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.

AI has moved quickly from possibility to priority, but many leaders are now confronting a more practical question: how ready is the organization to turn isolated wins into repeatable, governed progress? Early pilots and productivity gains matter, but they do not automatically create the leadership alignment, data foundations, governance practices, workforce readiness, or operating discipline required to scale AI across the business.

The AI Strategy Roadmap offers a practical guide for advancing Frontier Transformation. It helps leaders understand where they are today, identify the capabilities they may need next, and strengthen the conditions that can support AI as a durable business capability.

What the AI Strategy Roadmap is

The AI Strategy Roadmap translates insights and patterns observed among organizations advancing Frontier Transformation into practical strategies and actions leaders can consider. Its recommendations are informed by qualitative research conducted by Emerald Research Group on behalf of Microsoft, including in-depth interviews with 70 business and IT decision-makers actively leading AI transformations, as well as Microsoft’s own internal AI transformation experience and the work we’ve done supporting customer transformation efforts across industries.

The Roadmap reflects challenges described by leaders in transformation efforts and provides practical, experience-informed guidance for navigating them.

Why it matters now

This roadmap goes beyond the basic question of where AI can create value. It addresses the organizational conditions that can help teams move from isolated initiatives toward more repeatable, governed progress.

The research surfaced a consistent pattern among the organizations interviewed. Leaders described AI transformation as business transformation that occurs—and improves—over time. Organizations working through the shift often have strong ideas, but can get stuck controlling scope, aligning leaders, managing complexity, and building the workforce capability required to make change happen.

“We didn’t start with a big, fully baked AI strategy. It was much more about identifying a single area where we could realistically make a difference and testing it there first. Once we had something working and could show tangible impact, that’s what helped us expand and bring others along.”

Senior VP of Growth Marketing, Financial Technology

The five drivers of AI value

Every organization is somewhere on the AI readiness journey. Across the research and customer experience reflected in the Roadmap, five core drivers emerged as useful lenses for assessing readiness, prioritizing investment, and planning practical next steps.

1. Business strategy. Align AI investments to measurable business priorities and prioritize use cases that can earn sponsorship, funding, and momentum.
2. Technology and data strategy. Build trusted, governed, AI-ready data foundations and infrastructure designed to support growth, so AI initiatives can become more repeatable over time.
3. AI strategy and experience. Build trust through visible learning, experimentation, and repeatable AI delivery practices that can support user confidence, leadership engagement, and sustained adoption.
4. Organization and culture. Develop AI literacy, change management, leadership alignment, and operating-model practices that can help teams adopt AI more consistently.
5. AI governance and security. Embed responsible AI, governance, security, and compliance from the start, with transparency and human oversight that can help build trust as AI initiatives expand.

Developed in parallel, these drivers can help teams assess readiness, manage risk, and plan the next stage of AI transformation. Each one maps to a maturity model—Exploring, Planning, Implementing, Scaling, and Realizing—so leaders can assess where they are today and identify where to invest next.

Key research highlights

• Culture outweighs code. According to the Microsoft 2026 Work Trend Index, organizational factors drive 67% of the realized value from AI—twice the impact of individual behavior. For many organizations, culture can be a significant barrier to scaling AI initiatives.
• Leadership alignment shows up in the results. In the AI Strategy Roadmap research, more than 70% of organizations with advanced strategy and execution readiness say leadership communicates an AI vision clearly tied to business strategy.
• Direction beats enthusiasm. In the AI Strategy Roadmap research, among organizations that struggled with execution complexity, 15% named a lack of executive buy-in and strategic alignment as a barrier—a reminder that ideas without sponsorship can fragment.

Wherever you are on the journey

Frontier Transformation is a journey, not a destination—and no two organizations start in the same place. By pairing the five readiness drivers with a clear maturity model, the guide helps leaders locate their organization today, identify roadblocks, and focus on practical next steps for moving from experimentation toward more repeatable systems for delivering value at scale.

Start where you are

The next phase of AI transformation will require organizations to understand where they are ready, where they may be exposed, and what capabilities to build next. The AI Strategy Roadmap gives leaders a practical way to assess their current maturity, align teams around the next stage of progress, and strengthen the trusted foundations that can support AI initiatives as they scale.

Download the AI Strategy Roadmap

Discover the key actions leading organizations are taking to scale AI initiatives and advance their journey toward frontier leadership.

Download the roadmap

The AI Strategy Roadmap draws on qualitative research conducted by Emerald Research Group on behalf of Microsoft—70 in-depth interviews with IT leaders and business decision-makers across industries, conducted from February through March 2026—alongside Microsoft’s own Frontier Transformation experience.

The post The AI Strategy Roadmap: Five drivers of successful AI transformation appeared first on The Microsoft Cloud Blog.

This is our idea behind the AI Railroad Brain concept: a network-level intelligence layer that could help rail operators move from localized, reactive decision-making to system-wide orchestration. Instead of treating dispatching, maintenance, safety, workforce planning, and energy optimization as separate problems, it connects them into one operating picture so leaders can make faster, more consistent, and more profitable decisions.

Five freight rail challenges AI can help solve

Across freight rail, five challenges are limiting performance. Each is familiar on its own. What makes them urgent is how they now compound across the network.

1. Reduce network congestion and railcar dwell time

Rail networks lose value when yards, terminals, crews, and equipment are optimized locally instead of as one system. A decision that improves one terminal can create delays downstream. A small disruption can cascade across regions. The result is lower velocity, higher dwell time, underused assets, and constrained capacity.

The issue is not that railroads lack data. They already collect vast operational, asset, and network information. The gap is turning that data into real-time, coordinated decisions that improve the entire network, not just one node at a time.

2. Improve predictive maintenance and asset reliability

Railroads have invested heavily in sensors, wayside detectors, and condition monitoring. These tools can detect anomalies earlier, but detection alone does not improve outcomes. Maintenance teams still need to know which issue matters most, when to act, what resources to assign, and how the decision will affect service.

Without network-aware prioritization, teams can over-maintain some assets, under-respond to others, and miss the broader operational impact. The next step is not simply better prediction. It is better decision-making.

3. Strengthen rail safety and risk management

Rail safety has improved over time, but the risk profile remains asymmetric. Many incidents are low impact, while a small number of high-severity events can create significant operational, regulatory, financial, and reputational consequences.

Most safety systems are built for compliance and response. Modern rail operations require something more: the ability to correlate signals from track conditions, rolling stock, weather, human factors, and network context to anticipate compound risk earlier. The opportunity is to shift from incident response to integrated risk intelligence.

4. Address workforce knowledge loss

As experienced railroad workers retire, organizations risk losing more than labor capacity. They risk losing institutional knowledge: how to diagnose non-obvious failures, manage disruptions under pressure, and balance trade-offs in real time.

That knowledge often lives in people’s experience, not in systems. AI copilots can help capture and scale expertise by giving dispatchers, maintenance planners, safety analysts, and operators access to relevant context, recommended actions, and explanations in natural language. The goal is not to remove people from decisions, but to help every worker make better decisions faster.

5. Optimize fuel efficiency and reduce railroad emissions

Fuel is one of the most direct levers on railroad margin and emissions. Consumption varies by route conditions, train configuration, pacing, congestion, and operator behavior. Yet many fuel optimization approaches remain static, rule-based, and disconnected from real-time network conditions.

AI can help railroads optimize train handling, routing, consist planning, and dwell reduction together. That creates a dual benefit: lower operating cost and measurable progress toward sustainability goals, often without waiting for large-scale infrastructure change.

Building an AI Railroad Brain

The AI Railroad Brain is not a single application but a concept that will help address the challenges highlighted above. It is an intelligence layer that connects operational data, digital models, optimization engines, and human workflows. Its value comes from how these capabilities work together.

• A network digital twin provides a continuously updated view of tracks, yards, rolling stock, crews, terminals, and operating constraints.
• A real-time data platform unifies telemetry, maintenance history, GPS, weather, crew data, customer commitments, and enterprise systems into a shared foundation.
• Decision intelligence uses optimization, prediction, simulation, and machine learning to recommend actions across dispatching, maintenance, safety, workforce, and energy.
• Copilots and agents bring insight into daily work by helping teams ask questions, understand trade-offs, and act with confidence, drawing on the ontology and semantic layer to return answers that reflect rail-specific terms, relationships, and operating context.

Together, these capabilities help railroads move from fragmented insights to system-wide orchestration. A maintenance decision can account for dispatching impact. A safety signal can be evaluated in the context of weather, asset condition, and traffic density. A fuel recommendation can reflect congestion, service commitments, and crew availability. This is where AI becomes operationally meaningful: not by producing more dashboards, but by improving the decisions that shape network performance.

How railroads can start to transform

Railroads do not need to transform everything at once. The most practical path is to start with a high-value operational problem, prove impact, and scale from there.

1. Choose a measurable use case. Start with dwell reduction, maintenance prioritization, safety risk detection, workforce decision support, or fuel optimization.
2. Connect the right data. Focus on the operational signals needed to improve a specific decision, not on building a perfect enterprise data estate first.
3. Embed AI into workflows. Put recommendations where dispatchers, planners, and field teams already work.
4. Redesign decision rights. Clarify when AI recommends, when humans approve, and how outcomes are measured.
5. Scale across domains. Once value is proven, connect adjacent workflows so improvements compound across the network.

Adoption will matter as much as technology. Rail operations are shaped by thousands of daily decisions made by people across shifts, regions, and functions. AI creates value only when it becomes part of how those decisions are made, reviewed, and improved.

The next rail advantage is intelligence

The future of freight rail will not be defined only by more track, more yards, or more equipment. Those investments will remain important, but the next performance frontier is intelligence: the ability to coordinate decisions across the network in real time.

The concept of AI Railroad Brain offers a practical way to unlock more capacity, reliability, safety, workforce resilience, and fuel efficiency from existing infrastructure. For railroad leaders, the question is no longer whether AI will influence operations. It is how quickly they can turn AI from isolated pilots into an operating capability that improves performance every day.

The post The AI Railroad Brain: A new operating model for freight rail appeared first on The Microsoft Cloud Blog.

As we enter a new era for AI, Microsoft’s environmental sustainability work is entering a new phase—defined not only by ambition, but by how we deliver in a period of rapid technological change. In our pursuit of becoming a carbon negative, water positive, and zero waste company that protects ecosystems, the context has evolved, and so must our approach.

The global shift toward AI is reshaping economies, accelerating innovation, and becoming foundational to how technology is built and used. It is also increasing demand for the energy, water, land, and materials required to support that growth. As a company at the forefront of this transition, Microsoft has a responsibility to help ensure that technology strengthens, rather than strains, the systems and communities on which it depends. This imperative is reshaping the context for our work.

We are approaching this moment with clarity and conviction. We believe AI can deliver broad societal, economic, and environmental benefits, but innovation at this scale must be matched by responsibility at the same scale. For Microsoft, this means designing, building, and operating infrastructure that is more efficient, more resilient, and more grounded in the realities of the communities where we operate.

We do not see these dynamics as a reason to step back. We see them as a mandate to lead differently. That requires greater operational rigor, stronger integration across our sustainability priorities, and a sharper focus on durable outcomes for the local communities where we work and the global value chains that make our work possible. It also requires being transparent about where progress is advancing, where it is more difficult, and where new approaches are needed.

The path forward will not be defined by simple tradeoffs or single solutions. It will depend on how effectively we align innovation with stewardship. The systems we build to support the future must also support the long-term health of the planet and the communities we serve. Our experience makes clear that this is possible, but only with even greater discipline, partnership, and a willingness to learn and adapt as conditions evolve.

What this moment requires
Our aim is to build technology that gives more than it uses. Lasting progress depends on how we build it and whether that growth strengthens the places where it takes root.

This thinking is reflected in our Community First AI Infrastructure approach, which is helping shape a more integrated model for community partnership, responsible operations, and environmental performance as we grow. In this way, sustainability is not separate from growth; it is part of how responsible growth is defined.

While AI infrastructure is driving demand for energy, water, land, and materials, sustainability solutions are not scaling fast enough to meet demand. This tension is real, and it is also productive.

It is forcing sharper questions: Where do we need to move faster, invest differently, or rethink our approach? Which assumptions still hold, which ones need to evolve? Five years into this work, we have more operational data, more direct experience, and a clearer view of what measurable planetary progress actually requires. That perspective helps keep us focused on outcomes rather than attached to any single pathway.

We want to be clear about what this means—and what it does not. It means being more precise about what sustainability requires for Microsoft, and more willing to refine our strategies as conditions change, data improves, and tradeoffs become clearer. It does not mean we are lowering our ambition.

Progress amid growth
Our results reflect both progress and pressure. As we scale the physical infrastructure required to power the AI economy, our emissions are shaped by the impact of that growth and the actions we are taking to manage it.

The visual that follows illustrates this dynamic by comparing our reported emissions with a modeled view of where emissions may have been in the absence of four specific interventions: carbon free electricity, sustainable fuels, XBOX console efficiency, and Surface device decarbonization. While these examples represent only a portion of our emissions reduction efforts, they highlight an important lesson from our work to date: that well-designed, targeted interventions can deliver measurable progress even as demand for infrastructure continues to rise.

In FY25, we matched 100% of our annual global electricity consumption with renewable energy[2]. Microsoft will continue to push for an expansive focus on adding all forms of carbon-free electricity (CFE) [3] to the grids where we operate, complementing and building on our portfolio of renewable energy resources. We recognize that the world’s rising electricity needs require a balanced, all-of-the-above decarbonization strategy to meet global economic growth and environmental goals, and we will continue to support this approach moving forward.

Our total emissions (Scopes 1, 2, and 3) increased 25% year over year, driven primarily by the expansion of our datacenter infrastructure and pausing our use of non-additional, unbundled renewable energy certificates as we prioritize investments that bring net new power to grids. While this decision increases our reported emissions in the near term, it enables us to increase the development of new CFE rather than relying on certificates alone. We believe this change will create more long-term sustainability benefits. Growth-related emissions pressure was expected. The more important signal is where that pressure is concentrated.

Scope 3 remains the largest share of our footprint overall, but one of the clearest changes this year was the growing contribution of Scope 2, which represents 13% of our total emissions—up from nearly 2% last year. This development highlights how important the energy systems across our supply chain are in shaping environmental outcomes.

This year’s results also made clear that progress now depends on adapting how we work.

Water is one of the clearest examples. In FY25, we replenished for the first time more water globally than we withdrew—more than 14 million cubic meters—marking a major milestone on our journey to become water positive. Reaching this point reflects years of work to improve water efficiency, expand replenishment efforts, and scale partnerships around the world.

We are proud of this achievement but also know that replenishing global volumes is not enough. The next phase of our work is increasingly local. As we move forward, we are placing greater focus on helping restore more water to the watersheds where we operate than we withdraw while strengthening long-term water resilience. We prioritize projects in water-stressed regions that are locally relevant and designed in partnership with communities, delivering benefits not only for water availability, but also for ecosystems, economies, and people. Through this approach, we aim to ensure our growth supports and helps sustain the communities and environments where we operate.

Transparency remains central to how we work and how we report. Microsoft has eliminated nearly all single-use plastics in our primary product packaging, reducing the share that remained to just 0.07% at the end of calendar year 2025.[4] But we are not rounding down. We are staying accountable to the work required to eliminate them entirely.

Across our cloud operations, we achieved 92% reuse and recycling of decommissioned servers and components for the second consecutive year, diverted 90.5% of construction and demolition waste from landfills and incinerators, and expanded our Circular Centers to seven facilities globally. These results also reflect a broader shift toward solutions that have co-benefits—reducing both emissions and resource demand over time.

Throughout this journey, we have learned that progress in one area often depends on progress in another. Clean energy investments are essential to decarbonization. Water use is linked not only to our operations, but also to the energy systems that power them. And extending hardware life through circular approaches can reduce both emissions and material demand across the value chain.

That is why our priorities extend beyond tracking progress against individual commitments on water, carbon, waste, and ecosystems as though they move independently. Our experience has made clear that progress does not happen pillar by pillar. Some of the most consequential work ahead will be measured in whether we address system challenges and help build the conditions for long-term progress: more resilient grids, stronger markets for lower-carbon materials, more effective water stewardship, and infrastructure designed and operated with local realities and community priorities in mind.

For that reason, this year’s report takes a more integrated approach—placing progress against our commitments in the broader context of how those commitments are operationalized across our infrastructure and products.

What’s next
We are proud of what we have accomplished, and we remain humbled by the scale of the challenge ahead. Responsibly building the AI future requires clear accountability for what AI demands, candor about real constraints and tradeoffs, and sustained focus on outcomes that are durable and broadly shared. The chapters that follow show how we translate that intent into execution across our physical infrastructure, products, and value chain—where our sustainability commitments become operational reality.

Read the full report: https://aka.ms/SustainabilityReport2026

[1] The solid line represents Microsoft’s reported greenhouse gas emissions (Scopes 1, 2, and 3) for FY20–FY25, prepared in accordance with GHG Protocol and management’s criteria, and uses a market-based emissions approach. The dotted line represents an illustrative counterfactual scenario of estimated emissions had select, discrete carbon reduction initiatives not been undertaken. These initiatives include energy efficiency improvements for XBOX consoles, renewable energy purchases, sustainable aviation fuel (SAF) and sustainable marine fuel (SMF) certificates, and supply chain decarbonization of Surface devices. The difference
between the two lines is an estimate of emissions avoided through these specific initiatives relative to a scenario without those initiatives occurring. This estimate is directional in nature, does not represent the full scope of Microsoft’s decarbonization efforts, and is not part of our reported greenhouse gas inventory. It should not be interpreted as a comprehensive measure of total emissions reductions or as additive to other carbon reduction or removal claims.

[2] Microsoft defines renewable energy as electricity that comes from sources that are replenished at a rate greater than or equal to their rate of depletion, such as geothermal, wind, solar, hydro, and biomass. To date, Microsoft’s renewable energy target includes two primary categories: renewable energy from contracted projects and grid mix. The first is renewable energy delivered under PPAs or similar long-term contracting mechanisms, generally for new projects where our financial involvement in the project’s development is critical for its success. This category represents more than 90% of the renewable energy applied to achieve our 2025 target. The second category is “grid mix” – renewable energy supported via our standard utility relationships and rates, inclusive of policy programs such as renewable portfolio standards and state and utility decarbonization goals. Our 2025 100% renewable target does not include purchases from short-term, so-called “spot market” renewable energy credits (RECs) sourced from operational clean energy projects.

[3] Microsoft defines carbon-free electricity (CFE) technologies as technologies with zero direct emissions and biogenic technologies with lifecycle emissions equivalent to renewables. CFE technologies include wind; solar; geothermal; sustainable biomass; hydropower; nuclear; fossil fuels with complete carbon capture, utilization, and sequestration; and storage charged with CFE generation.

[4] By weight, as designed, portfolio average. More details can be found in our Environmental Data Fact Sheet.

The post Responsibly building the AI future 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 more time I spend working with AI, the more convinced I become that what matters most for her future isn’t how quickly she can answer. It’s whether she has the judgment to know when an answer can be trusted.

I’ve spent decades at Microsoft watching this tension play out: first building tools for other developers, then working across AI as models moved from research curiosities to systems deployed at scale. Now we’re building Microsoft IQ, where we’re exploring how an organization’s collective intelligence can become its greatest advantage. Through every one of those chapters, one thing has remained true: it’s never enough for a system to be powerful; it must also be trustworthy.

Trust is what turns assistance into delegation. When we can trust an agent to do what we intend, within the limits we set, we can hand off the work we never wanted to spend our lives on: the repetitive tasks that drain attention, the mundane work that fills a day without moving anything meaningful forward, the dangerous work humans should not have to do, the work too vast for any individual or team. Agents should take on that toil, extend our reach, and give us back our time for the work that calls for something only humans bring.

My daughter doesn’t know any of this yet. But by the time she’s grown, most of the work that rewards speed and repetition will be work we delegate. What will matter then is exactly what gave her teachers pause: the patience to stay with a hard problem, reason through it, and decide when she’s reached a conclusion she can trust. The very thing they feared might hold her back could be exactly what the next era prizes most.

So no, I’m not worried about the timed test. I hope she grows up in a world where software carries the toil and people are freed for the work that is unmistakably ours—to think, to judge, to create, to care for one another. That is the future I want agents to make real. But my hope is not evidence it will happen. The future I just described depends on a single question: can we trust agents to do the work? Trust is earned one task at a time. So, I went looking for evidence of where it’s been earned, and where it hasn’t.

We partnered with MIT Technology Review Insights on new research that draws directly from the technical leaders building this frontier: not the people talking about it, but the people doing it. We surveyed 300 technical experts across AI, data, and cloud domains, spanning 12 industries and 4 regions of the world, asking them to rank their confidence across 101 of the top tasks. What we got back is the 2026 Agent Confidence Index, an honest map of where agents are delivering real value, so our community can see what’s working and move forward together with conviction.

Learn from where confidence is highest

Across the 101 tasks measured, average confidence already lands at 64 out of 100, and thirty tasks clear 70. The highest scores cluster on work that is both predictable and draining: the late nights, the interruptions, the low-value repetition. Automated report generation leads at 83.5. Boilerplate code generation for new features sits at 82.5—the hours a developer no longer spends rewriting the same patterns, freed for the work that challenges them. Certificate expiration monitoring and renewal, at 81.5, ends the scramble that pulls engineers off high-stakes problems for something entirely routine. Real-time data stream monitoring follows at 80.5, and release note generation from commit history at 79.5—the manual end-of-sprint commit review, gone. This is where frontier teams are already delegating to agents, regularly.

The pattern holds across every discipline. In developer and AI workflows it extends to API client maintenance and code identification; in cloud operations, to ticket routing and cost optimization; in data, to anomaly detection. Wherever it sits in the stack, this is work technical teams now trust agents to own.

What matters most here isn’t what the data says about the tasks; it’s what it says about the people delegating them. When technical experts believe in something deeply enough to hand it real work, that belief ripples outward. It becomes the recommendation they make to their leadership, the solution they build for their customers, and the culture they create for their teams.

Even the toughest agent tasks are gaining traction

Here’s what strikes me most: the tasks ranked lower on the index are still high in absolute terms. Service mesh configuration and troubleshooting sits at 37.5, database schema migration scripting at 46.5, memory leak detection at 48.5. These sit at the very frontier, the interconnected, high-stakes work where investment and innovation are concentrated right now.

Consider what they demand. Service mesh configuration touches many systems at once. Database migration carries real stakes, requiring precision across data, application, and infrastructure layers at the same time. Memory leak detection means diving deep into a system’s behavior under load, accounting for conditions that shift from one deployment to the next. These are the challenges that have separated great engineers from exceptional ones—and even here, experts see agents helping. Not carrying the work alone, but contributing where it used to be unthinkable. That confidence is still climbing, and that’s telling.

We’re shipping new capabilities constantly to support this momentum. Database migration tooling in GitHub Copilot now covers not just scripts but the full application and infrastructure migration story. The Azure Site Reliability Engineering (SRE) Agent brings decades of experience operating Azure at scale and deep profiling capabilities directly into memory analysis and performance diagnosis.

Why human judgment remains paramount

When we asked technical experts how they’re navigating agent adoption, 59% named “keeping humans in the loop” as their top priority—ahead of better observability, ahead of governance documentation, and ahead of everything else. That’s a mark of maturity. Teams moving forward with clarity treat agent oversight as non-negotiable, regardless of how capabilities evolve.

The boundary itself is straightforward. Agents excel at well-specified, high-volume, reversible work: they synthesize data, automate known workflows, and surface anomalies at a speed and scale no human team could match. The moment a decision becomes high-stakes, context-dependent, or hard to undo, a human signs off. That isn’t a limitation of the technology; it’s the architecture of a trustworthy system.

What’s changing, and what remains underappreciated, is the skill it takes to draw that boundary well: the discipline of full-lifecycle evaluations and guardrails. Success means measuring agent output against intent and keeping behavior inside your business strategy. It’s new territory for most engineering teams, and it’s becoming table stakes for modern software faster than most organizations realize. The good news: the same tools generating the work can help you build the harness. Ask GitHub Copilot to write the evals and it will. Frontier teams are already doing this, and it’s why they’re pulling ahead.

Agents are opening career doors for engineering

Across system reliability and site operations, evaluations and quality assurance, and data pipeline management, 80% or more of respondents see meaningful career opportunity ahead. We believe this is one of the most significant moments in the history of building software, not because agents replace what technical people do, but because what’s left when they take on the toil is the work that defines a career: the judgment calls, the architectural vision, the reasoning to navigate complexity under pressure. That fluency will define the next generation of technical leadership.

We’re living this shift at Microsoft, right alongside our customers. Junior developers are using agents to explore codebases on their own and arriving at mentoring conversations with sharper, more sophisticated questions. Senior engineers are covering more ground because the repetitive work that used to fill their days is now delegated, and the work that’s left is harder, interesting, and consequential. Both are growing into more capable versions of themselves. For me, that’s the outcome I’ve always believed technology could deliver.

An integrated approach to intelligence and trust

Designing more sophisticated agent systems has made one thing clear: agents thrive in well-integrated environments, working best when your whole stack draws on a single source of truth. The high-confidence tasks are the ones we’ve already figured out; the meaningful frontier is the harder, interconnected work, and that’s exactly where observability, governance, security, and unified intelligence have to operate as one.

Microsoft IQ brings your enterprise context into a single, continuous intelligence layer. Within it, Work IQ builds semantic understanding of how your business operates across email, calendar, meetings, chats, files, people, and collaboration patterns. Such depth of knowledge is the reason technical teams choose us, and it’s what drives my focus and passion in learning how people actually work so their agents get them. My colleague Kim Manis, CVP of Product for Microsoft Fabric, has written specifically about what this means for data professionals, and the integral role of Fabric IQ.

It’s all part of the Microsoft Agent Platform, which is becoming the operating system for enterprise AI at scale. From building in GitHub and contextualizing with Microsoft IQ, to running in Microsoft Foundry and governing in Microsoft Agent 365, Microsoft is uniquely positioned to help customers bring together data, models, agents, and human judgment into a continuously improving and secure system.

Frontier transformation is being led by builders like you.

Next steps:

• Download The 2026 Agent Confidence Index from our partners at MIT Technology Review Insights. It is a free, ungated deep dive into all 101 tasks, broken out by role and workflow, with the patterns and reasoning behind where confidence is strongest and the frontier is expanding.

• Join us at the AI Engineering World’s Fair (June 29-July 2) where our very own Pablo Castro will keynote, and our teams will offer 16 breakout sessions and 4 labs. Swing by the Microsoft booth as well to explore an interactive 3D visualization of the Index data. We want to hear what’s working for you.

• Learn more about Microsoft IQ and how it connects across Work IQ, Fabric IQ, Foundry IQ, and the newly announced Web IQ. You can catch up on all the developer innovation from Microsoft Build through Satya Nadella’s keynote, Kyle Daigle’s blog post, and the Microsoft Build CLI.

What’s Working in Agentic AI

The 2026 Agent Confidence Index report reveals where agents are trusted, the challenges they face, and what leaders should do next

Download the 2026 Agent Confidence Index

The post The 2026 Agent Confidence Index: Where 300 builders see real momentum 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.

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

Discover more

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.

This momentum is changing not only how insights are generated, but also how decisions are prepared, governed, and executed. As Microsoft Build 2026 made clear, the next phase of agentic AI is not just better outputs. It is governed execution grounded in enterprise context, with systems designed to coordinate action across research, portfolio construction, risk, and compliance under human supervision. For asset managers, this shift is profound.

This is not simply a technology transition; it is a fiduciary moment. For asset managers, Microsoft’s Frontier Tuning points toward institution-shaped intelligence: models tuned not only on enterprise knowledge, but on the workflows, conventions, and decision patterns that define how a firm operates inside its compliance boundary. In a fiduciary setting, that is indispensable because trust depends not just on what AI knows, but on whether its outputs and actions align with approved controls, human oversight, and reproducible governance.

The critical challenge, then, is to maintain trust. AI cannot merely improve performance; its actions must be governed, explainable, and attributable in line with fiduciary duty.

From analytics to homeostasis: AI as a trust-control layer

In biology, homeostasis is how living systems stay stable by sensing change, making small corrections, and restoring balance. In asset management, agentic AI makes it possible to build a similar closed-loop layer across workflows defined by governance parameters, helping keep activity within agreed boundaries of risk appetite, policy, and intent even when markets move faster than humans can triage.

Historically, AI in asset management has centered on prediction, such as forecasting spreads, detecting anomalies, or scoring counterparties. Agentic systems add the ability to observe, decide, and act across workflows under human guidance, gathering evidence, reconciling sources, drafting outputs, escalating exceptions, and coordinating next steps. Used well, these systems can create a homeostatic effect, helping firms maintain operational equilibrium and resilience.

The shift to a homeostatic approach

AI is being used in capital markets not only to deliver new capabilities but to help strengthen trust as a cornerstone of infrastructure. Trust can be infused throughout the platform by design, helping to ensure that data and operations remain safe, responsive, and governed even in fast-changing conditions.

In trading workflows, for example, the LSEG and Microsoft partnership is integrating licensed market data into Microsoft 365 Copilot and agent experiences so that decisions can be informed with authoritative, permissioned context at the point of execution. This is increasingly the right pattern for agentic systems: not intelligence separated from governance, but intelligence grounded in trusted context and controlled inside the systems where work happens.

By combining licensed data with built-in governance capabilities, trust is embedded into how intelligence is delivered, orchestrated, and acted on across the workflow, rather than managed downstream.

Likewise, Moody’s has federated decision-grade credit intelligence directly into Microsoft 365 Copilot, Researcher, and Excel, grounding AI-assisted analysis in authoritative, auditable context so that trust can be enforced by the system, not retrofitted after the fact.² Similarly, Morningstar has embedded proprietary research as entitlement-aware context for copilots and agents, keeping advisor workflows reviewable and aligned to licensed sources and user permissions. UBS is also bringing together internal and market data into AI-assisted advisor workflows, enabling client advisors to access unified, permissioned insights in real time.

Further, at Nasdaq, AI is being applied directly into the boardroom experience while preserving the strict controls those workflows demand. Its Nasdaq Boardvantage platform helps members turn hundreds of pages of board materials into concise, decision‑ready insights. This reduces review time by up to 60% while ensuring that outputs are fully auditable, remain grounded in proprietary data, and are protected by design.

Together, these examples show that intelligence can scale when workflows are auditable, permissioned, and attributable by default.

Getting started: Three moves asset managers can make now

The path forward is not theoretical; it is operational. Asset management firms can begin by focusing on three priorities:

• Invest in data readiness. Start by unifying internal and external datasets into a governed data foundation that can support agentic AI. Platforms like Microsoft Fabric are increasingly designed not only to bring data together, but to create the shared business context that agents need to operate consistently across teams and workflows. That matters because the bottleneck in scaling agents is no longer just model capability. It is whether each system can access the same governed definitions, permissions, and operational data foundation needed to move from isolated experiments to production-ready agent systems.

• Operationalize governance and enterprise ontology. Define ownership, approval paths, and audit checkpoints directly into AI-enhanced workflows under human supervision. A broader contextual intelligence layer is now emerging to connect how people work, how the business is modeled, and how knowledge is grounded across enterprise systems. Within that picture, Microsoft Work IQ remains an important component, helping make sense of activity across emails, meetings, documents, and chats while honoring existing permissions, sensitivity labels, and governance controls. Applied consistently in AI-assisted experiences, this kind of shared context helps turn governance from a static framework into a living system embedded in everyday work.

• Enable model-agnostic intelligence. Explore the comprehensive catalog of AI models so that you can select the best ones as needs evolve. Microsoft Foundry increasingly represents the production layer for agents, bringing together models, hosted agent services, memory, observability, evaluation, and guardrails in one governed environment. That gives firms more flexibility as requirements change while helping them move from pilot use cases to durable, production-scale systems.

Once these foundations are in place, firms can layer agentic workflows across research, operations, and risk while keeping clear human ownership at every material decision point and standardizing evidence trails so material outputs are reproducible and traceable. The goal is not automation alone, but structural operating leverage that scales intelligence with observability, governance, and security.

Making trust measurable and operational

Trust is not abstract in capital markets; it is the product of repeatable controls including lineage, entitlements, validation, and oversight. In practice, this is increasingly operationalized within the systems where work already happens. The idea is to bring productivity, security, identity, and agent management into a more unified operational layer, so that trust-related controls are enforced upstream in the flow of work rather than applied after the fact. Microsoft 365 E7 for Enterprise is one example of this broader architectural direction.

Accordingly, many firms focus on AI-enabled workflows defined by a common set of “trust signals,” including:

• Traceability: Every output must link back to authorized data, policies, and model versions.
• Explainability: Decisions must be not just technically correct but contextually defensible.
• Human accountability: Ownership must be explicit: who reviewed, who approved, and who can intervene.
• Controls and monitoring: Guardrails must operate continuously, especially under stress conditions.
• Regulatory evidence: Every decision path must be reproducible when challenged.

These are not abstract ideals but key operational expressions of responsible AI practices, helping asset managers ensure that AI is compatible with fiduciary duty.

The bottom line: Improving operational leverage

As firms mature in AI adoption, the focus evolves from products to platforms and ultimately to systems. The difference is not incremental; it is structural.

Value is no longer created by isolated use cases, but by integrated intelligence loops running on trusted infrastructure that connect external market signals with internal data, workflows, and decision rights. When orchestrated well, AI can serve as a coordination layer for action at scale in line with fiduciary requirements.

That is what drives operational leverage. Instead of adding incremental tools or headcount, firms can embed intelligence directly into workflows so the organization can better align decision-making, sensing, and action. Trust is thus inherent within systems and is no longer merely a byproduct.

Take the next step

We work with institutions to move from experimentation to production by embedding governance into multi-agent systems and operations so that fairness, reliability, privacy, and accountability are built into the foundation where AI runs. Our end-to-end approach can help unify enterprise and market intelligence into a shared operating context that turns fragmented insights into coordinated action at scale. Because most financial markets professionals already use Microsoft tools, it can also help lower deployment effort and improve adoption while keeping human oversight at every material decision point.

This is a rare and urgent invitation to the custodians of fiduciary capital to architect a homeostatic system of intelligence. It marks the convergence of capital and cognition, redefining how enduring organizations create and sustain value over time.

Explore how to best adopt AI

To learn more about how your organization can adopt AI, start by engaging with your Microsoft representative or service provider and explore these resources:

• Read our blog for stories about how Frontier Firms are using agentic AI to gain an edge in capital markets.
• To learn how Frontier Firms in capital markets lead with AI, visit Microsoft for Financial Services.
• For a practical transformation framework, read our new ebook, Frontier Transformation in Capital Markets.

¹ EY, Unlocking strategic advantage: Generative AI in wealth and asset management, September 16, 2025.

² Moody’s, Moody’s advances decision-grade credit intelligence across enterprise AI workflows, powered by Microsoft 365 Copilot, April 21, 2026.

The post Trust as infrastructure: How agentic AI is rearchitecting asset management at scale appeared first on The Microsoft Cloud Blog.