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In a landmark move for Saudi Arabia’s digital economy, Huawei Cloud and Zain KSA have announced a strategic partnership to accelerate cloud adoption and digital transformation across industries. Signed at MWC Barcelona 2025, this collaboration aligns with Saudi Arabia’s Cloud-First policy and underscores both companies’ commitment to advancing cloud technologies in the Kingdom. Huawei showcased cutting-edge advancements at MWC, reaffirming its 5G, AI, and cloud computing leadership. The company introduced next-generation cloud solutions for scalability, security, high-performance computing (HPC), and AI-driven services that optimize business operations. Huawei Cloud’s latest offerings highlight its focus on sovereign cloud solutions, ensuring data compliance and security while delivering seamless AI-powered services. These innovations set the stage for the newly announced partnership with Zain KSA, reinforcing the Kingdom’s ambitions for digital transformation. Under the agreement, Huawei Cloud will provide advanced cloud services, AI-powered tools, and comprehensive technical support. At the same time, Zain KSA will play a pivotal role in delivering these solutions to businesses. This ensures that organizations in Saudi Arabia can seamlessly integrate cloud computing into their digital transformation strategies, fostering a more competitive and innovative business environment. source

To capitalize on the enormous potential of artificial intelligence (AI) enterprises need systems purpose-built for industry-specific workflows. Strong domain expertise, solid data foundations and innovative AI capabilities will help organizations accelerate business outcomes and outperform their competitors. Enterprise technology leaders discussed these issues and more while sharing real-world examples during EXL’s recent virtual event, “AI in Action: Driving the Shift to Scalable AI.” “The key to driving real impact lies in seamlessly integrating data and AI into the way businesses work,” said Rohit Kapoor, chairman and CEO, EXL. “It’s not just about implementing technology. It’s about orchestrating data, digital solutions and human intelligence to optimize decision-making and unlock new opportunities.” The year of agentic AI Agentic AI holds the key to unlocking these opportunities. With autonomous, self-regulating AI agents, enterprises can create automated workflows that adapt to real-world business complexity and augment their human experts to boost efficiency, accuracy and innovation. Kevin Ichhpurani, president of global partner ecosystem with Google Cloud, shared an example of a mutual client and how EXL and Google have helped them with customer service agents. The agents understand the consumer’s intent when they call, make      educated decisions through complex reasoning and then take action, such as initiating a product exchange or ordering a replacement unit. “We see [2025] as the year of delivering agentic experiences for clients, where we automate complete end-to-end business processes,” Ichhpurani said. To achieve this goal, EXL last month launched its agentic AI platform, EXLerate.AI. It orchestrates AI models alongside human expertise and analytics “to help businesses harness AI without getting slowed down by technical complexities,” Kapoor said. The virtual event also featured demos of EXL Code Harbor, a generative AI-powered code migration tool, and EXL’s Insurance Large Language Model (LLM), a purpose-built solution to the industry’s challenges around claims adjudication and underwriting. The Insurance LLM is trained on 12 years’ worth of casualty insurance claims and medical records and is powered by EXL’s domain expertise. Built on NVIDIA’s AI stack, the LLM delivers 30% greater accuracy and 30% lower costs than general-purpose models. “Insurance LLM assists claim adjusters to be more productive and accurate in a shorter time period,” said John Fanelli, vice president, enterprise software, NVIDIA. “It also delivers the best outcomes for both the insurers and the insured. Insurance LLM is a fantastic example of what we call an agentic AI system.” AI in the wild In two event panels, enterprise AI practitioners shared the trends they’re seeing this year and how they’re adapting. The first conversation focused on the evolving symbiosis between data and AI. “There used to be a discussion about how much data you have,” said Sidd Kuckreja, CTO with TruStage. “Now it’s about the quality of data as you think of the regulatory landscape, bias mitigation, privacy and ethical considerations.” Randy Huang, vice president and chief data scientist for U.S. business with Prudential, emphasized the importance of security and governance, because more people are using AI platforms to access and use sensitive data. “The focus on data is really changing based on how the data is generated and how the data is used,” Huang said. And Preetha Sekharan, vice president of Unum’s digital incubator, noted that while data can fuel AI innovation, the inverse is also true. “What is really interesting with genAI and newer technologies is how AI can accelerate how you generate, how you transform, how you understand data,” Sekharan said. “That is really a fascinating twist in how we think about data.” The second panel focused on how AI helps enterprises maintain a competitive advantage. NRG Energy uses AI to conduct ongoing scenario modeling, analyzing weather and forecasting its effects on customer demand and energy prices. “There’s a lot of data points, and … there’s a really good opportunity to use that to do better prediction,” said Dak Liyanearachchi, chief data and technology officer. Sarthak Pattanaik, head of the artificial intelligence hub at BNY, discussed the bank’s internal platform, which enables employees to build AI-powered systems while ensuring security, privacy, fairness, ethical usage, accountability, and transparency. “It democratizes access to AI in a responsible fashion, so it helps innovation at scale,” Pattanaik said. And Dr. Ashish Atreja, professor of medicine at University of California – Davis Health, spoke about AI improving patient access to care. “The biggest value for patients that’s going to happen is moving healthcare fundamentally from one-to-one care, where you have to be with a physician and a patient in the same space and time, to one-to-many care — how you can automate digital care pathways through digital avatars, through digital apps, through digital therapeutics,” Atreja said. A fundamental transformation Simply adopting AI is no longer enough. As industry leaders emphasized during EXL’s event, success requires integrating AI with high-quality data and deep domain expertise — while rethinking and optimizing business processes. “AI is not just a technological shift,” Kapoor said. “It’s a fundamental business transformation.” To learn more about what agentic AI and EXL can do for your business, visit here. source

However, smooth integration does not guarantee seamless execution. Successfully embedding agentic AI into an enterprise platform requires careful alignment with security, governance, and compliance standards, according to Abhivyakti Sengar, practice director at Everest Group. “Additionally, ServiceNow must ensure Moveworks’ AI enhances, rather than disrupts, existing customer workflows,” Sengar said. “Moreover, as regulatory scrutiny of AI and large-scale tech acquisitions intensifies, ServiceNow will need to demonstrate that this deal fosters innovation and competition rather than consolidating market power.” Despite such concerns over AI regulation, the deal is unlikely to attract major antitrust scrutiny due to Moveworks’ relatively modest market footprint. “Although Moveworks is considered a leader in the enterprise assistance space, its $100 million+ annual revenue is probably not enough to invite monopoly or trade scrutiny compared to the likes of OpenAI, Anthropic, Microsoft, Google, or other behemoths that have billions of dollars in revenue or billions of dollars in funding,” Park said. source

In addition, many benchmarks quickly become outdated. The rapid development in AI technology means that models are becoming more and more powerful and can easily handle tests that were once challenging. Benchmarks that were previously considered the standard thus quickly lose their relevance. This requires the continuous development of new and more demanding tests to meaningfully evaluate the current capabilities of modern models. Another aspect is the limited generalizability of benchmarks. They usually measure isolated abilities such as translation or mathematical problem-solving. However, a model that performs well in a benchmark is not automatically suitable for use in real, complex scenarios in which several abilities are required at the same time. Such applications reveal that benchmarks provide helpful information, but do not reflect the whole reality. Practical tips for your next project Benchmarks are more than just tests — they form the basis for informed decisions when dealing with large language models. They enable the strengths and weaknesses of a model to be systematically analyzed, the best options for specific use cases to be identified, and project risks to be minimized. The following points will help you to implement this in practice. source

In a competitive environment, with pressing responsibilities, IT leaders are prime candidates for mental health issues that put a drain on productivity and well being. source

Overview As we mark International Women’s Day, we celebrate the achievements of women driving innovation, leadership, and transformation across industries. In the Middle East, female leaders are shaping the future of technology, proving that diversity and inclusion are essential to progress.In this special episode of CIO Leadership Live Middle East, we are honored to welcome Yasmin Al Enazi, UAE Ambassador for Women in AI. Yasmin is a trailblazer in artificial intelligence, championing women’s participation in the field and paving the way for the next generation of female tech leaders. Join us as we explore her journey, insights on AI’s evolving landscape, and the role of women in shaping the future of technology. Register Now source

In transformation coaching, organizations should find out what their values ​​and goals are and what they need to achieve them. The vision developed is rolled out top-down by management to the various management levels and then to the individual teams. In this way, everyone is included in the joint work on target images and transformation strategies. Attention should always be paid to psychological safety, the feeling of competence in the team, careful interaction with one another through mindful communication, and a benevolent mindset. This is followed by implementation throughout the company.  Establishing new rituals But it can also be done more subliminally: Mental body scans help you feel more strongly and get in touch with yourself again. Attention is drawn to a positive feeling from a particular moment, which is then described in more detail: Where exactly is the feeling? If it were a color or a sound, what would it look or sound like?   Simply by focusing on physical sensations, thoughts, and feelings, mindfulness is created. Such strategies and prompts can also be used to manifest goals and create images in the mind, as well as to work through negative feelings. Afterwards, a defusion exercise from acceptance and commitment therapy helps to let go of these feelings again.  source

Essential to global prosperity yet subject to economic and geopolitical forces, companies across the entire energy value chain are under pressure to operate at high levels of safety, efficiency, and uptime. It’s the same story across all industries. According to a recent survey by Foundry, nearly all respondents (97%) reported that their organization is impacted by “digital friction,” defined as the unnecessary effort an employee must exert to use data or technology for work. Top impacts of digital friction included: increased costs (41%)increased frustration while conducting work (34%) increased security risk (31%) decreased efficiency (30%) lack of data for quality decision-making (30%) are top impacts. But organizations within the energy industry are in an especially precarious situation. These large-scale, asset-driven enterprises generate an overwhelming amount of information, from engineering drawings and standard operating procedures (SOPs) to compliance documentation and quality assurance data. Unmanaged, this asset information could be a serious liability, leading to extreme consequences even by the standards of today’s hyper-competitive business landscape, including lost productivity, unsafe operations, and poor uptime performance. Managed, on the other hand, it can boost operations, efficiency, and resiliency. Enter, AI. In another Foundry survey, decision-makers across all industries cited increased productivity (42%), improved decision-making (40%) and optimized content performance (40%) as top potential benefits of AI-enabled content management. Thanks to the sheer volume of asset information within the energy industry, these organizations are especially poised to capture those benefits. Safety The loss value of the industry’s costliest incidents has hit the lowest average amount for any two-year period in the last 25 years.[1] Despite remarkable improvements in safety and productivity, the journey to zero safety incidents is far from complete. AI-driven asset information management will play a critical role in that final push toward zero incidents. Predictive maintenance promotes safety by helping workers avoid injury due to equipment failures and emergency repairs, which can be difficult and dangerous. Asset information management is a key tool in predictive maintenance. Asset data, such as work orders, inspection reports, and images has intrinsic predictive value – it tells managers when repairs were performed, the condition of equipment when last inspected, and the expected lifespan of a piece of equipment, whether a wind turbine, drilling rig, or length of pipe.  A content management platform streamlines engineering document management organizes this information and connects it through secure, automated workflows with enterprise applications. By applying advanced analytics, maintenance teams can foresee failures and schedule maintenance work before any mishaps occur. AI enables operations personnel to consult an intelligent assistant that responds to inquiries by not only providing accurate answers but also referencing the exact asset documentation where the information is sourced. This transparency and accuracy reduce human error and speed response times for maintenance workers. Risk assessment depends on quick, reliable access to up-to-date information such as engineering drawings, SOPs, and compliance reports. A content platform that organizes and automates access to this information is essential. Without timely information, employees could make erroneous decisions and execute procedures incorrectly. Poorly organized data contributes to risk, as teams waste time searching for information resulting in delays to critical maintenance actions that increase the likelihood of equipment failures and accidents. Real-time monitoring is made more effective by AI-powered asset information management. By immediately linking alerts to actionable information, AI reduces response times and speeds resolution of safety risks. For example, an alert triggered by real-time equipment monitoring automatically provides operators with direct access to relevant asset documentation, including maintenance histories, troubleshooting guides, and current operational data. An intelligent assistant provides instant access to knowledge contained in asset documentation so thorough troubleshooting can take place before making a site visit. Project execution To meet global energy demand, global energy investment is set to exceed $3 trillion USD for the first time in 2024 and accelerate beyond that in the coming years .[2] As a result, just one decade from now, currently acceptable levels of project excellence in the energy sector will be considered average at best. The good news? There are opportunities for improvements in capital project execution, all made possible by AI-driven asset information management. Document management and accessibility are vital for teamsworking on construction projects in the energy sector. They need a single source of truth, whether engineering drawings, equipment specifications, contracts, quality certificates, invoices, or compliance documents. Otherwise, team members could be misled by outdated or incorrect information, leading to errors, rework, or project delays. Access to centralized project documents is accelerated by an AI content assistant that swiftly and accurately correlates projects with relevant assets. Collaboration and communication are improved when an asset information platform integrates withproject management software to provide real-time updates, facilitate document sharing, and automate workflow notifications. An AI-powered intelligent assistant reduces the time spent chasing information and coordinating between teams, leading to smoother project execution as well as improved transparency and accountability. Progress tracking is improved when an AI-powered intelligent assistant provides managers with an instant overview of the status of a project. Managers gain a concise summary, from which they can access associated documents at the touch of a button. The AI assistant presents project data to managers in real time, including a rundown of milestones achieved, upcoming deadlines, and potential risks. Meeting uptime goalsOperational excellence initiatives – such as intelligent oilfields, smart grids, smart refineries, and other smart asset programs – have made a measurable impact on productivity and operational excellence over the last 10 years. AI-driven asset information management has the potential to generate even greater results. Operational excellence depends on centralized, intelligently managed asset documentation that gives maintenance teams immediate access to the latest procedures, historical data, and performance metrics. An AI-powered intelligent assistant streamlines information access and decision-making, enabling maintenance teams to minimize equipment downtime, enhance asset reliability, and support continuous improvement in operational practices, all of which are key to achieving asset uptime goals. Enhanced troubleshooting is fed by valuable information in asset documents such as past work orders, inspection reports, and condition updates. When an AI-powered intelligent

For smaller projects, Lexmark asks if it’s worth experimenting with from a commercial perspective. If it is, they take a close look at the smaller vendors to make sure they don’t miss out on some of the innovation coming from the broader market. “If it doesn’t work, at least it won’t create a hole in our priorities for this year,” says Gupta. “And if it does work, it’s all upside.” Large software vendors are used to solving the integration problems that enterprises deal with on a daily basis, says Lee McClendon, chief digital and technology officer at software testing company Tricentis. It’s more common for smaller vendors to deliver point solutions to specific problems that may not have the level of connectivity and robustness that enterprises require. This applies to all technologies, not just AI. “But it’s important to consider whether multiple point solutions in the AI space are worth the management overhead given the complexities of managing data privacy and security in this rapidly evolving field,” he says. Lee McClendon, chief digital and technology officer, Tricentis Tricentis Concerning established vendors, a word of caution from Eric Helmer, CTO for Rimini Street, is that big companies may be bundling and up-charging for AI as part of their standard products. All the major software vendors are putting it into their products, he says. “If they haven’t done so already, it’ll be bundled in their next release,” he says. “Companies who have a lot of legacy applications may find themselves on an AI journey they didn’t ask for. You may go through the evolution of these very disruptive upgrades, only to find out the functionality you got will never be of use.” source