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Another approach to answer this question is to focus on a common problem facing IT departments and then demonstrate your solution. For example, many organizations’ AI ambitions are held back by siloed applications and outdated data management practices. Manish Sood, CEO, founder, and chairman of Reltio, suggests answering, “To unlock AI’s full value, we need unified, trusted, real-time data in motion across every system, every interaction, and every touchpoint. We need to transition toward data-centric computing, where data — rather than applications — is treated as the enterprise’s most critical, long-term strategic asset.” These questions come in many variants, and there’s always the oddball question that might come up. The best practice in handling executives’ questions is to avoid letting the mouth outpace the mind. Instead, pause, think, and then respond. Give your mind a chance to understand the question’s context and recall the executive’s background and interest. When you respond, focus on the business opportunity and value, not the technical details. source

To address these issues before larger trustee migrations, MPFA said it launched a comprehensive improvement program, which included simplified biometric registration, doubling the number of support personnel, a redesigned user interface, localized employer assistance teams, and expanding physical service access points across Hong Kong’s districts. These measures, shaped by early user feedback, aim to ensure stability as the platform targets full implementation by the end of 2025. Contractors address ongoing issues While authorities focus on improvements, contractors are addressing the eMPF platform’s challenges. PCCW Solutions, the primary contractor, defended its performance, saying, “We have swiftly addressed these issues and implemented proactive measures to incorporate feedback.” The firm pledged to maintain operations with a “rigorous and dedicated approach,” deploying user-experience improvements throughout the expansion period. Subcontractor iFAST Corporation, a Singapore-based wealth management firm, clarified that its role is limited to operational and user delivery services. The eMPF Platform, a non-profit entity operated by eMPF Platform Company Limited under the MPFA, is supported by PCCW and iFAST, who were awarded a HK$4.9 billion contract in 2021 to build and operate it. source

What does it take to be a leader and recognised year on year? In short, it is a testament to customer trust, of continued innovation and leadership in delivering future-ready network solutions. For any company to be successful, it is vital to have a clear vision of the company’s roadmap, which provides strategic direction and focus. It ensures employees and all stakeholders understand the company’s goals, and paves the way for better decision-making. To be recognised as a leader repeatedly surely speaks volumes about a company’s vision, strengths, and the ability to help enterprises succeed in a hyperconnected environment. Trust as the foundation to digital success Trust underpins all forms of economic activity and forms the foundation of our society. Trust has always been built through interpersonal relationships over time. An IDC report commissioned by Tata Communications notes that “Building trust and being trusted in the digital realm are not options. If you are not trusted online, you will not be able to transact online”. In today’s digital realm, organisations must give their stakeholders the confidence that they have the necessary measures to secure any transaction they conduct. Trust drives businesses, and digital trust drives digital businesses. 12 reasons to trust Tata Communications as a leader for global WAN services Why are we the trusted partner for enterprises to help them succeed in a digital-first world, for 12 consecutive years? Here are 12 reasons why: We run the world’s largest wholly owned subsea cable network.  Together with our strategic investments in other cable systems, we operate 500,000+ km of subsea optical fibre. Enterprises can achieve their global ambition with borderless growth. We operate a global Tier-1 IP network, with customers accounting for over one-third of the world’s internet routes.  Our internet edge capacity reaches 250 Tbps. Enterprises can have shorter paths with more resilient and secure internet connections, augmented by native threat intelligence and DDoS. Our IZO™ Internet WAN is the world’s first predictable internet services with guaranteed performance. It evolves to encompass different service variants including broadband internet, 4G/5G and satellite access, spanning across 150+ countries. Enterprises global access needs can be met with comprehensive, best fit solutions. We support a software-defined cloud interconnect service, IZO™ Multi Cloud Connect, with a 100% availability SLA, connecting the global cloud giants. Enterprise cloud connectivity can be simple and agile to satisfy changing needs. Our Network-as-a-Service (NaaS) comes with comprehensive options and zero-based bandwidth on a pay-as-you-go model. It can scale up to 100 Gbps and cover Private Line, MPLS, VPN, and internet services. Enterprises have a flexible consumption model for core network connectivity. We provide SDWAN-as-a-Service (SDWANaaS) and Wi-Fi-as-a-Service (WiFiaaS) with a “cloud-like” consumption model. Enterprises can avoid technical debt and have a faster provisioning lead time. We support Hosted SASE, which runs single vendor technology over our global network to offer a single-pass security advantage. We also have Hybrid SASE, which combines multi-vendor SDWAN and SSE. With 3.5TB of data analysed daily, 8K+ IOCs detected and blocked, and native SOAR enhancing MTTD and MTTR by 99%, we deliver cutting-edge network protection. Enterprise network and security needs can also be integrated with a single dashboard and management panel. We enable virtual network functions (VNFs), both at the gateway and cloud edge locations, as well as on premises as universal CPE. Enterprises do not need to invest in single integrated devices that lack flexibility and have longer delivery lead times. We maintain 99.8% first-time right deployments of SDWAN managed services and Security Service Edge, and over 95% of incidents are proactively identified by our management platform. Enterprises can be assured of smooth service migration with shorter incident resolution times. We offer simple, intelligent Managed Wi-Fi & LAN with multiple vendors and service packages. Enterprises’ end user experience is improved with better coverage, easier guest access, and the usage analytics required by business growth. Our unique AXIOM managed services framework, which focuses on “Assess, eXecute, Integrate, Operate & Manage”, takes care of the entire management lifecycle across Day 0, Day 1, and Day 2, augmented by value-added services. Enterprises can ensure that their network management staff focus on their core business. Our TCX platform helps manage the network with greater visibility, control, and ease. Enterprises can manage the entire lifecycle from design, delivery, and operations from a single pane of glass. Find out more about Tata Communications’ network solutions here. source

“RPA is still relevant for automating rule-based, repetitive, and redundant tasks, especially in industries where there is a big downside for an error like banking, insurance, and healthcare,” says Arjun Bali, staff data scientist at Rocket Mortgage. “It is not yet being replaced, but augmented with AI, allowing for smarter decisions within workflows.” Adaptable vs. cost While AI agents offer a powerful, adaptable, and autonomous approach to automation, good, old RPA, with its predictable outcomes, still has a place, says Shae Khan, AI research scientist at the IBM MIT AI Lab. AI tools may eventually replace some RPA deployments, but RPA can be cheaper and faster to deploy, while being less prone to errors than most AI tools, Khan adds. source

As a CIO, you’re no stranger to the excitement surrounding AI and its potential to transform your business. The rapid evolution to gen AI and agentic AI means it is time to take a closer look at the incredible opportunity to drive real business value with this technology.  AI has the potential to transform every aspect of a business, from customer experience to operations. At IBM, we really do believe the future of work is not about humans versus machines; it’s about humans and machines working together to achieve better outcomes. I’ll borrow from another leader who said, “AI is like Tony Stark with the Iron Man suit on.”  My team would tell you I’m a cynical optimist, but make no mistake, I am a huge optimist when it comes to using AI agents and AI assistants to drive business transformation. My optimist fundamentally believes in enterprise productivity with AI. Moreover, I believe that we can only reach our potential with an open approach. I see these tools as essential for automating routine tasks, providing real-time insights, increasing team effectiveness, and enhancing customer experiences.  Yes, there is a very real divide between this optimistic potential and the technical demands of realizing said potential. As a CIO, it’s a divide I navigate every day. The problem is that in a rush to use the shiny new toy (read: AI), many CIOs and tech leaders just add chatbots or copilots on top of existing software without having a clear strategy and ability to integrate AI at a deeper platform level.  To harness the value of AI for enterprise productivity, the business needs to take an introspective moment about how it operates, and the mindset that drives those operations.   What it means to become an AI-first enterprise  Let’s level-set first: AI is not just about automating tasks, it’s about augmenting human capabilities to help us make better decisions, work more efficiently, and focus on higher-value projects.  AI-driven transformation requires a cultural shift within an organization. From leadership to the newest employee, a business culture must be one that is willing to experiment, learn from failures, and adapt quickly to new ways of working.   Becoming a gen AI-first enterprise means putting AI at the forefront of your business strategy and leveraging its capabilities to drive transformation across the organization. It does not mean using AI all the time, every time, just for the sake of saying you did. It is strategic and purposeful and, yes, entirely doable.  Getting started with AI agents for productivity  As a CIO, you are uniquely positioned to harness the incredible potential of AI agents to transform your business. The journey to fully capitalize on AI begins with integrating it seamlessly into your existing infrastructure. Here are a few key steps to get started:  Integrate AI with existing systems  Ensure data is high quality and trustworthy  Unite the team around an AI-first culture  Integrating AI with existing systems   In basic terms, an AI agent is a tool. One with great power and potential, to be sure, but still it needs to be used correctly in order to reach its potential.  Think about your legacy systems as a vintage race car. Even though it was created in an era of older technology, the potential is there. You just need some select modernizations to ensure the latest and greatest technology can integrate with it. Do that and you’re racing stronger again than before.  We’ve seen firsthand what it looks like to try using AI agents before addressing critical platform integrations and data format inconsistencies. Outdated and siloed systems can be blockers to AI adoption. So, what can you do?   Map out critical workflows  Identify bottlenecks  Prioritize integrations to eliminate targeted data silos  For my team, we included optimizing infrastructure and application environments, as well as redesigning our end-to-end business processes. We invested in API-first architectures, strategic platform partner solutions, and IBM’s automation tools to help streamline interoperability between legacy and modern systems.  Use an enterprise’s own high-quality data   Alongside interoperability solutions, as client zero, we prioritized using high-quality data that drives explainability, transparency, and trustworthiness. IBM shines in this area, so I have faith in the data we use to train our models and prompt our own AI agents.   Getting your data ready for AI requires an AI-first mindset that includes accountability, transparency, and explainability, all established through clear governance policies and guidelines. This means:  Integrating data from multiple sources and systems  Addressing unorganized or siloed data  Methodically and responsibly curating and preparing data  Establishing governing practices for the responsible and ethical use of AI  The way I see it, responsible AI practices are a required part of the AI strategy. Companies that have those practices woven into their solutions, as IBM does, will be positioned to respond to challenges AI solutions might present in the future.  Uniting around an AI-first culture  Ever heard the Peter Drucker quote, “culture eats strategy for breakfast”? Well, I agree with him. And implementing AI agents into our business practices is a prime lesson in how powerful culture can be, for better or worse. Humans often resist change because they think it will bring a negative impact. Resistance to AI is no different, and it can hinder adoption and value realization.  Remember my earlier points around becoming an AI-first enterprise? That is a culture transformation as much as a digital transformation. And I, ever the optimist, believe that cultures can change for the better; I am seeing it firsthand at IBM.  How can you help foster an AI-first culture in a positive way?   Leadership buy-in and role modeling. At IBM, we work to foster an AI-first culture with top-down support and continuous learning for all. It’s not lip service. The buy-in must be visible for this kind of cultural transformation to take hold. So maybe you start small and get some wins. When my team implemented AI-driven automation and reduced manual tasks it resulted in cost savings and efficiency gains that helped earn organizational buy-in.  

The growth of AI has changed the roles of data scientists, who once worked primarily with neat rows and columns of structured data to create predictive analytics. Today’s data scientists feed raw text, images, video, and audio into intelligent systems and large language models (LLMs) that employ a fundamentally different approach to generating business insights. Data scientists face mounting pressure to deliver results at the breakneck pace of the AI industry, while working with increasingly complex tools and datasets. The challenge: How do organizations enable their data science teams to innovate rapidly without seeing costs spiral out of control? With growing expenses for AI-centric data science — including new tools, on-prem hardware, and significant compute time — CIOs need ways to balance increasing costs with the need for increased analytical output. There are conceptual frameworks that can help. The efficiency of choice The evolution of data science demands a diverse toolkit. But what might seem like complexity is an opportunity for optimization. Different types of analysis demand different approaches: SQL for some tasks, Python for others, and specialized AI frameworks for still others. Forward-thinking organizations are finding that this diversity of tools isn’t a burden but rather a powerful advantage to leverage. In addition to providing the potential for varied techniques leading to optimal outputs (a.k.a., using the right tool for the job), this flexible approach can reduce total cost of ownership (TCO) compared to forcing all workloads through a single processing engine or methodology. This approach also helps forestall common TCO pitfalls, such as shadow IT brought in when the unified platform lacks specific capabilities, and the cost of unmanaged or over-provisioned compute resources sitting idle. Success with this approach requires both technical readiness and thoughtful governance. On the technical side, modern “serverless” platforms can automatically scale computing resources to match exact workload demands, eliminating costly idle capacity while ensuring performance when needed. Infrastructure must be able to handle significant, fluctuating spikes in AI compute demand without manual intervention or runaway costs. Analytics teams need unified billing and resource management to maintain clear visibility into usage patterns. The complete cost equation extends beyond infrastructure expenses. Developer productivity — how quickly teams can move from idea to implementation — can have a much greater impact on TCO than raw compute costs. The business impact of providing developers with flexibility can be substantial: one major retailer found that optimizing a single recommendation algorithm added millions of dollars in weekly revenue by better matching products to customer interests. Building a sustainable data strategy When building a data strategy for the AI era, organizations must strike a balance between the benefits of integrated tooling and the need to adapt to rapid change. While tightly coupled solutions might offer immediate convenience, they create dependencies on specific technologies that may become outdated. Successful operations adopt modular approaches that preserve flexibility as the technology landscape evolves. The most effective organizations establish clear boundaries around security, compliance, and deployment standards, while allowing teams significant autonomy within those guardrails. This balanced approach helps maintain necessary controls while preserving the agility that data science teams need to innovate effectively. This approach has tradeoffs. Embracing a variety of cutting-edge tools can spark innovation, but it also raises security risks and complicates integration. Giving teams wider access to data speeds up discovery, but it can weaken governance. However, enforcing strict centralized standards can stifle creativity and slow projects down. The key is establishing clear policies around data access, model validation, and deployment processes that ensure outputs remain traceable and explainable, particularly crucial with AI systems. When data scientists can rapidly prototype and deploy solutions using familiar tools within this governed environment, they spend more time generating insights and less time managing infrastructure. The path forward CIOs who want to harness AI in their data science teams must juggle their demands while keeping long-term goals in sight. Start by pinpointing exactly how your teams spend their time today; this insight will guide your next moves. If data scientists are spending excessive hours locating and preparing data rather than building models and generating insights, that’s often a clear signal that your data architecture needs attention before scaling AI initiatives. Most importantly, measure success through business outcomes, not just technical metrics. Organizations that adopt this strategic view, building flexible foundations while maintaining appropriate governance, position themselves to leverage data science capabilities into a sustainable competitive advantage. Unlock greater value from your data by empowering data scientists with the best tools. Learn more here. source

Insights from data and machine learning algorithms can be invaluable, but mistakes can be irreversible. These recent high-profile AI blunders illustrate what can go wrong. source

Many organizations are already reaping the benefits. KocSistem, a well-known IT service provider in Türkiye, adopted Huawei’s all-flash high-end storage solution and achieved a 33% boost in storage performance—meeting critical requirements for high performance, reliability, and stability while enhancing the customer experience. Similarly, the Poznan Supercomputing and Networking Center in Poland built a high-reliability, high-performance HPC platform using Huawei’s high-performance storage, unlocking the full potential of its high-value scientific research data. Building a future-proof data storage solution Aligned with the vision of Future-proof Data Storage Power, at its 4th Huawei Innovative Data Infrastructure Forum, Huawei launched the AI Data Lake Solution to help enterprises overcome the limitations of traditional data infrastructure and deploy AI services more efficiently. “The solution provides key capabilities including data aggregation, model enablement, and data mobility, as well as extreme performance, to help enterprises break the limitations of traditional data infrastructure and deploy AI services more efficiently,” says Qiu. Huawei From data storage to data management and resource management, the AI Data Lake is made up of products like the OceanStor A series high-performance AI storage for large AI model training, the OceanStor Pacific All-Flash Scale-Out Storage for data analysis, and the OceanProtect Backup Storage for data backup. All of these solutions empower enterprises to fully leverage their data assets, streamline AI workflows, and accelerate transformation across diverse application scenarios. source