than meeting their short-term demands. A few challenges are listed below: Fragmented customer data makes it difficult to build a unified view of the customer journey. Without real-time insights across touchpoints, businesses struggle to personalise interactions or anticipate needs. Siloed teams and inconsistent processes hinder collaboration between marketing, sales, service, and product functions, leading to disjointed experiences for the customer. Internal resistance to change often derails even the most well-intentioned CX initiatives. Whether it’s reluctance from leadership to invest in long-term experience transformation or frontline teams clinging to legacy processes, cultural inertia can be a silent killer of progress. Overcoming these barriers is essential for organizations aiming to lead through customer-centric innovation. While the barriers to CX success are real, leading businesses have found strategic ways to turn these challenges into competitive advantages. Here are a few best practices that top performers consistently embrace: Leverage AI for personalised experience in the Generative AI (GenAI) era: GenAI enables companies to offer hyper-personalised experiences, enhance employee productivity, and drive collaborative intelligence. By leveraging AI-driven insights, businesses can better understand customer needs and preferences, leading to more tailored and impactful interactions. Embedding CX into the organisational culture: Delivering consistent and meaningful experiences requires leadership buy-in and a collective effort to embed customer-centric values into the fabric of the organization. A culture that prioritizes the customer in every decision and action leads to more consistent and meaningful interactions across all touchpoints. Build cross-functional teams and focus on upskilling: Successful CX implementation requires collaboration across various departments, including marketing, sales, customer service, and IT. Building cross-functional teams ensures that everyone is aligned towards the common goal of delivering exceptional customer experiences. Additionally, upskilling employees with the necessary CX competencies, such as data analysis and customer journey mapping, is essential for driving continuous improvement. Measure and optimise CX strategies: To ensure long-term CX success, it is essential to track important metrics within a well-defined measurement governance framework. Key CX metrics such as Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), and Customer Effort Score (CES) provide valuable insights into the effectiveness of CX initiatives. Regular monitoring and optimisation of these metrics help businesses identify areas for improvement and make data-driven decisions. The path to CX leadership isn’t about overhauling everything overnight; it’s about knowing where to focus first, and how to align your people, processes, and platforms around what truly matters: “the customer”. Get your guide to customer experience excellence when you download the IDC eBook, “The CX Blueprint: Implementing Game-changing Strategies for Market Dominance”.  source

From a small mechanical workshop in 1945 to a global leader of industrial connectivity solutions, today HARTING provides connectors, network components, and cable harnesses for machinery, transportation, and telecommunications.  As a pioneer of sustainable industry practices for decades, HARTING realized some of its practices were lagging behind. HARTING’s manual calculations to provide CO₂ product data for customers was slow and costly, so they did it for only a few products. However, HARTING has over 100,000 products, and CO₂ data has become a critical factor to include in bids to customers.  In addition, HARTING needed to comply with EU regulations for net zero in 2030. To do so, the company needed to provide verifiable, product level carbon footprint data on all its products.    Providing carbon footprint data on products is critical for competitive bids In 2024, HARTING set out to find a reliable and scalable way to calculate carbon emissions data across 100,000 products and 14 factories while continuing to meet customer and supplier sustainability expectations. Before they could provide CO₂ data, however, they needed to accurately measure the carbon footprint of raw materials and supplies, including energy consumption.  The goal was to implement a cost-effective solution that would automate the product-level measurement of emissions to verify CO₂ footprints and, thereby, support competitive bids to customers, demonstrate compliance with EU regulations, and help HARTING regain its  status as a sustainability pioneer. Complying with EU Corporate Sustainability Reporting Directive (CSRD) transparency The European Union (EU) has established ambitious sustainability goals for the industry, including sustainability performance reporting in an accounting-based format. The EU CSRD, as well as the Digital Product Passport (including the Battery Passport), require more transparency and data reporting in 2026 to track product-level sustainability.   HARTING needed to comply with these regulations as well as figure out how to achieve carbon neutrality (Net Zero) by 2030. While the company  uses a high percentage of recycled plastic packaging, in order to comply with the EU standard, it must be able to measure and report on the carbon impact of all its plastic packaging.  Verifiable, granular data for ‘one-click’ carbon footprint tracking across 100K products To complement its SAP S/4HANA application landscape and maintain established green production and renewable energy processes, HARTING chose the SAP Sustainability Footprint Management solution. This gives HARTING a data-granular, verifiable “one-click” solution that is integrated with SAP S/4HANA and HARTING’s SAP business applications.  Instead of relying on manual calculations using ERP and vendor data to determine the carbon footprint of just a few products, the new solution calculates CO₂ data almost instantly across thousands of products.  “The key to communicating our CO₂ emissions transparently and recognize potential for reduction, lies in the automated calculation and the granularity of the data provided by SAP Sustainability Footprint Management,” explains Dr. Stephan Middelkamp, general manager, quality and technology, HARTING Technology Group. SAP Sustainability Footprint Management provides traceability through every step of the Product Carbon Footprint (PCF) calculation by using real-time production data and by mapping the emission factors of purchased materials by access to a variety of databases directly in the solution,” says Nadine Frank, sustainability controller, HARTING. “In the end this enables us to calculate a very high number of PCFs with a click.” AI modeling enhances mapping of emission factors to ERP data to improve accuracy  The solution leverages AI to automate the mapping of emission factors to business processes and products. AI emission factor modeling – a feature of the SAP Sustainability Footprint Management solution – uses AI to streamline and enhance the mapping of emission factors to ERP data, reducing manual effort and improving accuracy in carbon footprint calculations. In order to calculate accurate carbon footprint data, the solution integrates with SAP S/4HANA, supplier data, and third-party data sources. For instance, HARTING can import and manage emission factors from various sources, including environmental life cycle assessment (LCA) databases and supplier data.  This comprehensive approach enables HARTING to automate data collection and emissions calculations at a scale that can achieve CO₂ transparency throughout its entire supply chain.  Back on track to meet 100% of sustainability goals and EU Net Zero by 2030 HARTING stands out as a sustainability pioneer, leading the industry with one-click CO₂ tracking. The new solution went live in 2024 across three factories in Germany and Switzerland, with plans to expand operations to all 14 factories (and 100K products) by 2026.  Now, real-time, granular data assures customers’ choice of HARTING’s environmentally sustainable “GreenLine” label products — those made with renewable materials — can offer up to a 70% reduction in CO₂ emissions. And, when it comes to its own carbon footprint, HARTING can measure and ensure that 85% of its packaging material is recycled.  With these capabilities, HARTING is on track to meet its 100% sustainability goals by 2030. For these future-minded accomplishments, HARTING Technology Group was selected as a winner of the SAP Innovation Awards for 2025 in the Sustainability Hero category. Learn more about how they calculate product carbon footprints in their pitch deck. source

Artificial Intelligence (AI) is revolutionising the way enterprises innovate products, address customer needs, and boost productivity. This transformation is enhancing their competitiveness not only on a global scale but also specifically in the Middle East, Türkiye, and Africa (META) region. In 2025, 40% of the core IT budget among the world’s 2,000 largest firms is expected to be spent on AI initiatives, driving a double-digit rise in product and process innovation rates. The META commitment to AI However, to fully harness the potential of AI and achieve scalable value realisation, enterprises must build robust foundational capabilities while systematically navigating the risks and complexities of organisation-wide AI implementation. Encouragingly, approximately 60% of organisations in the META region have already prioritised AI investments as a means of transforming their businesses (IDC EMEA Digital Executive Sentiment Survey 2024). Additionally, more than 80% of the organisations have been either investing in Generative AI (GenAI), doing proof of concepts, or exploring potential GenAI use cases (Source: IDC Data and AI Survey, 2024). This promising trend is propelled by key factors such as enhancing and expanding customer experiences, accelerating the introduction of digital products and services, and driving research and development for faster product design and innovation. The framework for becoming AI-ready Nevertheless, understanding and addressing AI-related challenges are pivotal for success, especially as GenAI is increasingly infused into various business processes and applications. These challenges encompass a range of issues, including security and privacy concerns, a shortage of skills and expertise, the cost of deploying and managing AI solutions, data quality issues, model bias, ethical considerations, and infrastructure readiness. Crucially, many organisations lack a structured, enterprise-wide AI strategy, which is fundamental to succeeding in their AI journey. To proactively overcome these obstacles and achieve AI readiness, enterprises should adopt an AI Readiness Framework. This framework identifies key foundational elements for success, both before embarking on the AI journey and throughout its enablement, ensuring measurable business impact. The framework’s essential pillars are outlined below. Key activities: Establishing a comprehensive, organisation-wide strategy, implementing a responsible AI policy with governance mechanisms, designing modern data architectures, and empowering employees through training. Core technologies: Gaining a thorough understanding of available data and the capabilities of core AI technologies. Infrastructure and platforms: Deploying cloud-native digital infrastructure for compute-intensive AI workloads and enabling data and AI platforms for AI lifecycle management. Trust and oversight: Implementing a robust trust and oversight programme to address transparency, bias, regulatory compliance, governance, and ethical considerations in AI. Use cases: Prioritising use cases with measurable outcomes and ensuring continuous value delivery across their lifecycles. The rapid pace of technological advancement demands close attention, particularly to the emergence of AI agents, which represent significant innovations across industries. These digital assistants that work reactively and cooperatively with humans to provide productivity and efficiency gains, along with AI advisors that offer enhanced insights and recommendations to organisations, have quickly become must-haves in modern software. AI agents independently perceive, evaluate, and act upon data to help organisations move toward more integrated and autonomous work practices. Since the beginning of 2024, technology suppliers and buyers have started exploring and investing in AI agents. IDC predicts that by 2027, 40% of the world’s largest 2,000 companies’ knowledge work will be transformed as agentic workflows reshape task delivery and performance, leading to a doubling in productivity. Although adopting such emerging technologies presents challenges, organisations prepared to seize such opportunities will enjoy a substantial competitive advantage in achieving their goals. Success in the AI journey also hinges on cultivating a robust partner ecosystem. Partnerships driven by ecosystem collaboration are crucial to successful AI initiatives, as they combine diverse expertise and resources from both technology and non-technology sectors. Technology partners supply critical infrastructure, tools, and support, empowering organisations to implement advanced AI solutions effectively. Additionally, organisations seeking to become data- and AI-driven will require technology service providers that serve as trusted partners, with comprehensive knowledge of both business and technology requirements, to guide them through this transformative journey. Meanwhile, non-technology partners—such as industry stakeholders, academic institutions, and policymakers—foster innovation through collaborative opportunities. To gain insights into how organisations can initiate and advance their AI journey, download e& enterprise’s Infobrief titled “Decoding AI: A CXO’s Handbook for Success.“ source

To become truly AI-first, your business needs a dual strategy of optimization and innovation source