Exactly one year ago, we outlined how generative AI is creating a new era of efficiency and innovation for logistics and supply chain. We mapped AI use cases across the value chain, from demand forecasting to AI-based customer service, and introduced two new reference architectures for logistics and supply chains: adaptive cloud and AI‑enhanced experiences, alongside innovations in Microsoft Dynamics 365.

Since then, technology has rapidly evolved. We are now in the agentic era of AI with agents being capable of reasoning, planning, and taking action across complex supply chain workflows. End-to-end agent hosting like in Microsoft Foundry and open protocols such as Model Context Protocol (MCP) have made it easier for AI agents to connect with each other as well as enterprise systems, tools, and data.

Additionally, there have been significant advances in 3D simulations, robotics, and embodied intelligence. Open platforms for physical AI like NVIDIA Cosmos with world foundation models (WFMs) as well as the OSMO edge-to-cloud compute framework on Azure enable machines and humanoid robots to act more effectively in the physical world, resulting in broader automation across warehouses, distribution centers, and transportation. This new article picks up Microsoft’s perspective on supply chain and logistics one year after our previous blog article and explores how our own logistics teams as well as frontier customers and partners use this new wave of innovations together with Microsoft.

Microsoft supply chains: Our own “customer zero” story

Microsoft operates one of the world’s most far-reaching cloud supply chains spanning more than 70 Azure regions, over 400 datacenters, and a network of more than 600,000 km of fiber. Our datacenters are the backbone of Microsoft Azure powering everything from AI infrastructure and collaboration tools to networking and security. Microsoft also runs supply chains for Microsoft Windows and Devices with Surface hardware and PC accessories as well as Xbox consoles and gaming hardware.

All of our supply chains have undergone a fundamental transformation over the past decade, evolving from a reactive, manual environment into a rapidly emerging autonomous, agentic supply chain. In the past, our operations were dominated by Excel-based reporting, limited visibility, and siloed data. In 2018, we consolidated more than 30 systems into a single supply chain supply chain data lake on Azure, enabling predictive analytics and the first generation of cognitive supply chain capabilities. In 2022, we began experimenting with generative AI, followed by the development of an AI platform to operationalize agents at scale. Today, this foundation is accelerating to fully autonomous agents, and more than 25 AI agents and applications have been deployed. Below are three examples:

• The Demand Planning Agent drives AI‑based demand–simulations for non‑IT rack components—improving forecast accuracy and reducing manual reconciliation.
• The Multi‑Agent DC Spare‑Part Space Solver uses computer‑vision‑driven monitoring and multi‑agent reasoning to forecast spare‑part storage needs and proactively mitigates space or stockout risks.
• The CargoPilot Agent continuously analyses transport modes, routes, cost structures, carbon impact, and cycle times—providing optimized shipment recommendations that balance speed, sustainability, and efficiency.

The goal is to operate over 100 agents by the end of 2026 and equip every employee with agentic support. The impact today is already huge: AI in logistics is saving our teams hundreds of hours each month demonstrating how agentic operations are translating directly into efficiency and business value. Both in our own Microsoft supply chain transformation and Frontier customers we work with, we have seen that unifying the data estate is key. Yet, it’s what organizations do next that truly generates value with AI.

In supply chain, we believe real value gets unlocked by driving three elements:

• Enabling AI-powered supply chain simulations.
• Building agentic supply chains.
• Integrating first physical AI innovations.

Simulations: The digital twins of supply chains

As supply chains become larger, more interconnected, and more exposed to global volatility, simulating scenarios before they unfold is becoming a critical capability to reduce risk and increase resilience. Discrete event-based simulations (DES) within supply chains enable the development of a virtual risk-free model to test how a complex system reacts to interventions and variables before implementation. With Microsoft’s advanced modelling tools such as Azure Machine Learning and the new machine learning model in Microsoft Fabric with Power BI semantic models, organizations in supply chain and logistics can simulate demand patterns, shortages, or supply chain disruptions.

Our partner paiqo offers with prognotix an AI-powered Forecasting Platform available on the Microsoft Marketplace. More than 70 algorithms enable supply chain experts to generate and optimize highly accurate demand forecasts directly within their Azure environment. Cosmo Tech offers an AI simulation platform for Advanced Supply Chain Risk Management on Azure, offering enterprise customers dynamic digital twins that simulate how disruptions and decisions impact system-wide performance. InstaDeep uses Azure in high-performance compute for AI-enabling deep reinforcement learning and predictive analytics that optimize last-mile delivery, inventory levels, and fleet utilization.

The next level of simulation combines multiple physical simulations in 3D environments and discrete event-based simulations to enable teams to build comprehensive digital twins of warehouses, distribution centers, production lines, and logistics networks. These virtual environments allow organizations to model both the physical behavior of assets and the dynamic flow of operations. By integrating these simulation methods within a digital twin and applying AI, teams can predict future outcomes, optimize performance, and prescribe actions that drive continuous operational improvements. This can help customers lower capital expenditure, shorten commissioning, and ramp up phases, as well as improve operational key performance indicators (KPIs).

Taking warehouses as an example, customers and partners can build advanced, AI-enabled 3D visualizations for four key scenarios:

• Warehouse planning (such as greenfield and brownfield).
• Warehouse monitoring (like real-time monitoring and people movement heatmaps).
• Warehouse improvement (for example trailer dwell time optimization and collision detection for safety and automation).
• Warehouse maintenance (like asset monitoring in real-time, detect quality issues, and reduce rework).

In collaboration with NVIDIA we offer access to NVIDIA libraries and frameworks including NVIDIA Omniverse™, NVIDIA Isaac Sim™, and NVIDIA Omniverse Kit App Streaming that enable developers to build applications and workflows to simulate and test intelligent machines in digital twins before building or deploying anything in the real world. Applications built on these libraries and frameworks allow developers seamlessly integrate geometry data (such as 2D, 3D, and point clouds), AI capabilities (for example large language models, Volume Shadow Copy Service (VSS), and Solvers), and Internet of Things (IoT) signals across operational technology (OT) environments.

The reference architecture below illustrates how to combine cloud and edge computing using NVIDIA Omniverse Kit App Streaming to visualize warehouse operations in real-time with graphics processing unit (GPU) accelerated Kubernetes clusters natively deployed on Azure to remotely monitor, analyze, and optimize warehouse performance with greater precision and situational awareness.

Inside the physical warehouse, operational data from robotic arms, conveyors, and warehouse sensors are captured on the edge using Azure IoT Operations running on Arc-enabled Kubernetes and using MQTT broker. The architecture adopts the Universal Scene Description format (OpenUSD) to ensure that 2D, 3D, and point cloud geometry from the warehouse can be seamlessly integrated into the digital twin. Microsoft Fabric takes up the data in the cloud to provide a unified analytics foundation. Eventstream and eventhouse capture incoming telemetry as real-time streams or batch data. Microsoft OneLake acts as the governed, centralized data lake that consolidates all warehouse data. Digital twin builder transforms raw IoT signals into a contextualized virtual representation by mapping telemetry to the warehouse’s digital model. Powered by NVIDIA Omniverse, high-fidelity simulation and spatial computing occur creating a real digital twin which is streamed directly to the browser—eliminating the need for high-end local hardware. Tools such as Microsoft Copilot Studio and Microsoft Foundry enable natural language interaction. Across all stages, security is maintained through Azure Arc, ensuring consistent governance, configuration, and policy enforcement across edge and cloud.

SoftServe has proven to be an excellent delivery partner for digital twin applications. Together with Microsoft, they seamlessly integrated AI agents built on NVIDIA libraries and open models into beverage production simulations at Krones, enabling physical-accurate digital twins that reduced cycle times from hours to under five minutes. Similarly, at Toyota Material Handling Europe, SoftServe built a digital twin for simulating autonomous forklifts in virtual warehouse environments, enabling rapid testing, optimization, and safer deployments, helping to reduce the training times of autonomous systems by more than 30%.

TeamViewer’s augmented reality platform Frontline provides an additional simulation angle. Wearables such as smart glasses or wrist-mounted devices bring data seamlessly to frontline workers to get guidance in a hands-free manner for picking and packing as well as AI‑assisted counting. At DHL Supply Chain, TeamViewer’s solution is deployed globally to support vision picking of over 1,500 workers across 25 United States sites with fully hands‑free processes.

Agentic supply chains: The multi-agentic web

Agentic supply chains mark a new era of autonomous AI systems that proactively manage and optimize end-to-end supply chain operations. These agentic systems aim to continuously improve overarching KPIs like operating margin or cash conversion as well as specific KPIs such as lead time or freight cost per unit, ensuring that every agentic action contributes to measurable business impact.

Agentic supply chains are built on today’s human-driven tasks and encode the underlying decision-making logic. They include single purpose agents such as “troubleshooters” that constantly diagnose issues and propose fixes as well as “orchestrator agents” like planners or organizers that coordinate multistep workflows. These agents become functional through modern data fabrics, robust systems of record, and event-driven architectures that provide real-time information and governance.

Below is an overview of supply chain agents we have identified along the value chain through multiple customer and partner discussions.

Frontier Firms have already created value with multi-agentic systems.

• CSX Transportation has deployed a multiagent system that validates customer eligibility, routes complex requests, and supports rail operations with multistage coordination.
• Dow Chemical operates invoice analysis agents that review thousands of freight invoices each day, automatically detecting discrepancies and saving the company millions across its global shipping network.
• C.H. Robinson has rolled out a large fleet of generative AI agents including fast quoting agents that deliver tailored freight quotes and automating key steps along the shipping lifecycle.
• Blue Yonder has created an off-the-shelve Inventory Ops Agent on the Microsoft Marketplace that identifies supply–demand mismatches in real-time and recommends corrective actions such as alternate sourcing or demand swaps to keep inventory levels optimized.
• Resilinc offers an agentic supplier risk platform on Azure with pre-built AI agents (like for disruption, tariffs, and compliance) that autonomously evaluate potential impacts, initiate supplier engagement and recommend mitigation strategies.
• o9’s Digital Brain platform on Azure has been enhanced with various AI agents taking over simple tasks like getting specific data and more complex like creating full demand reviews.
• GEP recently added to their source-to-pay GEP SMART and supply chain solution GEP NEXXE (both built natively on Azure), a portfolio of AI agents that cover sourcing, negotiation, contract lifecycle, spend analysis, and market intelligence.
• Kinaxis offers its Maestro supply chain planning platform including AI agents that sense disruptions, run scenario simulations, and provide prescriptive insights through natural language.

Additionally, several delivery partners have used Microsoft tools like Microsoft Foundry and Copilot Studio to build agents for customers at high speed.

• Avanade, for example, offers concrete off-the-shelf agents including a trade compliance agent, a digital twin agent, and lead-2-cash agents.
• Softserve offers a catalyst with 3 customized agents in 30 days.
• NTT DATA is developing a simulation-driven, agentic AI-powered decision-support system for supply chain network agentic rebalancing platform.
• PwC delivers end-to-end agentic AI consulting services, powered by distinctive industry expertise.
• Capgemini is currently building an agentic end-to-end offering using the latest Microsoft IQ technology to be released at Hannover Messe in April 2026.

Microsoft Work IQ, Foundry IQ and Fabric IQ together form an intelligence layer for supply chains—from demand planning to inventory and customer service—that connects how people work, how the business operates, and what the organization knows. This gives AI agents full enterprise context so that agents can reason, simulate scenarios, and act in line with real-world constraints and KPIs such as inventory turnover to support better decisions.

Together with our strategic partner Celonis we have developed a new reference architecture leveraging Fabric IQ and the Celonis Process Intelligence Graph to transform fragmented supply chain data into agentic workflows. A collaborative stack that integrates raw data at the bottom and creates intelligent, automated actions at the top.

On the System of Record (SoR) layer, data is often siloed and does not “speak the same language,” leading to a fragmented understanding within the supply chain. Microsoft Fabric unifies this data through mirroring, streaming, or multi-cloud shortcuts with the goal to create a zero-copy connection and ensure the data is fresh and accessible without the weight of traditional extract, transform, and load (ETL) processes. Fabric IQ provides a reasoning layer that translates raw, unified data in OneLake into context-aware insights. This is the basis for Celonis’ Process Intelligence (PI) Graph which sits between data and the automation and uses process mining to map out how the supply chain actually runs—generating operational supply chain insights and suggesting improvement potentials from a process point of view. It communicates with Microsoft Fabric through Rest APIs, providing the knowledge and context that AI needs to make sense of the data. The agentic layer is divided into three functions:

• Building: Use Copilot Studio, Microsoft Foundry, and Power Automate to create custom AI agents.
• Orchestrating: Use MCP and Agent2Agent Protocol (A2A) to manage how different agents work together.
• Overseeing: Use the control plane for agents in Agent 365 (generally available in May 2026) to monitor agent activities and ensure performance as expected.

On the top layer, with the help of Microsoft Entra ID, insights and suggested actions are shown in tools employees use, such as Microsoft Teams, Microsoft 365 Copilot, Dynamics 365, Power Apps or in the Celonis UI.

A large global pharmaceutical company is using the above architecture to unify fragmented logistics data, enabling real-time identification of temperature-critical pharmaceutical returns and designing an agentic return process that unlocks multi-million euro annual productivity gains. Uniper automated material and service needs with Celonis and Microsoft. Microsoft Copilot in Teams and Power Automate orchestrate approvals, SAP actions, and replace manual component planning with proactive, agentic workflows that ensure timely material availability.

Physical AI: From warehouse handling to last mile deliveries

Physical AI is the final evolution of supply chain intelligence, building on simulations and agentic AI and embodying that intelligence directly in the physical world. In the near future, humanoid robots and robotic systems will physically take over more and more operational tasks along supply chains and logistics: from trailer unloading and sorting, pallet handling and replenishment, to packing and labelling and autonomous last‑mile deliveries. As intelligence moves from screens into machines, supply chains and logistics may gain a new level of physical agility.

Microsoft is pushing the frontier of physical AI with it’s new Rho‑alpha robotics model that combines natural language, visual perception, and tactile feedback to make robots more adaptive and autonomous. Microsoft has launched an early access research program with selected partners to advance co‑training and domain adaptation and aims to integrate the model in Microsoft Foundry in the coming months. Already today, customers and partners may take the below robotics toolchain reference architecture to train and deploy warehouse robotics with NVIDIA Osmo on Azure.

This toolchain is an open-source, production-ready framework that integrates Azure cloud services with NVIDIA’s physical AI stack, from simulation to training and deployment. It combines Azure Machine Learning, Azure Kubernetes Services (AKS), Microsoft Fabric, Azure Arc, and NVIDIA’s robotics and AI stack. NVIDIA Isaac Sim and Isaac Lab enable high-fidelity simulation and reinforcement learning, while NVIDIA OSMO orchestrates scalable training workflows across cloud and edge environments.

Detailed information can be found here.

Hexagon Robotics has started to deploy this architecture using Azure IoT Operations as well as Fabric Real-Time Intelligence in Microsoft Fabric to provide production-ready humanoid robotic solutions. Their industrial humanoid robot, AEON, combines dexterity, locomotion, and unique spatial intelligence to tackle complex industrial use cases for warehousing and logistics such as inspection and inventory taking.

Figure AI, funded by Microsoft, enables the deployment of their humanoid robots in real-world logistics environments using Azure’s AI infrastructure. Their latest model Figure 03 can take over warehouse tasks such as sorting packages at conveyor belt speeds and help at last-mile delivery with near human-level precision.

KUKA and Microsoft jointly developed iiQWorks.Copilot, an AI-powered assistant that enables natural language robot programming and significantly simplifies automation tasks. By integrating Azure AI services, the solution allows users to design, test, and deploy robot workflows faster and more safely—cutting programming time for simple tasks by up to 80%. This has benefitted all KUKA robotics deployed in warehouses and logistics.

Wandelbots’ NOVA software layer combined with Azure cloud services unifies heterogeneous robots and brings adaptive automation to the shop floor. Wandelbots NOVA streamlines warehouse and fulfillment operations such as palletizing by simplifying robot programming, accelerating deployment, and enabling AI-powered path planning and scaling across multiple robot brands. Together, these capabilities position Wandelbots NOVA as a physical AI platform for orchestrating and scaling AI-powered automation across supply chain operations.

Get in touch with us

Contact us directly at screquests@microsoft.com or go to Microsoft for Manufacturing to explore how Microsoft technologies can transform your supply chain. Join us at Hannover Messe in April 2026 to hear directly from our industry leaders, explore cutting-edge ideas, and connect with peers.

The post Supply Chain 2.0: How Microsoft is powering simulations, AI agents, and physical AI appeared first on Microsoft Industry Blogs.

Yet despite broad cloud adoption, migration and modernization remain complex. Legacy architectures, fragmented environments, and persistent skills gaps continue to slow progress, pushing organizations to find ways to migrate faster while minimizing operational risk. 

The IDC study highlights agentic AI as a critical unlock. These intelligent systems automate assessments, orchestrate migration and modernization efforts, and optimize operations across hybrid environments—helping organizations shift from periodic, manual initiatives to continuous, adaptive modernization. This momentum is driving unprecedented growth, with IDC forecasting the public cloud services market will reach USD1.9 trillion by 2029. 

While migration frameworks may be horizontal, their real-world impact is industry-specific. Healthcare, financial services, and manufacturing each face unique constraints shaped by regulation, operational risk, and mission-critical systems. 

In this blog, we explore the key migration and modernization challenges across these three industries—healthcare, manufacturing, and financial services—through real customer stories that highlight the tangible impact cloud adoption is delivering today.

Healthcare: Modernizing securely while powering next-generation clinical experiences

Healthcare faces the toughest modernization headwinds: strict regulations (HIPAA/HITECH, HITRUST), fragmented clinical data across electronic health records (EHRs) and imaging systems, aging on-premises infrastructure resulting in high Capex, and heightened exposure to ransomware.¹ Clinical environments also demand extremely low latency and high reliability.

The IDC study notes that these constraints slow modernization—but accelerate the need for it, as organizations push to scale telehealth, imaging workloads, genomics pipelines, and AI-powered clinical workflows.¹ 

What healthcare organizations need, according to the IDC study: 

• Secure, compliant integration across EHRs, picture archiving and communication systems (PACS), genomics systems, and Internet of Things (IoT) medical devices.¹
• Elastic compute for high-throughput imaging and genomics. 
• Stronger disaster recovery and recovery time performance.¹
• Ambient documentation and AI-supported diagnostics.
• Secure clinician collaboration and modern patient digital front doors.

Customer spotlight: Franciscan Health

Facing aging infrastructure and disaster recovery risks, Franciscan adopted a pragmatic workload placement strategy—moving its Epic EHR to Microsoft Azure.

The results included: 

• $45 million in savings over five years after migrating Epic to Azure.
• 90% faster disaster recovery compared to the prior environment.
• Around a 30-minute failover, reduced from hours.
• $10–$12 million per day in potential downtime risk avoided.

Learn more about Franciscan Health’s journey to migrate its Epic EHR to Azure.

Healthcare’s modernization mandate is clear: reduce operational risk, meet regulatory demands, and harness cloud AI to improve patient outcomes. 

Financial services: Enabling real-time intelligence and automated compliance

Financial institutions operate in one of the most regulated environments, including the payment card industry data security standard (PCI DSS), the Sarbanes-Oxley Act (SOX), the Gramm-Leach-Bliley Act (GLBA), Basel capital frameworks, and know your customer (KYC) and anti-money laundering (AML) requirements, and rely heavily on legacy mainframes that are difficult to modernize. Today, regulatory pressure is intensifying further as new frameworks such as the EU’s Digital Operational Resilience Act (DORA) and the EU AI Act raise the bar for operational resilience, third-party risk management, model transparency, and ongoing compliance monitoring. Under DORA, financial services firms must demonstrate continuous information and communication technology (ICT) risk management, advanced incident reporting, and resilience testing across critical systems and cloud service providers. Meanwhile, the EU AI Act introduces governance requirements for high-risk AI systems, including explainability, data lineage, human oversight, and auditability—with direct implications for fraud models, credit scoring, and customer decisioning platforms.

IDC interviews highlight accelerating demand for real-time risk analytics, fraud detection, digital onboarding, and infrastructure elasticity to support peak activity—capabilities that are increasingly mandated, not optional.¹

Key challenges the IDC study identifies: 

• Strict data residency, model risk governance, explainability, and eDiscovery requirements.¹
• Heightened expectations for operational resilience, cyber defense, and third-party risk oversight.
• Legacy systems and common business-oriented language (COBOL)-based batch processes resistant to change.
• Rapidly evolving regulatory mandates requiring continuous compliance rather than point-in-time audits.

Cloud—especially especially platform as a service (PaaS) and managed services—helps financial institutions shift from static, batch-driven compliance to continuous controls and real-time observability. By reducing batch windows from hours to minutes, modern cloud platforms enable real-time insights, automated evidence collection, resilient architectures, and policy-driven compliance workflows aligned with DORA and AI governance requirements.¹ Learn more about how Microsoft can help financial institutions navigate these requirements. 

Customer spotlight: Crediclub

To accelerate product innovation and meet expectations from Mexico’s national banking and securities commission (CNBV), Mexican fintech Crediclub modernized its databases to a serverless platform as a service (PaaS) architecture and adopted microservices.¹

The impact:

• Uptime improved from around 80% to 99.5%.
• 90% reduction in network latency through Multiprotocol Label Switching (MPLS) and dark fiber.
• Rapid deployment of new financial products via Kubernetes and DevSecOps.

For financial institutions, modernization is no longer just about efficiency—it is foundational to resilience, trustworthy AI, and regulatory compliance at scale. 

Manufacturing: Unifying IT and OT for predictive, data-driven industrial operations

Manufacturers operate in one of the most complex operating environments—defined by legacy and proprietary operational technology (OT) protocols, historically air-gapped manufacturing execution systems (MES) and supervisory control and data acquisition (SCADA) systems, and globally distributed supply chains. Stringent low-latency requirements for safety-critical systems, intermittent connectivity at the edge, and the need to protect intellectual property further compound the challenge. The ability to modernize and unify these environments—without compromising safety, reliability, or performance—represents a critical inflection point for industrial transformation.

Unique modernization challenges according to the IDC study:

• Ultra-low latency requirements for safety-critical operations.
• Massive telemetry ingestion and time-series analytics at scale.
• Operational complexity across global, distributed supply chains.
• Secure protection of intellectual property across edge and cloud environments.

Opportunities unlocked by cloud:

• Predictive maintenance with IoT ingestion.¹ 
• Reduced unplanned downtime and improved overall equipment effectiveness (OEE).
• Digital twins for plants, lines, and products.
• Computer vision for real-time quality and safety. 
• High-performance computing (HPC) simulations for engineering and design. 
• Standardized, global data models.

Customer spotlight: ASTEC Industries

ASTEC unified fragmented systems across its rock to road value chain—from aggregate processing through asphalt production and paving—by adopting Azure, modernizing to timeseries databases, and building a universal connectivity platform using Azure IoT Hub, Azure Events Hub, and Power BI.¹

The results:

• Realtime operational visibility across fleets.
• Predictive maintenance for reducing downtime.
• New digital services supported by connected equipment.

Manufacturing’s modernization imperative: unify OT and IT, scale real-time intelligence, and enable global efficiency. 

Microsoft’s approach: Continuous, intelligent, collaborative modernization 

Microsoft’s strategy is grounded in a simple principle: modernization should be continuous, intelligent, and collaborative. The IDC study emphasizes that successful enterprises adopt a balanced, multipath migration strategy, blending rehost, replatform, refactor, and software as a service (SaaS) substitution based on workload criticality.¹

Microsoft enables this approach through a comprehensive set of tools and offerings, including Azure Copilot and GitHub Copilot. Agentic automation enables:

• Discovery and dependency mapping.
• Security assessment and 6R recommendations.
• Application refactoring, code remediation, and modernization. 

Azure Migrate provides unified discovery, assessment, migration execution, and modernization services. Azure Accelerate complements this with a coordinated framework that includes:

• Guided deployments through Cloud Accelerate Factory.¹ 
• Funding and Azure credits for planning, pilot, and rollout. 
• Expert partners and tailored skilling programs.

The IDC study concludes that organizations using Microsoft Azure for migration and modernization achieve lower operational costs, improved resiliency, faster modernization timelines, and stronger security postures—especially in regulated industries.¹

Looking ahead: Agentic modernization as the foundation for AI-ready enterprises

Across all industries, IDC’s findings are consistent: agentic AI is emerging as the new force multiplier for modernization, enabling organizations to keep pace with rising complexity, regulatory demands, and competitive pressure. 

Healthcare, financial services, and manufacturing each face unique constraints—but cloud modernization remains the foundation for innovation, operational excellence, and enterprise AI. 

Microsoft’s approach gives organizations the unified automation, intelligence, and tooling they need to modernize securely and at scale. 

• Explore how Azure Copilot accelerates cloud migration.
• Discover GitHub Copilot for app modernization.
• See how Azure Accelerate supports transformation.

¹ IDC White Paper, Cloud Migration and Modernization Strategies for Healthcare, Financial Services, and Manufacturing, February 2026.

The post Modernizing regulated industries with cloud and agentic AI appeared first on Microsoft Industry Blogs.

What is a Frontier Firm?

Microsoft defines a Frontier Firm as a human-led but AI-operated organization that integrates AI agents as core team members—enabling rapid scaling, agile operations, and enhanced productivity through hybrid human-agent collaboration and on-demand intelligence.

Microsoft identifies four key pillars of AI transformation:

1. Enrich employee experiences: Empower people with AI tools that remove friction and unlock creativity.
2. Reinvent customer engagement: Deliver transparency, personalization, and trust at scale.
3. Reshape business processes: Automate and optimize operations for speed, safety, and sustainability.
4. Bend the curve on innovation: Move beyond pilots to bold, repeatable frameworks that accelerate transformation.

Microsoft mining and metals customers Ma’aden, Petrosea, and Outokumpu bring these pillars to life and drive efficiency, productivity, cost reduction, safety, and sustainability. I’ll talk more about each one below.

From reactive to proactive: How AI and agents transform mining operations

Frontier Firms are deploying AI and agents across the mining value chain—not just to automate tasks, but to enable supervised autonomous systems that can monitor, reason, and act. AI-powered innovations are already delivering measurable results. For example, BHP and Microsoft have partnered to use advanced AI and machine learning technologies to enhance copper recovery at the world’s largest copper mine. AI-powered systems adapt in real-time to more variability. This optimizes recovery rates, improves throughput, and grade control. It also reduces downtime, waste, water usage, energy consumption, and costs.

With AI and agents, mining companies are not only addressing today’s challenges but are also building resilience and agility for the future—empowering their workforce, optimizing operations, and accelerating progress toward sustainability and growth.

The Frontier Firm in action: Empowering people with Microsoft Copilot and agents

Ma’aden, a leading mining and metals company, aimed to transform into a Frontier Firm by using digital innovation and AI to stay competitive in a resource-intensive industry while supporting sustainability and growth.

The company faced pressure to modernize operations without disrupting workforce roles—balancing efficiency gains with its commitment to empower employees rather than replace them and ensuring adoption of AI tools aligned with cultural and operational needs.

Ma’aden deployed Microsoft 365 Copilot and agentic AI capabilities across workflows—integrating generative AI into collaboration and decision-making. The focus was on augmenting human expertise, enabling employees to automate routine tasks, and free time for strategic thinking.

The transformation improved productivity, saved time, and enriched employee experiences—positioning Ma’aden as a Frontier Firm in mining. Employees reported higher engagement and confidence, as AI functioned as a trusted assistant, not a substitute—driving faster decisions, better collaboration, and sustainable growth.

“We intentionally gave Copilot to early adopters—people who are excited about technology—because they would act as change agents for the rest of their teams.”

—Khalid AlMutairi, Vice President, IT at Ma’aden

Turning obstacles into intelligent opportunities

Petrosea, a leading Indonesian mining and energy services firm, faced intense price wars and operational inefficiencies. To sustain growth and environmental, social, and governance (ESG) commitments, it needed to differentiate beyond cost and embrace innovation.

Legacy batch processes and limited data access hindered real-time decision-making. Remote sites and rising sustainability requirements amplified complexity, requiring a shift to advanced digital capabilities for competitive resilience.

Petrosea launched its 3D strategy: diversification, digitalization, and decarbonization—deploying the Minerva Digital Platform on Microsoft Azure, integrating Internet of Things (IoT) sensors, predictive analytics, and digital twins. It adopted Microsoft Azure OpenAI, Copilot Stack, automation agents, and advanced security.

The company achieved a 15% increase in productivity, a 9% reduction in operational costs, improved safety, and was selected by the World Economic Forum to join its Global Lighthouse Network. Petrosea transformed adversity into innovation, building competitive differentiation as a Frontier Firm through AI-powered workflows.

The integration of IoT sensors, predictive maintenance, and a Remote Operations Center reshaped their business processes—shifting from manual, site-based oversight to centralized, data-driven control that improved efficiency and safety.

“All these innovations led to a 9% reduction in operation costs, decrease in incidents, and enhanced safety measures with real-time corrective actions.”

—Krishna Nawacandra, Digital Project Manager, Petrosea

AI-powered sustainability as strategy

Outokumpu, a global stainless-steel leader, faced mounting pressure to meet ambitious climate targets and comply with Corporate Sustainability Reporting Directive (CSRD) reporting while embedding sustainability into its core strategy. Steel accounts for 10% of global greenhouse gas (GHG) emissions, making decarbonization critical.

Manual, fragmented sustainability reporting hindered transparency and efficiency. Outokumpu needed a unified, intelligent data approach to accelerate green value creation and explore AI-powered ESG innovations for competitive advantage.

Outokumpu partnered with Microsoft to deploy the Intelligent Data Platform, Microsoft Fabric, and Sustainability Manager—automating environmental data processes, enabling advanced analytics, and training leaders through the AI data-driven green value creation program.

Outokumpu achieved up to 75% lower carbon footprint versus industry average, launched Circle Green® stainless steel with 93% lower carbon footprint, and helps customers cut 10 million tons of CO₂ annually. Data and AI now fuel new business models, cost savings, and sustainable growth.

By using Microsoft Intelligent Data Platform and AI capabilities, Outokumpu is not just improving sustainability reporting—it is bending the curve on innovation by accelerating the development of new low-emission products and unlocking green business models that deliver both environmental and commercial impact.

“We have set a very clear goal for ourselves. We want to achieve something remarkable.”

—Heidi Peltonen, Vice President of Sustainability at Outokumpu

Advancing the Frontier for mining organizations

Across these three customer stories, a common thread emerges: transformation is not accidental—it is intentional. Frontier Firms combine human ambition with AI, Copilot, and agents to create scalable impact. Ma’aden reimagined productivity, while Petrosea transformed adversity into innovation, and Outokumpu turned data into a strategic asset.

What sets these leaders apart is discipline: they do not stop at adoption. They measure outcomes, codify frameworks, and scale with intent. Technology is a purpose multiplier, enabling safer operations, faster innovation, and sustainable growth.

As Frontier Firms continue to redefine what’s possible in mining, the horizon is filled with opportunities for AI-powered solutions—from predictive maintenance and autonomous operations to intelligent exploration, workflow automation, and sustainability platforms—each poised to unlock new levels of efficiency, safety, and innovation across the industry. The Microsoft GenAI for Energy Permitting Solution Accelerator applied to mining represents a promising step for Frontier Firms seeking to transform permitting from a bottleneck into a strategic advantage. Built on the Microsoft Cloud, the accelerator aims to help mining companies accelerate permitting timelines, improve compliance confidence, and enhance transparency with regulators and communities.

With these and other innovative solutions, the future belongs to Frontier Firms. Are you ready?

Discover solutions

Using Copilot in energy and resources

Explore the possibilities of AI transformation

Learn more

• Visit Microsoft for energy and resources and Microsoft for the mining industry.
• Explore Azure Arc.
• Learn more about Microsoft Security for enterprise.
• Discover over 1,000 AI customer use cases in the blog AI-powered success—with more than 1,000 stories of customer transformation and innovation.
• Read the latest International Energy Agency’s World Energy Outlook 2025.

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At Microsoft, we’re proud to partner with governments and innovators globally to advance AI-powered infrastructure. The latest Smart Cities World Trend Report, developed in collaboration with Microsoft, highlights how cities are moving from reactive planning to proactive resilience, using AI to anticipate, adapt, and act.

Moving from prediction to preparedness in Jakarta’s flood management 

In Jakarta, Indonesia, flooding has long posed a threat to millions of residents. The Jakarta Smart City program, in partnership with SAS,¹ deployed an AI-powered analytics platform that forecasts flood risks up to six hours in advance. By integrating data from rainfall sensors, river gauges, and weather services, the system lets authorities close floodgates, activate pumps, and issue alerts through the JAKI app before disaster strikes.

This shift from reactive to preventive action exemplifies how AI strengthens resilience. As Hannah Prior, Climate Resilience Lead for Microsoft’s Worldwide Public Sector, explains:

“We’re now entering an era where we genuinely don’t know what’s going to happen next… In the past, city planners would have said, ‘Let’s plan for a one-in-100-year flood.’ But those kinds of events have become far more common and therefore more difficult to plan for.”

Enhancing operations with AI: Evergy’s utility transformation 

In the United States, Evergy, a public utility serving 1.7 million customers across Kansas and Missouri, has embraced AI and automation to transform its operations. Using Microsoft Power Platform, Evergy developed over 275 automation solutions that save more than 120,000 hours annually. From drone image processing for power line inspections to intelligent data extraction for inventory management, AI is helping Evergy improve efficiency, reduce errors, and enhance resilience across its energy infrastructure.

These innovations not only streamline internal processes but also support Evergy’s transition to cleaner energy generation, with workforce adaptability and operational continuity in a rapidly evolving energy landscape.

Strengthening water resilience for future challenges 

In southern France, the Société du Canal de Provence (SCP) is tackling water stress through its REImu program, an intelligent water network initiative powered by Microsoft Azure technologies. By integrating IoT sensors, smart meters, and big data platforms, SCP can monitor consumption, detect leaks, and forecast demand across a 6,000-kilometer distribution network. The system also combines meteorological and agricultural data to provide adaptive irrigation advice and enhance drought preparedness.

The next phase will use AI to refine consumption forecasts and detect inefficiencies automatically, turning water networks into climate-resilient, data-driven systems.

Adopting a system-of-systems approach to plan for uncertainty

Beyond individual use cases, cities are beginning to adopt a system-of-systems approach, integrating data across water, energy, transport, and environmental domains to model complex interactions and plan dynamically. Platforms like Sentient Hubs in Australia exemplify this shift, allowing for near real-time scenario planning and collaborative decision-making.

“It’s really about moving from a static, five-year flood plan sitting in a PDF on a shelf to a dynamic, living plan that exists as a digital platform… People can access it at any time to understand, in near real time, what’s happening across their systems.”

—Hannah Prior, Climate Resilience Lead for Microsoft’s Worldwide Public Sector

This approach transforms resilience planning into an active, adaptive process, one that evolves with every new dataset and empowers cities to respond to uncertainty with confidence.

Advancing sustainability and efficiency through AI

AI’s value extends beyond resilience; it also helps cities meet sustainability goals. In Munich, Germany, the municipal utility Stadtwerke München uses Microsoft Azure IoT and Azure AI to optimize electric bus operations, forecast energy demand, and reduce waste. Ninety percent of Munich’s electricity already comes from renewable sources, and AI is helping the city move closer to full carbon neutrality.

In Singapore, the Smart P.U.B. initiative uses thousands of sensors and AI analytics to detect leaks and optimize water distribution, achieving 5% water savings and near-zero pipe bursts.² These examples show how AI can reduce emissions, conserve resources, and improve service delivery.

Building responsible, inclusive infrastructure for all

As cities scale AI-powered infrastructure, governance and fairness must remain central. Seattle’s 2025–2026 AI Plan sets a benchmark for responsible deployment, grounded in principles of innovation, accountability, fairness, and transparency.³ The plan mandates human oversight, bans harmful applications, and introduces a Proof of Value Framework to assess AI projects for responsible impact.

Used responsibly, AI can democratize resilience, making forecasting affordable and accessible, reducing bias in decision-making, and ensuring that infrastructure serves all communities equitably.

Microsoft for government operations and infrastructure

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Join us at Smart City Expo World Congress November 4–6

The journey toward resilient, sustainable infrastructure is underway, and AI is at the heart of it. From Jakarta to Kansas City, from Provence to Munich, cities are showing what’s possible when technology meets purpose.

To learn more about how Microsoft and our partners are helping cities build future-ready infrastructure, join us at the 2025 Smart City Expo World Congress. Discover the latest innovations, connect with global leaders, and explore how AI can help your city thrive amid uncertainty.

¹ https://www.sas.com/sas/partners.html

² High Fidelity Digital Twin-enabled Anomaly Detection and Localization in Singapore | The Year In Infrastructure | Bentley Systems

³ Seattle launches responsible AI implementation plan – Smart Cities World, September 25, 2025

The post How cities build resilient infrastructure with trusted AI appeared first on Microsoft Industry Blogs.

When 70% of Microsoft 365 Copilot users report that the integration of generative AI into their everyday applications and tasks makes them more productive,¹ firms can see how AI can fundamentally revolutionize their businesses—starting by empowering the people who keep their companies running.

Historically, technology innovations have often not focused foremost on the needs of the average worker. Rather, they were often focused on empowering executives or driving loftier business goals such as enhancing competitiveness and profitability or powering new products and services. Generative AI is distinct in that it is tailored to benefit employees first. 

Helping banks and other financial institutions take full advantage of AI is central to our work at Microsoft. In the past two years, we’ve worked intensively with firms around the world to explore new avenues of AI innovation, with use cases that span an incredible range of opportunities. None have been more noteworthy than employee empowerment.

For many users, this happens initially with Microsoft 365 Copilot, which is embedded into apps like Word, Excel, PowerPoint, Outlook, and Microsoft Teams to integrate AI directly into everyday work tasks. 

Copilot removes drudgery and empowers employees 

When every employee has an AI assistant that helps them work better and faster, the sky is the limit on innovation. And it couldn’t come at a better time. According to the Microsoft 2025 Annual Work Trend Index, 53% of leaders say their company’s productivity needs to increase, yet 80% of the global workforce reports lacking the time or energy to do their job.¹

This “capacity gap” is why 82% of leaders expect to use digital labor to expand their workforce in the next 12 to 18 months. For many, the journey starts with Copilot and related cloud solutions that remove the drudgery of work and help people do the same work better and faster. 

4 ways Copilot is delivering immediate impact in financial services 

Enhanced productivity is the broad term for an important set of benefits that generative AI can deliver. For financial services firms, the specific use cases and benefits span many areas but we will concentrate here on four in particular: summarization, content creation, process optimization, and real-time insights.

1. Summarization
One of the most valuable features of Copilot is summarization—the ability to instantly produce a customized summary of anything from a recently conducted meeting to a transcript of a customer conversation, to summaries of whitepapers and PowerPoint presentations. In a fast-paced environment where analysts, advisors, and other professionals juggle multiple tasks, having a tool that can immediately extract key takeaways and follow-up actions is invaluable.

A good example is the experience of Hargreaves Lansdown, a leading United Kingdom financial services company that was early to embrace Copilot. Until recently, advisors had to manually take notes for customer meetings and later transfer them into a branded document, a process that could take up to four hours. With Copilot summarization and Microsoft Teams Premium, the process is being cut to as little as one hour through the automatic generation of meeting summaries, documentation, and action items.

Copilot not only speeds summarization, “it’s also good quality information,” says Systems Operations Manager Daniel Toman. “We know nothing is being missed.”

2. Content creation
The process of drafting emails, building presentations, and writing important client documents can be time-consuming and frustrating. Copilot eases that burden by identifying relevant source materials, using natural language processing to create messages and documents, and pulling information from across the Microsoft Graph (an API that connects all Microsoft 365 data, documents, and users.) The benefits for financial services include improved client engagement, scalable workflows, and AI-powered insights.

Content creation is delivering major benefits to Bank of Queensland (BQQ), which adopted Copilot to enhance collaboration and productivity. It has helped decrease the time required to draft internal manuals by 99%, marketing content by 88%, and human resource document drafts by 75%. Those gains are credited with improved customer service and operational efficiency—plus greater innovation in a competitive market.

“Copilot puts the power in the hands of the employee to be able to find efficiency.”

—Hayley Watson, Head of Enterprise Capability, BOQ

And to offer another customer example, in the United Kingdom, Floww, a financial infrastructure platform provider, increased employee efficiency by up to 20%, using Copilot to process massive quantities of data spanning technical documents, regulatory compliance requirements, and financial information, then condensing and delivering reports in easily accessible, shared formats.

3. Process optimization
Too many important processes in financial services are still dependent on manual tasks that can slow productivity and drain resources. Copilot solves this by automating processes and enhancing collaboration. The net benefits for firms include streamlined operations, fewer errors, and more time for employees to focus on high-value work.

For example, Dutch wealth management firm Van Lanschot Kempen wanted to help advisors focus more on personal connections with clients and found that too much time was being spent on unautomated tasks. So, they enlisted Copilot to improve common workflows and processes. Copilot now helps save them time by drafting emails (in multiple languages) in response to prompts, taking notes in meetings, and identifying and automatically assigning action points. An added benefit is that it reduces the language barrier and increases the quality of emails and documents.

“Having to take notes and structure action points and recaps accounted for around 40% of my time, Copilot is now my assistant during and after meetings.”

—Johanna Albert, Digital Adoption Specialist

Elsewhere, LGT, a Liechtenstein-based international private banking and asset management group, is using Copilot in their legal and compliance departments—for instance, to simplify reviews of lengthy contract documents. What used to take up to four hours can now be done in about 30 minutes. And global payments platform Paysafe cut the amount of time spent building technical documentation by up to 50%, automating meeting documentation, information retrieval, and document creation.

4. Real-time insights
Copilot is redefining how financial services professionals work by providing real-time insights that empower employees and enhance decision-making. Imagine, for example, a mortgages operation manager who needs to ensure efficient loan processing while maintaining compliance and optimizing customer satisfaction. Throughout the process, Copilot instantly retrieves and summarizes critical data, improving both speed and accuracy. Across firms and roles, real-time insights help with everything from research and predictive analysis to collaboration, workflow automation, and decision-making.

The scope of the transformation this represents is reflected in Microsoft’s strategic partnership with Moody’s to co-create new products and services for research and risk assessment. Built on a combination of Moody’s robust data and analytical capabilities and the power and scale of Azure OpenAI Service, the partnership creates innovative offerings that enhance insights into corporate intelligence and risk assessment. One early offering is a new copilot, “Moody’s CoPilot,” deployed to Moody’s 14,000 global employees, which helps drive firm-wide innovation and enhance employee productivity in a safe and secure digital sandbox. 

Beyond Microsoft 365 Copilot: A new breed of financial services agents 

Microsoft 365 Copilot is really just the start. Microsoft also offers a range of Copilot solutions tailored for businesses sectors, including Microsoft Copilot for Finance, which connects to financial systems such as Dynamics 365 and other leading financial management platforms to assist with tasks like analysis, reconciliation, collections, and communications.

For firms who want to do more, Microsoft Copilot Studio makes it easy to build custom AI agents without requiring extensive coding expertise. It also includes a powerful new Researcher Agent that helps with complex, multi-step research. It delivers deep insights with greater quality and accuracy than previously possible to advance important tasks like market analysis, opportunity identification, and key reporting. 

Set the stage for new waves of AI innovation 

Employees who use copilots are not just more productive, they’re more likely to be engaged and innovative. For many, their embrace of AI will become career accelerators. Empowered employees will help drive the reinvention of even the most established firms, and make endless contributions to the upstarts and even those we can’t imagine today.  

As the rapid evolution of AI continues, we will soon see the adoption of new classes of AI agents that won’t simply provide assistance but will execute tasks and orchestrate action autonomously. The future is approaching rapidly: our research suggests that global business leaders expect their teams will be training (41%) and managing (36%) AI agents within five years.¹  

Soon, human-agent teams will upend the org chart, and every employee will become an agent boss. Now is the time to get them up and running. 

Learn more 

• Visit Microsoft for financial services to learn more about how Microsoft empowers financial services organizations to achieve more.
• Learn more about using Copilot in Financial Services and download the Financial Services scenarios kit.

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¹ The 2025 Annual Work Trend Index: The Frontier Firm is born.

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As the mining industry undergoes its digital and AI transformation, Microsoft remains committed to delivering innovative and secure solutions. From adopting AI and agents to streamlining business processes and unlocking efficiency to moving legacy systems to the cloud—we’re dedicated to working together towards a powerful and sustainable future of mining.

AI transformation for a more resilient future of mining

As we are seeing across the energy and resources industry, the mining sector is facing growing pressure to support the global energy transition, with AI emerging as a prominent solution. With demand for critical minerals expected to quadruple by 2040³, AI can help mining companies locate and extract resources more efficiently, with studies showing potential reductions of 20% to 30% in the time and cost of mineral discovery.⁴

From early stage exploration to downstream processing and logistics, AI has the potential to be embedded throughout the mining value chain. In upstream operations, it can enhance mineral prospectivity mapping, resource estimation, and production planning. Downstream, it can optimize ore blending, recovery, and processing. Even side streams like supply chain logistics are beginning to see gains, as AI-powered efficiencies ripple across operations. And in exploration, AI unlocks insights from vast geoscientific datasets—both legacy and real-time—enabling faster, more accurate decision-making.

The possibilities for AI use cases in the mining sector are abundant, and there are ways for organizations embarking on their digital transformation journey to get started today—such as with workforce productivity. AI adoption in this context is a powerful step towards the future of work, and Ma’aden, a mining company in Saudi Arabia, is a prime example of that. Ma’aden used Microsoft 365 Copilot, Microsoft Copilot Studio, and Microsoft Azure OpenAI Service to help employees be more productive in daily tasks, like getting quick answers on policies, summarizing content, and drafting presentations, emails, and meeting minutes. Ma’aden saw enhanced productivity, with Copilot users saving up to 2,200 hours monthly.

In addition to workforce productivity, Microsoft AI solutions are also enabling operational transformation, as seen in Sandvik’s approach to equipment optimization. Sandvik created a cloud-based service solution that uses data and AI to generate insights on the state of their machines to support the optimization of the operation of equipment. Powered by Microsoft Azure Cloud and its analytics and AI services, the solution uses data to produce actionable insights into equipment performance and status—helping to drive transformation across its business.

Foundations for AI-driven transformation in mining

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Unlocking potential: Bringing the cloud to mining operations

As the mining industry advances efficiency, safety, and sustainability goals, the adaptive cloud has emerged as a critical piece of this journey. Microsoft’s adaptive cloud approach uses cloud-native and AI technologies across hybrid, multi-cloud, edge, and Internet of Things (IoT) environments. By making operational technology (OT) cloud-enabled, mining organizations can unlock real-time insights, streamline operations, and enhance resilience. This union of cloud and OT supports smarter decision-making and predictive maintenance, and lays the foundation for innovation and scalability.

Boliden offers a compelling example of how cloud infrastructure can modernize mining operations at scale. The Swedish mining company needed to automate and centralize data collection, increase visibility across processes, and add new ways to analyze information. Boliden monitors the Garpenberg site with a network of 500 cameras that give management teams oversight of the mines, wells, and operations, helping to keep an eye on productivity and safety. The company now uses a combination of Microsoft Azure IoT Edge and Microsoft Azure IoT Hub to connect the cameras with other Boliden systems and the rest of its IoT network, which consists of thousands of sensors above and below ground, along with other devices. By working with a flexible, fully featured cloud infrastructure, the company can now bring more productivity and safety to all their sites.

Emirates Global Aluminium (EGA) also exemplifies how adaptive cloud infrastructure can overcome the limitations of traditional on-premises environments to support scalable, intelligent operations. EGA deployed a hybrid environment that connected private cloud services through on-premises datacenters. Deploying a hybrid environment helped to optimize latency, support advanced AI and automation solutions, offer sustaining commercial savings by applying intelligence at the edge, and streamline processing for massive amounts of real-time readings from sensors, machinery, and production lines.

Learn more about energy and resources solutions with Microsoft

No matter what your organization’s digital transformation may look like, Microsoft is committed to helping to drive progress in the mining industry and working to grow sustainable, secure, AI-powered businesses. Microsoft has always been built on trust and a robust security suite, and is committed to prioritizing security in the design, build, and operation of our products and services. To take a deeper dive into cybersecurity in the age of generative AI and building a foundation for AI-powered transformation in mining, read our latest e-book.

• Explore the possibilities of AI transformation with Microsoft.
• Read the AI Transformation in Mining whitepaper.
• Visit Microsoft for energy and resources and Microsoft for the mining industry.
• Read the new International Energy Agency report, Energy and AI.
• Learn more about Microsoft Security for enterprise. 

¹ Tracking the Trends 2025 | Deloitte US, Deloitte 2025

² 2025: The Year the Frontier Firm Is Born, Microsoft, April 2025

³ The energy transition will need critical minerals and metals. Here’s how to mine responsibly, World Economic Forum, June 2024

⁴ Now is the time to invest in sustainable mining technologies. Here’s why, World Economic Forum, September 2024

The post Embracing AI and adaptive cloud to drive digital transformation in mining appeared first on Microsoft Industry Blogs.

We see tremendous AI adoption across process manufacturing industries. The focus is shifting from experimenting with pilots to implementing AI in a way that delivers real business value. Leaders are now focused on how to get started and how to ensure a clear return on investment. Artificial Intelligence in Process Manufacturing: Preparing for an AI Future, a new manufacturing signals industry report published by Microsoft with research by IoT Analytics, presents insights into how manufacturers in process industries prioritize technology today and where AI fits into the picture. The report provides valuable insights for navigating the implementation of AI.

AI adoption is accelerating and entering a new phase

AI is gaining real traction in process manufacturing. Building on investments in Internet of Things (IoT), automation, and advanced process controls, manufacturers are focused on how AI can drive enterprise-wide decision-making and long-term value. This shift is no longer about if AI is worth pursuing—it’s about how to start effectively and drive measurable impact. As manufacturers move from pilot programs to broader deployment, the opportunity extends beyond task-level automation. AI is enabling predictive, real-time decision making across operations, research and development (R&D), and the supply chain—unlocking value that legacy systems can’t deliver alone. From my conversations with customers, the biggest barrier to generative AI isn’t the technology, it’s getting the data right.

This next phase of AI adoption depends on strong data foundations, grounded in enterprise data and context, with clear business alignment, and an organization-wide readiness to operationalize insights. Manufacturers that get this right are already seeing the results.

AI is supporting real business priorities

AI is helping manufacturers tackle two of their top business priorities: improving operational efficiency and driving revenue growth. By reducing waste, minimizing downtime, and optimizing output, AI-powered insights enable targeted operational improvements. The same data intelligence also fuels research and development (R&D), accelerates time-to-market, and uncovers opportunities for market expansion and business differentiation. One global chemical company reported that AI helped reduce the time-to-market for molecular enhancements from six months to just six to eight weeks¹—a powerful example of how operational innovation translates into business acceleration. 

The signals report also explores how industrial AI drives benefits beyond cost and throughput, from better data integration to improved customer satisfaction—ultimately enabling smarter, faster decisions across the value chain.

AI use cases with measurable business impact

The signals report surfaces real-world use cases where AI is delivering measurable results—not just technical improvements, but business transformation. From reducing downtime to accelerating product development, industrial leaders are applying AI in areas such as: 

• Process optimization
• Sustainability, energy efficiency, and waste reduction
• Research and development
• Predictive maintenance and analytics

Adoption is scaling fast: 80% of manufacturers surveyed are either using or planning to adopt generative AI. These solutions are driving change across every level of the organization—from frontline operations to management decision-making. 

A rubber and plastics manufacturer reported significant improvements to plastic design for more efficient production. A chemical company achieved a 90% reduction in demand forecasting costs and dramatically accelerated knowledge retrieval—enabling users to access answers in seconds instead of days.¹ And in the words of one life sciences organization: “Our employees have more power to support farmers, help cure diseases and see consumers healthier.”¹

These examples offer a compelling view into how industrial AI is already reshaping core operations, creating value well beyond the pilot stage.

Addressing security and complexity head-on

As more manufacturers embrace AI, leading organizations are not just navigating challenges—they’re building the strategies to overcome them. The signals report highlights two areas that require thoughtful planning: security and system complexity. 

Security remains a key consideration. Nearly half of respondents say concerns around data protection—from IP theft to regulatory compliance—impact their AI adoption decisions. In industries where uptime, safety, and proprietary processes are critical, protecting sensitive data is non-negotiable. 

Fortunately, security and AI aren’t mutually exclusive. Companies are investing in responsible AI practices, secure architectures, and governance models that enable innovation without compromising protection. 

Complexity is the other major hurdle. Legacy systems often lack interoperability, and introducing AI may require adapting long-standing workflows. But many manufacturers are proving that modernization is possible—and that the payoff is worth it. 

The signals report offers guidance on how to approach these challenges with the right foundation, so AI becomes a source of advantage, not friction.

Laying the foundation

Successful AI adoption requires a strong governance framework—it’s not about experimenting endlessly with every possible AI use case but rather focusing on the most strategic use cases that will deliver business value. Building this framework requires the right foundation to scale impact over time. Leading manufacturers are taking a structured approach: aligning AI investments to business goals, modernizing infrastructure, and investing in the skills needed to sustain innovation. 

The signals report outlines four practical steps manufacturers are taking to move from isolated pilots to enterprise-wide transformation: 

• Identify business needs
• Embrace structural flexibility
• Get the data in order
• Use AI to develop workforce capabilities 

These are more than recommendations—they reflect what real manufacturers are doing to turn AI into a competitive advantage. And for many, AI is no longer optional, but essential to unlocking the next wave of efficiency, innovation, and competitiveness. The signals report brings each step to life with examples from the field. 

Download the full report on Artificial Intelligence in Process Manufacturing to explore the research, benchmark your readiness, and take your next step toward AI-powered transformation. 

Preparing for an AI future

Artificial Intelligence in Process Manufacturing

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¹ Artificial Intelligence in Process Manufacturing

The post AI in process manufacturing: From operational gains to strategic advantage appeared first on Microsoft Industry Blogs.

As we witnessed in many leadership dialogues at CERAWeek 2025, AI adoption is taking place at an accelerated speed in the energy sector. The role of AI in meeting rising energy demand is multifaceted and transformative. AI can optimize operations, reduce  energy consumption, enhance grid capacity and reliability, and support renewable energy integration. It can also address energy security and sustainability efforts, such as carbon capture and storage, and methane management and mitigation. AI’s potential in the energy sector is robust. Critically, all these innovations are underpinned by the industry’s collective goal to mitigate environmental impact and continue to move towards a more sustainable future, while meeting the growing demand for energy.   

Microsoft is committed to helping to drive progress in the energy industry through technological innovation—working to empower the energy workforce, optimize operational efficiency and safety, advance net zero commitments, and grow sustainable, AI-powered businesses.  

CERAWeek 2025 takeaways 

In March 2025, we attended CERAWeek 2025, one of the most influential energy gatherings of the year, which provides a platform for over 8,000 attendees to connect, share and discuss insights, and explore where the industry is headed.  

This year, a few key takeaways from the event were: 

• AI is changing the game across industries, and energy is no exception.
  Jason Zander, Executive Vice President for Strategic Missions and Technologies at Microsoft, spoke in the session “Will AI Revolutionize the Energy Sector?” and discussed the ways in which AI is transforming the energy sector with innovative solutions that boost energy efficiency, optimize production, and integrate renewable resources. One of the recent breakthroughs that Jason highlighted is Microsoft’s Majorana 1, the world’s first Quantum Processing Unit (QPU) powered by a Topological Core, designed to scale to a million qubits on a single chip. This advancement has practical applications to help solve some of the most difficult global challenges. It brings the potential to revolutionize the energy industry with possibilities such as developing self-healing materials, enhancing safety, creating catalysts to break down plastics, and significantly advancing sustainability through more recyclable and reusable materials. 

• Powerful collaboration with industry-leading, strategic partners is key to growth and innovation.
  Several of our partners; Accenture, Cognite, Honeywell, Kongsberg Digital, Schneider Electric, and SLB showcased their industry-leading, real-world solutions in the Microsoft booth. These innovative AI-powered solutions illustrate the immense opportunity to transform the energy industry and empower the energy workforce with modern technology.
  
  One standout demo was Schneider Electric’s new One Digital Grid Platform, an AI-powered system that helps improve the reliability and efficiency of power grids. Using Microsoft Azure, this platform allows different software solutions to work together, helping utilities modernize their grids and provide cleaner, more affordable energy at a lower overall cost. Schneider Electric is leveraging Microsoft solutions to improve grid reliability, and is collaborating with Itron to do so. Solutions like these, built with strategic partners, address the need for a modern, digital grid to provide energy to all who need it.   

Transform and optimize resilient energy systems with AI 

AI’s potential in the energy industry is transformative, with the capability to optimize operations, strengthen security, and advance decarbonization. To provide the energy that the world will require, the energy industry needs intelligent solutions that address these needs with faster insights and increased productivity.  

The impact of AI solutions is directly correlated to the amount of data companies have at their disposal. For energy companies that may have a fractured data estate spanning diverse environments and proprietary data formats, establishing a unified data foundation is a difficult task. This is why Microsoft is committed to equipping industry leaders with powerful, enterprise platforms like the Microsoft Cloud to accelerate this transformation. And with Microsoft Fabric, data, AI, security, and applications are integrated to enable real-time data processing and AI-powered insights so that companies can make informed decisions more quickly. Microsoft Azure Data Manager for Energy is also an important piece of this energy data journey, and critical to carbon storage and planning. Most powerfully, it can create a repository for all of an organization’s data in a single location. Furthermore, by adopting our adaptive cloud approach and unifying siloed teams, distributed sites, and sprawling systems into a single operation, organizations can increase security, improve data modeling, and leverage cloud-native and AI technologies across hybrid, multicloud, edge, and Internet of Things (IoT) environments. 

From there, custom reports and insights can be generated with Microsoft 365 Copilot. These cutting-edge solutions are designed to help energy companies harness the full potential of their data with AI—enabling seamless integration, real-time insights, and predictive analytics to help drive efficiency and innovation across the sector.  

One vast opportunity for the energy sector when it comes to using AI is its power to help decarbonize the energy value chain. It’s clear that decarbonization is a critical step towards achieving a sustainable and net-zero future—involving reducing carbon emissions across all stages of energy production, distribution, and consumption. By integrating renewable energy sources and improving energy efficiency through adopting innovative technologies like AI, the energy sector has the potential to significantly lower its carbon footprint more quickly than was possible without the efficiency gains from AI. The recent International Energy Agency report, “Energy and AI” shares several potential examples of cost and energy savings in power plant operations and end-use sectors, such as manufacturing and transportation. For example, AI in transportation can enhance vehicle operation and management, potentially cutting energy consumption—and therefore emissions—up to 20%.² 

AI’s potential in enhancing security is immense, offering both vast implications and opportunities. Using AI to enhance security is already making a difference for companies, with a recent study of Copilot users showing that using Microsoft Security Copilot reduced mean time to resolution by 30%.³ Harnessing the power of AI to bolster security in the energy industry, we recently introduced six new Microsoft Security Copilot agents. These agents represent the power of AI to respond to an increasingly high volume of security threats that energy organizations face today. They are designed to autonomously manage high-volume security tasks, helping to provide robust protection for critical energy infrastructure. By leveraging advanced threat intelligence and machine learning, these agents can swiftly detect, investigate, and respond to security incidents, helping to mitigate risks associated with cyberattacks.  

Demonstrating the power of collaboration in AI innovation, new partner-developed agents will also be available in Security Copilot. These solutions offer industry-specific solutions, such as the ability to understand sector-specific compliance needs. This approach enhances operational efficiency and will help to strengthen the overall security posture for energy companies that adopt agents, allowing IT and operations teams to focus on their core operations while maintaining a resilient defense against ever-evolving threats.  

Our customers like Chevron are also already seeing the impact of AI in their operations, integrating real-time data from IoT devices, optimizing equipment and bandwidth costs, and accelerating decision-making with AI at the edge. 

Collaborating with Chevron on facilities of the future with Azure IoT Operations  

With its Facilities and Operations of the Future initiative, Chevron is reimagining the monitoring of its physical operations to support remote and autonomous operations through enhanced capabilities and real-time access to data. This includes ongoing efforts to use technology to unlock access to connected data across a vast network of IoT devices—ultimately for greater speed to decisions. Chevron worked closely with Microsoft to deploy Azure IoT Operations, enabled by Azure Arc. This edge-to-cloud data plane facilitates data capture from various devices like Wi-Fi cameras, thermal cameras, sensors, robots, and drones at the edge before sending it to the cloud.  Chevron chose Azure for its flexible infrastructure to control data and scale globally, unifying sites and systems into one AI-assisted control pane. Using AI at the edge, where sensors are located at the equipment, helps optimize equipment and bandwidth costs and accelerates speed to insights.  

Learn more about energy and resources solutions with Microsoft  

As we look to the future, AI is becoming an increasingly important force in the energy industry—enabling companies to achieve greater safety and efficiency, help secure their operations, and increase sustainability. Key to this growth and innovation is powerful collaboration with our strategic partners and secure, resilient solutions that meet the industry’s robust needs. Microsoft remains dedicated to supporting this journey, providing the tools and technologies needed to thrive in an ever-evolving energy landscape. 

• Explore CERAWeek 2025 highlights.
• Visit Microsoft for energy and resources.
• Learn more about Microsoft Security for Enterprise.
• Read about Microsoft responsible AI.

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¹ Global Energy Perspective 2024, McKinsey, September 2024. 

² Energy and AI, AI for energy optimization and innovation.  

³ Agentic AI and Microsoft Security Copilot: Revolutionizing cybersecurity, March 2025.

The post Empower a data and AI-powered, sustainable energy future with Microsoft appeared first on Microsoft Industry Blogs.

What are OSS and BSS?

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Modernizing OSS and BSS: From reactive to agentic AI

OSS and BSS have long been the operational and commercial backbone of telecoms. Generally speaking, OSS manages network operations—provisioning, inventory, and fault detection—while BSS handles transactional functions like billing and customer management. Traditionally, these environments have remained fragmented, hindering a unified view spanning the customer, the network, and the business.

Thanks to advances in data management, AI and generative AI, these systems can now move beyond reactive troubleshooting to automated, predictive, and—even more significantly—agentic solutions, in which AI autonomously orchestrates tasks end-to-end. Whether it’s proactively responding to service degradations or autonomously managing resolving customer issues, agentic AI promises unprecedented cost mitigation, efficiency, and agility. 

However, effectively harnessing the proactive benefits of agentic AI requires telecom providers to establish a unified source of data truth through seamless data accessibility, rather than trying to consolidate all data onto a single platform. By enabling unified access to network, operational, and business data through a singular data catalog—such as Microsoft Fabric, which utilizes shortcuts and mirroring—telecoms ensure AI-powered insights are accurate and comprehensive. Without cohesive access to high-quality data, AI-powered insights risk becoming fragmented or misleading, limiting the transformative potential of autonomous decision-making and potentially leading to inaccurate, risky decisions. 

The critical importance of data accessibility and cohesion is exemplified by AT&T’s migration to Azure Databricks, highlighting tangible benefits: 

1. Unified data access and operational visibility: Instead of traditional consolidation, unified data access through platforms like Microsoft Fabric provides comprehensive context, enabling AI algorithms to generate precise, actionable insights. AT&T’s migration to Azure Databricks illustrates how improving accessibility to quality data across silos empowers technical staff, enhances analytical capabilities, and improves decision-making accuracy—dramatically reducing the risk of overlooking critical dependencies or making suboptimal decisions.
2. It enables closed-loop intelligence: Agentic AI extends beyond merely analyzing data; it proactively acts in near real-time. A cohesive data access approach, like the one implemented by AT&T, facilitates rapid anomaly detection and automated corrective actions within network and revenue systems. This closed-loop intelligence is crucial for next-generation AIOps, enabling seamless and automated responses across the entire telecom infrastructure. 
3. It accelerates new revenue opportunities: Providing cohesive access to operational and business data creates agile, scalable monetization pathways. AT&T’s adoption of Azure Databricks accelerated its ability to launch new services by automating complex data processing and analytics tasks. Similarly, telecoms leveraging unified data access solutions can rapidly provision and monetize services such as customized 5G and 6G experiences or on-demand network slicing—shifting from manual processes to dynamic, programmable offerings.

A modern, agentic, cloud-native OSS and BSS environment built on public cloud principles doesn’t just serve the operator; it also creates a frictionless platform for third-party and ecosystem partners to plug in. Whether it’s Internet of Things (IoT) device vendors, over-the-top content providers, or enterprise service integrators, cloud-native OSS with open APIs allows rapid partner onboarding and co-creation. In turn, operators can easily expand their portfolio with new revenue streams—bolstering the business to business to everything (B2B2X) model—while still maintaining centralized oversight and robust security at scale. 

Agentic AI in action: From insight to autonomous operations

Faster time-to-market for new services

Traditionally, launching a new offering in telecom could take upward of 50 weeks, hindered by lengthy approvals, hardware provisioning, and siloed systems. In a cloud-native environment, operators can test, iterate, and deploy new products—like on-demand network slicing or advanced IoT bundles—in days or even hours. This speed is a game changer for operators transitioning from ‘telcos’ to ‘tech-cos,’ where continuous experimentation and rapid scaling of successful pilots are essential to staying competitive. Coupled with agentic AI that autonomously manages tasks, cloud-based OSS and BSS ensures you don’t just move faster—you move smarter. Leading telecoms are already laying the groundwork for agentic AI by adopting:

• Predictive analytics for network health: For instance, AI-powered anomaly detection can preempt equipment failures, but true autonomy means the system itself orders the replacement part, dispatches a technician, and reroutes traffic in the meantime—all driven by integrated data across OSS and field service management. 
• Proactive policy and billing: In a unified data environment, usage spikes or new IoT device activations can trigger dynamic policy updates in real time—while simultaneously adjusting billing parameters. This end-to-end automation requires that the network layer (OSS) and the revenue layer (BSS) share data instantly and accurately. 

Why run OSS on the public cloud?

As service catalogs explode and customer demands evolve more rapidly, operators need elastic, scalable infrastructure to shorten time-to-market and accommodate fluctuating loads. Public cloud delivers on-demand compute and storage, reducing capital expenses and enabling rapid innovation with built-in AI and machine learning services. Moreover, the global reach and reliability of platforms such as Microsoft Azure allow telecoms to replicate, secure, and manage their OSS across regions far more easily than traditional on-premises setups. By shifting OSS to a cloud-native model, operators can pivot from lengthy, monolithic upgrade cycles to nimble, iterative releases—critical for accelerating 5G and 6G services, IoT offerings, and B2B2X monetization scenarios.

Self-optimizing networks and beyond

While self-optimizing networks (SON) currently manages aspects of radio access networks, next-generation AI solutions extend self-optimization to the entire telecom domain. Microsoft Project Janus is an early example of how real-time AI-powered telemetry can proactively detect network anomalies, predict service degradations, and dynamically optimize network resources—laying the foundation for fully autonomous network operations. Telefónica España, for example, leveraged Azure AI and machine learning to achieve significant improvements in network performance and efficiency. By incorporating AI and big data technologies, Telefónica España is developing more intelligent networks capable of self-optimization and adaptation. This intelligence allows for a reduction in time to market for new solutions, enabling the company to swiftly implement innovations that enhance network performance and customer satisfaction. With advanced generative AI, AI-powered instructions can autonomously fine-tune network configurations, adapt capacity, and realign resources based on live traffic patterns. This orchestration is feasible only when AI has an enterprise-wide view of network, business, and operational data.

Embracing open standards and ecosystem collaboration

Just as critical as data consolidation is ensuring interoperability and flexibility. Many telecoms are turning to TM Forum’s Open APIs and adopting Open Digital Architecture (ODA) principles. These frameworks reduce vendor lock-in, streamline data exchange, and allow AI solutions to operate across heterogeneous environments. 

For example, TM Forum’s collaboration with Microsoft has accelerated the adoption of carrier-grade, open-source ODA canvases. By aligning Azure’s robust cloud capabilities with ODA standards, operators are now better equipped to innovate rapidly, simplify complex integrations, and significantly reduce the operational hurdles associated with legacy systems.

Microsoft plays a pivotal role in supporting these open standards, providing a cloud-native, modular approach fully aligned with ODA. A practical illustration is Sure Telecom’s adoption of Azure, where leveraging Microsoft’s open API framework allowed them to consolidate disparate data sources and achieve enhanced customer insights and operational efficiency. Microsoft’s platform delivers out-of-the-box integrations and open APIs that empower operators to harness AI-powered analytics and intelligent automation workflows, minimizing friction traditionally encountered during legacy system modernization. 

Achieving scale with cloud-native AI

A robust, cloud-native foundation is essential for scaling AI across complex telecommunication environments. Containerized microservices, DevOps practices, and serverless compute reduce operational overhead, allowing teams to focus on innovating rather than managing infrastructure. Within such environments: 

• Azure AI services streamlines the training, deployment, and monitoring of AI models across OSS and BSS workloads. 
• Microsoft Fabric fosters seamless data ingestion, orchestration, and transformation—critical for building that unified data estate necessary for agentic AI. 

By converging data and AI workloads in the cloud, telecoms can more quickly test and deploy innovative services that leverage advanced analytics for both operational efficiency and new revenue streams.

In addition to the operational and technical upsides, running on public cloud offers a more predictable and flexible cost model. Instead of large capital expenditures tied to peak capacity, operators pay only for what they consume. This shift in economics not only aligns with sporadic traffic spikes—common in modern usage-based and event-driven architectures—but also frees up budget to invest in strategic AI initiatives. By reducing hardware overhead, maintenance, and upgrade costs, telecoms can reinvest in higher-value activities such as AI-powered product innovation and partner ecosystem growth. 

Microsoft’s unique value: Building a telecom foundation for agentic AI

Microsoft combines a partner-centric approach with end-to-end technology solutions—bringing actionable capabilities to telecoms that want to realize AI-powered OSS and BSS at scale.

Key value streams include: 

1. Telecom-specific cloud and data services: Telecom-optimized solutions from Microsoft and its partners help unify network, operational, and customer data into a single source of truth. 
2. First-party AI agents: Microsoft’s growing suite of autonomous agents, such as those integrated within Dynamics 365, automate complex business processes—enhancing efficiency and decision-making across various telecom operations. 
3. Alignment with industry standards: Microsoft’s active support for TM Forum and ODA ensures an open, interoperable environment. Operators can adopt AI without overhauling existing infrastructure or incurring vendor lock-in. 
4. Security and compliance: As AI-powered automations become central to business functions, Microsoft provides enterprise-grade security and governance—critical for protecting sensitive network and customer data. 
5. Partner ecosystem: Collaborations with leading vendors—such as Amdocs, CSG, Blue Planet, ServiceNow, Netcracker, and system integrators—create end-to-end workflows that accelerate modernization and reduce complexity. Through these partnerships, Microsoft’s AI tools seamlessly integrate with telecom-specific applications.

Positioning for revenue impact and the autonomous future

When OSS and BSS data is unified and AI-powered processes take over routine tasks, telecoms can prioritize innovation that directly impacts the bottom line. Whether rolling out new network services or offering real-time network slicing for enterprise customers, the ability to act on consolidated data in an autonomous fashion sets operators apart in a hyper-competitive market.

Short-term gains include faster time-to-market for new services, reduced operational costs, and improved customer experiences. Longer term, fully autonomous, self-healing networks that optimize themselves and require minimal manual intervention, unlock new revenue streams through AI-powered insights. Project Janus is already demonstrating this shift—showcasing how AI-powered network intelligence moves beyond predictive analytics into autonomous, self-optimizing operations that reduce operational overhead and ensure peak performance with minimal human intervention.

Project Janus demonstrates how AI-powered network intelligence can move beyond predictive analytics into autonomous, self-optimizing networks—reducing operational overhead and ensuring peak performance with minimal human intervention. 

Ready to transform your operations?

The industry is moving beyond point solutions toward a future where agentic AI and unified data estates power autonomous operations. For telecom leaders, now is the time to ensure OSS and BSS modernization strategies align with open standards, prioritize data consolidation, and prepare for the emergence of fully autonomous networks.

Microsoft and its partners are here to guide you on this journey—from building robust cloud-native foundations and consolidating your data estate to delivering intelligent, revenue-focused transformations across OSS and BSS. By embracing this approach today, you’ll ensure your operations not only keep pace with evolving market demands but lead the next era of telecommunications innovation. 

Learn more about our AI and generative AI solutions for telecommunications and discover how we can help you lay the groundwork for the agentic AI revolution—starting with your most strategic asset: your data.

The post The transformative impact of AI and generative AI on OSS and BSS in telecommunications appeared first on Microsoft Industry Blogs.

• Data collection and processing at the edge: Providing secure, reliable, and low-latency data transfer and processing in highly sensitive and distributed environments.
• Secure communication and interoperability: Ensuring seamless integration and communication across different domains and platforms.
• Data security: Protecting sensitive information from cyber threats and unauthorized access.
• Real-time analytics: Providing real-time insights and analytics across a fusion of many different data types, to support decision-making.

By solving these challenges, decision-makers can act on near real-time updates and intelligence, enhancing situational awareness and enabling mission success.

How Microsoft Cloud helps solve legacy system challenges

Microsoft is well placed to respond to these challenges through the hyperscale cloud capabilities of Microsoft Azure, encompassing a global network of data centers, servers, and networks that power cloud services, including:

• The Microsoft Adaptive Cloud approach, which lets organizations use cloud-native and AI technologies across hybrid, multi-cloud, edge, and Internet of Things (IoT) environments. This helps defense organizations ensure consistent operations by extending cloud services to on-premises and multi-cloud environments, and it simplifies operations with centralized management, enhanced security, and seamless integration across diverse and complex environments. Additionally, it allows for easier application deployment and a common data foundation across environments.
• Azure Local, enabled by Azure Arc, which is a specialized offering designed to bring cloud computing capabilities directly to the edge, closer to where data is generated, and decisions need to be made. For defense and intelligence customers, this means enhanced security, reduced latency, and improved operational efficiency by processing data locally rather than relying solely on centralized cloud services. This approach is crucial for defense and intelligence operations, where timely and secure data handling significantly impacts mission success.

Adaptive Cloud and Azure Local solutions in action

By way of illustration, consider a joint task force assigned to secure a national border as part of a multi-domain operation (MDO). The objective is to identify and address potential threats, including unauthorized crossings, smuggling activities, and aerial incursions. This is achieved by using advanced technologies, which can potentially benefit the following warfighting functions:

• Land forces patrolling the coastline
• Naval units monitoring the sea lines of communication
• Air units conducting intelligence, surveillance and reconnaissance (ISR) collection
• Cyber units ensuring secure communication and protecting against cyber threats
• Space units ensuring satellite availability for communications and geospatial intelligence collection

Let’s look at some specific scenarios and how technology can help achieve success:

Real-time data collection and edge processing

IoT data collection

Data is collected and processed directly from IoT devices in real-time, close to the source, reducing latency and enhancing security.

How it works:

• Ground sensors and drones equipped with cameras and motion detectors monitor coastline activities.
• Buoys and unmanned surface vehicles (USVs) collect data on maritime traffic and environmental conditions.
• Drones and aircraft equipped with radar and cameras provide aerial surveillance.
• Azure IoT operations deployed on Azure Local securely process and normalize this data at the edge.

Edge processing

The data collected from sensors is processed and transmitted to Azure Local instances deployed at mobile command centers.

How it works:

• Local AI inferencing, such as Azure AI Video Indexer, allows the processing of data at the source. By conducting real-time analysis directly within an environmental context, defense organizations can respond faster and more accurately to emergent situations using AI and machine learning models to analyze patterns, detect anomalies, and provide actionable insights to field commanders.
• Azure Local supports both legacy systems and modern containerized applications, allowing the defense organization to run a mix of applications needed for the mission, from traditional command and control systems to advanced AI-powered analytics.
• Through edge processing, critical information can now be filtered prior to its transmission to the cloud—for instance, identifying potential threats, such as unidentified aircraft or submarines, and alerting the command center for appropriate action.
  • For all tactical units, where traditional terrestrial connectivity is limited or unavailable, low earth orbit (LEO) satellite connections provide connectivity to remote and mobile units, such as ships at sea, aircraft in flight, or land-based command and control nodes. Satellite communication can ensure continuous and secure data transmission, critical to information sharing in a joint operation.
  • Forward operating bases (FOBs) process data on Azure Local, securely transmitting it to the cloud using Azure ExpressRoute, which provides a private connection between the edge and Azure, bypassing the public internet and supporting encryption technologies like MACsec and IPsec to ensure data confidentiality and integrity.

Command and control (C2) situational awareness

The task force sustains a thorough and current operational overview by using data transmitted to Azure from Azure Local. With cloud technologies, command and control data flows seamlessly from collection to actionable insights. The C2 node assesses the situation and determines the appropriate response such as route planning, resource allocation, and threat assessment.

How it works:

• Real-time intelligence managed with Microsoft Fabric, a unified AI data and analytics platform, enables a C2 node to swiftly analyze data from the edge using technologies like Azure Event Hubs and AMQP for data ingestion, and Microsoft Power BI for visualization. The real-time hub provides a unified interface for managing streaming data sources, allowing for rapid decision-making and enhanced situational awareness. Data is further processed and made available to Azure AI Foundry, for use in advanced AI applications.
• AI Foundry uses this data to deploy AI models assisting commanders in analyzing battlefield data and suggesting optimal strategies—for example, using AI models to perform sentiment analysis on communication data from the field. By analyzing the sentiment of messages, AI can identify potential stress or urgency in communications, providing valuable insights to commanders. Additionally, AI can detect patterns and anomalies in the data, such as unusual movements or activities, and alert the command center for further investigation.
• Units can then swiftly adapt to the updated operational plan. Analyzed data and directives from the C2 node are sent to Azure Local. Military applications running on Azure Local Virtual Machine receive directives from the C2 node. The units reconfigure their routes based on the optimized path provided, ensuring efficient movement and resource utilization. They allocate resources as per the new directives, prioritizing critical areas identified by the C2 node. Additionally, the units enhance their threat assessment protocols, incorporating the latest intelligence to mitigate potential risks.

By using Azure Local, the joint task force’s multi-domain operation not only addresses immediate threats but also establishes a robust framework for ongoing border security enabling seamless coordination and integration across land, sea, air, cyber, and space domains. By extending Azure services and security to distributed locations, apps and data are better safeguarded against advanced threats, ensuring reliable protection and operational efficiency.

• Real-time situational awareness: Rapidly assess and respond to emergent situations ensuring the border remains secure.
• Enhanced security: Secure communication channels and robust cybersecurity measures protect sensitive information from cyber threats. This ensures that all units can communicate effectively and securely, maintaining the integrity of the operation.
• Efficient decision-making: Advanced analytics and AI-powered insights enable quick and informed decision-making. The command center can process vast amounts of data in real-time, allowing for swift and accurate responses to emerging threats.

Benefits of Microsoft Adaptive Cloud and Azure Local in defense operations

Enhanced security

• Hardened security posture: Azure Local instances are configured with secured-core settings and automatic data encryption by default, protecting sensitive military communications and intelligence data from cyber threats.
• Microsoft Defender for Cloud Integration: Azure Local integrates natively with Microsoft Defender for Cloud, offering comprehensive monitoring and advanced threat protection. This ensures that potential security breaches are promptly detected and mitigated.
• Network Security Groups (NSGs): NSGs in Azure Local manage and secure network traffic within the Azure environment, allowing for control of inbound and outbound traffic to virtual networks, subnets, and network interfaces with defined security rules. These rules can permit or deny traffic based on various criteria such as source and destination IP addresses, ports, and protocols.
• Trusted launch: Enhance protection against sophisticated threats such as malware-based rootkits and bootkits with Trusted launch security. This includes secure boot, which guarantees that only trusted software is loaded during the boot process, and a virtual trusted platform module (vTPM), which securely stores keys, certificates, and secrets.

Operational flexibility

• Disconnected operations: In areas with limited or no connectivity, Azure Local supports disconnected operations, allowing joint forces to maintain situational awareness and make informed decisions even when not connected to Azure. Data can be synchronized with the C2 node once connectivity is restored.
• Flexible hardware options: Azure Local’s extensive catalog supports rugged hardware suitable for harsh environments, ensuring reliable performance even in extreme conditions.
• Scalability: In support of mission needs, additional Azure Local instances can be quickly deployed to new locations, providing the necessary computing and storage resources to support expanding operations.

Explore Microsoft for defense and intelligence

Learn how Microsoft Cloud can help achieve mission outcomes to promote stability and security:

• Contact your Microsoft representative or technology solution partner.
• Visit the Microsoft for defense and intelligence website

Microsoft for defense and intelligence

Explore solutions

The post Microsoft Adaptive Cloud: Advancing edge computing in the defense sector appeared first on Microsoft Industry Blogs.