CIO CIO

Editorial Director CIO and CSO ASEAN Foundry Estelle leads CIO and CSOonline coverage across 10 ASEAN diverse markets, focusing on the evolving priorities of IT leadership roles including CIO, CTO, CISO, CDO, and CPO. Her reporting will address the most pressing concerns facing today’s technology executives, from digital transformation and change management to the strategic adoption of public, hybrid, and private cloud (IaaS, PaaS, SaaS), AI, Generative AI, automation, and intelligent enterprise software. source

That means a good overall compensation plan, but it also means being recognized for the value they bring to their organization, McCarley says. That may mean being asked to present, or being thanked publicly, or getting exposure to other departments on special projects, an opportunity that demonstrates the company’s trust in them and their work. Caiafa doles out quarterly bonuses, posts the names of workers doing top work on leaderboards, and offers prizes for winners of quarterly hackathons — all of which he has found keeps workers engaged. And to encourage workers of all capabilities, he lets anyone with an idea pitch to judges in a shark tank-type competition, with winners getting to develop a proof of concept. “These things really highlight achievements,” he says, noting that while top talent may naturally get the most wins, the programs encourage all workers to strive. 6. Offer targeted training Ensono has a robust training program, which supports employee retention at all levels. But Lobo says retaining top talent often requires a more targeted approach where their managers direct them into career development programs that can really accelerate their professional advancement. “We focus on the individual’s development and make sure we have the right individual going to the right programs,” he says. source

AI is the latest innovation transforming the tech industry and it’s increasingly impacting daily business operations everywhere. As a result, IT leaders are quickly finding themselves in the position of heading organizational change around AI adoption. It’s a daunting task to get an entire organization on board with widescale change, but being equipped with the right knowledge will help to smoothly handle the transition.   Skills surrounding AI have exploded in demand, yet it’s a relatively new skill, especially in the widespread applications we’re seeing today. From chatbots and work management tools, to data-driven decision making, IT leaders are juggling a lot of change. To keep from getting overwhelmed, utilizing what’s best to implement AI are key assets in any CIO’s arsenal, and these six courses can help sharpen your skills for this emerging era. LinkedIn Learning The LinkedIn Leading With Generative AI: Master Change Management for Success is a succinct course that offers 15 modules focusing on how to navigate AI change management, and maintain stability and growth in the organization. There’s a focus on the Faster Framework, the historical context of AI, and how the introduction of AI compares to past technological advances and workforce adaptations. This course also qualifies for PMI Professional Development Units (PDU), so it’s a smart choice for any leaders maintaining a PMI certification. This is a quick course that can be completed on your own time, and can easily fit into a busy schedule if you’re just looking to brush up on the basics. source

Without strong safeguards against threats like adversarial attacks, data misuse or intellectual property theft, large-scale adoption becomes difficult. No one wants to deploy an AI tool only to discover later that it leaked sensitive data, exposed proprietary IP or became a new attack surface for adversaries. Beyond the immediate operational and security fallout, there’s also the risk of lawsuits over data misuse, regulatory penalties or contractual breaches. For many organizations, the uncertainty of those risks is enough to slow or even halt adoption until stronger safeguards are in place. In fact, a recent Forrester report shows that data privacy and security concerns remain the biggest barrier to generative AI adoption. Building trustworthy AI requires attention to privacy, cybersecurity and AI governance. AI isn’t just a race for speed; it’s a race for trust AI isn’t just about faster chips, bigger models or who gets to market first. It’s about whether enterprises, governments and individuals feel confident enough to use it in the first place.  The hesitation around DeepSeek, a Chinese artificial intelligence system, illustrates this point, as many potential users and governments remain wary due to unresolved privacy and cybersecurity concerns that undermine trust in the system and threaten national security. We don’t have to speculate about what happens when trust is ignored. The crypto industry offers a cautionary tale for revolutionary technologies. Without regulation tailored to the unique nature of blockchain, the space was plagued by cyberattacks, privacy failures, security breaches and widespread illicit use. Now, as regulators begin clarifying the legal landscape, such as by requiring regular public disclosures and compliance with anti-money laundering and export control laws, many in the industry argue that digital assets can finally gain legitimacy and move into the financial mainstream. source

More than 10,000 of the world’s 34,500 large enterprises—nearly 30%—have already committed to becoming their own data and AI platforms. And within just three years, over 95% of global enterprises say they want to do the same, regardless of where they operate—from Japan and India to the US, EMEA, and MEA. This isn’t just a regional trend. It’s a global imperative. And it’s accelerating. While the first three waves of digital transformation took a decade or more to reshape industries, the agentic revolution may unfold in less than three years. That’s not just speculation—it’s the view of 2,050 executive leaders across 13 economies surveyed in our 2025 global study. Their consensus is clear: the time to act is now. This is a tale of two futures. For the 13% of enterprises already getting it right, the advantages are profound. They report five times the ROI of their peers and are deploying agentic and generative AI capabilities at twice the scale. Perhaps more tellingly, these leaders are 250% more confident in their ability to remain at the forefront of their industries over the next three years. Their platform choices are not just strategic—they’re producing measurable economic outcomes. Meanwhile, the other 87% aren’t necessarily struggling—but they are behind. Their return on AI investment is, at best, a fraction of what the leaders are generating. At worst, they’re five times less productive, still reliant on legacy infrastructure, and unable to meet the rising expectations of customers, regulators, and shareholders. The conviction to move toward sovereign data and AI infrastructure is shared across the globe. Leading enterprises in Scandinavia, the United States, Saudi Arabia, the UAE, and Japan are rapidly building their own platforms. And among the 13% of high performers, the highest concentration of success stories is emerging in Germany, Saudi Arabia, the UAE, and the US. These regions are showing us what’s working—and why. Rule one: Mission-critical sovereignty enables scalable agentic AI What distinguishes the 13% is not just what they do, but how they think. Nearly 90% of these organizations share one defining mindset: they see sovereignty over their AI and data as mission-critical. They believe that all their data—structured and unstructured—must be accessible in real time, regardless of where it’s stored. They prioritize eliminating silos, enforcing compliance, and creating an environment where data and AI can work together seamlessly. This belief isn’t theoretical—it’s validated. In our research, across more than 15,000 simulations that combined 15 agentic and GenAI capabilities with seven economic metrics and 35 data infrastructure variables, the organizations that focused on real-time sovereign access and control consistently outperformed. Statistically, this pattern of success showed an exceptionally high predictive score (CHAID and regression: 0.982), confirming that this mindset isn’t just useful—it’s essential. These leaders aren’t treating AI and data as separate disciplines—they’ve fused them into a single, sovereign engine that fuels operations across more than a dozen business areas. They’ve built platforms that don’t care where the data lives because the design ensures access, security, compliance, and scale. And as a result, they’re moving faster, innovating more, and outpacing competitors. Every day, an average of 58 new enterprises begin this transition. That’s the scale of change we’re witnessing. The question for those not yet moving is simple: do you have the infrastructure—and the mindset—to thrive? Rule two: Compliance, observability, security, and agility must be designed in Sovereignty isn’t just about ownership. It’s about architecture. The most successful organizations don’t just talk about sovereignty—they’ve engineered it across four interdependent layers: compliance, observability, security, and agility. They’ve built environments where AI can interact with the real world without compromising data integrity or breaching regulatory obligations. Financial forecasting agents, for example, need to pull from CRM, customer experience, sales funnel, ledger data, and external benchmarks. Edge agents may need to ingest signals from vehicles, sensors, or telecom infrastructure. Sovereign AI requires the infrastructure to support all of this in a unified, compliant way. Among the deeply committed, over 40% operate on a truly hybrid infrastructure—one that supports full observability across environments, from cloud to on-prem, and delivers centralized control through a single pane of glass. This allows them to respond to competitive threats, regulatory changes, and customer demands simultaneously. They’ve reduced reliance on global infrastructure providers, built flexibility into their architectures, and are realizing more than double the innovation and efficiency gains of their peers. These are not abstract advantages. These are measurable business outcomes that compound over time—what many refer to as the sovereignty flywheel. When you have unified control, innovation becomes faster. When compliance is built in, agility increases. When observability is complete, optimization becomes constant. And when security is inherent, trust becomes an asset. This is why the sovereignty movement is growing so quickly. It’s not just the right thing to do. It’s the profitable thing to do. Redefining success in the agentic and gen AI era What’s happening is a global migration—not just to AI—but to sovereign AI. More than ever, enterprises are realizing that just “having” AI isn’t enough. Real success comes from owning the infrastructure, the data, and the intelligence end-to-end. Open source platforms like PostgreSQL are powering this shift. Flexible, hybrid, and battle-tested, Postgres is enabling enterprises to unify AI and data across environments, accelerate their sovereignty strategies, and build resilient systems that scale. Because at the end of the day, just anything isn’t data and AI sovereignty. Building the right architecture, with the right mindset and the right principles—that’s the definition of success in a world where AI is no longer optional. The race is on. The winners are emerging. And the rules are now clear. To learn more about EDB, visit here. source

Esteemed voices in the AI community are speaking of a near-term horizon where AI significantly reshapes the job market, potentially leading to major reductions in white-collar employment.[1] It’s a conversation that understandably stokes anxiety and inevitably begs the question: Are cybersecurity jobs also at risk of being automated into obsolescence? As someone who has dedicated his career to AI, navigating AI’s evolution from theoretical constructs to the powerful agents we see today, my answer is emphatically “no.” The narrative for cybersecurity is different — and far more empowering. AI will not lead to obsolescence; it will lead to evolution. In fact, it already has. While it will automate routine tasks, it will simultaneously elevate the demand for professionals who understand uniquely human skills. Therefore, the future of our field will be defined by enduring archetypes: The strategic risk translator, who negotiates the critical trade-offs between risk and operational needs. The AI-augmented hunter, whose intuition and creativity outmaneuver intelligent threats. The AI governance specialist, who provides essential human judgment and accountability for these powerful new systems. AI as the great enabler, not the great replacement Before we dig into AI’s impact, let’s first acknowledge a foundational truth: Cybersecurity is an incredibly demanding discipline. For decades, even with significant advancements in automation, we’ve grappled with a persistent shortage of experienced professionals capable of navigating its complex landscape. The sheer difficulty of doing cybersecurity right, and staying ahead of determined adversaries, means that human expertise remains an invaluable and often scarce resource. This isn’t a gap that conventional automation alone has been able to close. AI arrives into this challenging environment as the most potent amplifier for human ingenuity, not as its replacement. Its role is to augment our capabilities, allowing us to achieve more, faster, and with greater precision. Imagine providing your security analysts with an intelligent partner that tirelessly sifts through petabytes of data, identifies the signals of a sophisticated attack, and prioritizes critical alerts with superhuman speed. Here, AI acts as a force multiplier, enabling teams to achieve what was previously impossible — coverage at both scale and depth. Instead of choosing between lower-resolution monitoring across the landscape or deep analysis on a few critical assets, your teams can now apply forensic-level scrutiny everywhere. This enables your teams to manage the massive queue of potential issues and protect the entire organization with unprecedented speed and precision. AI is poised to revolutionize how we cultivate talent. For new team members, AI can act as an intelligent apprentice or a personalized mentor, guiding them through complex tasks and organizational specifics that once took months — if not years — to fully comprehend. This dramatically shortens the learning curve, enabling new hires to become effective contributors much faster and reducing the overall cost and time associated with talent development. For seasoned experts, AI offers a new dimension of insight. It can synthesize global threat intelligence in real time, identify emerging attack vectors previously obscured, and help veterans apply their deep expertise across an ever-expanding digital attack surface. The expanding digital universe: More to secure, not less While AI undoubtedly boosts efficiency, the digital frontier we must defend continues its inexorable growth. Every new technology adds layers of complexity, but none more so than AI itself. This creates a fascinating paradox: The tool we use to manage scale is simultaneously creating a vast new continent of risk that requires securing. The discipline of securing AI systems is becoming deeply interwoven with traditional cybersecurity. It’s difficult to fathom a future where we entrust the security of these powerful, autonomous agents exclusively to another AI. As these systems proliferate, the need for human oversight, strategic judgment, and ethical governance will persist and intensify. AI, therefore, doesn’t shrink the workload. It helps manage a rapidly expanding one while creating an entirely new domain that demands enduring human talent. Judgment, accountability, and the art of security For all its analytical power, AI is not a CISO. It cannot replicate the uniquely human attributes essential for true security leadership. Yet, who is responsible when an AI makes a mistake or when the path forward is clouded by ambiguity? The accountability for that decision rests firmly with people. And this isn’t a responsibility reserved for the C-suite alone; it is distributed throughout the entire security organization. It’s present in the countless judgment calls made every day — from the frontline analyst validating an AI-flagged alert to the security engineer assessing the business risk of an automated response. In an AI-powered world, every security professional becomes a crucial steward of accountability and context, a role that technology enhances but cannot replace. Moreover, security is rarely a binary decision. It often involves negotiating trade-offs with the business, balancing risk mitigation with operational needs and strategic goals. AI can inform these decisions with data, but it cannot navigate the nuanced discussions or make the value judgments inherent in these tradeoffs. Human interface: Where people meet peril — and respond We must also remember that cybersecurity is not solely a battle of machines. Many of an organization’s most significant vulnerabilities lie at the interface between humans and technology. Social engineering, business email compromise, insider threats, and ensuring data loss prevention and compliance are fundamentally human problems that require human understanding, training, and effective communication as part of the solution. And when an incident does occur, managing the response in a calm, effective manner requires working with people who are often under immense stress. This demands empathy, integrity, trust, relationships, and leadership — qualities that are, for the foreseeable future, uniquely human. Enduring adversarial dance Finally, we operate in an inherently adversarial world, which is not a problem technology, even AI, will “solve” in perpetuity. As defenders get better and leverage AI, attackers will inevitably respond in kind, developing their own AI-driven tools and techniques to attack at scale or craft increasingly sophisticated exploits. This new AI-driven landscape is inherently adversarial. As defenders, our strategic advantage comes from leveraging comprehensive data within a well-architected

I recently attended a series of marketing-centric webinars hosted by industry-leading enterprise data cloud vendors, all proclaiming that the business intelligence (BI) dashboard is unofficially dead and that Generative AI-based cloud data platform interfaces would provide a renaissance, if not a clear path to redemption for the future of business intelligence. Further, some recent architectural efforts I’ve been associated with also suggested that many large enterprises in multiple verticals struggle with data operations and the need for dynamic, outcome-driven interaction with data beyond investments in data lake houses, medallion or lambda architectures, and semantic models. All of these are foundational to making BI useful. After some reflection, I was forced to consider the very real possibility that the BI landscape had not really diversified significantly in at least a decade. To be fair, industry leaders like Microsoft and Salesforce (Power BI and Tableau respectively) according to Gartner, have a significant installed base in the BI client arena and provide tooling that is sophisticated and evolved enough to engage business decision makers with compelling data and data visualizations yet, as I noted in Data trust and the evolution of enterprise analytics in the age of AI, 58% of business decisionmakers rely on gut feel or experience rather than data and information. While much of the issue is data trust, a larger portion is also based on the need for democratized inquiry, interaction, discovery and most of all, time-to-execution. Creating a semantic layer that is chained to static dashboards doesn’t really provide a significant advantage for anyone who needs to operate at the speed of business. The new basis of competition and market differentiator is not just time-to-insight, it is also time-to-execution. According to Besemer Venture Partners, unlocking data with a path to execution as opposed to aggregating and storing data means moving from “systems of record to systems of action.” So, what is the path forward to enabling both insight and action? source

Salesforce AI Research on Wednesday announced three advancements geared toward helping customers transform into agentic AI enterprises. They include a simulated enterprise environment framework for testing and training agents, a new benchmarking tool to measure the effectiveness of agents in enterprise use cases, and a new Data Cloud capability for autonomously consolidating and unifying duplicated account data. Simulation testing and training for AI agents Salesforce AI Research has built on the previously released CRMArena, an environment for testing single-turn B2C service tasks, with the new CRMArena-Pro, a simulated enterprise environment framework that enables the testing of AI agent performance in complex, multi-turn, multi-agent scenarios including sales forecasting, service case triage, and Configure, Price, Quote (CPQ) processes. source

We’ve entered the intelligent age, in which vast quantities of data are being put to work with AI to improve products, boost efficiency, enhance healthcare and transportation, and provide more personalized experiences. Emerging AI use cases like autonomous vehicles, healthcare monitoring, smart manufacturing, and precision farming promise to make our lives and our world better. As GPUs become more affordable and accessible, AI is likely to become ubiquitous, with businesses of all sizes taking advantage. But to achieve all the benefits of AI-driven insights and efficiencies, companies need the right IT infrastructure in the right places. For cutting-edge AI use cases, real-time data processing at low latency is a must. And that requires processing power in edge locations, close to where the data is generated and used. IT models that rely too heavily on public cloud simply can’t meet the real-time processing requirements of many AI applications. Moving data to far-off public cloud data centers not only increases latency, but it can also introduce data privacy and security concerns. To succeed with AI, companies need interconnected hubs in edge locations to complement their public cloud use. A robust hybrid multicloud infrastructure that’s well connected to global ecosystems in edge locations will make businesses unstoppable in the AI-powered future. Data is forever, but who owns it is not Data is now being gathered at an unprecedented scale. Every day, hundreds of millions of terabytes are created worldwide. AI and machine learning are already demonstrating how rich with possibilities that data is. At the same time, organizations are facing numerous data-related risks: Data breaches Changes of ownership through mergers, acquisitions, bankruptcy, and private equity takeovers A growing number of data regulations globally Proprietary and sensitive data that needs to be kept private Companies are therefore scrutinizing how they can best protect their most valuable resource. In industries like healthcare and life sciences, financial services, and government and defense, highly sensitive data underpins organizational research, development, and competitive advantage. It’s therefore crucial to process data locally in a secure environment where companies have control of data and can minimize exposure to external threats. If data is forever, and it’s the differentiator for your business, the maintenance, privacy, security, and provenance of that data are essential. And that may mean rethinking where you put it and how you transport it. The problem with going all in on cloud For a time, companies were laser-focused on cloud adoption. Enterprises seemed to be racing to move as many workloads to the cloud as possible. And it made sense at the time: Businesses were trying to reduce risk by increasing the flexibility and scalability of their IT infrastructure. They wanted to break free from 60-month cycles of buying gear and get access to things like high-performance computing. Over time, many have learned that going all in on cloud didn’t deliver the cost savings and risk reduction they’d hoped for. Even companies that were born on cloud have begun to realize that as they mature, they need greater control of their data. Many are undergoing cloud rebalancing initiatives. In the era of AI, organizations are now working with so much data and need so much real-time processing that a cloud-first approach to IT is no longer the best strategy. New AI use cases and opportunities require edge The world is in an interesting historical moment with AI. We’re in the early stages of what’s sure to be a transformative technology. The internet was groundbreaking, but it took decades to get from the initial idea of hypertext in the 1960s to HTTP and the World Wide Web in the 1990s. The world of AI is evolving very quickly, but we’re still in the beginning of what’s likely to be a renaissance in how we live and work. New AI models are being trained all the time, and they’re operating in all kinds of places: in smartphones, cars, wearable devices, self-checkout kiosks. IoT sensors are collecting data everywhere, and 5G networks are expanding to transport it. As an industry, we’re all still trying to figure out the best use of the technology, with clever and novel ideas landing in production every day. Companies already recognize AI’s potential to help them solve their problems: In healthcare, remote patient monitoring and AI-powered diagnostics are improving patient outcomes. In manufacturing, digital twins and supply chain management solutions are transforming factory operations. In transportation, self-driving vehicles and fleet management solutions are improving safety and efficiency. In smart cities initiatives, traffic management and public safety services are reducing congestion and accelerating emergency response. As more AI wins accumulate, companies will see that machine learning is the only way forward. But these AI applications require real-time data processing close to where data is generated and used. For data-driven decisions, companies can’t afford to transport data—even at the speed of light—to a far-off cloud or on-premises data center. They need infrastructure at the edge. AI requires an infrastructure rethink To meaningfully and securely reduce latency to support the response times of many AI use cases, organizations are rethinking their infrastructure strategy to include a mix of cloud and edge. There are multiple ways for companies to meet their edge computing needs, using cloud availability zones, company-owned sites and branch offices, or colocation facilities. Regardless of how they do it, they’ll still need access to public clouds and a robust network that can securely connect all their resources. In fact, the network is crucial for AI, because it doesn’t matter how sophisticated your AI equipment is if you’re not plugged into a good network. If you have sensitive or highly regulated data, it’s even more important that you have private connectivity options. At Equinix, organizations can connect their private infrastructure with all the leading clouds, network service providers, and other IT services using any connectivity option they want, whether that’s public or private, physical or virtual. With 260+ data centers in 74 markets, we can help you find your edge, wherever in the world it might be. And you can continue to have that cloudlike infrastructure

While managing more than 1.5 million annual service tickets in approximately 40 countries, Bosch Power Tools — leveraging its deep customer service expertise and internal digital transformation strategy — identified a strategic opportunity to optimize routing efficiency. At the time, nearly 50% of the service tickets — or customer requests — were misrouted, requiring manual intervention. Since customers depend on the long-established company to handle inquiries with expertise and offer solutions tailored to each client’s specifications, Bosch was looking to increase customer satisfaction and repeat purchases. In addition, the company sought to reduce lead times and IT maintenance costs. Fortunately, as a frontrunner in innovation, the company had already taken steps to rejuvenate its corporate AI strategy, paving the way for forward-thinking solutions and groundbreaking advancements. Consequently, the new ticket handling system would revolutionize the service environment, creating a personalized experience for customers and enhancing responsiveness and satisfaction. AI co-pilot Bosch Power Tools appeals to both professionals and DIY enthusiasts looking to successfully complete projects either on the job at a construction site or in and around their home.  The upgraded platform features an AI extension — an “intelligent knowledge base” co-designed by Bosch’s CX and IT teams in collaboration with SAP, incorporating Bosch’s domain-specific service data to address complex customer inquiries with precision. This capability marks a turning point in how the company manages service, fostering innovation, reducing complexity, and reimagining productivity. Since 1979, Bosch has built a strong relationship with SAP, the multinational software leader. This bond grew even deeper in 2023 when Bosch and SAP agreed to collaborate on shared AI initiatives.  With that strategy in place, the revised ticket handling system was formulated — utilizing agentic AI, particularly SAP Joule — designed to augment productivity and automate complex processes within SAP’s cloud applications, as well as SAP Customer Experience solutions. The Joule agent was developed to dilute the routing challenges of the recent past, helping customer service specialists increase their understanding of user intentions while providing tailored assistance in each customer’s native language. Revving the innovation motor As soon as the upgraded system was tested in 2024, Bosch management noticed an incredible accuracy: More than 90% of tickets are now correctly classified.  Within seven months, the company launched its first “go-live” at a flagship contact center in April 2025, with phased global rollout underway. As planned, the complex and conflicting workflow rules have been replaced, removing manual efforts in call centers, resulting in a seamless, more precise ticket routing, and guiding customers with intuitive, natural language directions. “Integrating the AI Classification Agent replaced hundreds of routing workflows with a single prompt, significantly improving accuracy and resolution times,” said Florian Haustein, head of Digital CX for Bosch Power Tools. Using the digital assistant, call center agents can provide ticket summaries of an entire conversation in ten lines, along with bullet points about customer queries and solutions offered and articles on how to address similar questions in the future. Free from the labyrinth of IT-managed workflow rules, business teams are being empowered with greater self-service capabilities. Challenges that once took hours to solve can be remedied in minutes, saving up to 2,500 hours of annual contact center time and adding a significant amount of “man-free days” to the calendar of the IT department. Haustein has characterized the transformation as “a true leap forward in intelligent service management.” Extending the customer relationship For its rapid development of a streamlined routing system that has enriched both UI and UX and saved time and cost by shrinking manual involvement, Bosch Power Tools was honored as a winner in the CX category at the SAP 2025 SAP Innovation Awards. Bosch’s goal is to capitalize on the new tools to expand the system to other hotlines and extend the heightened customer relationship management (CRM) philosophy to sales teams. Observed Marcel LeCompte, Bosch Power Tools’ Lead Product Owner for CRM Sales & Service, “Our service agents report a fundamental change” that has translated to “more assured, empathetic interactions that build customer relationships beyond the immediate problem.” To learn more about what Bosch did to earn SAP’s prestigious award, check out their Innovation Awards pitch deck. source