These stories don’t have much in common on the surface, but together, they point to a broader shift: AI is helping nonprofit organizations across the globe tackle some of society’s most pressing challenges.  

Animal Protection Denmark, Answer ALS, and Everything Suarve are three nonprofits that turned to Microsoft to solve different problems. Today, AI is helping them expand opportunity, strengthen communities, and create new possibilities for the people, animals, and causes they serve—extending their impact far beyond what their size or resources might otherwise allow.  

Animal Protection Denmark: From guesswork to insight in kitten care

Every year, Animal Protection Denmark prepares for “kitten season,” when shelters see a surge in vulnerable animals needing care. As the organization grew, so did the volume of data flowing across shelters, foster networks, volunteers, and supporters. Teams often spent long hours every month reconciling information from different systems, making it harder to forecast needs and coordinate resources.

Using Microsoft data and AI capabilities, Animal Protection Denmark unified that data into a single source of truth. Teams can identify trends earlier, forecast demand for foster placements, and monitor shelter capacity in real time. Staff can also access complete records for each animal, helping ensure continuity of care and supporting faster adoption decisions.

By combining a unified data foundation with AI-powered insights, Animal Protection Denmark spends less time managing information and more time acting on it. The result is earlier planning, faster decisions, and better outcomes for the animals that depend on them.

Answer ALS: Turning data into discovery

Answer ALS was created to accelerate progress toward treatments and a cure for amyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disease that affects over 450,000 people worldwide. Together with Microsoft, the organization built Neuromine, one of the world’s largest ALS research hubs, bringing together trillions of data points contributed by more than 2,500 people living with the disease. Azure AI Search powers Neuromine’s query function so researchers can quickly learn details about an individual’s disease trajectory or even their DNA, which helps pinpoint cell lines to further study.

Additionally, Answer ALS is developing a chatbot in Microsoft Foundry that uses generative AI to answer user questions and direct researchers to relevant data. Before Neuromine, researchers often spent months, and sometimes more than a year, assembling data and biological samples before meaningful analysis could begin. Today, researchers around the world can access hundreds of patient lines and related clinical data in hours rather than months, helping them move more quickly from questions to insights.

By making high-quality research data easier to explore, share, and build upon, Answer ALS is helping accelerate research by as much as 65% over the next few years. The platform is enabling researchers around the world to collaborate more effectively, uncover new patterns in the data, and advance the search for better treatments and, ultimately, a cure.

Everything Suarve: Scaling second chances with AI 

Everything Suarve helps young people in Australia rebuild their lives through job training, mentorship, mental health support, and practical life skills. Many participants arrive after facing challenges such as housing insecurity, unstable home environments, or trauma. For a small team, the work depends on trust, consistency, and showing up for young people when they need support most.

As demand for its programs grew, so did the administrative burden. Referrals arrived through emails, paper forms, and phone calls, while staff tracked participant progress across disconnected systems. To streamline operations, Everything Suarve used Microsoft data and AI capabilities to build a solution that centralizes enrollment, case management, reporting, and communications. The organization also uses Microsoft 365 Copilot to help with grant writing, document summarization, and other administrative tasks, reducing the time spent on manual work.

Today, Everything Suarve’s staff can manage referrals, case notes, reporting, and participant communications in a single workflow. The new platform saves up to eight hours per participant during enrollment, while Copilot reduces grant application work by as much as two weeks. By automating processes and putting AI to work, Everything Suarve is reducing administrative overhead and creating more time to help young people build confidence, skills, and a path forward toward employment, education, and long-term stability.

What other nonprofits can learn from these stories

These three organizations serve different communities and pursue different goals. Yet together they show that organizations create the most value from AI when they use it to strengthen human expertise and start with a problem worth solving or a mission worth advancing.

At Animal Protection Denmark, staff can spot needs before they peak. At Answer ALS, researchers are compressing years of discovery into months. At Everything Suarve, staff spend less time on forms and more time with young people rebuilding their lives.

Together, these stories reflect a broader shift already underway across organizations of every size. AI is becoming part of how decisions are made, how services get delivered, and how people work. Success is no longer defined by the technology alone. It comes from combining technology with human judgment, experience, and purpose.

We call this Frontier Transformation: to move beyond isolated tools and individual tasks to rethink how they achieve their mission. Sometimes the result is greater efficiency. Often, it creates something more meaningful: the ability to reach more people, respond more quickly, and extend impact in ways that were previously out of reach.

As AI becomes more accessible, these nonprofits are showing that meaningful transformation is not reserved for the largest enterprises. It can begin anywhere people are willing to pair innovation with a clear sense of purpose.

The post Inside nonprofits using AI to extend their impact appeared first on The Microsoft Cloud Blog.

As operators move from experimentation to execution, the opportunity is not just to adopt more AI, but to build systems that combine intelligence with trust across networks, data, and customer operations. TM Forum DTW Ignite 2026 focuses on how those capabilities come together to drive measurable business outcomes.

Composable IT and ecosystems

Complexity continues to slow progress across telecom. Tightly coupled systems, fragmented integration paths, and years of technical debt make it harder to launch new services, adapt quickly, and deliver consistent experiences across channels.

TM Forum continues to position Open Digital Architecture as both a practical migration path away from legacy complexity and the architectural foundation for AI-native operations, with tools, standards, Open APIs, and reusable components designed to support more modular, cloud-native, and interoperable platforms.

That is why the Composable IT and Ecosystems summit sits so centrally in this year’s event: it reflects a broader industry shift from digital transformation as a long-running aspiration to reinvention for the AI era, where openness, composability, and governance become prerequisites for scaling intelligent operations.

For operators, the value is practical. Modular environments make it easier to modernize in stages, improve interoperability across business and network domains, and create a stronger foundation for ecosystem plays such as CAMARA and Open Gateway. This is where solutions such as Infobip’s Network API offering become relevant—helping operators turn standardized capabilities like number verification, SIM swap detection, and device location into practical services that expand revenues, strengthen trust, reduce fraud, and reach more developers.

With Microsoft, teams can standardize platforms, simplify integration, and create an environment where new capabilities can be introduced with greater speed and confidence.

That conversation will continue in the panel discussion “The AI-Native ODA Roadmap.” The session will explore how operators can build AI-native operations on common foundations across IT and networks, with emphasis on interoperability, governance, reusable patterns, and business value. It is a timely example of how TM Forum DTW Ignite 2026 is connecting architectural change to the strategic and operational priorities telecom leaders are navigating now.

That same modernization path is also reshaping the business side of telecom through Agentic Business Support Systems (BSS). Instead of hard-coded workflows, operators can use AI agents to interpret intent, orchestrate actions across catalog, ordering, billing, and care, and accelerate the path from request to resolution.

With Microsoft, TM Forum Open APIs, and a composable architecture, BSS can evolve into a more adaptive, outcome-driven system that enables faster service creation, stronger customer experiences, and new monetization opportunities across the ecosystem.

This direction is reinforced by recent Microsoft Build 2026 announcements, where capabilities such as Work IQ and Agent 365 introduce a consistent way to ground AI agents in enterprise context, apply governance by design, and orchestrate actions across systems. Together, these capabilities make it easier for operators to move from isolated automation to agent-driven workflows that are composable, secure, and production-ready at scale.

This evolution also reflects the emergence of more persistent, context-aware agents—exemplified by experiences such as Microsoft Scout—that can operate continuously across tasks and systems, moving from reactive copilots to proactive digital coworkers that help coordinate work across the enterprise.

Autonomous networks

The industry is moving beyond manual processes toward systems that can interpret signals, recommend next actions, and automate response within clear controls. That shift sits at the heart of the Autonomous Networks summit at DTW Ignite 2026, where the focus is on how operators can make autonomy practical in day-to-day operations.

AT&T offers a strong example for incident management. By improving observability, creating better context across operational data, and accelerating the path from detection to action, operators can improve reliability and shorten the time between issue discovery and resolution. Microsoft supports this direction with cloud-scale data services, AI capabilities, and a platform approach that helps teams operationalize intelligence rather than isolate it in pilots.

 Microsoft’s Network Operations Agent (NOA) framework extends this vision by enabling operators to deploy coordinated AI agents across network operations—helping interpret signals, orchestrate actions, and drive closed-loop automation with greater consistency and control.

The broader direction is clear. Operators are moving toward more predictive operations, more intelligent assurance, and more trusted automation that improves resilience, experience, and efficiency.

AT&T will also bring this story to the Park stage at DTW Ignite 2026, where the discussion is expected to highlight how End-to-End Incident Management uses current context, observability, and AI to move faster from detection to action. It adds a timely, customer-led example of how operators are applying agentic decision-making to improve reliability, reduce disruption, and make autonomy more practical in live network operations.

By bringing more context and intelligence into incident management, we are improving how quickly teams can identify issues, coordinate response, and restore service with confidence. This type of advanced agentic technology is helping us keep our customers connected and provide a better experience for them.

—Mark Austin, Vice President Data Science, AT&T

Underpinning this shift is the emergence of unified data and AI platforms that can ground agents in real-time operational context. With Microsoft Foundry and Foundry IQ, its knowledge layer, operators can connect data across network, service, and customer domains, enabling agents to reason more effectively and support closed-loop decisioning that moves from insight to action with greater speed and consistency.

Microsoft’s recent work in Azure Networking operations adds an important operational lens to this shift. Rather than treating agents as simple assistants, the model is evolving toward more persistent digital coworkers that help coordinate the messy middle of operations across incidents, repairs, vendors, and validation steps. In large-scale environments, that can reduce manual effort, shorten mitigation time, and free engineers to focus more on judgment, oversight, and higher-value decisions while agents keep execution moving across systems and teams.

Trustworthy AI and data

AI creates value only when it is grounded in trusted data, strong governance, and a clear path to action. In telecom, that standard matters because operators work in highly regulated environments and depend on reliable decision-making across critical services.

That is why the Trustworthy AI and Data summit at DTW Ignite 2026 matters. The conversation is not only about model capability. It is about data readiness, governance, observability, and the controls required to scale AI responsibly across the business.

This is reinforced by Microsoft’s perspective on local AI, where operators extend the AI execution model closer to where data is generated—improving latency, resiliency, and control while keeping data private. Running inference locally or at the edge can also shift the cost profile, reducing dependency on centralized compute for time-sensitive or high-volume workloads while enabling new real-time scenarios.

SoftBank brings that point into focus through its AI-powered customer platform, where the value shows up in responsiveness, service quality, and workload reduction. In its work with Microsoft, SoftBank is applying Microsoft Foundry, Azure OpenAI in Foundry Models, Azure AI Search, and Semantic Kernel to help build a more intelligent call center experience that reduces wait times, improves response quality, and supports more scalable customer operations. The example reinforces that AI success depends on the right data foundation and a disciplined link to measurable outcomes.

Microsoft sees telecom ontologies as an important part of the foundation for trustworthy AI. By creating a shared semantic model across network, service, customer, and operations data, they help AI systems reason with more context, improve explainability, and support more consistent decisions across domains. Combined with Microsoft Fabric as the unified data foundation, Fabric IQ as the intelligence layer, and Microsoft’s broader data and AI platform, that structure can help operators reduce fragmentation and move with greater confidence from isolated use cases to governed, reusable intelligence.

That theme will also come through in Alberto Manuel Fernandes Dias’ panel, “Data products at scale—the blueprint for AI-native telcos.” The discussion reinforces that scaling AI in telecom depends on more than models alone. It requires well-designed data products, clear ownership, and disciplined governance to turn fragmented information into reusable, high-quality, and AI-ready assets. For operators, that blueprint matters because it creates the consistency, trust, and lifecycle discipline needed to support autonomy at speed and scale.

Accelerating transformation through partner innovation

Transformation in telecom does not happen in isolation. Across TM Forum DTW Ignite 2026, Microsoft and its partners are working together to help operators modernize networks, simplify operations, and bring AI into real business processes.

From composable IT and cloud-native BSS modernization to agentic marketplaces, digital twins, and voice AI pilots, these collaborations reflect how the industry is moving from experimentation to scalable, outcome-driven innovation.

• Amdocs is highlighting continued momentum in cloud-first modernization through its expanded work with Lumen, helping accelerate enterprise billing transformation on Microsoft Azure. Separately, Amdocs is also showcasing how its Entitlement Server on Azure is setting new performance benchmarks for digital service delivery, reinforcing the value of scalable, AI-ready cloud infrastructure for telecom providers.
• Beyond Now is also demonstrating how AI is reshaping telecom monetization through its work on agentic marketplace experiences. In collaboration with Microsoft, the company is highlighting how operators can use intelligent automation and AI-powered ecosystems to accelerate service innovation, streamline partner engagement, and unlock new digital revenue opportunities.
• Kenmei is advancing how operators can make network data more usable for AI-powered operations through its new Network Performance Data Product built on Microsoft Fabric. Running natively in Azure, the solution combines governed network KPIs with a telecom ontology layer, helping operators accelerate agent-ready use cases while keeping data secure, unified, and under their own control.
• By integrating Nokia Data Suite with Microsoft Fabric, operators can access on-demand telecom data products that simplify data integration across complex, multi-vendor environments. This unified data foundation enables faster adoption of AI, generative AI, and automation, supporting the journey toward autonomous networks.
• Norwood is demonstrating how voice AI is becoming more practical for telecom providers through its OpenSpan Voice platform. In collaboration with Microsoft, the company is piloting AI-powered small and mid-sized businesses (SMB) voice capabilities with a major United Kingdom and Australian telecommunications provider, demonstrating how operators can deliver more intelligent, responsive customer communication experiences at scale.
• Tech Mahindra and Microsoft are advancing telecom innovation with a 5G Network Digital Twin, an intelligent virtual replica that enables AI-powered optimization, proactive issue resolution, and new monetization opportunities. Built on Microsoft Azure and Fabric, the solution unifies data, simulates network scenarios, and drives measurable impact across cost efficiency, network slicing, service assurance, and autonomous operations, while maintaining enterprise-grade governance and data sovereignty.

Customer momentum: From blueprint to production

The strongest signal in telecom today is that customer stories are moving from blueprint to production. Leaders want to see where transformation is working in practice and where AI is delivering value beyond the pilot stage.

• MEO stands out because its modular AI factory connects strategy to execution, giving the company a more repeatable path to scale new use cases across the business. AT&T highlights the operational side of the story, with incident management illustrating how better data and more intelligent workflows can improve detection, triage, and resolution.
• TIM Brazil adds a security-focused example, showing how integrated signals and intelligent protection can strengthen operational resilience while reducing noise and improving response. Together, these examples show how operators are turning transformation into something more tangible, operational, and repeatable.

TM Forum also shows how quickly AI can move from experimentation to scaled impact. Using Microsoft 365 Copilot and Microsoft Copilot Studio, the organization moved rapidly from early pilots to production with internal and member-facing agents. Its cross-functional productivity agent, Buddy, is already delivering measurable ROI across multiple departments, while Navigator—launched in time for TM Forum DTW Ignite 2026—simplifies access to standards and articles for members. The result is a strong example of how AI can improve internal productivity, strengthen knowledge access, and create a foundation for broader support across the innovation cycle, from catalyst to production.

That momentum is visible across the telecom landscape. Recent public examples show operators using Copilot to strengthen leadership decision-making, workforce productivity, and sales execution—from Singtel and Ooredoo Qatar to KPN—while large-scale deployments across Accenture, Cognizant, Infosys, TCS, and Wipro reinforce how quickly the supporting ecosystem is scaling the skills, services, and implementation capacity needed to help enterprises and operators move from pilots to broader adoption. Together, they point to the same shift: AI is becoming part of day-to-day work, with measurable impact on speed, decision quality, and business outcomes.

That is why TM Forum DTW Ignite 2026 feels timely. Innovation matters, but adoption matters more. The next chapter of telecom will be shaped by operators that can turn transformation into repeatable outcomes.

Join us in Copenhagen

TM Forum DTW Ignite 2026 arrives at a pivotal moment for the telecom industry. TM Forum has structured the event around three mission summits all designed to help operators move from ambition to real change:

• Composable IT and Ecosystems
• Autonomous Networks
• Trustworthy AI and Data

We look forward to continuing these conversations in Copenhagen and sharing how Microsoft, together with customers and partners, is helping telecom organizations translate strategy into measurable progress. In the end, the next chapter of telecom will be shaped by operators that can pair intelligence with trust at scale.

Microsoft for telecommunications

Accelerate your business performance and growth with Microsoft AI

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The post TM Forum DTW Ignite 2026: From strategy to execution in the AI era of telecom appeared first on The Microsoft Cloud Blog.

Summary How responsible AI is giving the world’s most stretched profession its time, its insight, and its humanity back.

Shared as part of the 2026 APHSA National Human Services Summit, this article speaks to the Summit’s theme: Leadership for a Stronger Tomorrow. For the leaders of state, county, municipal, tribal, and territorial human services agencies, that tomorrow will be shaped by a single decision—how to put AI to work in ways that strengthen the workforce, sharpen practice, deepen community trust, and turn evidence into action. The paragraphs that follow share what Microsoft is seeing across the world’s most ambitious human services organizations—and where the next opportunity lies.

Microsoft’s booth at APHSA National Human Services Summit is located above the lobby in the Atrium area.

Behind every case file is a person—a child waiting for a safer home, an elder who has stopped answering the door, or a family one missed appointment away from losing housing. And behind every one of those people is a social worker carrying a caseload that often exceeds what any human should reasonably bear.

Social workers are the connective tissue of modern society, bridging many services. Yet the systems built to support them often work against them—pulling professionals away from the very people they entered the field to serve. AI has the potential to change that, not by replacing the human heart of social work, but by giving it room to breathe.

A profession under pressure

The numbers tell a structural, not cyclical, story. The United States Bureau of Labor Statistics counts 810,900 social workers in 2024 and projects roughly 74,000 openings each year through 2034.¹ Across the OECD, health and social services sit among the highest-vacancy sectors, with shortages intensifying as populations age.² In England, 75% of social workers surveyed stated that they feel that society does not value the profession and 42% would not recommend it as a career.³ At Cafcass, 93% of family-court advisers reported unpaid overtime they could not take back.⁴

Behind these numbers is a more painful one: too much of a social worker’s day is spent on documentation rather than people. The United Kingdom Department for Education has now dedicated an entire workstream of its National Workload Action Group to AI in case recording—a quiet acknowledgement that paperwork, not policy, is where time is being lost.⁵

A global shift already underway

From local councils in the United Kingdom to child welfare agencies in the United States, from health systems across Europe to nonprofits serving the world’s most vulnerable, public organizations are moving from reactive to proactive, from siloed to coordinated, and from paper-driven to insight-driven. With administrative friction reduced, the worker–beneficiary relationship—the actual instrument of change in social work—is being restored. Four impact areas show where the evidence is now in.

1. More time with the people who need it

The single most powerful thing AI can do for social workers is give them their time back. Generative AI can draft case notes from a recorded conversation, populate forms from existing records, and turn a 45-minute write-up into a five-minute review. Backlogs shrink, compliance improves, and workers can spend more time in deeper client engagement—enabling higher quality assessments, and more timely care. Returning that time is also a workforce strategy: it eases burnout, raises retention, and gives agency leaders a tangible answer to the recruitment pressures the sector now faces.

The evidence is now consistent:

• In Wales, Torfaen County Borough Council rolled out Microsoft 365 Copilot across children’s services and corporate teams: minute-taking that previously consumed two to three hours per session is now produced in minutes, and frontline workers describe the time returned as time given back to families.
• In the United States, Washington, DC’s Child and Family Services Agency built its platform on Microsoft Dynamics 365, Power Apps, Azure AI, and Microsoft Copilot Studio—saving caseworkers around 45 minutes per intake, delivering new features roughly 20 times more cheaply than the legacy system, and using Copilot Studio agents to automate routine intake work.
• Barnsley Council is taking a similar path on Microsoft 365 Copilot to serve its residents with increasing demand for services with fewer resources.
• Buckinghamshire Council also calls out potential to provide users with Copilot licenses to support accessibility and inclusion, outlining how some of the capabilities mesh well with the needs of the neurodiverse workforce and workers with other accessibility needs.

2. Real-time insights from a single source of truth

Social workers shouldn’t have to be detectives. Yet building a complete picture of a beneficiary often means logging into five systems and hoping nothing falls through the cracks. AI on a unified data foundation changes that—surfacing a 360° view, supporting transparent triage, forecasting demand, and routing each request to the right service the first time. It also gives evaluation and research teams the foundation they need to measure outcomes consistently, link evidence to practice, and demonstrate impact to the communities they serve.

Real world examples include:

• North Yorkshire Council is transforming children’s social care with Microsoft Azure and Azure OpenAI, helping social workers quickly see the full context around a child or family and focus their time on early help and meaningful intervention.
• The Department of Human Services South Australia built its Family Safety Portal on Dynamics 365, Microsoft Power Platform, Azure, and Power BI, replacing paper-based information sharing with a real-time, multi-agency view for high-risk domestic and family violence cases. Ten government agencies including police, health, justice, child protection, and housing now coordinate around a single source of truth, with Chief Information Officer Shikha Sharma noting that centralized data and real-time analytics have transformed how decisions get made.

3. Cross-sector collaboration for person-centered care

The lives of vulnerable people don’t fit inside any single agency’s mandate. A child in care interacts with schools, doctors, courts, and community programs. AI-powered case management combined with modern citizen contact centers is finally making these worlds work together. This is the interoperability work behind aligning Medicaid, housing, public health, child welfare, and economic supports around the same family—and it is where modern AI is now meeting longstanding system-modernization goals.

• Healthcare institutions like the City of Hope are empowering physicians with the tools for more focused, personalized patient care. By using the power of Azure, they developed a generative AI solution that could rapidly process and summarize hundreds of pages of patients’ medical history documents, transforming physician workloads, and enriching new patients onboarding. A similar approach could be applied in social care, supporting the onboarding of new cases with complex, multi‑agency histories.
• Derby Council is using Azure OpenAI to automate around 43% of customer interactions, handling over 1.1 million telephone and web queries, and freeing officers to handle the most complex cases personally.

One experience for the citizen, one coordinated response from government.

4. The social worker of the AI era

The future of social work isn’t a profession diminished by automation—it’s a profession amplified by it. Mornings begin with an AI-prepared briefing of the families a worker is seeing today; home visits are captured by voice-to-text and drafted into case notes for review, not authoring; AI-powered agents flag a school-attendance dip or a missed appointment before it becomes a crisis. Strategic decisions—to escalate, to remove, and to reunify—remain firmly in human hands, supported by transparent and auditable evidence. Designing that future well means co-creating it with the people who use these services, including those with lived experience—so AI strengthens trust rather than displacing it.

Responsible innovation

Empowering social workers with AI demands the same care we expect from social workers themselves: privacy and security by design, fairness testing at every stage, transparency so workers can challenge what AI suggests, human oversight on every consequential decision, and co-design with frontline workers—not just for them. These are the disciplines documented in the Microsoft 2025 Responsible AI Transparency Report and the foundation on which Microsoft 365 Copilot, Azure AI, Dynamics 365, and Microsoft Power Platform are deployed in public sector organizations in selected deployments.

A good example is Northumbria Healthcare NHS Foundation Trust, which uses Azure Machine Learning and the Microsoft responsible AI dashboard to build surgical risk and triage models with explicit fairness and bias checks built-in—a working blueprint for social-care contexts.

King County Housing Authority chose Microsoft 365 Copilot to draft scripts, build training materials, and experiment in real time, enabling secure, compliant adoption aligned with public sector requirements.

As a public agency, we have a responsibility to protect data, comply with records requirements, and maintain trust. Copilot gave us a way to move forward with AI inside the guardrails we already rely on.

—Steven Hellyer, Chief Technology Officer, KCHA

A call to the sector

AI in social work is no longer experimental—it is already delivering impact.

The opportunity now is to move beyond pilots and scale what works, responsibly and with the workforce at the center. There are many common challenges across global social care but also shared opportunity.

Done well, this is not about technology transformation. It is about restoring the conditions for better human decisions—earlier, clearer, and closer to those who need them most.

More time. Better understanding. More human connection.

Meet Microsoft at the 2026 APHSA National Human Services Summit

June 14 to 17, 2026—Arlington, Virginia

Microsoft is proud to be a Summit Sponsor of the 2026 APHSA National Human Services Summit. Stop by our booth above the lobby in the Atrium area to see these capabilities live, share what is working in your jurisdiction, and explore what is possible together.

Beyond technology: How Microsoft partners for the long term 

Embracing the paradigm shift of generative AI obviously begins with technology. The table stakes of modernization for government include migrating to a modern cloud platform and the adoption of a comprehensive AI development solution from a vendor who demonstrates a deep commitment to security and responsible AI practices. 

Microsoft invests heavily in all these areas. However, success involves much more than just technology. Governments also depend heavily on the contributions of trusted solution providers, and we believe our global partner ecosystem sets us apart, with expertise in all corners of the world. Then, the final unique benefit we offer is the deep experience of our industry advisors and the many highly experienced government veterans on the Microsoft for government team. 

Our job is to help build the bridge between the technical and the strategic, on realistic terms. When we sit down with customers, we help clarify challenges and goals, educate on important challenges (for example, how governments can tackle cybersecurity and AI skilling), and share our experiences with other governments facing similar challenges—sometimes even connecting them to help foster learning.

Then, we embark on identifying and exploring use cases, evaluating impact, and taking the knowledge gained for further innovation.

Discover more

We are excited to work with governments to empower social workers with the tools and skills for deeper client engagement. To learn more about how Microsoft is helping to create opportunities that support vulnerable communities, contact your account team to learn how Microsoft can address your organization’s challenges. You can also visit our Microsoft social services and public health website, and learn more about Microsoft for government. 

¹ U.S. Bureau of Labor Statistics, Occupational Outlook Handbook.

² OECD Employment Outlook 2024.

³ Social Work England, Social Work Employment Survey Results 2024.

⁴ Community Care, Caseloads fall at Cafcass but social workers still struggling.

⁵ GOV UK, National workload action group: reports on social worker workload.

The post 4 impactful ways AI is empowering social workers appeared first on The Microsoft Cloud Blog.

Our recent research study by Microsoft AI for Good Lab, Microsoft Sustainability, and Azure, published in the peer-reviewed energy journal Joule, answers this question. For organizations evaluating AI adoption, understanding per‑user energy and water impact is essential for scaling responsibly. When a user sends a text request (“a query”) to a large language model (LLM), like the AI models powering Microsoft Copilot, the system reads the input and then generates a response one piece at a time. Each piece is called a “token,” roughly equivalent to three-quarters of a word. This process, known as “inference,” runs on specialized hardware inside datacenters.

The energy used per query depends on how many tokens are read and generated, how fast the hardware processes them, how large and resource-consuming the LLM is, and how efficiently the whole system is managed.

The key finding of this study: AI at scale is significantly more efficient than previously reported in literature and media. The analysis, focused on serving AI at large scale, finds that a typical AI query to some of the largest and most capable LLMs uses between 0.16 and 0.60 watt-hours of electricity, depending on the length of the query, the LLM used, and datacenter specifications. This is equivalent to the amount of electricity used by a PC (~40 W1) for 15 to 60 seconds or running a home microwave oven (1000 W2) for 0.6 to 2 seconds. That is 4 to 20 times less energy than previous measurements, as described in the study, mainly because those past reports didn’t account for how efficient large-scale AI systems are.

Understanding energy per query also allows us to estimate the amount of cooling water consumed by a typical query. For large production models under conservative assumptions, we estimate that a typical query uses in the range of 0.0 to 0.067 mL of water, with a median water use equivalent to about one-hundredth of a teaspoon or less than a single drop. As datacenter designs continue to evolve, including our rollout of zero water datacenter designs, this amount of water is expected to decrease further.

Bigger systems unlock greater efficiency

Our analysis considered the efficiency of AI inference at scale: usually the bigger an LLM serving system is, the more efficient it becomes for each individual query or user. Think of it as a major airline versus a small regional carrier. A small airline running just a few flights can’t do much if a plane is half-empty—that’s just wasting fuel or underutilizing aircraft. But a large airline running thousands of flights every day can constantly adjust, fill up planes, reroute aircraft, and apply fuel-saving techniques across every single flight at once.

AI works the same way. When billions of queries are served by a hyperscaler such as Microsoft Azure, thousands of requests can be processed at the same time, multiple efficiency optimization techniques can be applied at various stages of the AI inference process, and trade-offs can be made to reduce the resource consumption of the whole system or product without compromising user experience or response quality. Usually, the bigger the system, the more efficiency improvement compounds.

At a billion queries a day, efficiency cuts energy use in half

Leading AI products already serve in the order of a few billion queries every single day. The analysis in the study shows that serving one billion queries, assuming those are conversational queries with a few hundred tokens per interaction, takes about 0.7 gigawatt-hours (GWh) of electricity at baseline, roughly comparable to about 0.4% of the energy US households use watching TV each day. But when smart efficiency improvements are applied, that number drops by more than half, to about 0.3 GWh.

Even with 10% of queries consisting of longer, more complex tasks that consume more than ten times the tokens—such as code generation or multi-step reasoning—our study showed that efficiency improvements still cut total energy use by more than half relative to the baseline, effectively mitigating overall consumption.

Microsoft is actively investing in multiple efficiency levers

Efficiency at scale doesn’t happen on its own. It takes deliberate research and development and investment. The study estimates the impact of three main categories of efficiency improvements:

• Optimized models and the right model for a task. Carefully designed and specialized models, such as Microsoft’s Fara-7B and Phi models, can match the performance of much larger ones at a small fraction of energy and cost. In the same way, intelligent model routing, such as Microsoft’s Model Router in Azure AI Foundry, is designed to automatically direct simple questions to lightweight models and reserves large models for complex tasks. Similar model improvements, as described under the modeling assumptions in the study, can lead to 5 to 10x reductions in energy use in the near term.
• Smarter AI serving. Beyond models, queries must be orchestrated in a datacenter to maximize efficiency while providing a great customer experience. Techniques such as disaggregated serving or adapting serving being implemented by Microsoft can reduce energy use substantially. For long queries generating thousands of tokens, these serving optimizations in general are especially impactful, with estimated efficiency gains in the study leading to up to 5x reductions in energy use.
• Better hardware. Next-generation chips deliver substantially more computation per watt. Together with datacenter-level energy use improvements, the study estimates that advances in GPU hardware offer over at least 1.5x to 2.5x energy reduction per query. And custom AI chips built for inference, such as Microsoft’s Maia 200, can provide even larger efficiency gains.

These improvements build on each other. In the study, we estimate that these efficiency gains, many currently being implemented or scaled up, have a combined near-term reduction of energy per query of 8 to 20x. An efficiency gain made in one area becomes the new starting point for everything that runs on the platform going forward.

Scaling AI responsibly

AI is becoming something that billions of people rely on every day—to learn, to work, and to create. As that happens, it is important that we make sure growing access to AI doesn’t mean growing pressure on local energy grids or on water supplies.

This research shows that scaling AI does not require proportional increases in energy or water use. With the right engineering and investment decisions, organizations can grow AI adoption while improving efficiency. Microsoft remains committed to making that possible—combining advancing capability with infrastructure.

Learn about Microsoft’s sustainability efforts

• Microsoft combines commitments, strategic partnerships, and the transformative power of AI to accelerate progress in sustainability.
• Explore the steps Microsoft is taking toward zero waste and building a more sustainable future.

The post Scaling AI with 8 to 20x energy efficiency appeared first on The Microsoft Cloud Blog.

Financial service institutions have long been among the sectors requiring the greatest levels of security, compliance, and reliability. Today, in the age of AI, organizations in the financial sector are looking to apply AI to alleviate some of these burdens, while also unlocking meaningful competitive advantage through AI applications.

The good news: If you’re in this industry you will likely have decades of sensitive data you can use for learning and insights that can lead to real customer solutions.

The bad news: Yesterday’s data infrastructure might not be up to the task. Delivering the scale, speed, predictive maintenance, access, and performance that today’s financial data platforms need—along with the standard security and compliance—requires rethinking your database solution for the modern era.

The stakes are higher with sensitive data

Maintaining always-on services and meeting stringent regulatory requirements have been baseline expectations in finance for years. Now, with surging digital transactions and AI-powered projects, the pressure has only intensified. In some financial organizations, even a few minutes of downtime can be disastrous, given the reliance on every day availability. Aging, self-managed databases struggle to keep up with high-volume transactions and real-time analytics demands. The operational overhead of managing such systems (like patching, scaling hardware, and manual failovers) drains resources that could be better spent on innovation.

It’s telling that predictive maintenance and infrastructure automation have become focal points for banks to avoid costly outages. Yet, acting too fast also carries risk: one fintech leader recently cautioned that “in financial services, harm historically scales faster than benefit,” underscoring that if you scale up without a solid foundation, problems can amplify rapidly. In other words, bad data or brittle systems will only wreak havoc faster when accelerated by AI. This reality makes it clear that simply layering AI on top of old infrastructure isn’t viable—the core data platform needs modernization.

An investment in PostgreSQL on Microsoft Azure

Azure PostgreSQL managed services, including Microsoft Azure Database for PostgreSQL, address these modern challenges, combining PostgreSQL’s versatility with Azure’s cloud resilience and ecosystem. It’s a fully managed service, meaning Azure handles the heavy lifting of maintenance, updates, and scaling behind the scenes, so teams can focus on value-add work instead of upkeep. Crucially for financial institutions, Azure Database for PostgreSQL offers flexible performance scaling. You can start with a small instance and scale up to large multi-vCore servers or even scale out across elastic clusters to distribute heavy workloads across multiple nodes. This elasticity ensures that sudden surges in trading volume or customer usage won’t degrade application performance.

Enterprise-grade resilience and availability

Downtime isn’t an option for these critical applications, so continuous availability is baked into Azure PostgreSQL services. With a few clicks, you can enable zone-redundant high availability, deploying a fully synchronized standby server in a different Azure availability zone. In the event of an outage or even an entire datacenter zone failure, the service triggers an automatic failover to the standby typically within 60 to 120 seconds with zero data loss. This architecture delivers up to a 99.99% availability service level agreement (SLA) for mission-critical workloads, which is a key assurance for financial apps that cannot go down.

For read-intensive scenarios, Azure Database for PostgreSQL supports read replicas which asynchronously replicate data and allow you to offload analytics or reporting queries without impacting the primary database’s performance. These replicas can even be in different Azure regions, doubling as a disaster recovery option to keep services running through regional disruptions. The bottom line: whether it’s handling a hardware failure or scaling out reads, the service preserves uptime and consistency so your customers and applications see uninterrupted service.

Security, compliance, and an integrated ecosystem

Azure Database for PostgreSQL helps simplify compliance for sensitive and highly regulated data by providing layered security controls out of the box. All data is encrypted at rest by default, and you have the option to use customer-managed keys for encryption if you need full control over key rotation and access. Network isolation is straightforward: you can deploy your PostgreSQL server into an Azure Virtual Network with private endpoints, so that database access stays entirely on your private Azure network with no exposure to the public internet.

For identity and access management, Azure Database for PostgreSQL supports Microsoft Entra ID authentication, allowing you to manage database users and permissions through centralized Entra ID identities instead of static credentials. This means you can use existing corporate security policies and easily onboard and offboard users per compliance needs. Together, these features help meet strict standards like payment card industry data security standard (PCI DSS) and Security Operations Center (SOC) compliance by controlling who has access to what data and ensuring data is protected at rest and in motion.

Because it’s an Azure service, PostgreSQL integrates naturally with the broader Microsoft ecosystem. You can connect your data to analytics and AI services (such as Microsoft Fabric and Azure AI) without complex Extract, Transform, and Load (ETL), accelerating the development of AI-powered apps on top of your operational data.

In fact, after modernizing its platform, BNY Mellon reported that its teams could “innovate faster in areas such as data management, analytics, AI, and machine learning” once they were running PostgreSQL on Azure. Developers also retain the full power of PostgreSQL’s extensibility. Azure’s managed service supports a wide range of popular Postgres extensions (from PostGIS for geospatial analysis to pg_cron for scheduling), so developers can continue to use specialized plugins for financial calculations, time-series analysis, or even graph queries as needed.

A transformation with returns in nine months

To see these benefits in action, consider BNY Mellon, a global financial services company that modernized a critical data platform by migrating to Azure Database for PostgreSQL. BNY Mellon’s Data Vault system ingests and manages mission-critical, multitenant data for clients—it demanded high resilience, scalability, and agility that their legacy self-managed database couldn’t easily provide. Working closely with Microsoft, BNY Mellon moved this workload to Azure Database for PostgreSQL, completing the migration in just nine months.

By adopting Azure’s fully managed Postgres, the company achieved simplified data storage and analytics and built a “cohesive, customized solution” aligned with their microservices architecture. Resiliency improved immediately, with Azure’s high availability and backup capabilities, and BNY Mellon’s engineering teams gained more time for innovation now that routine database maintenance is offloaded to Azure. This new foundation is not only handling today’s needs but is flexible enough to evolve with future AI and analytics initiatives, exemplifying how a modern cloud database can empower a venerable financial institution to stay on the cutting edge.

A step toward readiness for the era of AI

Modern financial services requires a database platform that can scale effortlessly, stay secure and compliant by default, and free up your teams to innovate with data. Azure Database for PostgreSQL, with its combination of performance, high availability, advanced security, and rich PostgreSQL compatibility, rises to that challenge. It’s a solution that lets developers and Database Administrators (DBAs) spend less time wrestling with infrastructure limitations and more time building the next generation of financial applications.

Ready to take the next step? Explore our PostgreSQL for Financial Services solution guide for architectural best practices and implementation tips.

The post AI is requiring financial services to modernize their data platforms appeared first on The Microsoft Cloud Blog.

Property and casualty (P&C) insurers face growing challenges, including macro-economic factors and cyber-attacks, but none is bigger than climate risk. Catastrophic events are nothing new, of course. What has changed is the scale and frequency of weather-related losses and the operational strain that follows. Swiss Re estimates global insured losses from weather‑related natural catastrophes have exceeded $135 billion in 2024, marking the fifth consecutive year insured losses topped $100 billion, and underscoring a structural escalation in climate‑related risk.¹

In response, many insurers are rethinking how to best deliver customer value, profitability, and growth. Mutual and cooperative insurers are under sustained pressure to balance financial strength with their purpose of providing protection in an environment marked by increasingly severe risks and closer regulatory scrutiny. It is a challenge that AI is well suited to answer, helping to expand the role of insurers from risk transfer providers to proactive risk partners.

Insurers and AI: early adoption and opportunity

A 2024 survey by the International Cooperative and Mutual Insurance Federation (ICMIF) found that 62% of respondents were already using AI, with a further 19% planning adoption within the next year. In practice, however, most deployments were commonly concentrated in specific functional areas, such as supporting underwriting, claims processing, and customer interactions. About 67% of insurers expect AI to become more central to their operations, even as many cite data quality and talent gaps as key challenges.²

According to a recent BCG study, only about 7% of insurers have successfully scaled initiatives, with 67% engaged in pilots, fragmented across functions. The opportunity now is to move from isolated use cases to AI embedded across end‑to‑end processes, extending to more automated, interconnected workflows and setting the stage for a shift toward risk prevention.³

How AI helps improve efficiency, service, and relationship management

Prevention does not replace excellence in risk transfer. Forward-looking organizations pursue both. They modernize service and core operations across the customer engagement cycle, while investing in prediction and prevention-oriented capabilities that help reduce future risk and strengthen long-term resilience.

One area where AI delivers important benefits is in enabling faster, more consistent client service by helping representatives locate and validate policy information faster. At Unum Group, for example, a new AI-powered application lets representatives search across 1.3 terabytes of policy and related documents and receive highly relevant answers in four to five seconds, with reported accuracy of up to 95%. This reduces time spent on manual lookup and frees representatives to focus on higher-value client interactions.

Likewise, NFU Mutual uses Copilot for Sales with Microsoft Dynamics 365 to establish a centralized “single source of truth” for customer data and interactions. By capturing and summarizing communications in real time, employees can quickly understand customer needs and respond with greater precision, helping to reduce response times and deliver more informed, personalized engagement.

AI can also streamline First Notice of Loss by ingesting call transcriptions, images, and videos, and guiding representatives to capture the right information in the first conversation, helping accelerate remediation.

In claims review, AI can turn static documentation into insights that inform action. Gallagher, for example, built an internal AI platform that summarizes complex claims files in minutes rather than hours, helping adjusters move faster and apply those insights more effectively across claims and client workflows.

In cases of widespread impact, such as a storm that causes power outages that result in many food spoilage claims, AI can route low-complexity claims through specialized AI agents that can help validate coverage, correlate weather data, detect fraud, calculate payouts, and generate audit trails. This increases service representative capacity for higher-impact cases by addressing low-risk claims with autonomous AI.

These innovations use document processing, contextual summarization, natural language interface and workflow automation, all of which can be used to help improve other processes across core insurance capabilities, customer service, and relationship management.

How AI helps with prevention and protection

The impact of prevention‑led approaches, whether applied to customer risk or enterprise risk, is twofold: financial resilience and stronger trust. This positions insurers as partners that mitigate, not just transfer risk for their customers.

Prevention‑led use cases extend well beyond field‑level interventions, such as property risk scoring or event‑readiness outreach. Increasingly, they focus on identifying and reducing risks earlier, before disruptions, security incidents, or service failures occur.

This shift is visible in how organizations are applying AI to support faster, more informed decisions. At Aon, which has an enterprise grade platform that can operate across solution lines, teams use AI-enabled tools to better assess and respond to risk. To enhance decision quality while maintaining strong governance, they built an Azure-based AI platform called AonGPT that securely connects data and supports consistent, governed analysis, especially in fast-moving situations. During recent California wildfires, Aon’s teams combined near real-time satellite imagery with proprietary data to generate timely insights that helped clients assess damage and plan their response.

AI also enables a shift from paying claims to helping customers reduce exposure before losses occur. Zurich Insurance Group deployed more than 200 AI tools to interpret unstructured inputs in the form of images, reports, and emails in multiple languages, and translate them into clear, consistent risk signals for underwriters. This improves the accuracy and timeliness of risk assessments, helping customers anticipate and reduce potential exposures before losses occur, and supports better informed underwriting decisions.

Prevention can also take the form of making dormant risk visible early enough to act. For example, AI can analyze large volumes of historical risk engineering reports to identify patterns, such as construction materials or design features that are associated with higher structural risk. This can distinguish specific higher-risk properties for expert review—in weeks rather than months in some cases—letting insurers engage earlier, prioritize inspections, and reduce the likelihood of disruption.

Emerging external data sources help improve risk prevention

Many prevention types depend on spotting and interpreting early signals, often from outside of core insurance systems. Using generative AI and machine learning, insurers can integrate third-party signals with internal data to help create new ways to refine risk selection, pricing, event readiness, customer outreach, and more. Sources such as external research, disclosures, regulatory filings, sensor data, and geospatial imagery can have immense impact, provided they are reliably accessible.

Initiatives from Microsoft Research and AI for Good highlight advances in third-party data that can significantly enrich the power of predictive solutions:

• First, Aurora is a foundation model of the atmosphere that produces fast, high-resolution forecasts, especially during extreme and fast-moving conditions. For insurers and reinsurers, that means more timely environmental intelligence to support underwriting, catastrophe modeling, claims surge planning, and reinsurance response.
  
• Second, SPARROW uses solar-powered devices with cameras, microphones, and sensors to detect meaningful changes on the ground and send near real-time insights to the cloud. For insurers, it shows how AI and sensor data can enable earlier risk detection, faster intervention, and reduce loss severity.
  

Earlier, more precise forecasting can inform proactive risk alerts, giving customers and commercial clients time to take preventive actions (for example, securing property or adjusting operations) and support coordination among insurers, risk engineers, brokers, and public authorities. The objective is straightforward: Improve analysis, lead time, and decision quality to mitigate large losses.

Priorities for success with AI and risk prevention

For leaders, realizing measurable value from AI across the business, including enhancing prevention, can happen in a matter of months or quarters. Microsoft’s view of industry patterns indicates that successful approaches often prioritize the following:

• Define a clear strategy and start with a small number of high‑value, extendable use cases aligned to core business priorities.
  
• Build strong data foundations and effective governance.
  
• Balance innovation with credibility and responsible adoption.
  
• Pursue business-led process re-architecture, change management, and talent skilling.
  
• Commit to stretch goals with active leadership, resourcing, and accountability.
  

Insurers who employ this comprehensive approach and tailor AI to their unique business requirements can improve the most critical aspects of their operations. Critically, they can enhance prevention as an important part of their future growth strategies.

Learn more

• To explore how leading insurers are using agentic AI to transform claims, underwriting, and customer experience, read our ebook.
• To explore solutions and resources for insurers, visit Microsoft for Insurance.
• To learn how frontier firms in financial services are using AI to improve efficiency, innovation, and customer satisfaction, get the e-book.
  
• Visit our blog for stories of how Microsoft for Financial Services helps firms accelerate business value.
  

¹ Swiss Re, “Hurricanes, severe thunderstorms and floods drive insured losses above USD 100 billion for 5th consecutive year, says Swiss Re Institute,” December 2024

² International Cooperative and Mutual Insurance Federation, “Balancing AI innovation with member-driven values at mutual and cooperative insurers,” February 26, 2025

³ BCG, “Insurance Leads in AI Adoption. Now It’s Time to Scale.” September 04, 2025

The post From risk transfer to risk prevention: How AI supports long-term financial resilience in insurance appeared first on The Microsoft Cloud Blog.

As Australia looked to extend its winning streak to four straight Ashes on home soil, it was clear that left-handed batter Travis Head was leading the way for Australia as the runs piled up. But just how good was his performance? Fans using the Cricket Australia Live app had an instant answer. 

Thanks to the app’s new AI Insights feature, which provides live insights on player milestones, records and key moments using OpenAI’s GPT-5 within Microsoft Foundry, cricket aficionados and newcomers can now access much-needed context to better engage with the game. They can also dig deeper by asking follow-up questions about the insights provided. It’s an exciting development for Cricket Australia, the governing body of the sport in the country. 

“The recent series where England were here in Australia had a couple of key moments where I saw the insights come to life in real-time,” says Cricket Australia CEO Todd Greenberg. “And you can see the engagement through the analytics and the tracking that when something is delivered in the right time frame, in the right format, into the right hands, it has a huge effect.” 

Indeed, AI Insights showed that Head’s 172 runs for the match were his fifth-highest aggregate total in a test. His only higher efforts were 220 runs against Sri Lanka, 213 against West Indies, 181 against India and 180 against England. Head’s big day earned him Player of the Match honors and helped Australia claim a five-wicket victory in the match and a 4-1 Ashes series victory against its archrivals. 

Going beyond the box score 

“Scores and highlights tell you what happened. But the context tells you why you should care about it,” says Balamurugan P M, chief technology and digital officer at Cricket Australia. 

“It comes down to the storytelling. From my perspective, I thought it was essential for fans to learn more about the story rather than just following the scores or watching highlights. So, we wanted to give a different experience.” 

Cricket Australia had a corker in its arsenal as AI Insights came into focus – an extensive archive of official scorecards that dates to 1886, providing a wealth of historical data that could bridge the gap between the past and present. Those scorecards were carefully integrated over a period of three months to ensure the information would pass muster among the serious cricket experts. 

“We had hundreds of years of data, and when it comes to fans, trust is non-negotiable,” Balamurugan says. “When you’re dealing with records and milestones, you can’t make mistakes. There are some hardcore fans who know these stats like the back of their hand. History is core to cricket’s identity. And instant context turns a scoreboard into a story. 

“Getting that volume of data, integrating it and surfacing greater context for live games required huge data alignment and validation. With our systems and with the skilled team that we’ve got, that was made possible.” 

Creating a solution fans can use in real time 

Cricket Australia joined forces with Microsoft, alongside technical partners Insight Enterprises, HCL Tech and Skewer, to create the new iteration of the app. With the important Ashes and T20 international tournaments on the horizon, time was of the essence to launch the app before the bats were raised on those key fixtures. 

The app is anchored by Microsoft Azure, the cloud foundation that Cricket Australia uses to run and scale its digital platforms and the app experience. AI Insights takes advantage of Azure OpenAI Service in Microsoft Foundry, which generates the real-time, match-aware insights that serve as a companion to what fans are seeing on the field. 

“What we’re talking about is a really good example of solving a fan-facing problem with deep technical capability and a shared vision on delivery,” Greenberg says. “Microsoft brought world-class cloud and AI foundations. Without them, we would not have been able to get as far as we have. And our partners have helped accelerate the build, the integration and, importantly, operational readiness.” 

One of the biggest challenges with AI Insights is ensuring that fans watching a match and using the app can get updates and context within the flow of the game, making it an additional resource for fans at the grounds or watching alongside with commentary. 

Azure Cosmos DB supports Cricket Australia’s ecosystem of apps – including Cricket Australia Live with AI Insights and PlayCricket, which hosts scores for up to 7,000 community matches a weekend. The technology provides a fast, scalable data layer that can update quickly during live play, always keeping fans aware of the latest scores. 

“All live sport has one thing in common. There are no pauses,” Greenberg says. “It’s not like reality television. So, the experience has to be fast, reliable and consistent, especially when it’s under peak demand and when you have millions of people enjoying it at the same time.” 

An experience for every type of fan 

While cricket has its ardent supporters, especially in Australia, it can also be difficult for newcomers to pick up. As Cricket Australia looks to cultivate the next generation of fans, Greenberg realizes that the app can prevent sticky wickets for the sport’s novices. 

“I mean, we play a crazy sport that goes over five days and sometimes at the end of the five days, you still don’t get a result,” Greenberg says. “We can’t expect people to be tuned in at every moment, but what we can do is we can hyper-personalize the way they would like to engage with the sport during the contest.” 

The Seddon Cricket Club in Melbourne has been in existence since the 1920s and is now home to several senior, junior and all abilities sides that compete in associations across Australia. It is also home to a loyal supporters group, featuring fans who love the game in all forms. For them, the AI Insights on the Cricket Live App has been a value add as they go deeper into the game. 

“It’s definitely made it more interesting to follow along and learn more about the players,” says Cassie Gray, a Seddon Club supporter and cricket fan. “You could follow a player, you could see what they’re known for, as well as figure out what’s their next step or what do they need to get an amazing moment next. 

“Cricket is a game of history. It’s been around for a really long time, and the players influence other players, and countries influence other countries. With the insights, it gives me an understanding of not just what’s happening today, but what’s led up to that in the game itself.” 

The next step for AI insights is to create greater personalization within its levels of information for different types of fans. A user can select “newcomer,” “history buff” or “stats guru” and receive insights tailored to their persona. 

“We want to understand every fan and cater to how they want to be served by the app,” Balamurugan says. “We have moved from scores to storytelling, but we want to move from storytelling to fans setting up the narrative themselves. Fans should hear the story how they want to hear it. That is one of our lodestars.” 

With the initial success of the AI Insights feature, Greenberg said other sports organizations have reached out to learn more about how it was developed and the impact on the fanbase. Most people working at Cricket Australia have a deep love of the sport, often having played for many years. Greenberg hopes the app’s success and further innovation can continue the sport’s momentum. 

“The thing we’ll never know until much later on is the impact that we’re having on young kids falling in love and choosing cricket as their preferred sport,” he says. “And if we help them love it, what we can create for a fan on their journey between the ages of 8 and 80 is astronomical for a sport like cricket. And so, we’re very mindful of ensuring kids get the opportunity to engage in cricket so we can form lifelong partnerships.” 

Top Image caption: Supporters at the Seddon Cricket Club in Melbourne love the game in all forms, and the Cricket Live App featuring AI Insights has allowed them to gain further insights into the sport, whether they are a novice fan or stats guru. Photo by Graham Denholm for Microsoft.  

Elliott Smith writes about AI and innovation at Microsoft, from how the Premier League is transforming its online presence to why AI may play a major role in saving the Amazon rainforest. Previously, Smith worked as a sports reporter in Washington, D.C., Washington state and Texas, covering high schools to the pros. You can contact him on LinkedIn. 

The post Cricket Australia uses AI Insights to bring fans closer to the action appeared first on The Microsoft Cloud Blog.

MWC26 Barcelona, the GSMA’s flagship connectivity event, brought the global ecosystem together at scale: GSMA reported over 105,000 attendees from 207 countries and territories. In that backdrop, one theme kept surfacing in nearly every discussion I had: telecoms have moved past debating whether AI creates value and into the harder question of how to scale it across the enterprise with the right security, governance, and operating model.

In other words, the industry is shifting from isolated pilots to enterprise execution, embedding AI into customer engagement, network operations, and day-to-day workflows. This recap shares what we heard, what we showed, and what it signals for the next phase of telecom transformation.

Ahead of the event, we shared our point of view on how telecoms can realize AI ROI with a unified, trusted AI platform in our industry blog: MWC 2026: Microsoft Helps Telecoms Realize AI ROI. We described how Microsoft helps telecoms achieve return on intelligence and trust by scaling AI through a single intelligence platform—Microsoft IQ—with built‑in, carrier‑grade trust and governance so operators can innovate with confidence. During the week, additional customer and partner momentum included:

• AT&T unveiled Connected AI for Manufacturing and Connected Spaces for Enterprise, powered by Azure, enabling real-time data integration across factories, supply chains, buildings, and campuses to improve efficiency, safety, and decision-making.
• Tech Mahindra launched an ontology-driven agentic AI platform built on Microsoft Fabric and Azure AI Foundry to help operators modernize complex data environments and move from AI experimentation to production across use cases such as network optimization, fraud detection, and revenue assurance.
• Orange introduced Sharlie, a next-generation voice assistant for Sosh built with Microsoft AI, expected to handle more than 3 million customer conversations annually at full rollout.

What changed at MWC this year

The most important shift I saw wasn’t a single product announcement, it was a change in posture. Telecom leaders are increasingly treating AI as a core capability to be industrialized, not a set of experiments to be evaluated. The questions sounded less like “What use cases should we try?” and more often pointed to a simple reality: Scaling AI is a systems challenge. It requires bringing data, security, governance, and operational processes together so insights consistently turn into action. That’s the idea behind Microsoft’s Return on Intelligence—measurable business outcomes created when intelligence is embedded end-to-end across the telecom value chain.

At MWC, our goal was to make this practical, showing how AI can be applied across customer experience, operations, and growth, with trust built in from the start. Three themes came up repeatedly in these conversations:

• Data readiness: Connected intelligence that brings network, customer, and operational data together so models and agents can act with context.
  
• Trust at scale: Security, privacy, compliance, and governance that are designed in, not bolted on after pilots.
  
• Operationalization: Integrating AI into workflows, tools, and KPIs so teams can adopt it and leaders can measure outcomes.

That’s why we focused on an end-to-end story: Not just what AI can do, but how it can be delivered responsibly and repeatedly across the business. The show floor is where those ideas get tested quickly, so we designed the booth experience to reflect the real priorities operators are working on now.

What we showed: Turning intelligence into action

In the Microsoft booth, we brought Return on Intelligence to life with hands-on experiences designed around real operator workflows. The intent was simple: show how AI moves from insight to execution when it’s connected to the data people rely on, the tools they already use, and the guardrails organizations need.

Across 14 interactive demo stations, we explored five priorities many operators are investing in right now. Each one reflects a different place AI can create value and a different set of operational requirements to get it into production.

1. Copilots and AI agents for employees to reduce toil and speed decisions across customer care, operations, and field teams.
   
2. Agentic customer experiences that resolve issues faster, personalize interactions, and escalate to humans when needed.
   
3. Intelligent business operations that streamline order-to-cash and service fulfillment with better orchestration.
   
4. Autonomous network operations to detect, predict, and remediate issues—moving from reactive to proactive operations.
   
5. AI-enabled growth and monetization that helps identify opportunities and launch new offers faster.

What connected these scenarios wasn’t a single model, it was the operational pattern behind them: Unified data, secured access, governed AI, and integration into the workflows where work actually happens. That’s what turns a compelling demo into something a team can deploy, adopt, and measure.

The level of engagement reinforced the momentum behind this shift. Over the course of the week, more than 12,000 customers and partners visited the Microsoft booth. More than 3,200 attendees took part in more than 30 demos across 14 stations, and 1,387 people joined more than 38 in-booth theatre sessions with Microsoft and partner speakers. We also held 396 executive meetings with priority customers and partners—many focused on what it will take to move from pilot success to enterprise-scale execution.

Beyond the booth: Keeping the momentum going

MWC is four days on the calendar, but it’s really a milestone in a longer journey. The weeks before and after the show are where teams align on priorities, validate approaches, and translate interest into concrete next steps.

Our announcement blog helped frame the week by sharing Microsoft’s approach to scaling agentic and autonomous AI on a unified, trusted platform—and we continued the dialogue through customer and partner communications, follow-ups with teams exploring next steps, and ongoing industry programs.

Four takeaways from the week:

1. AI is an operating layer, not an add-on. The most consistent message was that AI is being stitched into how telecoms run: across customer experiences, operations, and growth. That shift changes what leaders prioritize, from isolated tools to enterprise foundations.
   
2. The maturity journey is speeding up. Many conversations reflected the same evolution: From pilot projects to targeted productivity improvements, to enterprise-wide transformation and growth. The winners will be the teams that can standardize what works and scale it across functions.
   
3. Agentic experiences raise the bar on trust. As copilots and AI agents take on more autonomous work—from customer interactions to network operations—security, privacy, and governance can’t be optional. Operators want guardrails, monitoring, and controls that work in production, not just in proofs of concept.
   
4. Outcomes depend on integration. AI delivers ROI when it connects to real data, real processes, and real workflows, so it can move from insight to action repeatedly. That’s why unifying data and AI, embedding security, and governing end-to-end matters: It’s what makes execution scalable.

Together, these themes point to the same conclusion: Telecoms that operationalize AI, securely and at scale, will move faster and compete differently.

What comes next: Moving from momentum to measurable outcomes

The post-MWC opportunity is straightforward: take the excitement and turn it into a repeatable operating model. For most operators, that means industrializing AI as a trusted layer, grounded in enterprise data, secured by design, governed end-to-end, and integrated into the workflows where customer experience and operational performance are won.

MWC 2026 made one thing clear: The telecoms that lead in the next cycle won’t just deploy AI, they’ll operationalize it. The organizations that can reliably turn intelligence into action, measure impact, and scale what works will set the pace for the industry’s next wave of transformation.

Continue the conversation

• Consolidated list of MWC announcements and news: Return on Intelligence: Powering the AI-First Telco
• Fierce Network: Microsoft & Nokia; Microsoft & Lumen; Microsoft, PWC & Orange

Explore related content

• YouTube videos: Mobile World Congress 2026
• MWC announcement blog
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• Rise of Agentic BSS in the IQ Era
• AI-Powered RAN and the Intelligent Edge
• Accelerating revenue in telecommunications through agentic sales processes

The post MWC 2026 recap: From AI pilots to enterprise execution in telecom appeared first on The Microsoft Cloud Blog.

Return on intelligence and trust

For media and entertainment organizations, unlocking value from AI at scale depends on two things: intelligence and trust.

Built on three complementary elements—Work IQ, Fabric IQ, and Foundry IQ—Microsoft IQ is the intelligence layer that connects AI, data, and context across the media value chain. It gives AI agents a deep understanding of how creative teams work, how content moves through production and distribution, and how business decisions are made. This shared intelligence accelerates content creation, personalizes audience engagement, streamlines operations, and opens new paths to monetization—all while keeping human intent and creativity at the center.

None of this works without trust. Media organizations operate under intense intellectual property (IP), regulatory, anti-piracy, and contractual constraints. Frontier transformation depends on intelligence that is secure, governed, and observable by design. Microsoft delivers this through an AI control plane, with Agent 365 providing unified governance, identity, and observability across agents—ensuring they are discoverable, auditable, and policy‑controlled as they operate across creative, operational, and business workflows. Combined with Microsoft’s end‑to‑end security and compliance stack—spanning Microsoft Entra, Microsoft Purview, Microsoft Defender, Fabric, and Foundry—media organizations can scale AI confidently while protecting creative IP on a global scale. 

At NAB Show 2026, Microsoft is showcasing how media companies can move beyond experimentation to real business impact with AI. Through a single, unified platform that brings together AI, data, intelligence, and governance, Microsoft enables connected, actionable insights that help media organizations use intelligent work, AI-powered creation, agentic operations, and new growth with AI.

Read more to see where frontier media organizations are already achieving this.

Intelligent work

Frontier media organizations start by transforming how people work. Instead of being spread across dashboards and systems, intelligence shows up directly in the flow of work through Copilot, agents, and Microsoft IQ. See how a few examples of frontier media organizations are embedding intelligence into everyday work:

• Publicis Groupe announced it is expanding its partnership with Microsoft to enable intelligent, agent‑driven work for its more than 110,000 employees while powering the future of agentic marketing for its customers worldwide. Publicis is rolling out Microsoft 365 Copilot across its workforce to embed AI into daily work. Additionally, Publicis Sapient’s Slingshot framework will use Microsoft’s cloud, while Sapient AI solutions will integrate Microsoft Copilot Studio, Microsoft Agent 365, and Microsoft IQ, enabling customers to embed AI directly into core business processes. Sapient’s Bodhi platform will then allow organizations to deploy and scale secure, enterprise-grade AI agents across the business. The partnership is anchored in Epsilon, Publicis’ IP intelligence layer. AI agents built on Microsoft Fabric and powered by Epsilon will be able to reason, decide, and act on trusted, real-world data, to deliver impact that extends beyond model performance to sustained business value.
• The New York Jets are using technology to turn one of the most high‑pressure moments in sports—the NFL Draft—into an example of intelligent work in action. Through their Microsoft powered Titan platform and Copilot enabled tools, coaches, scouts, and front office leaders bring together film, analytics, historical data, and real‑time insights to make faster, better‑informed decisions. By augmenting human expertise with AI and cloud intelligence, the Jets show how intelligent work helps teams operate with speed, alignment, and confidence when every decision matters.

Together, these organizations show how intelligent work starts by meeting people where they already work and embedding intelligence directly into daily media workflows.

AI-powered creation

For creators and content teams, intelligence must move as fast as the moment. Frontier media organizations connect content, audience signals, and creative context in real-time, so insight immediately translates into action. See how frontier media organizations are using AI-powered creativity to scale their content:

• Collective Artists Network is working with Microsoft to support creators with AI-native content systems that keep human storytelling at the center. By embedding intelligence into filmmaking workflows, the collaboration aims to help teams iterate faster while preserving director-led creative vision.

We’re using technology being developed here in India to take our culture and history to a global audience, at a scale that wasn’t possible earlier. For us, this is a long-term priority, building stories that are rooted in who we are, but can travel anywhere in the world.

—Vijay Subramaniam, Founder and Group CEO, Collective Artists Network

• The NBA uses Microsoft Azure AI to power dynamic highlights, real‑time stats, and in-game insights embedded directly into fan touchpoints like the NBA App—bringing fans closer to the action through personalized, data‑driven experiences.

Microsoft has also announced new Microsoft AI models in Microsoft Foundry and Microsoft AI Playground to help media organizations further accelerate this shift. MAI-Transcribe-1 delivers state-of-the-art speech-to-text transcription across the top 25 most-used languages.¹ MAI-Voice-1 generates natural, realistic speech, that preserves speaker identity even across long-form content. MAI-Image-2 was created with photographers, designers, and visual storytellers, delivering natural lighting, accurate skin tones and texture, and clear in-image text for diagrams, layouts, and graphics.

Empowering creators is not about adding AI features. It is about orchestrating intelligence across content, data, and delivery—so creativity becomes action in real time.

Agentic operations

The most profound transformation in media today is operational. Frontier organizations are embedding intelligence across the entire media supply chain—from production and post to rights, distribution, and monetization—using agentic systems to replace manual handoffs with coordinated, end-to-end workflows.

• Penguin Random House is using agentic AI to modernize accessibility at scale, embedding governance and human oversight into core publishing workflows to improve efficiency and compliance.

Penguin Random House leverages Azure AI to scale the creation of high‑quality, context‑aware Alt-Text content across our e-book catalog. This initiative advances our accessibility commitments while materially reducing manual effort, cost, and operational complexity. By embedding Azure OpenAI into our accessibility workflow with a human‑in‑the‑loop governance model, we can generate image descriptions at scale, strengthening regulatory compliance and enabling a more accessible and efficient publishing process. 

—Christopher Hart, CIO Penguin Random House 

• The International Tennis Federation (ITF) is using Microsoft Azure and AI orchestration to power a real‑time intelligence platform that unifies match telemetry and delivers instant, on‑court insights to coaches and players. By processing more than 700,000 data points per match and generating over 1,500 statistical combinations in real time, the ITF is enabling teams to make faster, data‑driven decisions during play through applications like Match Insights, helping standardize access to advanced analytics across more than 140 competing nations regardless of their resources.

• Kantar is using Microsoft Copilot Studio to deploy teams of AI agents that automate complex data preparation tasks across its global operations. By breaking down manual workflows such as translating documents, validating policies, and organizing HR content into smaller subtasks handled by specialized agents, Kantar enabled its People Team to clean, tag, and structure 4,000 artifacts into 400 policy documents in just six weeks, laying the operational foundation for scalable, agent‑driven workflows that support employee queries across 60 countries.

With Foundry IQ and Fabric IQ, agents now operate with shared context across data, workflows, and knowledge—allowing operations to scale without chaos and intelligence to move end-to-end.

Additional partner solutions continue to enable agentic operations:

swXtch.io will introduce swXtch.ai and the swXtch AI Router, a platform that integrates with Microsoft Fabric and NVIDIA AI to enable real-time AI in live media workflows through a simple chat-driven interface, reducing the need for custom pipelines or specialized expertise.

New growth with AI

The clearest signal of frontier leadership is how media organizations innovate. Instead of experimenting at the edges, leaders are building AI‑native platforms that unlock entirely new creative and commercial opportunities.

See how some of these frontier organizations have experienced new growth with AI:

• Microsoft recently announced a partnership with the Mercedes‑AMG PETRONAS Formula 1 Team to apply cloud and enterprise AI across race strategy, team operations and business intelligence, transforming massive volumes of telemetry into real‑time intelligence from the factory to the circuit. With each car generating more than a million data points per second, Microsoft technology helps turn complex race data into faster insights that power smarter decisions and more effective strategies in the moments that matter most. Together, the companies are harnessing data as intelligence to drive performance and strategy, enabling teams to move from raw information to sustained competitive advantage both on and off the track.
• Art Basel is using Microsoft Foundry to power the Art Basel Companion app, unlocking new digital pathways for audience growth and artist discovery across its global fairs. With AI‑powered features such as personalized recommendations and instant artwork recognition through the Art Basel Lens, the platform creates new opportunities for deeper visitor engagement—helping attract new audiences, increase return visits, and expand how collectors and fans interact with galleries through AI‑enabled discovery.
• The Premier League is using Azure AI and Foundry to unify decades of match statistics, editorial content, and video into real‑time, personalized digital experiences for its global fanbase. By enabling rapid innovation through agentic AI and real‑time personalization, the League has unlocked new forms of fan engagement across its owned platforms, driving a 20% year‑over‑year increase in engagement and activating more than 60 million users in the early months of rollout.

Additional partner solutions continue to unlock new growth with AI:

SymphonyAI’s Revedia is an AI‑first platform supporting over $40B in industry content revenue, rapidly ingesting and normalizing third‑party data to deliver accurate revenue and viewership insights at scale. Beyond data management, the Revedia Suite provides prescriptive intelligence—recommending actions and forecasting outcomes to maximize distribution performance and revenue. Revedia is trusted by a broad cross‑section of the media industry, including major studios, broadcasters, cable networks, and Direct-to-Consumer (D2C) platforms.

The Microsoft and MediaKind partnership continues to accelerate, with MK.IO emerging as the proven cloud-native streaming platform for live sports. Built on Azure, MK.IO supported DAZN’s delivery of the FIFA Club World Cup 2025, streaming 63 matches to audiences across over 200 markets with consistent, broadcast-quality performance. It reflects a broader industry shift toward platforms that combine reliability with the agility of API-driven services. A transformation MediaKind is showcasing at NAB 2026 through MK.IO’s self-serve platform and large language model (LLM)-optimized documentation, with live demonstrations in Microsoft’s booth highlighting AI-assisted workflows in action. This momentum continues to grow through MediaKind and Microsoft’s collaboration on some of the most prestigious sports ecosystems in the world, including ongoing work supporting top-tier football experiences such as the Premier League. 

Join us at NAB Show 2026

Frontier media organizations are already proving what is possible when intelligence, data, and trust come together on a single platform. Join Microsoft at NAB Show 2026 to see how Copilot, agents, Microsoft IQ, Foundry, and Fabric come to life through real deployments, live demos, and customer stories shaping the future of media.

¹ Top 25 languages by Microsoft product usage

The post Powering intelligent media: How frontier organizations realize a return on intelligence with Microsoft appeared first on The Microsoft Cloud Blog.

• Watch global leaders discuss the future of industrial intelligence: Register now 
• Get the latest insights on industrial transformation: Get the e-book 
• New: The Procurement Agent in Dynamics 365 Supply Chain Management 

Three global industrial leaders—ABB, Krones, and TK Elevator (TKE)—are redefining their industries by using advanced AI and trusted cloud platforms to become Frontier industrial organizations. With Microsoft, they’re turning data, processes, and context into intelligence that drives efficiency, agility, and innovation. Every Frontier organization gets two fundamental things right: intelligence and trust. They amplify what’s unique in their people and operations with AI that is governed securely on their own terms. Trust isn’t added at the end; it ensures intelligence is used responsibly and outcomes happen as intended. Hannover Messe 2026 is where these transformations take center stage, showing how Frontier organizations are shaping the next era of manufacturing.  

• ABB—AI as a Real-Time Industrial Co-Pilot: ABB, a global technology leader in electrification and automation, will showcase its cloud-powered Genix Industrial AI platform running on Microsoft Azure. Genix acts as a real-time industrial co-pilot on the factory floor by analyzing streaming data from equipment and sensors and delivering actionable insights and recommendations to operators and managers in real-time. At Hannover, ABB’s demos illustrate how Genix enables closed-loop, AI-driven optimization of production processes on the fly, for example, by automatically adjusting parameters to improve energy efficiency, asset performance, and reduce unplanned downtime. Designed with a modular, scalable architecture, Genix integrates seamlessly with existing industrial systems, eliminating the need for large‑scale platform replacements and enabling rapid time to value. With the integration of generative and agentic AI, the platform not only provides insights, but also automates actions, while keeping humans “in the loop” for all critical decisions to ensure safety and operational decisions. This approach demonstrates how ABB is moving toward autonomous, self-optimizing operations without sacrificing human oversight, a hallmark of a Frontier organization. 

• Krones—From Bottling Machines to “Bottle-as-a-Service”: Krones, one of the world’s largest bottling equipment manufacturers, is using AI to reinvent its business model and engineering process. With Microsoft’s help and the ecosystem of trusted partners Ansys (part of Synopsys), NVIDIA, Softserve, and CADFEM, Krones integrated advanced AI-based fluid simulation into its digital twin of a filling line, packaged with a multi-agent experience, for their engineers to create these complex simulations with natural language queries. This innovation has slashed simulation times from four hours to under five minutes (a 95% reduction), allowing engineers to optimize machine parameters virtually and dramatically shorten commissioning time. The payoff is huge—Krones can now rapidly tailor designs for each customer and ensure optimal throughput. At Hannover Messe 2026, Krones will demonstrate how these AI-powered digital twins let them forecast and fine-tune production faster and more flexibly than ever, turning a traditional machinery business into a Frontier digital services company.

• TK Elevator—Digital-Native Elevators and Agentic AI: TK Elevator (TKE) is revolutionizing mobility for 1.5 billion users by combining digital-native products, secure cloud and data platforms, and agentic AI, all in partnership with Microsoft. At Hannover Messe, TKE highlights its EOX and HELIX elevators, which are eco-efficient, AI-ready, and IoT-enabled as part of the MAX on Azure platform. Azure Databricks supports their unified analytics, ensuring data governance and enabling scalable AI workflows. TKE’s specialized AI agents, alongside the Digital Operations Center, streamline service by assembling contextual briefings before technician visits and capturing insights afterward, turning technician knowledge into organization-wide intelligence. 

These three examples are among many Microsoft customers and partners joining us at Hannover Messe 2026, with live demos showing how industrial intelligence turns data into faster decisions, safer operations, and more resilient manufacturing. 

Industrial Intelligence Unlocked, Microsoft’s overarching theme for Hannover Messe 2026, reflects the belief that manufacturing’s next era will be driven by human ingenuity and AI—grounded in trust. Microsoft provides a unified intelligence layer for the tools your employees use; Work IQ understands how people collaborate and decide. Fabric IQ delivers real-time visibility across assets, production, and supply chains. And Foundry IQ combines institutional knowledge like procedures, standards, and history with AI. Together, they help manufacturers connect teams, processes, and technology across the value chain. 

1. Redefine product lifecycle intelligence

This neighborhood focuses on uniting engineering and operations through data-driven intelligence, so manufacturers can design and deliver better products in less time. Here you’ll see how Microsoft is helping companies create a closed-loop product lifecycle —connecting every stage from design and simulation to production feedback. For example, Microsoft and NVIDIA are collaborating to power the next generation of physical AI by integrating NVIDIA Omniverse libraries with Microsoft Fabric.  

By blending real-time data, AI, and virtual simulation in one environment, companies can iterate designs faster with greater confidence. Imagine optimizing a new machine design virtually (with accurate physics and live data) before anything is built—reducing costly physical prototypes and accelerating time-to-market.  

In short, the product lifecycle intelligence zone shows how integrating data + simulation + AI yields smarter product decisions and faster innovation. 

Microsoft ecosystem partners showcased in this area: Aras, Brembo Solutions, Celebal Technology, NVIDIA, PTC, Tata Consultancy Services (TCS).

2. Run AI-powered factories 

In our AI-Powered Factories area, we demonstrate how to coordinate machines, materials, and people with AI, turning traditional facilities into adaptive, self-optimizing operations. Microsoft supports the ability to scale these operations with a unified intelligence layer powering AI insights and a consistent framework for managing agents, models, data and infrastructure with the adaptive cloud approach. 

A highlight here is Microsoft’s approach to industrial edge AI. Foundry Local on Azure Local enables manufacturers to deploy and run AI models, including those from the Foundry model catalog —directly on factory equipment or on-premises servers for scenarios that require ultra-low latency, data locality, or offline operation. This capability supports high-speed vision inference for quality inspection, anomaly detection, and predictive maintenance, all in real time without relying on constant cloud connectivity. Manufacturers can choose curated open-source models from the managed catalog or deploy custom OCI/Docker models on CPU or GPU systems.

Discover how the latest Azure IoT Operations release simplifies OT data management—now with no-code pipelines, seamless device control from cloud to edge, and direct support for third-party MQTT brokers and Litmus Edge gateways. In addition, upgrades to Azure IoT Hub and firmware analysis, enabled by Azure Arc make it easier for industrial organizations to securely manage and update large device fleets with unified Azure security and certificate management via Azure Device Registry integration. Learn more about how Microsoft and our partners are providing the foundation to initiate and scale industrial AI projects in our two-part blog series: Making Physical AI Practical for Real-World Industrial Operations: Part 1 and Part 2. 

Together, these capabilities come to life in Microsoft’s Factory of the Future demo—showing how adaptive cloud, edge intelligence, and Physical AI work together in a real manufacturing environment. The Factory of the Future demo shows how Physical AI comes to life when design, simulation, and execution are connected into a single, adaptive manufacturing system. In collaboration with Hexagon, Siemens, NVIDIA, KUKA, Advantech, and others, Microsoft demonstrates an end-to-end scenario where AI-assisted product design is validated in simulation and then executed in a live manufacturing cell. Real-time telemetry flows from the factory floor through Azure IoT Operations at the edge and into Microsoft Fabric, where AI agents’ reason across operational signals to proactively detect issues and support action.  

Microsoft ecosystem partners showcased in this area: Accenture, Advantech, Avanade, AVEVA, Hexagon, Kuka, NVIDIA, Schneider Electric, Siemens, Sight Machine, Rockwell Automation.

3. Build trust across human–agentic teams

As AI agents move from pilots to daily operations, trust becomes the factor that separates insight from impact. In manufacturing environments, AI only delivers value when people are confident enough to act on its recommendations. For frontline workers, trust means clarity at the moment of action. AI agents assemble contextual briefings that bring together equipment performance, recent alerts, maintenance history, and safety guidance, so technicians arrive informed and prepared. Recommendations are visible, explainable, and designed to support human judgment, not replace it. For engineers, planners, and operational leaders, trust means confidence at scale. As AI agents operate across factories, service networks, and supply chains, organizations need visibility into how decisions are made, what data is used, and when human approval is required. Governance, auditability, and clear accountability ensure AI actions align with operational priorities and policies. 

Manufacturers can now use Researcher in Microsoft 365 Copilot in Dynamics 365 Field Service. Powered by WorkIQ, teams can bring together signals from work orders, service history, parts availability, and Microsoft 365 context to investigate issues faster and take informed action, improving first-time fix rates, reducing downtime, and maintaining governance. 

This human–agent operating model reflects Microsoft’s approach to industrial AI. Intelligence proposes that. People decide. Trust is built into the system so AI can move beyond insights and support real operational action across the enterprise. 

Microsoft ecosystem partners showcased in this area: Bosch Connected Industry, Cognite, Kongsberg Digital, SymphonyAI. 

4. Orchestrate supply chains with AI agents

The fourth booth zone looks beyond the factory floor to the end-to-end value chain, where volatility, constraints, and customer expectations converge. Here we show how manufacturers can go from reactive coordination to agentic supply chains. From networks of suppliers, plants, and logistics partners connected by AI agents that continuously scan for change, reason across data, and support action in real time. These systems go beyond visibility, helping leaders anticipate disruption and respond with speed and confidence. 

Procurement is often first to feel disruption, where speed, context, and control matter most. The Procurement Agent in Dynamics 365 Supply Chain Management helps teams handle supplier communications and exceptions, assess downstream impact, and keep people in review.  

AI-assisted agents reduce manual effort while keeping our people in control…strengthening collaboration and improving outcomes.

—Andre Scheepers, Chief Digital Officer, Farmlands Cooperative

AI agents help organizations move from delayed reaction to proactive control. By detecting demand volatility, supplier risk, or inventory imbalances earlier, teams can evaluate tradeoffs, align cross functional responses, and act before issues escalate into revenue loss or excess cost. Embedding these insights directly into operational workflows shortens decision cycles, reduces manual intervention, and improves outcomes such as on-time, in full delivery, inventory turns, and working capital efficiency. 

This approach reflects a shift in how supply chains create value. AI strengthens human decision‑making by improving speed, consistency, and coordination across the value chain. Thus, enabling supply chains to operate with greater predictability, control, and customer confidence, even in volatile environments. 

Microsoft ecosystem partners showcased in this area: Resilinc, Fractal, C3.ai 

Join us—onsite or online—Live from Hannover Messe 2026

Microsoft is hosting a series of executive conversations at our Hannover Messe booth, where top manufacturing leaders will share how they’re navigating the journey to an AI-powered, data-driven future. The conversations feature voices from companies like Siemens, Accenture, Schneider Electric, TK Elevator, Bosch Connected Industries, and more. Register here to watch for strategic insights into how global manufacturers are using AI to connect data, systems, and workflows.

We’re also thrilled to invite everyone to the Hannover Messe Center Stage keynote by Deb Cupp, Microsoft’s President and Chief Revenue Officer. Deb will present “Return on Intelligence: The Next Frontier of Manufacturing,” exploring how organizations can move beyond incremental efficiency gains to achieve transformative growth with AI. This keynote takes place on April 20 at 2:00 PM CET (opening Monday) on the main stage.  

For a quick recap of Microsoft’s Hannover Messe 2025 presence and to see what to expect in 2026, check out the 2025 recap video: 

Learn more about how Microsoft helps Frontier organizations prioritize efficiency, agility, and innovation 

• The Industrial Frontier: Four ways manufacturers can unlock intelligence across the value chain. Get the e-book

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