Tag: Edge Computing

  • How Advanced Power Management Will Drive the Next Wave of Electronics

    How Advanced Power Management Will Drive the Next Wave of Electronics

    For decades, the electronics industry has focused on making devices faster, smaller, and more capable. But as technology evolves, a new engineering challenge is taking center stage: delivering power efficiently to all those increasingly complex components. Processors, sensors, wireless modules, memory—each has its own voltage, current, and timing requirements. Add the demand for longer battery life, compact form factors, and dependable performance, and power management becomes a cornerstone of product design rather than an afterthought.

    Power Management Gets More Sophisticated

    Supplying electricity to a device might seem straightforward, but managing dozens of components that need different voltages, operating states, and startup sequences is anything but simple. Engineers must regulate these requirements while minimizing energy waste and protecting sensitive circuitry. As products grow more intricate, relying on numerous discrete components for these tasks consumes valuable board space and introduces additional design complexities.

    That’s where specialized power management ICs (PMICs) come into play. Instead of treating power as a uniform requirement, these circuits address the specific needs of particular processors, applications, or system architectures. They can handle voltage regulation, power sequencing, monitoring, conversion, and protection—all in a single integrated solution.

    This approach matters because efficiency is no longer measured solely by how little electricity a device consumes. Engineers also need to weigh heat dissipation, physical space, reliability, and how effectively available power reaches each component. Better control helps designers tackle several of these goals simultaneously.

    Miniaturization Demands More

    Miniaturization has transformed both consumer and industrial electronics. Devices that once required several circuit boards now fit into packages small enough to wear on a wrist or embed inside machinery. Yet shrinking a product doesn’t necessarily reduce its computing demands—often, smaller products are expected to do more than their larger predecessors.

    This creates a fascinating engineering paradox. Every additional component takes up space, draws power, and generates heat. Designers can’t simply add more hardware without considering the knock-on effects on the entire system. Integrated power management reduces the number of discrete components needed for certain functions, freeing up precious board space. Efficient power conversion also matters because wasted energy often becomes heat—in compact electronics, there’s little room for elaborate cooling systems. Managing electricity effectively, therefore, influences not only battery life but also the size, durability, and real-world performance of the final product.

    AI Moves Beyond Data Centers

    Artificial intelligence has thrust computing power into the spotlight, but much of the conversation centers on massive data centers. Meanwhile, a quieter revolution is happening at the edge: AI processing is making its way into phones, vehicles, cameras, industrial equipment, and other devices that perform complex tasks locally.

    This shift places new demands on AI hardware. Advanced processors may require multiple voltage rails and rapid power delivery changes as workloads fluctuate. A system performing an intensive calculation one moment might drop to a low-power state the next. Managing these transitions efficiently affects energy consumption, thermal performance, and overall reliability.

    Edge computing amplifies the challenge. Devices operating away from centralized infrastructure often have limited battery capacity or strict energy budgets. Some sensors and remote equipment must function for extended periods without frequent maintenance. In these scenarios, raw computing power means little unless the supporting power architecture can sustain it. As AI spreads into more categories of electronics, designers will have to consider processing capability and power management together—not as separate decisions.

    Efficiency Becomes a Design Priority

    Consumers notice battery life, but energy efficiency matters well beyond portable gadgets. Industrial systems contain thousands of sensors and electronic controls; vehicles increasingly rely on cameras, radar, displays, processors, and communication systems; smart buildings integrate connected equipment into lighting, climate control, and security.

    Individually, small efficiency gains might seem trivial. But multiplied across millions of devices running for years, they become significant. Manufacturers also have practical incentives to cut wasted energy: lower power consumption reduces thermal stress, enables smaller batteries, and slashes cooling requirements.

    The result is a richer definition of performance. Speed will always matter, but manufacturers now must consider what that performance costs in electricity and heat. A processor that delivers impressive capability while demanding excessive supporting hardware could force compromises elsewhere in the product. Power architecture is thus becoming part of the earliest design discussions, rather than something addressed after the headline components are selected.

    The Supporting Chips Matter

    Semiconductor conversations tend to revolve around processors because their capabilities are easy to grasp. Faster chips enable better graphics, more sophisticated software, and increasingly capable AI. Yet processors can’t operate in isolation—they depend on a suite of supporting technologies that determine whether performance can be delivered efficiently and reliably.

    Power management sits near the top of that list. As processors become more sophisticated and electronics integrate more specialized components, delivering the right amount of electricity at the right moment becomes harder. Designers may also need systems that monitor conditions, protect components, and adjust power dynamically as workloads shift.

    This reality could make supporting semiconductor technologies increasingly important across consumer electronics, transportation, manufacturing, communications, and connected infrastructure. The next generation of electronics will certainly rely on better processors, sensors, and software. But it will also depend on engineers becoming much smarter about the electricity that keeps everything running.

  • How IoT and AI Are Reshaping the Automatic Door Industry in 2026: 5 Key Trends

    How IoT and AI Are Reshaping the Automatic Door Industry in 2026: 5 Key Trends

    The automatic door at your office entrance is no longer just a mechanical convenience. In 2026, it is a data-generating, self-diagnosing node in a building-wide intelligence network — and the companies manufacturing these systems are undergoing a transformation just as dramatic as the technology itself.

    The global automatic door market, valued at approximately USD 2.8 billion in 2025, is projected to grow at a CAGR of 6.2% through 2030. But the real story is not the market size — it is how IoT connectivity and AI-driven predictive maintenance are reshaping the entire value chain, from contract manufacturers to facilities managers.

    Here are five shifts redefining this industry.

    1. Predictive Maintenance Replaces Reactive Repairs

    Traditional automatic door maintenance follows a simple pattern: something breaks, someone calls a technician, and the building pays a premium for emergency service. IoT sensors are flipping this model. Modern door operators from brands like Dormakaba and Geze now embed vibration, temperature, and cycle-count sensors that stream data to cloud platforms. Algorithms detect degradation patterns — a worn drive belt, a drifting motor controller — weeks before failure.

    For facilities managers overseeing hundreds of entry points across multiple sites, this shift is transformative. Instead of scheduling reactive maintenance visits, they receive automated alerts ranked by urgency. The result: up to 40% fewer unplanned downtime events, according to early adopter case studies from European commercial real estate firms.

    2. Contract Manufacturers Become the Innovation Layer

    The brands that dominate automatic door installations — Dormakaba, Geze, Record, Assa Abloy, Gilgen, and BEA — rely on a network of specialized contract manufacturers to produce critical components. These manufacturers are no longer passive suppliers executing blueprints. Increasingly, they are co-developing next-generation parts: IoT-ready motor controllers, energy-efficient drive systems, and sensor arrays compatible with Building Management Systems (BMS).

    DoorDynamic, for example, serves as a contract manufacturer for several of the world’s leading automatic door brands, producing both genuine OEM components and certified compatible alternatives. This dual-track model — genuine brands alongside 100% compatible parts — gives distributors and facilities managers flexibility on cost without compromising on performance or warranty coverage.

    3. The Rise of Compatible Alternatives Disrupts Pricing

    One of the most significant trends in the automatic door aftermarket is the growing acceptance of compatible replacement parts. Historically, building operators had no choice but to purchase OEM components at premium prices. Today, manufacturers like DoorDynamic offer certified compatible alternatives for drive motors, controllers, drive belts, safety sensors, and door operators — all backed by warranties of up to five years.

    This shift mirrors what happened in the automotive aftermarket a decade ago. Independent manufacturers proved they could match OEM specifications at a fraction of the cost, and the market adjusted. In the automatic door industry, compatible parts are projected to capture 25% of the aftermarket by 2028, driven by cost-conscious facilities managers and distributors seeking better margins.

    4. Edge Computing Brings Intelligence to the Door

    Not every smart door use case requires cloud connectivity. Edge computing — processing data locally on the door controller — is emerging as the preferred architecture for latency-sensitive applications like safety sensor fusion and real-time obstacle detection. BEA’s latest sensor systems, for instance, combine radar and laser scanning to create a 3D detection zone that adapts in real time, reducing false triggers in high-traffic environments.

    This trend favors contract manufacturers with embedded systems expertise. The ability to integrate edge-capable controllers into existing door form factors — without requiring full system replacements — is becoming a key differentiator.

    5. Sustainability Drives Component-Level Upgrades

    Green building certifications like LEED and BREEAM are pushing building owners to evaluate the energy footprint of every system, including automatic doors. The good news: upgrading a door’s motor, controller, and sensors can reduce its energy consumption by 30-50% without replacing the entire door assembly.

    Contract manufacturers are responding with high-efficiency brushless motors, low-standby-power controllers, and LED-integrated sensor housings. For buildings pursuing net-zero targets, these component-level upgrades offer a fast ROI — often under 18 months — compared to full door replacements.

    What This Means for the Industry

    The automatic door industry is at an inflection point. IoT and AI are not just adding features to existing products — they are restructuring how components are designed, manufactured, and serviced. Contract manufacturers that invest in smart-component capabilities are moving up the value chain. Distributors that embrace compatible alternatives are unlocking new margins. And facilities managers that adopt predictive maintenance are cutting costs while improving reliability.

    For decision-makers evaluating building access solutions, the takeaway is clear: the intelligence of your automatic doors now depends less on the brand on the box and more on the capabilities of the manufacturers and suppliers behind it.

  • The Future of Retail: 2027 Trends in AI, Robotics, and Connected Store Technology

    The Future of Retail: 2027 Trends in AI, Robotics, and Connected Store Technology

    Retail is entering a new phase as artificial intelligence, computer vision, connected stores, and automated commerce become more deeply integrated into the shopping experience. The transformation is no longer limited to online marketplaces — physical stores are also deploying advanced technologies to improve inventory management, personalize customer journeys, and reduce checkout friction.

    Research from Deloitte, Gartner, McKinsey, NRF, KPMG, and Intel points to several technologies that could shape the smart retail landscape in 2027. Here’s a closer look at the trends retailers and consumers should watch.

    Agentic AI Could Change How People Shop

    AI agents are emerging as a new retail channel. These systems can search for products, compare prices, build shopping baskets, and eventually complete purchases on behalf of consumers. Deloitte advises retailers to prepare product data and technology systems for this shift, making information accessible to AI-driven purchasing decisions.

    AI-Driven Personalization

    Artificial intelligence is playing a growing role in how retailers tailor recommendations, promotions, and customer experiences. Deloitte’s 2026 retail outlook found that 67% of surveyed retail executives expected AI-driven personalization capabilities within the following year. The goal is to deliver more relevant experiences based on customer behavior and preferences.

    Computer Vision Enters Physical Stores

    Computer vision is becoming an important part of the smart-store technology stack. Camera-based systems can monitor shelves, understand customer behavior, automate checkout, and identify potential losses. This helps stores respond to situations in real time while reducing manual monitoring for routine retail operations.

    RFID Improves Inventory Tracking

    RFID systems can detect multiple items simultaneously and provide better item-level inventory information. Compared with traditional barcode scanning, RFID offers a more efficient way to track products, stock levels, restocking, and checkout processes.

    Smart Carts and Frictionless Checkout

    Smart carts are gaining attention as a way to minimize friction at checkout. These carts combine sensors, displays, payment options, and AI to help customers navigate the store without necessarily passing through a traditional checkout counter. Gartner lists smart carts as a technology retailers should consider.

    Edge Computing Gains Importance

    The growing number of cameras, sensors, AI systems, and connected devices in stores is increasing demand for faster data processing. Edge computing processes data closer to where it is generated, reducing latency and supporting real-time decisions. Intel and Comcast identify edge computing as a key part of connected retail infrastructure.

    Robotics Moves into Retail Operations

    Robots and smart automation are moving beyond warehouses and into day-to-day store tasks, including stock tracking and other frontline jobs. Gartner expects robots and drones to be used more widely across companies, so stores may adopt self-directed systems over the next few years.

    AR Brings Immersive Shopping Experiences

    Augmented reality could play a larger role in shopping. AR lets consumers visualize products, try items virtually, or see how products fit into their surroundings before buying. Shopify identifies 3D and AR shopping experiences as an important part of retail’s digital transformation, especially as retailers bridge digital and physical shopping.

    Stores Become More Connected

    The traditional distinction between online and offline shopping is becoming less important as retailers connect inventory, customer data, fulfillment, and store experiences. McKinsey expects physical stores to increasingly focus on fulfillment, convenience, product validation, and differentiated experiences, transforming them into connected parts of a wider retail network.

    AI Governance and Cybersecurity

    As retailers let AI systems operate more independently, security, privacy, and clear rules become more important. NRF has pointed to dangers tied to agentic AI, while KPMG highlights governance gaps, fragmented technology platforms, and cyber risk as key roadblocks for wider AI use in retail.

    Heading into 2027, retail technology decisions won’t just be about plugging in AI tools. Retailers will need strong back-end systems and guardrails to handle the security and privacy issues that come with more connected stores. Overall, the trends point to a retail world where AI, automation, connected systems, and physical locations continue to blend. Each retailer may choose different tools, but the direction is clear: more tasks become automated, shoppers get more tailored help, and the point of sale becomes less of a hassle.

    FAQ: Smart Retail Trends for 2027

    What are the biggest smart retail trends for 2027?

    AI agents, smart carts, computer vision, RFID, robotics, augmented reality, and connected stores are expected to shape retail experiences.

    How could AI agents change shopping?

    AI agents could search products, compare prices, build shopping baskets, and eventually complete purchases automatically for consumers.

    What role will computer vision play in retail?

    Computer vision can monitor shelves, understand customer behavior, support automated checkout, and help retailers identify potential losses.

    Why are smart carts becoming important?

    Smart carts combine sensors, displays, payment systems, and AI, helping shoppers navigate stores while reducing checkout friction.

    Will physical stores remain important in 2027?

    Yes. McKinsey expects stores to increasingly focus on fulfillment, convenience, product validation, and differentiated shopping experiences.

  • Data Science Goes to the Edge: 5 Trends Reshaping Modern Analytics

    Data Science Goes to the Edge: 5 Trends Reshaping Modern Analytics

    Data science is moving closer to the edge. More analytics workloads are being processed where data is created, enabling faster responses and reducing unnecessary data transfers. From edge AI to real-time intelligence, these five trends are reshaping modern analytics.

    Edge AI

    Cameras, sensors, smartphones, and industrial machines can analyze information without constantly sending raw data to the cloud. The result is faster decisions, lower latency, and greater resilience when connectivity is limited.

    Real-Time Data Streaming

    Real-time streaming allows analytics systems and AI agents to react to events almost immediately. Gartner identifies agentic data streaming as a major data and analytics trend as organizations demand faster intelligence.

    Smaller AI Models

    Techniques such as quantization and pruning can reduce model size while retaining useful performance. This makes local inference more practical for smartphones, industrial equipment, healthcare devices, and other resource-constrained systems.

    AI-Powered Data Management

    AI agents can help identify patterns, recommend actions, and support routine data processes in real time. Gartner expects agentic approaches to increasingly reshape how organizations manage complex and rapidly growing data environments.

    Analytics Meets IoT

    Edge analytics allows connected devices to filter and interpret information locally before sending only useful insights onward. This approach is especially relevant to manufacturing, logistics, healthcare, energy, and smart-city applications.

    Privacy Moves Closer to the Data

    This is especially useful when devices handle sensitive images, health information, or industrial data. Edge architectures therefore offer another tool for organizations trying to balance analytics performance with privacy and security requirements.

    5G Adds More Momentum

    5G can connect distributed devices with nearby computing infrastructure while supporting data-intensive applications. Together, 5G, specialized AI chips, and optimized models are helping push analytics beyond traditional cloud-only architectures.

    From Dashboards to Instant Decisions

    Modern analytics is moving beyond simply explaining what happened. Streaming data, AI agents, and edge processing can help systems detect events and support decisions as they happen.

  • 7 Key Technologies Shaping the Next Generation of AI Data Centers

    7 Key Technologies Shaping the Next Generation of AI Data Centers

    AI data centers are undergoing a major transformation to keep pace with the demands of next-generation artificial intelligence workloads. From advanced chips to smarter cooling and networking, here are seven technologies driving the evolution.

    1. AI Accelerators and Chiplet-Based Processors

    New AI accelerators and chiplet-based processors pack more computing power into each rack, enabling data centers to handle large-scale AI training and inference tasks more efficiently.

    2. Liquid Cooling for High-Density Racks

    As powerful chips generate more heat, liquid cooling is becoming essential. This technology improves thermal efficiency and reduces energy consumption in high-density AI racks.

    3. CXL Memory Technology

    CXL (Compute Express Link) memory lets processors and accelerators share memory resources efficiently, helping AI systems manage growing data and memory demands.

    4. High-Speed Optical Networks (800G and 1.6T)

    High-speed 800G and 1.6T optical networks move massive amounts of data between servers, switches, and accelerator clusters with lower power consumption.

    5. Silicon Photonics

    Silicon photonics uses light to transmit data across data center networks. Co-packaged optics and optical links improve bandwidth while reducing connectivity bottlenecks and energy demands.

    6. AI-Powered Power Management

    AI-driven power management monitors workloads, cooling, and equipment in real time. Smarter systems adjust resources, detect problems, and reduce unnecessary energy use.

    7. Purpose-Built Inference Chips and Edge Data Centers

    Purpose-built inference chips are gaining traction as AI shifts from training large models to running real-time applications like chatbots, coding tools, and voice agents. Edge data centers bring computing closer to users and devices, while renewable power, batteries, and microgrids support growing electricity needs.

    Together, advanced chips, liquid cooling, CXL memory, optical networking, smart power systems, and edge computing are reshaping data centers for faster and more efficient AI workloads.

  • Battlefield AI: Key Lessons from Ukraine’s Real-World Implementation

    Battlefield AI: Key Lessons from Ukraine’s Real-World Implementation

    There is a significant gap between an AI model that performs well in a test environment and one that can be effectively deployed in the field. In the lab, teams see a number in a report. On the battlefield, the question becomes: what will that number be in a week if imaging conditions shift, new targets emerge, connectivity degrades, or the model runs on different hardware?

    Ukraine faces these questions daily. That’s why its experience matters far beyond its borders. In Ukraine, AI systems receive what laboratory development often lacks: a constant stream of real-world data, continuous testing under actual conditions, and tight feedback loops between users and developers.

    In March 2026, Ukraine’s Ministry of Defense announced it would open access to real battlefield data for international partners to train AI models. This move underscores a critical insight: for computer vision, the gap between a curated dataset and reality can be enormous. In tests, cameras see objects clearly. In combat, the same object might be partially obscured by smoke, viewed from an unfamiliar angle, poorly lit, or appear entirely different from training data. A high benchmark score then tells little about real-world performance.

    Data Becomes Part of the System

    This principle is central to Valkyria, a Ukrainian defense AI company. Valkyria works with machine learning, computer vision, autonomous systems, and embedded AI, testing solutions in active combat. Developers don’t work with hypothetical “combat” datasets; they use data that actually occurs in production. Instead of training a model once and checking accuracy, they must constantly monitor current data, identify where the model makes mistakes, and adapt to changes since the last deployment.

    Hardware Constraints Are Not an Afterthought

    Benchmarks also hide another problem. A model that runs in 40 milliseconds on a powerful lab GPU may take 180 milliseconds on the target edge device, making it useless for its intended task. The model might not even fit within available computing resources. For defense AI, this is a typical engineering reality. The system must run on a specific platform with defined power, memory, and compute limits. Optimization cannot wait until after the model is ready; the hardware must be considered during development. Valkyria, for example, builds on embedded and edge platforms like NVIDIA Jetson, Hailo-8, and TI Jacinto/TDA4. An FP32 checkpoint that works perfectly in the lab must undergo quantization, compiler-level optimization, profiling, and adapt to completely different constraints in the field.

    The Battlefield Changes Faster Than Development Cycles

    Ukraine’s experience also shows that system requirements can evolve faster than a typical development cycle. What worked yesterday may need refinement today. Conditions, data, equipment, and methods of application change. The U.S. military has noted a similar dynamic: in 2025, the commander of U.S. European Command highlighted that systems can quickly lose effectiveness due to environmental changes, requiring rapid adaptation from industry. Therefore, deployment cannot be the final stage. Instead, it follows a cycle: field data → training → model → deployment → testing → feedback → new field data. The faster a team completes this cycle, the more quickly it can adapt to new conditions. This may be one of the most valuable lessons from Ukraine for the global defense tech market.

    From Benchmark to Operational Readiness

    The U.S. Government Accountability Office reached a similar conclusion from a different angle. In its analysis of AI in weapon systems, the GAO specifically highlighted challenges with data quality, integration into existing systems, and continuous monitoring of deployed capabilities. AI requires large volumes of data and ongoing monitoring; standard software development approaches are insufficient. As a result, the key question for defense AI is shifting from “How well does the model perform?” to “How well does the entire system perform—on the right hardware, with real data, under the conditions it was designed for?”

    For Valkyria, this is no theoretical question. The company builds AI/ML software to bridge this gap, from computer vision and autonomous systems to edge deployment and field testing. Ukraine’s experience demonstrates why this approach is increasingly critical. Winning a benchmark isn’t enough. The model must survive deployment, real data, hardware limitations, and field feedback—and then improve. That is the next level of defense AI: not just building a model, but learning to rapidly transform reality into the next version of a working system.

  • What Happens in One Second in the Cloud: The Invisible Infrastructure Powering the Internet

    What Happens in One Second in the Cloud: The Invisible Infrastructure Powering the Internet

    Every second, billions of digital interactions rely on cloud infrastructure that spans data centers, fiber-optic networks, and edge computing facilities worldwide. This article explores what happens behind the scenes, how cloud platforms process massive workloads, why latency and energy consumption matter, and why the cloud has become the invisible backbone of the modern internet.

    The Scale of a Single Second

    When you tap a button on your phone and a video loads, a payment clears, or a map recalculates your route, the experience feels effortless. But behind that instant response, the global cloud infrastructure executes hundreds of millions of operations across server farms, fiber networks, and data centers spanning multiple continents. Amazon’s AWS processes roughly 100 million API calls every second. Google answers approximately 99,000 search queries in the same window. Microsoft Azure handles tens of thousands of authentication requests per second. Across the global internet, more than 3.5 million gigabytes of data are transmitted every minute, according to Domo’s 2025 Data Never Sleeps report.

    What the Cloud Actually Is

    The cloud is a global network of physical data centers—enormous buildings filled with racks of servers, storage hardware, and networking equipment. Amazon Web Services, Microsoft Azure, and Google Cloud operate facilities on every inhabited continent. These are not abstract digital spaces; they are physical structures that consume vast amounts of electricity, require continuous cooling, and are staffed around the clock by engineers.

    The Infrastructure Behind the Magic

    At the physical layer, submarine fiber-optic cables carry data between continents at the speed of light, connecting data centers through a network of cables stretching more than 1.3 million kilometers across the ocean floor. At the software layer, virtualization technology allows a single physical server to run hundreds of isolated virtual machines simultaneously. Container platforms like Kubernetes orchestrate millions of software instances across thousands of servers, automatically redistributing load when traffic spikes and routing around failures before users notice.

    The Latency Problem

    Every millisecond of distance between a user and the server handling their request adds delay. For a video call or an online game, even 50 milliseconds of additional latency is perceptible. Cloud providers build edge computing infrastructure—smaller, geographically distributed data centers positioned closer to population centers—to handle latency-sensitive applications locally. The global cloud is a nested hierarchy of facilities at different scales, each optimized for a different balance of capacity, cost, and proximity.

    The Energy Reality

    Global data centers consumed approximately 415 terawatt-hours of electricity in 2025, roughly 1.5% of total global electricity consumption, according to the International Energy Agency. That figure is growing rapidly as AI workloads intensify computational demands. A single large language model training run can consume as much electricity as several hundred households use in a year. Major cloud providers have made significant commitments to renewable energy, but the absolute energy consumption of the cloud continues to climb even as efficiency improves.

    Why This Matters

    Understanding that the cloud is a physical, energy-consuming, globally distributed system changes how we think about data sovereignty, the companies that hold the infrastructure on which economies depend, the impact of a single provider outage, and the environmental footprint of increasingly compute-intensive applications. The hidden engine behind the internet isn’t hidden because it is small—it is hidden because it is so large, so reliable, and so seamlessly woven into daily life that it has become invisible.

  • Revolutionizing Retail: How AI, Cloud, and Self-Healing Systems Are Reshaping the Industry

    Revolutionizing Retail: How AI, Cloud, and Self-Healing Systems Are Reshaping the Industry

    In an era where retail technology is evolving at breakneck speed, senior software engineer and architect Arundhati Kumar offers a deep dive into the innovations driving the future of omnichannel commerce. With over 15 years of experience building high-performance distributed systems across healthcare, banking, and now global retail, Kumar shares insights on how AI, cloud computing, and self-healing infrastructure are transforming the way retailers operate at scale.

    The Challenges of Engineering at Massive Scale

    When you’re supporting thousands of physical stores with diverse hardware—from legacy cash registers to modern mobile checkout apps—the biggest challenge is ensuring speed and reliability across unpredictable network conditions. Kumar explains that her team had to build custom adapter layers to standardize conflicting data payloads, and engineer systems that can instantly drop into offline mode, saving transactions locally and syncing back to the cloud once connectivity is restored—without losing a single cent of revenue.

    Checkout Doctor: A Self-Healing Solution

    Inspired by store associates struggling with checkout glitches, Kumar designed Checkout Doctor, an automated real-time diagnostic assistant. Instead of waiting for centralized IT support, the system analyzes local device logs, isolates issues like a scale malfunction or peripheral disconnect, and triggers alerts directly on the associate’s mobile device. This shifts the support model from reactive to proactive, keeping lines moving and reducing customer frustration.

    How AI Is Changing Site Reliability Engineering

    Large language models (LLMs) are revolutionizing incident detection by reducing the time engineers spend digging through disparate dashboards and logs. Kumar notes that feeding real-time error logs and alerts into an LLM provides on-call engineers with a plain-English summary of the problem, along with context from past incidents. This acts as an intelligent co-pilot, dramatically cutting the time to understand and resolve critical outages.

    The Measurable Value of AI in Operations

    Rather than fully automated fixes, Kumar believes the greatest value of AI today lies in extreme noise reduction and early triage. In massive enterprise setups, a single microservice failure can trigger cascading alerts. AI correlates telemetry spikes, filters out background noise, and points engineers directly to the root cause. Additionally, machine learning models can flag anomalies—like a spike in database latency—long before a threshold is breached, enabling preemptive patching.

    Lessons from Migrating to Cloud-Native Architecture

    Leading a migration from legacy relational systems to Azure Cosmos DB, Kumar learned that you can’t simply replicate old database patterns. Her team had to completely rethink data organization, grouping by store numbers and register lanes as partition keys, and accepting some data duplication. This trade-off slashed expensive cross-partition queries, kept cloud costs low, and ensured maximum availability during peak shopping seasons.

    Event-Driven Architecture for Real-Time Decisions

    Event-driven architecture replaces batch processing by treating every register action—a barcode scan, a voided item, a payment—as an instant event broadcast via websocket servers. This gives business leaders a live view of store performance and enables automated decisions, such as sending an alert to a nearby associate when an unusual pattern is detected during a live checkout session. It catches faults and errors before the customer leaves the lane.

    Combating Retail Shrink with Intelligent Automation

    Retail shrink is a multi-billion-dollar problem, but Kumar’s approach prevents losses at the checkout lane by linking real-time register logs with overhead camera feeds. If a missed scan or barcode mismatch is detected, the system pauses the session mid-transaction with a friendly nudge to rescan. If the issue persists, a silent notification goes to an associate’s mobile device, protecting the bottom line while keeping the experience seamless.

    Emerging Trends: Autonomous Infrastructure

    Looking ahead, Kumar is most excited about autonomous, self-healing infrastructure and edge-computed intelligence. Instead of using AI reactively to summarize logs after an outage, future systems will run predictive models directly on local edge hardware—like thousands of retail registers. These models will monitor health and transaction patterns, fixing anomalies autonomously (e.g., routing around a slowing database) long before a human engineer is alerted. This marks the end of the traditional on-call nightmare, turning software into something that actively looks after itself.

    Advice for Enterprise AI Implementation

    Kumar offers two key pieces of advice for leaders: First, build systems that survive locally without relying on the cloud. Local hardware must be smart enough to run in offline mode, catch errors, and make decisions on the spot. Second, treat AI as an intelligent co-pilot, not a replacement. Use a tiered intervention model: let the system handle noise reduction and simple self-healing, but when complex failures occur, use AI to arm engineers with a plain-English context summary rather than letting the machine guess blindly. Build guardrails that empower teams, not black boxes.

    As retail continues to evolve, the integration of AI, cloud, and self-healing systems is not just a trend—it’s a necessity. Kumar’s insights provide a roadmap for building resilient, customer-friendly technology that can handle the demands of modern commerce.

  • Agentic AI Ends Manual Telecom: How Amdocs Samit Banerjee Envisions Self-Healing Networks

    Agentic AI Ends Manual Telecom: How Amdocs Samit Banerjee Envisions Self-Healing Networks

    Telecom operators have long relied on automation and analytics to improve network performance and customer experience. However, a new era is dawning where artificial intelligence not only analyzes data but also makes intelligent decisions, adapts, and takes autonomous action.

    In a recent episode of the Analytics Insight Podcast, host Priya Diyalani spoke with Samit Banerjee, Division President of Customer Business Services at Amdocs, about how agentic AI is revolutionizing telecom operations. The conversation explored autonomous networks, India’s AI advantages, enterprise opportunities, and the critical role of human oversight in AI-driven decisions.

    What Sets Agentic AI Apart from Traditional Automation?

    Banerjee explains that traditional automation is rule-based, following predefined instructions within strict limits. “Agentic AI brings intelligence to decision-making,” he says. “The AI agent can monitor the network, recognize anomalies, and make decisions automatically.” For example, telecom providers can dynamically allocate resources, create virtual network slices for enterprise clients, and maintain quality of service without human intervention.

    India’s Unique Position in AI-Driven Telecom

    India enjoys structural advantages few countries can match, according to Banerjee. The nation’s digital public infrastructure provides a robust foundation for identity verification and fraud prevention. With one of the highest volumes of mobile data globally, India offers vast datasets for training AI models. A strong fiber network, vibrant engineering community, and multilingual population enhance natural language processing technologies. Government initiatives like the IndiaAI Mission further unite public and private sectors to advance AI development.

    New Enterprise Services Through Agentic AI

    Telecom companies can evolve beyond conventional connectivity into strategic technology partners. By leveraging edge computing, they can run AI services close to end users, reducing latency in banking, manufacturing, healthcare, and smart city sectors. Telecom firms may offer environments where enterprises train AI models without transferring data remotely. AI solutions will also bolster cybersecurity by detecting threats before they penetrate enterprise networks. Intelligent networks can power applications like drones, surveillance, logistics management, and industrial robotics.

    Striking the Right Balance Between Autonomy and Oversight

    Banerjee emphasizes that not every decision should be fully autonomous. Routine operational decisions like traffic balancing and resource allocation can be handled by AI because they are repetitive and easily reversed. However, decisions involving major financial investments, public safety, or emergencies require human judgment. “The role of AI should be to provide suggestions and undertake automated operations, while human beings make critical decisions,” he states.

    The Future of Telecom in an Agentic AI World

    Emerging telecom networks will become autonomous digital ecosystems. Rather than mere bandwidth carriers, network providers will offer intelligent infrastructure that configures, heals, and optimizes itself in real time. Telecom companies will use AI for predictive maintenance, personalized customer experiences, and innovative enterprise offerings through edge intelligence. As these capabilities mature, telecom providers will act as facilitators of AI across healthcare, finance, manufacturing, logistics, and smart cities. Banerjee concludes, “The key issue is not about making AI replace humans.”

    Listen to the full discussion on the Analytics Insight Podcast.

  • Schneider Electric CAIO Philippe Rambach on Scaling AI with a Business-First Strategy and Critical Thinking

    Schneider Electric CAIO Philippe Rambach on Scaling AI with a Business-First Strategy and Critical Thinking

    In a wide-ranging interview, Philippe Rambach, Chief Artificial Intelligence Officer at Schneider Electric, shares how the global energy management and automation company is scaling AI across more than 160 factories and 140 countries. The key, he explains, is a disciplined business-first approach that prioritizes real-world value over technological novelty.

    Business Value Over Technology

    Rambach warns against “technology tourism”—the temptation to run hundreds of pilots without a clear business case. Instead, Schneider Electric starts by identifying a specific business impact, then asks if AI can help. Each use case passes through gate reviews that test both technical feasibility and business value. Projects are halted immediately if either criterion fails.

    “We do a pilot that keeps in mind what we want to deliver at scale,” Rambach notes. This rigor prevents “pilot purgatory” and ensures that lab successes translate to real factory results.

    Agentic AI and the Sera Assistant

    Schneider recently launched Sera, an AI agent that transforms how users interact with environmental data. However, Rambach cautions against overhyping generative AI. “Generative AI agents do not replace forecasting, anomaly detection, prediction or optimisation—we still need that,” he asserts. Sera adds a conversational layer, allowing operators to ask complex questions in natural language, moving beyond rigid dashboards.

    Critical Thinking as a Core Skill

    To counter blind faith in AI outputs, Schneider has introduced an “AI for All” training program with the same institutional rigor as safety training. The centerpiece is critical thinking. “We want people to keep critical thinking when AI gives an answer,” Rambach says, acknowledging that AI can be wrong or partially wrong. The company also hosts “promptathons” and a virtual “Genius Bar” for employees to share prompting strategies.

    Edge vs. Cloud: A Matter of Physics and Policy

    Deciding where to run AI depends on data sovereignty and latency. In industrial settings, sensitive data often must stay local, and real-time applications like visual inspection require edge processing for speed. Schneider’s solutions are designed to be versatile enough for both environments.

    Addressing the Energy Density Crisis

    Schneider Electric, which builds data center infrastructure, is acutely aware that AI training consumes vast energy. Rambach notes the irony: AI is part of the solution. The company uses AI to optimize power management for its own factories and partners like NVIDIA to precisely manage energy in AI factories. He also advocates treating AI as an “unreliable component,” building reliable systems with human-in-the-loop checks, as in customer care where AI drafts responses but humans verify them.

    Navigating Regulation and Ethics

    Operating under the EU AI Act, Schneider has published an external Trust Charter that explicitly bans facial recognition. Rambach says compliance with the Act does not slow the company down. The ethical framework is backed by a dedicated responsible AI team within the AI Hub.

    The Future of the CAIO Role

    Rambach admits he is “very scared of becoming the bottleneck” as AI becomes pervasive. He sees the CAIO role evolving toward defining technical policy and supporting large, bespoke use cases. His overarching message is to not forget centuries of human wisdom: “My big message is we should not forget everything we have learned in the last 2,000 years, just because there’s a new technique.”

    For industrial giants, AI is not a replacement for human engineering but its most powerful tool—and mastering it lies in the critical mind of the user.