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.

Summary In the days following the United Nations Micro-, Small, and Medium Enterprises (UNMSME) Day, we take a closer look at what the data shows, and why it matters for the 400 million businesses that power the global economy.

This year, I want to address something we’re watching happen in real time: small and medium businesses (SMBs), also referred to globally as Micro, Small, and Medium Enterprises (MSMEs), are stepping into AI leadership, moving quickly, and applying it directly into the work that drives their business forward.

We mark UNMSME Day to raise public awareness of their contribution to sustainable development and the global economy. The numbers deserve repeating every year: SMBs represent 90% of all businesses worldwide, 50% of global gross domestic product (GDP), and 70% of the world’s workforce.¹

SMBs are not a segment of the economy; they are a foundational part of it. They operate under real pressure. The median small business carries just 27 days of cash reserves.² There is no room to bet on the wrong transformation, they must pick the right direction and get it right the first time. That pressure is exactly why what is happening right now matters so much.

AI has moved from conversation to competitive advantage

The latest Microsoft Work Trend Index 2026 makes something clear: AI is no longer a productivity add-on. It is shifting what work is possible. 58% of AI users say they are already producing work they could not have done a year ago and 66% report spending more time on higher-value work as AI takes on execution.

In a small team, this shift affects capacity and growth. For a financial planning firm streamlining client reviews, a law firm cutting document preparation time, a title company accelerating closings, or a property management team eliminating administrative overhead, that is not a marginal improvement. It represents a structural advantage.

A different pattern is emerging among SMBs. Organizations that move beyond individual use and embed AI into how work happens across teams, workflows, and decisions are seeing step-change results. Not just better work, fundamentally different work. And because SMBs have leaner structures and shorter decision cycles, they can get there faster than large enterprise organizations ever could.

What Frontier Transformation looks like in practice

Frontier Transformation becomes clear when the SMB journey moves from simply experimenting with AI to achieving transformation at growth and scale. Frontier Firms are the ones making that shift real by embedding AI into productivity tools they already are using across their teams workflows and decision processes. The result is not just better output, but a different operating model and one that unlocks more creativity, innovation, and growth.

Here are three SMB’s doing exactly that

Turning bottlenecks into real-time workflows

At Dunaway, a multi-discipline design, planning, and engineering firm in Texas, regulatory research and compliance checks were once manual, time-consuming steps that slowed project delivery. By bringing AI agents into the workflow, engineers can access regulatory insights in real time, answer questions faster, and apply knowledge consistently across teams. The result: a 90% reduction in research time and roughly 10,000 hours saved annually. What once lived with a few experts now works for the whole team. That is what a Frontier Firm looks like in practice.

When I first saw that number, 10,000 hours, I sat with it for a moment. That is not an efficiency gain. That is an entire team’s year given back.

Scaling craft and personalization with trusted intelligence

Businesses built on craftsmanship, trust, and deeply personal service, where consistency is hard to standardize, must tackle a different kind of scale challenge. Chow Tai Fook, a 97-year-old global luxury jewelry brand based in Hong Kong with thousands of stores across Asia, integrated AI across its operations. The company moved from isolated digital efforts to a connected, real-time intelligence model—giving frontline associates the insights they need in the moment to better understand each customer and deliver more personal, relevant experiences at scale. The result is more than 70% efficiency gains across millions of monthly interactions, and a 97-year-old brand that feels personal at every counter.

For me, it always comes back to the customer. When sales associates are empowered with the right insight in the moment, we’re not just making the business more efficient, we’re creating richer, more personal experiences. That’s the power of AI when it’s done right, it doesn’t replace human expertise, it amplifies it.

Making security part of how the business runs

DT Swiss AG, a Swiss manufacturer of high-performance cycling components with teams across Europe, North America, and Asia, faced complexity from fragmented systems, manual compliance processes, and administrative overhead. By moving toward a unified security model, it made identity, access, and governance part of daily operations rather than separate layers of work. The result was a 60% reduction in administrative overhead and a stronger compliance posture. Security did not slow the business down. It made scale more practical.

This is the story I find myself telling most often right now. Security is not a tax on transformation. Done right, it is what makes transformation sustainable.

Across these examples, the pattern is consistent. The SMBs pulling ahead are not simply adopting AI earlier. They are applying it with more intention, moving from isolated use cases to integrated workflows, from individual productivity to team-wide execution, and from security as a separate control to security as the foundation for growth.

Trust is the precondition, not an afterthought

One thing these businesses share: AI adoption and security are unequivocally connected. A 2024 Microsoft Security study found one in three SMBs hit by a cyberattack in the past year, at an average cost of USD254,445. 94% consider cybersecurity critical. And 81% say AI increases the need for stronger controls.³

The businesses moving fastest are solving productivity, data protection, identity, governance, and compliance together. SMBs do not have the time or resources to make five separate technology decisions for one business outcome. Security by design is not a feature, but a foundation for lasting AI adoption.

The partner ecosystem is the multiplier

No SMB transforms alone. Across these customer stories, partners play a consistent role: they help leaders decide where to start, where to incorporate technology into real workflows, and how to support adoption after deployment.

The Microsoft Partner ecosystem brings AI, productivity, and security into one practical conversation. For many SMBs, that begins in Microsoft 365 Copilot supporting how teams create, communicate, and make decisions. Next, extend those workflows using Microsoft Copilot Studio, connect data, and add security tools like Microsoft Defender for Business and Microsoft Purview all working together on a foundation with built-in, secure AI. With more than 1,400 connectors to third-party business applications, these solutions integrate into how businesses already operate across a broader secure cloud foundation.

The Microsoft Partner blog post, “Partner-led momentum, broader availability for SMB: Microsoft 365 Business with Copilot,” has more information on our Microsoft Defender for Business bundles.

• If you are an SMB owner or leader: Start where the time cost is most visible. You do not need a grand transformation plan. You need a first process, a secure foundation, and the decision to act.
• If you are a partner: Almost every SMB conversation is now an AI conversation. Customers are ready. Many still need help knowing where to begin. The partners and Managed Service Providers (MSPs) who lead with outcomes, secure adoption, and real workflow change will be the ones SMBs trust to reach the frontier.

Recognizing UNMSME

I am grateful for the resilience and ambition of small business owners everywhere. I know firsthand, the challenges of operating a business are real. In addition to my role at Microsoft, my husband and I run a small design-build construction company. This experience shapes how I see Frontier Transformation. Together, we are proving that the AI era will not be defined by company size but by leadership. SMBs are leading this moment.

What is the one workflow your team has transformed with AI this year? Connect with me and look forward to the conversation.

¹ United Nations, Micro-, Small and Medium-sized Enterprises Day, June 27, 2026.

² JPMorgan Chase Institute, Cash is King: Flows, Balances, and Buffer Days.

³ Microsoft Security, New research: Small and medium business (SMB) cyberattacks are frequent and costly, 2024.

The post Small and medium businesses aren’t waiting for an AI invitation—they’re already leading appeared first on The Microsoft Cloud Blog.

The shift is already underway. Major financial institutions are quickly moving beyond limited use cases toward agentic AI solutions designed to help drive impact at scale. Frontier Firms—organizations reimagining business processes around human-agent collaboration—are integrating intelligence into the workflows, data environments, and governed systems where decisions are made and work gets done.

The challenge is that in the heavily regulated constraints of financial services, access to data is tightly controlled, critical data is often fragmented across legacy platforms, and workflows span multiple systems, teams, and jurisdictions. Internal data must be integrated with external data, including market data, research, and third-party insights. Firms must be certain that the right people can access the right data in the right ways, while keeping it all safe.

Our partnership with Microsoft is shaping the next era of AI ready data and intelligence‑driven workflows. We are meeting clients where they work—embedding S&P Global’s trusted, high‑quality data directly into their workflows and unlocking agentic capabilities that turn insight into action, enabling accurate, faster, more-informed decision‑making. Together, we are accelerating how clients drive growth, manage risk, and capture opportunity in an increasingly complex and fast‑moving market.

Sally Moore, Chief Client Officer, S&P Global

Trust is paramount, and the best way to scale AI is to leverage the inherent strengths and attributes of a trusted cloud environment and the applications that professionals use to do their work. This way, data used in agentic solutions can be configured to inherit existing permissions, licensing constraints, and governance controls, rather than requiring firms to rebuild trust around new layers.

This requires viewing AI as an operating capability that unifies data, governance, tools, and workflows, enabling intelligence to show up where work happens. Approaches that simplify access to critical data and embed AI into everyday decisions and actions are best positioned to succeed.

Putting AI at the point of work—where data, decisions, and action come together

Financial work rarely happens in a single system. Professionals move across data, spreadsheets, meetings, messages, and business applications, in scenarios that span all aspects of financial services. To name just a few:

• A banker preparing for a client meeting needs more than a market summary; they need relevant signals, prior context, comparable activity, potential risks, and a path to the next best action.
• A risk professional needs intelligence that can be applied within existing controls, not outside them.
• An investment analyst needs current financial data in the spreadsheet where assumptions are being tested, not a separate answer that has to be copied and reconciled later.

AI creates more value when it is embedded in workflows across the value chain, helping to reduce handoffs, preserve context, and close the gap between analysis and action. AI that sits apart from the workflow can generate useful responses, but it still requires that data be copied, reconciled, and validated across systems.

The next phase of AI in financial services is about connected intelligence that brings the right data into the right context at the right moment. Our connectors ensure Morningstar’s independent research and trusted data sit alongside firms’ own data within their workflows, enabling investment professionals to generate deeper insights, move with greater speed, and make more confident decisions with clarity and control across the entire investment process.

Adam Wheat, Chief Technology Officer for Direct Platform, Morningstar

Our focus is to narrow the distance between trusted information, professional judgment, and the action that follows—in other words, to enable governed AI execution in context, allowing people to move from insight to action within familiar tools. This is why we, and our partners, are bringing financial data and AI capabilities directly into the everyday applications and tools many professionals use to get work done, including Microsoft Excel, Teams, Outlook, and Microsoft 365 Copilot. It enables intelligence that can be accessed in the spreadsheet where analysis is being built, in the meeting where a client discussion is being prepared, or in the collaborative space where teams coordinate the next step.

The platform for advanced AI experiences

For financial institutions, the next model for AI begins with the data that anchors the business: market intelligence, risk signals, research, client information, transaction context, policies, and operational knowledge.

Microsoft’s approach brings together trusted data, enterprise context, and intelligence on one integrated platform. It spans a rich ecosystem of financial services data providers, helps to orchestrate models, and is built upon the core strengths of the Microsoft Cloud: enterprise-grade security, compliance, and governance.

In this approach, the following key features work together to connect trusted data, enterprise context, and AI-powered capabilities:

• Federated connectors provide live data by accessing information without moving or copying it.
• Synced connectors provide enterprise data by indexing content from business and partner systems.
• Microsoft IQ provides context by understanding how people work, how the business operates, what the organization knows, and how the broader business and market environment is changing.
• Skills provide expertise by defining how specific business tasks are performed.
• Plugins combine connectors and skills to package data access and task expertise into reusable capabilities.
• Copilot Cowork helps coordinate and assist with tasks across systems, people, and workflows—bringing agentic capabilities into Microsoft 365 Copilot and, by extension, into Excel, Word, PowerPoint, and Outlook.

Together, these features empower financial services professionals to ask more precise questions, receive more relevant answers, and take action directly within their workflows, all while supporting compliance efforts and helping preserve context.

Expanding the ecosystem of financial data providers

In recent months, we’ve unveiled this strategy through milestones that reveal an expanding ecosystem of financial data providers and capabilities.

Federated Copilot connectors extend Copilot’s reach by securely integrating external real-time data directly into Copilot in Excel, Copilot Chat, and Researcher agent. Recently, we announced federated Copilot connectors from LSEG and Moody’s, and the momentum continues today with new federated Copilot connectors from CB Insights, Daloopa, FactSet (in preview), Morningstar, PitchBook, and S&P Global. Together, these integrations can provide access to up-to-date market data, company intelligence, research, portfolio analytics, investment data and more—within supported workflows, subject to provider availability, licensing, and system integration.

As financial institutions scale AI, the combination of trusted data and enterprise platforms becomes critical. Our collaboration with Microsoft enables clients to bring LSEG’s data into AI-powered workflows, supporting more consistent, informed decision-making across their business.

Emily Prince, Group Head of Enterprise AI, LSEG

Copilot Cowork plugins embed specialized partner expertise directly into task-oriented workflows, enabling Copilot to support role-specific financial analysis and decision-making. Building on the recent launch of Copilot Cowork plugins from LSEG and S&P Global Energy, we’ve now expanded the list of partners with Copilot Cowork plugins from CB Insights, Moody’s, Morningstar, and PitchBook. These plugins package proprietary data, models, and domain knowledge within defined workflows, depending on partner capabilities and implementation, allowing financial services professionals to move more readily from insight to action.

The defining question for financial institutions today isn’t whether to use AI, it’s whether they can trust the intelligence powering it. From Microsoft 365 Copilot to Excel to Copilot Cowork, Moody’s is embedding its decision-grade connected intelligence directly into the Microsoft workflows where consequential decisions are made every single day, allowing our customers to move with greater confidence and speed without sacrificing the rigor, transparency and accountability that high-stakes decisions demand. 

Cristina Pieretti, Head of Digital Content and Innovation, Moody’s

We’ve also announced new skills and capabilities for Copilot in Excel, one of the most widely used tools in financial services. In addition, we’ve introduced a starter library of pre-built finance skills, alongside the ability for organizations to create and integrate their own. Combined with new customization and control features, these enhancements enable financial services professionals to customize their workflows, apply consistent logic, and scale analysis more efficiently.

This integrated platform supports a shift from information retrieval to governed execution. For firms and institutions, this can mean more efficient preparation, more consistent use of trusted information, less manual stitching across applications, and a stronger connection between insight and execution.

Trust as the foundation

For financial institutions, this shift can only succeed if trust is built into the architecture. Organizations must be able to innovate without separating intelligence from the controls that make enterprise adoption possible.

This is where Agent 365 makes the trust model tangible. It lets organizations see which agents are operating, understand what data they are using, monitor the actions they take, and apply policies that govern how they work. It also supports the traceability and citations institutions require to understand how data was used, what sources informed an output, and how agent-driven work was generated.

As agentic workflows become the new standard for how analysis gets done, the data underneath must be trustworthy and verifiable. Through Kensho’s AI innovation and S&P Global’s domain expertise, we’ve done that work, creating a trusted data retrieval foundation designed for how AI systems reason and act. Our partnership with Microsoft is about bringing that foundation into the AI-native experiences where the industry is already headed.

Bhavesh Dayalji, Chief AI Officer, S&P Global and CEO, S&P Kensho

The same environment that brings data and AI closer to work should inherently provide the capabilities required to use agents responsibly at scale. In regulated industries, this combination is a material advantage: firms should not have to choose between moving faster with AI versus maintaining control over how data and actions are managed.

For financial services leaders, the next phase of AI will not be defined by isolated experiments, disconnected tools, or AI architectures that require firms to add a separate layer of trust, permissions, and context on top of the systems they already use. As firms move from experimentation to scaled adoption, Microsoft’s advantage is the ability to combine trusted data, context, workflow integration, and governance in one coherent operating model for AI, allowing firms to leverage the platforms they already depend on.

Learn more

• Learn how agentic AI is rearchitecting asset management at scale.
• For a practical framework on using AI to drive coordinated, enterprise-scale impact across the firm, read Frontier Transformation in Capital Markets.
• Visit Microsoft for Financial Services to learn more about how Microsoft empowers financial services organizations to achieve more.

The post AI in financial services: Bringing trusted data into the flow of work appeared first on The Microsoft Cloud Blog.

With the global banking system intermediating hundreds of trillions of dollars in funds, corporate and commercial banking sits at the center of the global economy.¹ It spans geographies, regulatory regimes, client types, and balance sheet structures. And critically, it relies on decision‑making that unfolds across extended, non‑linear processes.

Where past transformation efforts often fell short in accelerating decision-making and streamlining cumbersome processes, the emergence of agentic AI is changing what is possible. Combined with enterprise‑grade platforms, new AI capabilities are being designed to help coordinate work securely, governably, and at scale.

This shift is best understood through four ways that AI is starting to reshape how corporate and commercial banking can operate.

1. Empowering relationship managers

Relationship managers have long been the lifeblood of positive client experiences, often serving as the trusted human face of the business. In this role, the ability to synthesize insights from complexity is a critical skill. Yet even the best managers often struggle to manage the growing sprawl of data and requirements.

In many cases, relationship managers function as human middleware—responsible for manually aligning information that the organization itself cannot easily connect as environments become more fragmented across treasury and credit/lending systems, client data, risk platforms, documents, and collaboration across channels.

Agentic AI addresses this by shifting from passive or responsive assistance to active work coordination. AI-powered agents can assist in monitoring where a client, deal, or request sits across multiple systems, identify what is missing, and surface the right context at the moment decisions are made.

Standard Chartered equipped more than 6,000 bankers with a unified platform spanning 53 markets, giving relationship managers real-time insights and more time to spend on client-centered engagement. Likewise, UBS deployed Microsoft Copilot across its employee base (including relationship managers), transforming legal research with an AI assistant that surfaces precise clauses across 26 million documents using natural language queries. The system eliminates manual search and accelerates information retrieval, freeing experts to focus on judgment-intensive work and giving relationship managers more time for client interactions.

The result is not “AI advice,” but improved decision readiness for teams. Relationship managers spend less time assembling inputs and more time applying expertise, consistently, audibly, and earlier in the process.

2. Improving the quality of client interactions

Clients do not experience the bank as a set of systems. They experience it as a series of moments across onboarding, credit, treasury, and servicing. Too often, these moments are disconnected, which creates friction and can erode confidence.

Agentic AI helps close these gaps not just by connecting interactions, but by making them more meaningful. AI agents enable financial professionals to understand the client in context by maintaining continuity across all channels, while also helping anticipate needs, surface relevant insights, and making sure requests move forward without repeated inputs or dropped handoffs.

Commerzbank, for example, built an AI agent that now handles more than 30,000 customer conversations per month, resolving approximately 75% of requests autonomously. By maintaining shared context across interactions and orchestrating workflows in real time, the bank reduces the need for customers to repeat information while ensuring requests move forward without disruption. The result is a more consistent, responsive experience at scale.

AI also helps bankers maintain continuity across interactions and generate consistent, empathetic responses by unifying previously siloed workflows in sales, service, and communications. First National Bank is using Copilot for Sales to reduce fragmented touchpoints and strengthen ongoing, relationship-driven engagement with commercial clients across channels and teams.

This extends across other complex and high-stakes moments in banking. In investment banking, for example, AI-assisted meeting preparation pulls together internal context and external market data, so bankers walk into conversations already aligned to the client’s situation. In corporate and commercial banking, onboarding and KYC agents can intelligently scan documents, cross-reference sanctions lists and adverse media, and surface key findings for relationship managers.

This can turn what has traditionally been a weeks-long, manually intensive process into a more efficient and consistent experience for both the bank and the client. 

The result is a shift from fragmented, episodic service to coordinated, proactive engagement, where interactions are more timely, more relevant, and more aligned to the client’s evolving objectives.

3. Modernizing risk and core systems

Banking operations have rarely struggled because of a lack of automation alone. They often struggle because complexity compounds.

Credit assessment depends on risk inputs, risk depends on documentation, documentation depends on counterparties, onboarding depends on compliance and core systems rely on all of the above. These processes span teams, systems, and timelines that were often not designed to operate in sync, and as a result work can slow down, fragment, and stall.

Agentic AI helps address these challenges by not only automating tasks but also coordinating workflows across stages. It can track progress across processes, resolve handoffs dynamically, and surface exceptions to the right experts when judgment is required, reducing friction by connecting workflows, aligning dependencies, and helping work move more smoothly across systems and teams.

Bank of Queensland, for example, used Microsoft Copilot to streamline complex, multi-step workflows, reducing risk analysis from weeks to a single day while improving quality by 22%. By automating document-intensive tasks and simplifying cross-functional coordination, teams accelerated decision-making and enabled more adaptive, efficient execution across core banking processes.

At an ecosystem level, Swift applied federated learning and confidential computing to detect financial crime across institutions without centralizing data, which enabled more coordinated risk modeling while preserving privacy and regulatory boundaries.

At Scotiabank, rather than replacing core systems, the bank embedded AI agents into its payment operations to coordinate data transformation, reconciliation, and exception handling across fragmented workflows. As a result, processes that once required weeks can now be executed in seconds, with clear auditability and control.²

When processes are streamlined and coordination costs fall, measurable outcomes can improve. Faster credit decisions, strengthened compliance, improved reconciliation and materially better banker and borrower experiences are enabled, without requiring wholesale system replacement.

4. Transforming trade finance

Innovation in corporate and commercial banking typically falls short because complexity has historically resisted scale.

In trade finance, structured lending, and cross-border treasury, work spans multiple stakeholders, documents, and exceptions across boundaries. These are not linear processes, but complex, judgment-driven environments where traditional automation can break down.

The shift enabled by agentic AI is reimagining how these processes operate. Instead of forcing complexity into fixed workflows, banks can execute work adaptively, coordinating across participants in real time and responding to exceptions as they arise.

Agentic systems open the door to different operating models. They can address deviations, help route actions to the right specialists, and capture how complex work is completed. Over time, these patterns can be encoded into reusable playbooks, so banks can scale expertise, deliver more consistent outcomes, and maintain continuity across complex, multi-party processes.

A glimpse of this future can be seen in a Microsoft-led trade finance proof of concept with ANZ, HSBC, and Lloyds. By embedding AI agents within ERP systems, the solution parses letters of credit, cross-checks them against invoice and shipping data, flags discrepancies, and securely transmits structured, standards-aligned information to bank platforms. Rather than simply accelerating existing steps, it demonstrates how trade workflows can evolve toward a more consistent, data-driven model, ultimately reducing fragmentation, minimizing manual rekeying, and improving traceability across trade finance processes.

What emerges here is not just more efficient execution, but a potentially fundamental new way of operating, one in which complex, exception-heavy processes can be coordinated, governed, and continuously improved at scale.

Realizing this shift depends on enterprise-grade AI platforms that combine advanced AI models, secure integration across systems and partners, and scalable data and analytics foundations.

Leading institutions are using GenAI to streamline trade finance and drive the shift from paper-based to digital platforms.

From experimentation to operating advantage

Experienced banking leaders are right to approach AI with caution. The industry has seen many moments where enthusiasm outpaced operational reality. What differentiates this phase is not model capability alone, but fit. Agentic AI aligns with how corporate and commercial banking functions. It streamlines key processes, respects variability, reacts to change, and reduces coordination costs rather than oversimplifying work.

Equally important, platforms matter. Without security, governance, and integration at enterprise scale, AI coordination becomes a risk rather than an advantage.

This is where disciplined adoption—not experimentation for its own sake—will separate leaders from laggards.

Learn more

• For a practical playbook to improve customer experiences, embed AI in high‑value workflows, and act on the shift to agentic AI, read The Microsoft Business Blueprint for Agentic Assistants.
• For a practical blueprint to scale AI, drive productivity, and deploy across banking, insurance, and capital markets, read Frontier Transformation: AI in Financial Services.
• To help unlock business value and deepen customer relationships in the era of AI, visit Microsoft for Financial Services.

¹ McKinsey & Company, “Global Banking Annual Review 2025,” October 23, 2025.

² Scotiabank, “How AI Agents are Transforming Scotiabank’s Payment Operations,” September 17, 2025.

The post How AI is reshaping corporate and commercial banking 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.

1. Will AI amplify the intelligence of my organization and the attributes that make my company unique within its industry to grow my business; or will it use my intelligence for its own benefit, learning from my most important business flows and leveraging my intellectual property?
2. Can I trust that the outcomes are providing durable return on investment and that these solutions are running within the confines of my governance and security standards?
3. How do I get the visibility, control, flexibility and business model innovation needed to manage the costs associated with AI and maximize value?

I consistently advise customers that they need to build their own IQ on a platform of intelligence that is model-diverse, open and heterogeneous at every layer of the stack. Models are commoditizing. No company should be dependent upon any one model or any one model’s harness. Over the weekend, Satya warned of a world where every company across every sector is ceding value to a few models that eat everything they see. AI that is intended for growth should amplify the intelligence of an organization so that it compounds from within.

Companies also need an observability platform that provides governance, management, security and Financial Operations (FinOps) to ensure the ROI with AI. This enables AI to be trusted within the environment over which it reasons and puts the business in control of the outcomes.

Intelligence + Trust is embedded across Microsoft 365 Copilot, GitHub Copilot and Copilot Studio, where model diversity aligns cost and performance to each task. Microsoft IQ optimizes workflows, so context is routed efficiently and reduces unnecessary compute. Agent 365 is the control plane to observe, govern, manage and secure agents. We have built a system to manage AI spend as a core enterprise capability, not an afterthought. It is delivered across clouds and model providers without locking customers into a single approach.

Managing costs at scale

As agent usage scales, organizations need a clear set of levers to manage cost:

Model diversity. Any given inferencing model, model harness or agentic loop on its own does not help build out an organization’s IQ in ways that compound its intelligence. Both Microsoft 365 Copilot and GitHub Copilot are model-diverse by design without locking customers into a single provider. Different models — like GPT-5.5 or Claude Opus 4.8 — serve distinct roles with different economics. Matching the right intelligence to each task optimizes performance and cost.

Your IQ. Agents struggle with raw data. Significant compute is spent interpreting structure and context before useful work begins. The Microsoft IQ platform empowers your IQ by turning raw data into usable intelligence, continuously building a semantic understanding of how your organization operates across Microsoft 365 and line-of-business systems. It provides agents with the context they need upfront rather than requiring them to reconstruct it. The result is measurable: faster execution, higher accuracy and lower token usage. This is how intelligence compounds within your organization.

Financial operations. FinOps became critical when companies moved to the cloud and requires even greater attention as AI shifts from fixed pricing to usage-driven models. With Foundry and Agent 365, we are providing tools to help our customers optimize their AI costs today.

Frontier business models

Business models are evolving as we use AI to drive business outcomes. The User Subscription License (USL) has become the foundation, providing a package of capabilities for a predictable per-user-per-month fee. Usage-based licensing has emerged for long-running, multi-tasking agents, where cost aligns directly to the work performed.

Microsoft gives customers a unique combination of business model flexibility and integrated product experiences that is unmatched in the market. Microsoft 365 Copilot and GitHub Copilot use both models — a USL offering with not only value and capabilities, but flexible consumption on top. Today we’re announcing the general availability of Copilot Cowork worldwide, which requires the Microsoft 365 Copilot USL and is then usage-based.

Our model-diverse strategy allows customers to purchase capacity with the flexibility to use the right model for the job based on model strengths, economics and the latest innovations. Microsoft Agent Factory provides a single consumption model spanning Microsoft 365 Copilot (including Cowork), GitHub Copilot and agents built in Fabric, Foundry and Copilot Studio.

Our integrated product experiences put AI in the flow of work for both knowledge workers and software developers and manage capacity fluidly across the two. Historically these personas have been distinct, but increasingly the line between them is blurring. Coding is becoming a mainstream knowledge worker skill and chat and Cowork are becoming modalities important for software development. With Microsoft 365 and GitHub, we offer market-leading tools for both roles and make it easy to seamlessly manage capacity based on availability and need.

Agent 365: The control plane

As organizations adopt agents from Microsoft, another provider or build their own, a control plane is essential. Agent 365 gives IT and security leaders a single place to observe, govern, manage and secure agents across the organization. It builds on the Microsoft stack that enterprises trust: Entra for identity, Defender for threat protection, Purview for data governance and Intune for endpoint management. We are extending Agent 365 to include cost management, so organizations can monitor and manage agent spend alongside security and compliance. As the Frontier Firm operating model takes hold, leaders will manage human and agentic work as a single system, with visibility into both performance and cost.

The two most essential elements in any AI solution are Intelligence + Trust. At Microsoft, this conviction shapes how we design every layer of our AI platform. Microsoft IQ enables organizations to harness their own unique IQ, bringing context to data and embedding AI directly into the flow of work to deliver faster, more accurate and more trusted outcomes while safeguarding assets and protecting intellectual property. Agent 365 provides that trust layer, ensuring every agent and AI artifact is observed across the environment so organizations can move decisively from experimentation to enterprise impact with confidence. As Jay Parikh put it at Build, AI alone will not change your business. The system running it will.

We have built this system for our customers and partners, where intelligence compounds from within and every agent operates with control, visibility and trust. Together, we can scale human ambition and define how AI delivers measurable business impact across every role, organization and industry.

Judson Althoff is the chief executive officer of the commercial business at Microsoft. He is responsible for the product strategy, sales, services, support, marketing, operations and revenue growth of the company’s commercial business, which operates in more than 120 regional and national subsidiaries globally.

The post Achieving success with AI 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.

This is a defining moment for healthcare and the decisions made now will shape outcomes for years to come. The question is no longer whether to adopt AI—it’s how to alleviate these pressures with agency, security, and trust.

AI for Better Health is our ambition to enable every person on the planet to live healthier by transforming how care is discovered, delivered, and accessed at scale. We are redefining how AI works alongside people to transform health across three priorities: 

1. Accelerating lifesaving breakthroughs.
   
2. Transforming the healthcare experience.
   
3. Advancing global health equity.
   

Accelerating lifesaving breakthroughs

Across healthcare and life sciences, organizations are bringing human judgement together with AI to enable discovery—helping researchers identify patterns that may support earlier insights and accelerate innovation.

In practice

• Novo Nordisk aimed to scale a pipeline of drug discovery, development, and data science capabilities with AI and machine learning. The teams built a Novo Nordisk AI platform and amplified its culture of innovation across many use cases, including predictive AI models for advanced risk detection in cardiovascular diseases. The algorithm may be able to predict patients’ cardiovascular risk better than current clinical standards. 
  
• In clinical settings, AI is helping clinicians act sooner and with greater precision. For example, AI-supported tumor boards are helping organizations like Providence surface potential data insights for clinician review, such as identifying biomarkers or matching patients to clinical trials, to support decision making.
  

These innovations are helping advance research so it can reach more patients, more communities, and more health systems worldwide.

Transforming the healthcare experience

As new discoveries move into practice, the way care is delivered is beginning to change. Increasingly, AI is being embedded directly into workflows with tools designed to reduce friction, connect information, and provide insights in context. As routine work is streamlined, people gain the time and clarity to focus on deeper impact—strengthening care, discovery and operations in service of better health for all.

In practice

• At Piedmont Healthcare in Atlanta, for example, an OB‑GYN is using Microsoft Dragon Copilot to capture and structure clinical conversations in real time—freeing her to listen more deeply, build trust, and apply more informed judgment during sensitive moments like pregnancy and postpartum. This shift toward more empathetic, patient-centered care improves reproductive health outcomes for women of color at her clinic.
  
• These capabilities extend beyond documentation. AI is beginning to surface new signals within the flow of care. For example, identifying patterns in vocal characteristics may provide additional context for clinician evaluation, subject to appropriate validation and oversight. Baptist Health in Kentucky is beginning to apply this approach within an ambient workflow, adopting tools from Canary Speech to support earlier and more informed intervention.
  

What begins as support for individual interactions can scale across teams, specialties, and health systems. And as some of the constraints on their time and attention are lifted, providers can focus more fully on their patients—bringing the human element back to care.

Advancing global health equity

Ensuring that better health is accessible to everyone remains one of the most important and complex challenges in healthcare.

AI has the potential to extend the reach of clinicians and care teams—bringing health information to remote communities, supporting resource-constrained environments, and providing individuals with more direct access to trusted health information.

As these capabilities scale, they can help reduce barriers tied to geography, infrastructure, and access to specialized care—supporting more equitable access to care.

In practice

• Through innovations like Microsoft Copilot Health, AI-powered health companions are helping individuals make sense of complex health information. By bringing together clinical records, wearable data, and medical knowledge, supported by Microsoft security and privacy technologies that help protect data, individuals can better understand their health. This can help them feel more empowered in discussions with their care teams.
  
• Partnerships are helping redesign care models and address long-standing inequities. For example, Microsoft is collaborating with Kearney to mobilize a global community of innovators through the Women’s Health Tech Manifesto—using data and technology to help close gaps in women’s health.
  
• Through the Rural Health Transformation (RHT) Collaborative, a multi-sector public and private partnership, co-chaired by Microsoft and others, we are uniting technology providers, health systems, payers, and non-profits to help states deploy CMS’s rural health funding into ready-to-adopt AI-enabled care models. Together, we are supporting efforts to expand access to primary care, telehealth, and remote monitoring for rural communities at risk of losing critical health services, while strengthening the cybersecurity and interoperability foundations more than 700 rural hospitals already rely on.
  

Ultimately, advancing health equity will depend not only on leveraging AI, but also on the responsible design, deployment, and use of these technologies with appropriate human oversight, transparency, and accountability.

Building the future of health—together

The decisions being made today will shape how AI is used in healthcare for years to come.

Progress will not be uniform. Every worker, leader and organization is learning how to harness AI-enabled workflows that amplify what we as humans can do to make meaningful change. Each step forward unlocks new possibilities for organizations and communities.

Taken together, these outcomes move us toward a broader vision for the future where every person on the planet can live healthier. That’s the goal of AI for Better Health.

Our ambition of AI for Better Health is grounded in use cases that create impact today.

• Get our e-book, AI for Better Health: Enabling transformation in healthcare, to explore how organizations are applying this approach in practice.
  
• Explore Microsoft for Healthcare to see how to drive innovation and improve healthcare experiences with trusted, AI-powered solutions.

The post AI for Better Health: Enabling every person on the planet to live healthier 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.