How enterprises can harness AI responsibly
AI is changing everything — work, learning, security. The key for enterprises? For one CTO, it’s been to use it smartly, openly and responsibly. source
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Volkswagen Japan sales streamlines IT with analytics
Volkswagen Japan Sales (VJS) is an automobile company with 11 stores in the Tokyo metropolitan area, managed directly by Porsche Holding Salzburg, Europe’s largest automotive retail company. At VJS, employees use a range of devices to perform important tasks, from client management, quoting, ordering, vehicle management to parts management. “Our stores are scattered across metro Tokyo, and with consideration to physical distance and the speed of information sharing, we would not be able to do our daily jobs without our devices,” says VJS President Akihiko Nishida. “However, information-storing devices face various problems such as server inaccessibility and freezing. Our IT staff had to go up against these unpredictable problems every day.” President Nishida says time to repair is his highest priority: “We respond to failures remotely, and sometimes we had to go in blind and test multiple ways to fix the problem because we were unable to immediately find the cause. We’d be able to respond more quickly if only we were able to grasp the situation more clearly.” With the end of support for Windows 7 and the transition to Windows 10, a rebuilding of the entire support system, including the introduction of devices, came under consideration. “The system at the time was managed by different vendors for each phase such as device manufacturer, implementation support and post-implementation help desk,” President Nishida explains. “As such, it was difficult to locate problems after the system was implemented. Not only that, two members of the IT team managed some 400 devices, and it was critical to visualize the entire system to reduce this burden.” Creating a consolidated, easy-to-manage IT environment There were key criteria selected as part of renovating the system, President Nishida explains. “The first point is the visibility of IT costs. Predictability of monthly and annual IT costs allows for more effective budget planning. The second point is the visibility of IT issues. We must identify the cause as soon as possible, increase response speed and reduce the burden on the IT team,” he says. “HP Services accurately provides proactive device health monitoring and analytics reporting.” Implementing HP Device as a Service (DaaS) provided a complete solution, uniting devices, repair services and analytics into a single monthly agreement and provides a simple and easy-to-manage IT environment. President Nishida says the ease of management integrating HP Services proved critical: “With HP DaaS, we were able to combine the different vendors for each phase into one enabling a streamlined lifecycle management. For IT staff, this is a great advantage.” HP TechPulse reporting resulting from proactive device monitoring drastically improves device management visibility, says President Nishida: “All device insights such as software performance, CPU/memory usage and device utilization can be viewed at a glance on the HP TechPulse dashboard. We can also immediately check the BIOS and Windows 10 update information for all devices, which helps us provide appropriate prompting updates and support to each employee,” he says. With proactive device monitoring and analytics with HP TechPulse, President Nishida explains that IT can now not only rapidly respond to failures, but also predict and alleviate problems — such as a battery or disk failure — before they occur. “We can take pre-emptive measures, such as backing up data or replacing a battery before performance declines. This type of intervention helps decrease stress for both employees and IT staff, and we can focus our abilities on more productive tasks,” he says. “And these hidden costs have greatly decreased.” Business outcomes: Maintaining optimal device lifecycle HP Services provides consistent service in all phases of the device lifecycle. The same goes for the help desk and support systems after implementation. As part of the implementation of the HP DaaS solution, the VJS help desk also transitioned to Comture Network Corporation, as it partners easily with HP. “As a help desk, we receive support not only for devices, but also for Microsoft Office and other information systems in general,” President Nishida explains. “After implementation, there were very few inquiries made to the help desk. I believe this is because, in addition to the high quality of the HP devices, the help desk has visibility to the analytics reporting by HP TechPulse to take preventive measures.” President Nishida says the result is a device management process that is scalable, secure and more streamlined: “Proactive device monitoring and data analytics through HP DaaS also created a robust support system including the help desk. We were able to streamline our operations. And when it comes to devices, HP notebooks are both durable and light enough to carry with ease. We have gotten closer to my ideal of streamlined devices and systems. “Since our business is based on close cooperation with the headquarters in Germany and our Japanese subsidiaries, we cannot proceed with a system build on our own. We are, however, moving in the direction where all the necessary tools for the automotive business will be integrated into a single system to centralize information and extract the maximum benefit from the minimum number of devices.” President Nishida says the engagement with HP DaaS reflects a broader shift of mindset within IT from ‘owning’ to ‘using’. “There is no doubt that the shift to cloud computing and subscriptions will continue to accelerate. Ideally, I’d like to be able to manage a minimal number of devices and build an IT environment with only a monthly fee base. HP DaaS allows you to add or change services as needed, so you can always operate effectively in an IT environment matching your current situation. This, in turn, is leading to better service. Learn more at hp.com/hp-services source
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Weighing the true cost of transformation
“Cultural transformation is the most significant and costly part of digital transformation because it’s essential to bring the entire company on board,” Dimitri says. “You need to explain the reasons behind the project, share the vision for the future, and explain that the company doesn’t exist without its people. Technologies can be scary because we’re immersed in a culture of alarmism, but management’s role is to reassure people that the goal is to do better and that people are always central to any organization.” Avoiding the hidden costs According to Nabil Moussli, technology strategist at Accenture, almost all the hidden costs of transformation are tied to cultural change, like comprehensive training, for instance, and a few hours’ course or an intuitive interface isn’t enough. Employees must not just learn to use a new tool, but adopt new ways of thinking since training shouldn’t be a one-off, but an ongoing commitment, he says. Without a structured approach to change, even the best technological tools fail as resistance manifests itself in subtle delays, passive defaults, or a silent return to old processes. Change, therefore, must be guided, communicated, and cultivated. Skipping this step is one of the costliest mistakes a company can make in terms of unrealized value. source
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Building industrial AI from the inside out for a stronger digital core
A manufacturer was running an AI training workload on a cobbled together system of GPUs, storage, and switching infrastructure, believing it had all the necessary tech to achieve its goals. But the company had put little thought into how the components actually worked together. Problems surfaced quickly. Training cycles dragged on for days instead of hours. Expensive hardware sat idle. And engineering teams began to wonder whether their AI investment would ever pay off. This experience isn’t unique. As AI becomes a critical element of industrial operations worldwide, many organizations are discovering a counterintuitive truth: the biggest breakthroughs come not from piling on more GPUs or larger models, but from carefully engineering the entire infrastructure to work as a single, integrated system. Engineering for outcomes What became of that cobbled-together system? When it was properly engineered to balance compute, networking, and storage, the improvement was quick and dramatic, explains Jason Hardy, CTO of AI for Hitachi Vantara: a 20x boost in output and a matching reduction in “wall clock time,” the actual time it takes to complete AI training cycles. “The infrastructure must be engineered so you understand exactly what each component delivers,” Hardy explains. “You want to know how the GPU drives specific outcomes, how that impacts the data requirements, and demands on throughput and bandwidth.” Getting systems to run that smoothly means confronting a challenge most organizations would rather avoid: aging infrastructure. Hardy points to a semiconductor manufacturer whose systems performed fine—until AI entered the picture. “As soon as they threw AI on top of it, just reading the data out of those systems brought everything to a halt,” he says. This scenario reflects a widespread industrial reality. Manufacturing environments often rely on systems that have been running reliably for years, even decades. “The only places I can think of where Windows 95 still exists and is used daily are in manufacturing,” Hardy says. “These lines have been operational for decades.” That longevity now collides with new demands: industrial AI requires exponentially more data throughput than traditional enterprise applications, and legacy systems simply can’t keep up. The challenge creates a fundamental mismatch between aspirations and capabilities. “We have this transformational outcome we want to pursue,” Hardy explains. “We have these laggard technologies that were good enough before, but now we need a little bit more from them.” From real-time requirements to sovereign AI In industrial AI, performance demands often make enterprise workloads look leisurely. Hardy describes a visual inspection system for a manufacturer in Asia that relied entirely on real-time image analysis for quality and cost control. “They wanted AI for quality control and to improve yield, while also controlling costs,” he says. The AI had to process high-resolution images at production speed—no delays, no cloud roundtrips. The system doesn’t just flag defects but traces them to the upstream machine causing the problem, enabling immediate repairs. It can also salvage partially damaged products by dynamically rerouting them for alternate uses, reducing waste while maintaining yield. All of this happens in real-time while collecting telemetry to continuously retrain the models, turning what had been a waste problem into an optimization advantage that improves over time. Using the cloud exclusively introduces delays that make near-real-time processing impossible, Hardy says. The latency from sending data to remote servers and waiting for results back can’t meet manufacturing’s millisecond requirements. Hardy advocates a hybrid approach: design infrastructure with an on-premises mindset for mission-critical, real-time tasks, and leverage the cloud for burst capacity, development, and non-latency-sensitive cloud-friendly workloads. The approach also serves the rising need for sovereign AI solutions. Sovereign AI ensures that mission-critical AI systems and data remain within national borders for regulatory and cultural compliance. As Hardy says, countries like Saudi Arabia are investing heavily in bringing AI assets in-country to maintain sovereignty, while India is building language- and culture-specific models to accurately reflect its thousands of spoken languages and microcultures. AI infrastructure is more than muscle Such high-level performance requires more than just fast hardware. It calls for an engineering mindset that starts with the desired outcome and data sources. As Hardy puts it, “You should step back and not just say, ‘You need a million dollars’ worth of GPUs.’” He notes that sometimes, “85% readiness is sufficient,” emphasizing practicality over perfection. From there, the emphasis shifts to disciplined, cost-conscious design. “Think about it this way,” Hardy says. “If an AI project were coming out of your own budget, how much would you be willing to spend to solve the problem? Then engineer based on that realistic assessment.” This mindset forces discipline and optimization. The approach works because it considers both the industrial side (operational requirements) and the IT side (technical optimization)—a combination he says is rare. Hardy’s observations align with recent academic research on hybrid computing architectures in industrial settings. A 2024 study in the Journal of Technology, Informatics and Engineering1 found that engineered CPU/GPU systems achieved 88.3% accuracy while using less energy than GPU-only setups, confirming the benefits of an engineering approach. The financial impact of getting infrastructure wrong can be substantial. Hardy notes that organizations have traditionally overspend on GPU resources that sit idle much of the time, while missing the performance gains that come from proper system engineering. “The traditional approach of buying a pool of GPU resources brings a lot of waste,” Hardy says. “The infrastructure-first approach eliminates this inefficiency while delivering superior results.” Avoiding mission-critical mistakes In industrial AI, mistakes can be catastrophic—faulty rail switches, conveyors without emergency shutoffs, or failing equipment can injure people or stop production. “We have an ethical bias to ensure everything we do in the industrial complex is 100% accurate—every decision has critical stakes,” Hardy says. This commitment shapes Hitachi’s approach: redundant systems, fail-safes, and cautious rollouts ensure reliability takes precedence over speed. “It does not move at the speed of light for a reason,” Hardy explains. The stakes help explain why Hardy takes a pragmatic view of AI project success rates. “Though 80-90% of AI projects never go
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AI for data and data for AI: Developing new age architecture
Data validation AI enhances validation by learning from historical data patterns to set dynamic validation rules. Instead of relying solely on static validation scripts, machine learning models can adapt to evolving data characteristics. Suppose an e-commerce platform notices a sudden spike in certain product returns; AI can validate whether these anomalies stem from genuine demand shifts or data entry errors, allowing for proactive intervention. Data qualification Data qualification determines whether a dataset is suitable for a specific purpose. AI can assess data quality dimensions — such as completeness, consistency and accuracy — using intelligent scoring systems. For instance, a marketing team might use AI to qualify potential leads by analyzing demographic, psychographic and behavioral data, focusing resources on those most likely to convert. Preparing the right data for AI-based solutions The effectiveness of AI models, particularly large language models (LLMs) and Specialized Language Models (SLMs), hinges on the quality, relevance and representativeness of the training data. Preparing data for AI is an iterative process requiring meticulous curation and management. source
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From pilot to profitability: How to approach enterprise AI adoption
From central authority to shared ownership In conversations with other IT leaders, I’ve noticed a common pattern in how AI programs evolve. Most began with a centralized team — a logical first step to establish standards, consistency and a safe space for early experiments. But over time, it became clear that no central group could keep pace with every business request or understand each domain deeply enough to deliver the best solutions. Many organizations have since shifted toward a hub-and-spoke model. The hub — often an AI center of excellence — takes responsibility for governance, education, best practices and the technically complex use cases. The spokes, led by product or functional teams, experiment with AI features embedded in the tools they use every day. Because they’re closer to the business, these teams can test, iterate and deliver solutions at speed. When I look across industries, the majority of AI innovation is now happening at the edge, not the center. That’s largely because so much intelligence is already embedded into enterprise software. A CRM platform, for instance, might now offer AI-based lead scoring or predictive churn models — capabilities a team can enable and deploy with little to no involvement from the center of excellence. source
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How i-Health, a company that sells digestive health products, got relief by adopting automation
Gut health is an important aspect of overall well-being, though it is not often addressed openly. Yet, according to the American Gastroenterological Association, up to 70 million Americans suffer from some type of gastrointestinal issue. And that’s where i-Health comes in with relief. Based in Connecticut, i-Health, a subsidiary of dsm-firmenich, offers products for many overlooked and unspoken issues, including digestive and hormonal health, with brands such as Culturelle, Lacteol, and Estoven. The company sells its products through online and brick-and-mortar retailers, such as Walmart, CVS, Costco, Amazon, Walgreens, and others. To address its needs, i-Health sought help with managing orders from its retailers—a solution that SAP was able to provide. i-Health’s success relies on efficiency Every year, i-Health’s fulfillment center handles, on average, 30,000 shipments for 180,000 sales order line items, with 95% of all orders arriving through electronic data interchange (EDI). So, it’s critical to have an efficient processing system to ensure the operation runs smoothly. i-Health’s legacy system, which included manual procedures, faced challenges due to the increasing volume of shipments and demands from its retailer customers. A situation that caused distress to the system Each retailer imposes strict, varied requirements on i-Health, from carrier selection and tight delivery windows to detailed label printing for pallets and cartons. The legacy system struggled to keep up, leading to inefficiencies and potential penalties. The company needed a solution customized to each retailer’s requirements. “In today’s high demanding customer packaged goods (CPG) industry, i-Health must be agile and efficient. Adopting a proven solution was vital to reduce costs while elevating our fulfillment capabilities and flexibility to meet business requirements,” says Susan Armstrong, Vice President of Operations for i-Health. i-Health worked with its IT partner, HCLTech, and SAP to implement a solution that uses SAP S/4HANA Public Cloud and SAP Business Technology Platform (BTP) Integration Suite. Aligning and advising on the balance of business needs vs. best practices was crucial to the success of the implementation. Boosting shipping efficiency with regular support from SAP In the past, sales order processing and ship-to date calculations were handled manually, which made the process slow and prone to mistakes. This often caused issues for both the sales and fulfilment teams, as well as uncertainty among customers regarding shipping times. The rate of accurately determining when orders would leave i-Health’s fulfillment center was just 20%. To address these issues, SAP automated the calculation of ship-to dates and other manual procedures. As a result, the time and effort spent on processing sales orders decreased by 20%. The accuracy of shipping date calculations soared to 95%, greatly enhancing the efficiency of sales order management, fulfilment operations, and overall customer satisfaction. “That means we can now print the orders in priority of target ship date and ship on the right day to arrive at our retailer’s dock on their requested delivery date,” says Marquis Jimenez, Global Fulfilment Manager at i-Health. “This is a game changer for setting the pace of the floor and understanding if we’re ahead or behind in order fulfillment each week. Having system-driven dates means no more guesswork and happier staff and customers.” The solution also includes custom fields and logic to support on-time delivery. Seeing the results in print i-Health customers need unique pallet and carton labels to ship orders, but their legacy system couldn’t meet these varied requirements. A custom SAP BTP-based application was built for on-demand label printing on any printer. This scalable solution enables staff to create multiple label specs efficiently, boosting label output by 200 times. What’s more, the automated solution has cut the onboarding time for new customers from a couple of weeks to a couple of hours. Improving order management health has its rewards i-Health was recently honored with the “Industry Leader” award at the SAP Innovation Awards 2025 for successfully changing and improving how the company conducts business in the CPG industry (and providing relief for staff and customers). If you’re looking for a remedy to improve your order management, start by checking out the i-Health pitch deck. source
Product management in the era of AI
1. Data-instrumented products Products must be built as continuous feedback systems from day one. Telemetry, behavioral data and customer signals should flow seamlessly into product workflows, providing teams with the live intelligence needed to prioritize work, refine roadmaps and respond to user needs dynamically. For example, a SaaS company started instrumenting its onboarding flow with telemetry, revealing where users dropped off within the first minute of interaction. This insight led to a refined user experience that improved activation by ~25% over the next three months. 2. Continuous, AI-driven optimization Where traditional teams optimize based on periodic reviews and lagging metrics, AI-first product organizations enable continuous optimization. AI Agents analyze real-time data streams to guide dynamic backlog adjustments, identify emerging opportunities and automate routine prioritization, turning reactive planning cycles into proactive, adaptive operations. One enterprise product team uses an AI anomaly detection tool to flag unusual drops in engagement within hours, triggering real-time hypothesis testing and backlog reprioritization, removing the need to wait for quarterly product reviews. 3. AI-augmented workflows AI Agents function as virtual team members, automating reporting, generating recommendations and handling operational tasks like backlog grooming, performance analysis and opportunity scoring. Rather than relying solely on human analysis, teams collaborate with embedded AI Agents to make faster, smarter decisions throughout the product lifecycle. In practice, some teams deploy AI Assistants to monitor product metrics and auto-generate weekly status summaries, saving several hours of reporting each sprint. source
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ServiceNow Zurich release introduces agentic AI to the platform
The user can even ask an agent, in natural language, to make changes or additions to a playbook if, for example, a step in a process is missing. Despite these features, Moor Insights’ Kramer said that Zurich’s success will all come down to execution. “Zurich shows ServiceNow moving in the right direction: less dashboard fatigue, more actionable insights, and better integration with external data,” he said. “But the real test will be execution. If the AI summaries are accurate, if the integrations stay reliable, and if the UX actually reduces complexity, customers will see value. If not, it risks being another release that looks good in demos but frustrates in practice.” He added, “Competitors like Microsoft, SAP and Salesforce are pushing similar ideas, so customers will have options. ServiceNow needs to prove that Zurich isn’t just riding the AI wave but actually making daily work smoother.” source
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10 generative AI certifications to grow your skills
AWS Certified AI Practitioner Amazon’s AWS Certified AI Practitioner certification validates your knowledge of AI, machine learning (ML), and generative AI concepts and use cases, as well as your ability to apply those in an enterprise setting. The exam covers the fundamentals of AI and ML, generative AI, applications of foundation models, and guidelines for responsible use of AI, as well as security, compliance, and governance for AI solutions. It’s targeted at business analysts, IT support professionals, marketing professionals, product and project managers, line-of-business or IT managers, and sales professionals. It’s an entry-level certification and there are no prerequisites to take the exam. Cost: $100 Certified Generative AI Specialist (CGAI) Offered through the Chartered Institute of Professional Certifications, the Certified Generative AI Specialist (CGAI) certification is designed to teach you the in-depth knowledge and skills required to be successful with generative AI. The course covers principles of generative AI, data acquisition and preprocessing, neural network architectures, natural language processing (NLP), image and video generation, audio synthesis, and creative AI applications. On completing the learning modules, you will need to pass a chartered exam to earn the CGAI designation. source
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