These challenges are unfolding against a backdrop of unprecedented scientific complexity, growing volumes of data, and rising expectations for speed, quality, and compliance.

This is where AI is beginning to change the equation.

At Microsoft, this belief has a name: AI for Better Health, our ambition to enable every person on the planet to live healthier by transforming how healthcare and life sciences organizations discover, develop, manufacture, and deliver innovations at scale. By combining trusted data and responsible AI with human ingenuity, organizations can help strengthen decision-making, accelerate innovation, and improve outcomes across the healthcare ecosystem while amplifying human expertise.

Importantly, this transformation is no longer theoretical.

The pharmaceutical industry has entered a new phase of AI adoption. What began as experimentation is increasingly becoming embedded in core scientific, manufacturing, operational, and commercial workflows. The leaders creating value are no longer asking whether AI works. They are determining how quickly they can scale it across the enterprise. While the use cases vary, the objective remains the same: help to bring therapies to patients faster, operate more efficiently, and improve health outcomes.

Three shifts are emerging among leading pharmaceutical organizations:

1. AI is moving from productivity gains to workflow transformation.
2. Competitive advantage is increasingly determined by data readiness and governance, not model access.
3. Organizations are shifting from isolated copilots to AI embedded across the value chain.

The examples that follow illustrate how pharmaceutical leaders are operationalizing these shifts to help accelerate innovation and improve business outcomes.

The AI shift is real. The opportunity is significant. The impact is human.

Accelerating scientific and business insights

AI’s immediate value in research and development (R&D) is not replacing scientific judgment. It is expanding the number, speed, and quality of decisions researchers can evaluate while keeping scientific expertise and human oversight at the center. Across life sciences, organizations are applying AI to access evidence faster, test more hypotheses, uncover institutional knowledge, and make more informed decisions across the discovery process.

Scientific discovery has always depended on the ability to generate, test, and refine hypotheses. What is changing is the speed at which researchers can access evidence and evaluate opportunities. Microsoft is advancing through Microsoft Discovery, a new platform designed to connect scientific knowledge, data, specialized tools, and human expertise into an evidence-driven discovery process that keeps researchers at the center of decision-making.

What separates leaders from laggards is no longer access to AI technology. It is the ability to connect scientific knowledge, enterprise data, and human expertise into a repeatable system for decision-making. The following organizations illustrate how this shift is already reshaping research and development.

Novo Nordisk built a governed AI reasoning agent on Microsoft Azure to help researchers analyze clinical data, validate hypotheses, and improve R&D decision-making. The result was a reduction in time-to-insight from weeks to minutes while increasing evaluation capacity from approximately 5 to 10 ideas per quarter to more than 50 opportunities, enabling scientists to explore substantially more innovation pathways.​

Amgen built its Catalyst Copilot on Microsoft Copilot Studio in just six weeks, giving drug developers a Q&A interface that ingests, filters, and reasons over reports, presentations, and knowledge resources across the organization. By making institutional knowledge searchable through natural language, Amgen is helping to shorten discovery cycles and connect researchers to the information and expertise they need to move drug development forward more quickly.

Almirall developed an AI-powered research assistant capable of searching across over 50 years of R&D knowledge and more than 400,000 documents. Researchers can now retrieve critical information in seconds instead of hours or days, helping to improve access to institutional knowledge and accelerating research workflows.

UCB built its SKAI platform on Azure and Microsoft Foundry to support enterprise-wide AI adoption. The platform helps enable compliant deployment of AI assistants across research and operations while protecting sensitive patient and intellectual property data, creating a trusted foundation for scaling AI throughout the organization.

Taken together, these examples reveal a broader pattern. AI’s greatest impact in pharmaceutical R&D is not automating science. It is expanding the number of questions researchers can explore, increasing access to institutional knowledge, and accelerating evidence-based decision-making while keeping scientific expertise at the center.

Translating innovation into validated production

Scientific breakthroughs only matter if organizations can reliably scale them. As AI matures, pharmaceutical leaders are extending transformation beyond research and into the manufacturing environments where quality, compliance, and operational efficiency ultimately determine business and patient impact. Pharmaceutical leaders are using AI, cloud platforms, and connected data to help modernize production processes, improve productivity, and scale innovation while maintaining the quality, trust, and compliance required in regulated environments.

Heathrow Scientific modernized finance and manufacturing operations with Dynamics 365 Business Central and Power BI. The organization reduced order processing time by 20%, decreased data-repair time for one employee from 3 days to just 2 hours, and improved visibility, resilience, and operational decision-making.

Körber is helping pharmaceutical manufacturers modernize one of the industry’s most complex operational challenges: managing manufacturing recipes at scale. Powered by Azure OpenAI and Foundry, the platform is designed to help reduce recipe-management timelines from months to hours and in pilot implementations has improved recipe-digitalization cycle times by approximately 30% while reducing manual work by up to 40%. Internal testing showed that PharmaGuardrails delivers reported precision rates near 99% for pharmaceutical tasks and 100% numerical precision for production-critical values, enabling AI-assisted workflows to be trusted, validated, and audited on the shop floor.

Together, these examples show how pharmaceutical organizations are moving beyond isolated experimentation to modernize validated production at scale. The goal is not productivity alone, but reliable and compliant manufacturing that helps turn scientific progress into medicines for patients.

Organizations looking to help accelerate this transformation can explore additional examples and best practices in Microsoft’s Manufacturing and Supply Chain e-book, which highlights how AI, data platforms, and digital capabilities are helping organizations improve operational performance and compliance readiness.

Building resilient and adaptive operations

Once production scales, resilience across supply and operating networks becomes the next critical requirement. Pharmaceutical organizations must be able to respond to disruption, connect decisions across markets and functions, and reliably move therapies through complex global networks. By modernizing core platforms and unifying operational data, industry leaders are helping to improve visibility, increasing agility, and building the foundations needed to deliver therapies to patients worldwide.

Rohto Pharmaceutical established a globally integrated platform connecting supply chain, finance, and reporting functions. In addition to reducing manual data-entry time by 50%, the transformation created a standardized global operating model capable of supporting future AI-powered supply chain planning and forecasting.

Astellas completed a large-scale infrastructure modernization, migrating approximately 250 servers, transferring 500 terabytes of data, closing six global datacenters, and completing the migration in just six months. This transformation established the scalable cloud foundation needed to support future innovation and operational agility.

These examples demonstrate that resilient pharmaceutical operations depend on connected platforms and a scalable data foundation. Modernizing core systems can help improve visibility, support faster decisions, and give organizations greater agility as operating conditions change.

Additional strategies for building connected manufacturing and supply chain operations can be found in Microsoft’s Manufacturing and Supply Chain e-book, which explores how AI and intelligent operations can strengthen resilience while lowering cost to serve.

Empowering people and improving experiences

AI can help create meaningful value when trusted insights reach employees, clinicians, partners, and other decision-makers at the moment they are needed. Across commercial and enterprise functions, pharmaceutical organizations are using AI to improve access to information, increase productivity, strengthen engagement, and support better decisions. Some of these advances improve operational and commercial performance directly, while others extend that value into interactions that support patients and the broader healthcare ecosystem.

CustomerInsights.AI built an AI-powered incentive compensation solution on Azure that achieved 100% calculation accuracy, reduced compensation report processing times by 70%, lowered operational costs by up to 60%, and increased sales team engagement by 30%.

Hanmi Pharmaceutical deployed Microsoft 365 Copilot and Surface Copilot+ PCs to support more efficient work and give field teams real-time access to information through 5G-connected devices. The company also plans to explore AI agents using Copilot Studio and Foundry.

Pierre Fabre launched its PLA.I.GROUND generative AI platform on Azure OpenAI. The platform has been adopted by more than 50% of employees and is helping 3,400 pharmacists deliver better support to patients while helping to improve employee productivity across content creation, data analysis, development, and routine business tasks.

Collectively, these examples show a continuum of value, from improving the productivity and decision-making of employees to helping healthcare stakeholders identify and support patients more effectively.

From transformation to impact

Across the pharmaceutical value chain, a clear pattern is emerging: AI is moving beyond experimentation and becoming embedded in the workflows that shape how therapies are discovered, developed, manufactured, and delivered. From accelerating scientific insights to strengthening operational resilience and improving commercial decision-making, leading organizations are demonstrating that AI can deliver measurable business and patient impact.

What distinguishes these leaders is not simply their adoption of new technology, but their ability to combine data, governance, and human expertise to drive meaningful change. The greatest value is not created through isolated pilots or productivity gains alone. It comes from redesigning critical workflows so that insights can be translated into faster decisions, stronger execution, and better outcomes.

The evidence suggests that successful AI transformation is ultimately an organizational challenge, not a technology challenge. Sustainable advantage is emerging among companies that embed AI into consequential scientific, manufacturing, operational, and commercial processes while building trust, accountability, and human judgment into every step.

The pharmaceutical leaders creating lasting value with AI are not asking what the technology can do. They are reimagining how work gets done. As the industry moves from experimentation to enterprise transformation, the organizations that operationalize AI responsibly and at scale will be best positioned to help accelerate innovation, strengthen competitiveness, and bring new therapies to patients faster.

From Vision to Value

AI Use Cases Transforming Healthcare

Get the white paper

The post The AI shift is real: How pharmaceutical leaders are operationalizing AI appeared first on The Microsoft Cloud Blog.

Understanding the people you serve

The word “customer” can take on different meanings depending on the industry. In retail, it may mean shoppers. In healthcare, patients. In education, students and teachers. In nonprofits, the communities they support.

Despite this difference, the leaders we spoke with at several Microsoft AI Tours around the world described a common challenge with the people they serve: expectations continue to rise. They expect experiences that are timely, relevant, and tailored to their needs. At the same time, organizations are being asked to do more with limited resources.

That tension is why reinventing customer engagement has become one of the clearest opportunities for AI—and one of the key paths toward Frontier Transformation.

Many organizations are using AI to better understand their customers and make engagement more meaningful at scale by bringing together information from across interactions and getting deeper insight into their needs.

At Lifeline Australia, a nonprofit that provides crisis support and suicide prevention services, AI is helping their teams gain deeper insight into the services they provide and the people who rely on them.

We are looking into more specialized, tuned AI […] from a data science point of view, to really tease out some of those insights or look at behaviors. Getting that insight into the actual service that we deliver, not just how we deliver it, has been a key game changer for us.

Mark West, Head of Data and Insights, Lifeline Australia

When organizations have a clearer understanding of the people they serve, engagement becomes more relevant, responsive, and impactful.

AI can help organizations uncover deeper insights, identify emerging needs, and make more informed decisions. That understanding creates the foundation for more personalized and meaningful experiences.

Personalization that scales

Personalization has always been one of the main goals of customer engagement. The challenge has been delivering it consistently across large and diverse audiences.

AI is helping organizations address that challenge.

At Brisbane Catholic Education, which supports approximately 80,000 students across 150 schools in Australia, AI is helping educators tailor learning experiences to individual student needs.

With AI we’ve been able to get to the point of what I would call hyper personalization, where a teacher can write just one curriculum document and then have Copilot come in and personalize that for every single child in the class.

Leigh Williams, CIO and Education Executive, Brisbane Catholic Education

Healthcare organizations are seeing similar opportunities.

At Sciensus, a European life sciences organization that helps healthcare partners bring the right medicines to the right patients faster, leaders believe that when AI helps manage information and surface relevant insights, it creates more room for people to focus on relationships, empathy, and expertise.

Sciensus recently launched the CareTranscribe pilot project, an AI-enabled initiative designed to improve clinical documentation workflows so clinicians can spend more time with patients during home treatments while improving visibility into patient interactions and care journeys.

Behind the scenes, Sciensus is also building knowledge systems and using AI to help teams learn from interactions and prescriptions and make information more accessible across the organization. Patient data stays protected at every step, with privacy, security, and responsible AI practices built into how these systems are designed and used.

We massively spiraled into an area where a lot of ours is production-ready AI. So, we’re building live agents, we’re building knowledge bases that we didn’t have beforehand, using unstructured data that’s available across all our domains. Copilot has really helped in that because of the secure aspect of it.

Joseph Frost, Director of Data Science and AI, Sciensus

Building stronger relationships

For many organizations, the real return on AI is what they can do with the time given back to their people. Freed from documentation and repetitive administrative work, employees can spend that time listening, understanding what someone needs, following up, and building the kind of trust that only comes from being present. Customers, patients, students, and communities all feel the difference.

At Everything Suarve, a nonprofit that supports at-risk youth across Australia, AI takes care of the case notes and reporting, so staff can give the young people they serve their full attention and spend more time with them.

What AI has done is actually enabled us to get a lot of time back to spend it where it’s needed. For us to make a difference it’s about being with the young person and not sitting behind a computer.

Joseph Te Puni-Fromont, Founder, Everything Suarve

Uniting NSW.ACT, a community services organization that provides care and support services for older adults, families, and communities across Australia, is seeing similar benefits. Its AI-powered platform, Buddy, helps frontline workers to retrieve and create content in less time, so they can spend more time supporting communities.

Activities that used to take 10 to 15 minutes are now taking one to two minutes. So, times that by 9,000 frontline workers, it’s an incredible amount of time back in the day for them to actually look after our customers and provide better aged care and community care.

Andrew Dome, Chief Digital Information Officer, Uniting NSW.ACT

Overall, through the conversations with customers, we’re hearing that AI is helping them create experiences that are more relevant, more personal, and more responsive to the people they serve. As engagement becomes more contextual and individualized, organizations can build stronger relationships while extending their reach and impact.

Make three practical moves now

1. Start with understanding. Use AI to bring together signals and context so teams can better understand the people they serve and what they need.
2. Personalize with purpose. Look for opportunities where AI can help tailor services, experiences, and communications at a scale that wasn’t previously possible.
3. Remove friction, not relationships. Apply AI to administrative and repetitive work so employees can focus on meaningful interactions and higher-value conversations.

Learn more

• Visit the Frontier Transformation site to explore how you can put AI to work, build trust, and scale transformation with Microsoft across everyday workflows
• Download the e-book Four Paths to Business Value with AI to learn how you can move from AI experimentation to Frontier Transformation with intelligence and trust.

Next in the Accelerating Frontier Transformation series: Reshape business processes.

The post Accelerating Frontier Transformation: Reinvent customer engagement appeared first on The Microsoft Cloud Blog.

Empowering people to thrive at work

When thoughtfully integrated into everyday work, AI has the potential to expand what we can do. It can help us explore and solve new kinds of problems, synthesize information to support decisions, and reduce time spent on some repetitive tasks. This can ultimately create more space for us to apply our expertise, develop new skills, contribute ideas, and focus on more meaningful, higher-value work.

The leaders we spoke with at several Microsoft AI Tours around the world described how these changes can give employees greater opportunities to learn, create, and grow. They also shared how a stronger employee experience can contribute to better outcomes for their organizations.

This is one part of what Frontier Transformation looks like in practice.

Providing broader access to AI at work

Two factors can help accelerate AI adoption: broader access and deeper integration into everyday workflows. When AI is embedded in the tools people already use, it becomes part of how work happens rather than a separate initiative.

Leaders at EY, a global professional services organization, describe a shift from experimentation to everyday use. By making AI broadly available across the workforce, they have helped create a culture where employees at every level are finding new ways to work and create value.

The biggest shift in our organization with AI has been moving from curiosity to everyday use.

Jenelle McMaster, EY Regional Deputy CEO and People & Culture Leader, Oceania

There are a couple of things that help you get beyond the experiment. One is democratizing AI for everyone […] As a result of that culture, we don’t have a constraint around AI adoption.

Katherine Boiciuc, EY Regional Chief Technology and Innovation Officer, Oceania

For Scope, a UK disability equality organization, accessibility is central to its approach. Leaders of this organization see AI as a way to put powerful capabilities into the hands of more people regardless of technical expertise, helping more people contribute ideas and participate in problem-solving across the organization.

You do not have to be a coder. You do not need to have studied AI for four years at a college or university. All you need is a desire, a problem you are trying to solve, and the passion to see it through and resolve that problem.

Kwesi Afful, Executive Director for Digital, Data, and Technology, Scope

The Salvation Army UK and Ireland has taken a people-first approach to adoption. Rather than starting with technology, the organization focused on helping employees, officers, and volunteers build confidence using AI so they could better serve communities and further their mission.

When it came to AI, it started off with putting people at the center. Our first major focus was rolling out Microsoft Copilot across the organization and supporting the rollout with structured training to give every officer, employee, and volunteer the confidence to serve our people with compassion and innovation.

Lev Malinin, Head of Enterprise Systems, Data and AI, The Salvation Army UK and Ireland

Measuring returns beyond efficiency

Organizations often begin their AI journey looking for efficiency gains. But the leaders we spoke with described returns that go well beyond time savings. They shared that AI can help reduce repetitive work and administrative burden, giving employees the opportunity to focus more on activities where their expertise creates the greatest value.

At EY, one of the biggest benefits has been enabling people to apply their knowledge and skills in new ways. One of their leaders describes seeing employees become more confident in their ability to create value with AI and contribute in ways that were previously out of reach.

The most meaningful outcome for employees at EY because of AI is the ability to do something new. We sell expertise, we sell knowledge, content and information. We still do that, but AI enables us to do that in a new way.

Mary Elizabeth Porray, EY Global Vice Chair, Client Technology

At The Salvation Army UK and Ireland, value is measured by helping people focus on the work that matters most. As the organization expands its use of AI, leaders have emphasized the importance of implementing it thoughtfully and responsibly so it can deliver lasting impact.

It’s really about maximizing value over everything and doing things right. Starts with caution and thinking about what you need to get right.

Lev Malinin, Head of Enterprise Systems, Data and AI, The Salvation Army UK and Ireland

The same theme emerged at Advania UK, an IT services provider. Leaders there see AI creating value not simply through efficiency, but by helping employees spend less time on routine work so they can focus on applying their expertise to higher value activities.

It’s actually about the experience that we’re creating for our employees in the workplace […] It’s going to help us all to become better versions of ourselves as we work across the businesses, and allow our staff to really enjoy the work that they do because they’re not doing the mundane […] they’re able to apply their skills to higher value activities, whether they’re in creative roles or analytical roles.

Nick Isherwood, Chief Operating Officer, Advania UK

AI that works alongside people

Based on these and other customer conversations, we can see that the most effective leaders use AI to support their people while keeping them in charge of making decisions. Organizations pursuing lasting value view AI not as a replacement for human expertise, but as a tool that helps people achieve more.

Baringa, a global management consultancy that helps organizations navigate AI transformation, encourages clients to focus first on building trust and proving value before scaling their efforts.

Have the bigger picture. Have a vision. Have a strategy. But start from a use case, which is going to be small enough for you to be able to build trust and to build confidence that that use case is going to deliver value.

Stela Bokun, Partner, Baringa

Educators can use AI tools to support learning, while remaining responsible for evaluating outputs and directing the learning experience. 

Teach For America, a nonprofit dedicated to expanding educational opportunity, is using AI for exactly that. Through AI incubation programs and hands-on experimentation, employees and educators are exploring new ways to support learning while keeping teachers at the center of the experience.

We don’t want AI to replace great teachers. We want it to augment that. We want it to help them in the classroom. I need to transform this organization to really reimagine how learning happens through AI. That’s virtual coaches and tutors and things like that. Things that we couldn’t do before that.

Alan Murray, Chief Data & Technology Officer, Teach for America

The organization’s Reinvention Lab illustrates what happens when employees are given both the tools and the freedom to experiment. Educators have been trained through hackathons and hands-on learning experiences, resulting in hundreds of AI-powered tools designed to address real classroom challenges.

We have our Reinvention Lab. They have educated over 1,200 educators through a whole bunch of hackathons to educate these folks on how to use AI. That has then resulted in over 720 tools that these educators have built. And these are all kinds of things built in AI for the classroom.

Alan Murray, Chief Data & Technology Officer, Teach for America

Across these stories, we can see a common pattern: organizations can create value when AI works alongside people, supporting human judgment, creativity, and expertise rather than replacing them.

Make three practical moves now

1. Start with the friction. Find the repetitive or draining tasks that slow people down, and identify appropriate ways AI can help address those moments.
2. Widen who can contribute. Use AI to help more people build skills and take on higher-value work, expanding what the team can achieve together.
3. Build confidence. Keep people in the lead, make the role of AI clear, and show where it adds value and where human expertise should guide decisions.

This is where Frontier Transformation to enrich employee experience begins. 

As Kwesi Afful of Scope put it:

AI is the train that won’t wait for us. If we avoid it, we will probably be doing a disservice to your organization or the community or customers […] so cautiously embrace it, start small and look for the practical value.

Kwesi Afful, Executive Director for Digital, Data, and Technology, Scope

Learn more

• Visit the Frontier Transformation site to explore how you can put AI to work, build trust, and scale transformation with Microsoft across everyday workflows
• Download the e-book Four Paths to Business Value with AI to learn how you can move from AI experimentation to Frontier Transformation with intelligence and trust.

Next in the Accelerating Frontier Transformation series: Reinvent customer engagement.

The post Accelerating Frontier Transformation: Enrich employee experience appeared first on The Microsoft Cloud Blog.

Everything we are doing at Microsoft is empowering Frontier Transformation: Copilot enables AI in the flow of human ambition, Microsoft IQ amplifies and protects an organization’s IQ and Agent 365 is the trust platform that enables observability at every layer of the stack.

Businesses are not static, and neither are the AI systems that support them. As organizations evolve, AI systems must continuously learn and improve. Agentic workflows need to be built, observed and tuned against the outcomes organizations seek and the ROI they demand. Microsoft’s open, model-diverse and heterogenous platform powers that improvement loop. We also recently announced Microsoft Frontier Company, bringing our AI engineering approach to customers around the world to help them build these AI systems to accelerate measurable business outcomes.

Throughout the past year, we saw customers put these capabilities to work in powerful ways — embedding AI into core business processes, building agentic systems, strengthening security, accelerating innovation and creating new sources of value. The stories below highlight organizations leading Frontier Transformation, demonstrating how intelligence, trust and human ambition come together across industries.

To advance its journey to become a global AI-powered company, Atos Group deployed Microsoft 365 Copilot to 56,000 employees across 54 countries — from consultants to engineers to frontline workers — and was one of the first organizations globally to adopt Microsoft 365 E7: The Frontier Suite. Using Microsoft Foundry, Microsoft Copilot Studio and Agent 365, Atos is building, operating and governing a growing ecosystem of 19,000 AI agents through a unified operating model that brings together productivity, security, compliance and agent governance. As Atos embeds secure agentic AI across its workforce, the company is creating a repeatable model to continuously improve thousands of agents at scale while applying the same playbook to help customers accelerate adoption across highly regulated industries.

Facing state-sponsored threats and complex global operations, ASM is strengthening cyber resilience with Microsoft Security Copilot, helping protect the intellectual property behind advanced semiconductor manufacturing. By bringing threat investigations into a unified AI-powered experience, ASM enables analysts to investigate incidents faster, apply consistent decision-making across global operations and accelerate the development of cybersecurity talent. The company reduced incident triage time by 68%, cut laptop compromise investigations from 25 minutes to eight and now saves 337 hours each week on investigations while redeploying 20% of its security operations staff to governance, risk and compliance initiatives.

Banco Popular Dominicano, the largest private-sector bank in the Dominican Republic, transformed operational risk management from periodic, sample-based reviews into continuous, AI-powered supervision. Using AURA — an ecosystem of specialized agents built on Microsoft Copilot Studio and Microsoft Power Platform — the bank monitors 100% of its operational risk universe in real time, up from roughly 40% coverage, and can automatically analyze changes, validate controls and surface issues as they occur. The shift has delivered seven times greater analytical capacity, reduced manual operating effort by 70%, achieved 98% methodological accuracy and enabled continuous processing of approximately 80,000 documents per week and more than 300 cases per day. Just as importantly, risk teams have moved from reacting to problems after the fact to anticipating and preventing deviations before they occur. These results demonstrate the power of AI democratization. By enabling business teams to build intelligent solutions themselves through low-code tools, Banco Popular transformed operational risk management while fostering a culture of innovation led by domain experts.

To help reduce the manual burden for employees while meeting the pharmaceutical industry’s strict data security requirements, Cactus Life Sciences modernized scientific workflows with Microsoft 365 Copilot and agents. The company has deployed more than 30 custom automation agents to streamline document review and structure data extraction and information retrieval across scientific writing and project management teams. Supported by a centralized knowledge repository and the Copilot Champions community, the company reports efficiency improvements of approximately 35% to 50% in structured data extraction. By automating labor-intensive tasks and maintaining human review and quality controls, the company is enabling scientific writers to focus on deeper analysis, synthesis and delivering exceptional science to clients.

Chow Tai Fook is redefining luxury retail with Microsoft 365 E5, Microsoft Purview, Microsoft Azure OpenAI Service, Microsoft Fabric and Microsoft Foundry. The company has deployed over 400 customized AI agents supporting more than 24,000 employees, with millions of AI interactions each month and core business-process efficiency gains exceeding 70%. Through its AI Fook super-agent ecosystem, frontline associates can instantly access product expertise, inventory insights and personalized recommendations, helping drive sales conversion improvements of up to 57% while delivering hyper-personalized omnichannel experiences at scale. With hundreds of AI agents operating across the business, Chow Tai Fook is creating a foundation where customer, product and operational intelligence can be applied across every interaction, helping personalize experiences and improve decision-making across its global retail network.

To accelerate AI adoption, EY moved AI from experimentation into enterprise-wide transformation. After deploying Microsoft 365 Copilot to 150,000 employees and realizing a 15% productivity gain, the firm is expanding the Microsoft 365 Frontier Suite across its global workforce of more than 400,000 people, embedding agentic AI capabilities across the enterprise. As Client Zero, EY is applying Microsoft technologies across its own operations, including Microsoft Power Platform, Microsoft Copilot Studio, Microsoft Azure, Microsoft Foundry and Microsoft Fabric. The results include 95% faster lead times, a more than 37% reduction in finance operating costs and up to a 90% reduction in manual workloads across key business processes.

To reimagine the grocery shopping experience, Grandiose Supermarkets created an AI-powered shopping companion — GrandChef — built on Microsoft Foundry and Azure OpenAI Service. By connecting meal inspiration, recipe discovery and product purchasing into a single experience grounded in live product and inventory data, GrandChef helps shoppers move from intent to purchase faster and with greater confidence. This has led to a 31% increase in conversion, a 20% lift in average basket value and shopping journeys that are 40% faster. Together, these gains are helping Grandiose Supermarkets create more personalized customer experiences while driving measurable business growth. By connecting customer intent, product data and purchasing decisions in a single experience, Grandiose is creating a feedback loop that helps continuously improve recommendations and shopping experiences.

To support audits across its global organization, Grupo Bimbo built two agents with Microsoft Copilot Studio and deployed them through Microsoft 365 Copilot and Microsoft Teams: the result was Audit Assist, an idea generated from a Microsoft-supported internal hackathon and Comatrix. By connecting auditors directly to approved guidance, procedures and templates in SharePoint, the company is helping teams work more efficiently across 39 countries while improving consistency and audit quality. The solution/AI agents reduced planning-phase audit time by 20% and accelerated risk and control matrix creation from days to seconds, enabling auditors to spend less time searching for information and more time on analysis and decision-making. By making approved audit knowledge instantly accessible across its global audit organization, Grupo Bimbo is creating a foundation where expertise can scale with the business — improving consistency, reducing rework and enabling auditors to focus on higher-value analysis and decision-making.

To create a unified foundation for data, automation and AI across its operations in 47 countries, Navien built a connected, intelligent operating model with Microsoft Fabric, Microsoft 365 Copilot, Microsoft Foundry and Microsoft Copilot Studio. By connecting fragmented data and processes across procurement, manufacturing, quality and customer service, the company is enabling more consistent, data-driven decision-making across its operations. Navien saved 28,000 hours annually through AI agents and automation, with 32% of employees using self-service analytics to make decisions without IT support. Through its migration to Azure, it also expects more than 1.4 million in total cost-of-ownership savings over five years. By connecting data, insights and workflows across the business, Navien is enabling faster, more consistent decision-making at scale across its global operations.

NHS England is accelerating AI adoption across the healthcare system with the largest implementation of its kind in the healthcare sector. Following a trial involving 30,000 workers across 90 NHS organizations — where users saved an average of 43 minutes of administrative time per day — NHS England is rolling out Microsoft 365 Copilot to over 500,000 clinicians and support staff. Organizations can use Microsoft Copilot Studio to build and deploy AI agents that streamline clinical, operational and administrative workflows. Through Agent 365, NHS England can govern and scale those agents across the healthcare system while enabling individual trusts to build solutions for local needs — creating a secure framework where agentic workflows can be deployed, managed and expanded consistently to improve service delivery, reduce costs and create more time for patient care.

Novo Nordisk is using AI to help researchers make faster, more quantitative decisions in pharmaceutical R&D. Working with Microsoft’s AI Acceleration Studio within the Forward Deployed Engineering team, the company built a governed reasoning agent on Microsoft Azure with its proprietary dataset, including more than 200,000 patient-years of harmonized clinical trial data, while maintaining rigor, oversight and compliance. The system has expanded the team’s capacity to evaluate potential opportunities from 5 to 10 strong ideas per quarter to more than 50, and the company expects it to reduce time to insight for exploratory analyses from weeks to minutes. By grounding AI in its datasets and governed workflows, Novo Nordisk is helping scientists turn decades of institutional expertise into a reusable intelligence layer that can help accelerate discovery across the organization.

As AI becomes part of investment decision-making, SimCorp is helping financial institutions bring AI into investment workflows without compromising governance, auditability or control. By unifying SimCorp One on Microsoft Azure and leveraging Microsoft Foundry, the company is helping portfolio managers, risk analysts and operations teams access insights faster, automate manual processes and spend more time on higher-value decisions. By standardizing how AI is deployed and governed across global investment operations, SimCorp is creating a foundation for trusted AI at scale, helping organizations embed AI into investment workflows while maintaining the controls required in highly regulated markets. One study found SimCorp One customers realized 134% ROI over three years, improved operational efficiency by 45 percent, saved 10 hours per person per week and accelerated time to market by 50 to 60 days.

Stellantis is accelerating AI-led strategy and digital transformation across its global business, co-developing more than 100 AI initiatives across sales, customer care, product development and operations. By applying AI-powered insights across the business, the automaker is streamlining product development and validation, advancing predictive maintenance and bringing new digital features and services to market faster for customers. Stellantis is also deploying an AI-driven global cyberdefense center to help protect vehicles, customers and operations worldwide while modernizing its infrastructure on Microsoft Azure with a targeted 60% reduction in its datacenter footprint by 2029, powering a more scalable and interconnected digital ecosystem for future digital and connected services and resilient operations.

Facing a growing volume of cyberthreats across a complex healthcare environment, St. Luke’s University Health Network is using Microsoft Security Copilot to help protect the systems clinicians and patients depend on every day. Across 15 campuses, 300 outpatient sites and more than 2.5 petabytes of data and patient records, the organization needed a unified view of threats across a complex environment. Security Copilot connects Microsoft Defender, Microsoft Sentinel, Microsoft Entra, Microsoft Purview and other security tools, helping analysts correlate threats faster, eliminate silos and respond with greater precision. St. Luke’s is saving nearly 200 hours each month in phishing alert triage and creating incident reports in minutes instead of hours, helping security teams focus more time on protecting patient care.

University of Kentucky unified more than 150 AI initiatives across classrooms, research labs, healthcare settings and administrative offices through its CATS AI governance framework. Standardizing on Microsoft’s AI portfolio — including Microsoft 365 Copilot, Microsoft Dragon Copilot, GitHub Copilot and Microsoft Azure — the university achieved campus-wide deployment, providing more than 70,000 students and employees with access to AI capabilities. Clinicians are using Dragon Copilot to reduce documentation burdens and spend more time with patients, and students are using GitHub Copilot to become active builders of digital solutions, such as the Socratic Tutor: an AI-powered learning platform aimed at helping medical students master complex curriculum.

Looking back on FY26, I am inspired not only by the continued pace of AI innovation, but by what our customers are achieving. Across every industry and segment, organizations are turning their unique IQ into strategic advantage with continuously improving agentic workflows. The companies leading this next phase are building, observing and tuning agentic workflows against business outcomes. Intelligence compounds, trust scales and AI can work in the flow of human ambition. As Frontier Firms redefine what is possible with AI, we remain focused on helping our customers amplify and protect their intelligence so they can transform how they operate, compete and grow.

The post Looking back on Microsoft’s FY26: From AI experimentation to Frontier Transformation appeared first on The Microsoft Cloud Blog.

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

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

Animal Protection Denmark: From guesswork to insight in kitten care

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

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

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

Answer ALS: Turning data into discovery

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

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

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

Everything Suarve: Scaling second chances with AI 

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

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

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

What other nonprofits can learn from these stories

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

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

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

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

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

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

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

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

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

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

AI has moved from conversation to competitive advantage

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

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

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

What Frontier Transformation looks like in practice

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

Here are three SMB’s doing exactly that

Turning bottlenecks into real-time workflows

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

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

Scaling craft and personalization with trusted intelligence

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

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

Making security part of how the business runs

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

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

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

Trust is the precondition, not an afterthought

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

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

The partner ecosystem is the multiplier

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

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

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

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

Recognizing UNMSME

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

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

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

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

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

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

The U.S. Department of Energy announced the Genesis Mission as a historic national effort to use the power of artificial intelligence to accelerate scientific discovery, strengthen national security, and drive energy innovation. The Genesis Mission brings together the DOE’s 17 National Laboratories, advanced experimental facilities, large-scale scientific datasets, and next-generation computing resources into a more integrated scientific discovery platform. The goal is to double the productivity and impact of American research and innovation within the decade by embedding AI directly into the scientific process.

This initiative builds on the Removing Barriers to American Leadership in Artificial Intelligence Executive Order. It also advances America’s AI Action Plan, a directive to reduce dependence on foreign adversaries and unleash the full strength of America’s scientific enterprise. DOE officials describe Genesis as building a “national AI platform for scientific discovery,” where labs can combine traditional simulation with generative and agentic AI for fields ranging from fusion and grid optimization to climate modeling and materials science.

Introducing SPARK: Microsoft’s Answer to the Mission

Microsoft’s Scientific Partnership Advancing Research & Knowledge (SPARK) is a mission-aligned program designed to help DOE National Laboratories accelerate AI-for-science outcomes for the Genesis Mission. SPARK combines disciplined program execution, hands-on technical delivery, and significant investments in AI and scientific computing services — so high-value pilots can be selected quickly, delivered predictably, and scaled into secure, compliant, repeatable production workflows.

SPARK alignment focuses on 5 key pillars:

1. A Dedicated Program Management Office

Microsoft is standing up a dedicated PMO to serve as the front door for Genesis Mission collaboration and the coordinating hub for the full breadth of resources Microsoft brings to bear. The PMO will orchestrate engagement across Microsoft program, technical, research, engineering, security, compliance, partner, and field teams — ensuring DOE and the National Laboratories have a clear, consistent path to access Microsoft expertise, align priorities, remove blockers, and move high-value use cases from intake through delivery. This includes project intake and selection, sprint and checkpoint cadence, cross-team resource coordination, capacity planning, and an operational interface with DOE for alignment, reporting, and sustained partnership.

2. An AI-for-Science Center of Excellence

Operating under the PMO, Microsoft’s COE is an integrated delivery team helping DOE labs move AI-for-science use cases from concept to secure, compliant, scalable implementation — spanning solution design, data and AI engineering, HPC-enabled training and inference, and production workflow automation. The COE provides enablement through office hours, hackathons, and targeted training, with emphasis on responsible AI and reproducible research pipelines. It also maintains reach-back to Microsoft research and engineering teams to help labs evaluate and leverage emerging capabilities.

3. Substantial Azure Cloud Service Credits

Microsoft is making available significant Azure cloud service consumption credits to support DOE-sponsored research initiatives. Credits will be allocated to projects that use Microsoft technology to accelerate science, with preference for rapid adoption of AI services and joint DOE–Microsoft researcher participation.

4. Professional and Technical Services Investment

Microsoft is making available dedicated investment funds to support professional and technical services for DOE National Laboratories — helping labs adopt and operationalize Microsoft AI-for-science capabilities. These services can be delivered by Microsoft teams or Microsoft-approved partners.

5. Joint Research & Development

Microsoft and DOE labs will jointly select a focused set of Genesis-aligned challenge problems for co-development — combining Microsoft research expertise with DOE scientific leadership and facilities. Collaboration may include joint experimental and computational work, scientific validation using DOE lab capabilities, and joint AI-for-science model training and evaluation. Milestones and success criteria will be co-defined; publications, IP, and data use will follow mutually agreed terms.

“An undertaking the magnitude of the Genesis Mission only works when partnership and discipline match the energy behind it. The SPARK PMO’s role is to keep Microsoft and DOE moving as one team — capitalizing on every bit of momentum to deliver tangible outcomes quickly to turn our shared ambition into mission-scale science.”

– Clementine Wall, Director, SPARK Program Management Office

The Technology Behind SPARK

SPARK is powered by the full breadth of Microsoft’s enterprise cloud and AI portfolio — a proven, secure, and FedRAMP-authorized technology stack built for the demands of government science. Key capabilities include:

Azure Cloud Infrastructure — Hyperscale compute, storage, and networking purpose-built for high-performance scientific workloads, with government-grade security and compliance accreditations spanning unclassified to Secret environments.

Azure AI Foundry & AI Models — A unified platform for building, deploying, and governing AI and machine learning models at and agents scale, giving DOE researchers access to frontier AI capabilities within a controlled, auditable environment.

Scientific Computing   — GPU and CPU resources optimized for simulation, model training, and inference workloads that complement DOE’s on-premises HPC systems at the National Laboratories.

Cybersecurity Tools — Microsoft Defender, Sentinel, and Entra ID provide layered, Zero Trust security across the research environment, protecting American science by meeting NIST 800-53 standards and supporting continuous compliance monitoring.

Data Lakes & Analytics — Azure Data Lake and Fabric enable labs to ingest, store, and analyze massive scientific datasets across disciplines — breaking down silos and creating a unified data foundation for AI-driven discovery.

Microsoft Discovery — Building on these foundational components is Microsoft Discovery — Microsoft’s integrated science platform that brings together AI models, simulation, data, and experimental workflows in a single governed environment. Microsoft Discovery is designed to match how modern science operates: across labs, disciplines, and data types.

Advanced cognition capabilities enabling autonomous workflow orchestration: AI-driven planning, execution, and adaptation of complex scientific workflows under researcher direction. Together, this technology stack gives DOE scientists the tools to move faster — from hypothesis to discovery — without compromising the security, reproducibility, or governance standards that mission-critical research demands.

Why This Matters

Under its Genesis Mission agreements, Microsoft is effectively becoming part of the federal R&D stack — providing hyperscale compute, advanced models, and tooling that runs alongside DOE’s own high-performance computing systems at national labs like Argonne and Los Alamos. The program is explicitly framed as a way to boost scientific productivity and reduce reliance on foreign technology as AI demand continues to grow.

Ultimately, SPARK is designed to turn Genesis Mission priorities into measurable scientific outcomes — combining disciplined execution, hands-on delivery, and targeted investment to move the most promising use cases from selection to mission-scale impact across the DOE and National Labs.

America’s scientific leadership is not just an academic achievement. It is a national security imperative and an economic engine. With SPARK, Microsoft is committed to making sure that leadership is not just maintained — but accelerated.

For more information, reach out to your Microsoft Account Team or email genesismission@microsoft.com

The post Microsoft SPARK: Powering America’s Genesis Mission for Scientific Discovery appeared first on The Microsoft Cloud Blog.

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

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

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

1. Empowering relationship managers

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

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

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

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

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

2. Improving the quality of client interactions

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

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

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

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

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

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

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

3. Modernizing risk and core systems

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

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

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

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

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

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

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

4. Transforming trade finance

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

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

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

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

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

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

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

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

From experimentation to operating advantage

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

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

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

Learn more

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

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

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

The post How AI is reshaping corporate and commercial banking appeared first on The Microsoft Cloud Blog.

Safety, quality, energy, maintenance, production targets, and supply volatility now collide in real time. In many plants, the response has been more dashboards, more alerts, and more analysis. But what manufacturers need next is intelligence embedded in daily work—systems that help teams understand what matters, decide faster, and act with confidence while the plant is running. 

That is the promise of agentic AI for plant operations: AI that works alongside people, grounded in industrial context and governed by operational guardrails. At its best, it does more than explain what happened—it helps teams determine the next best step and move from insight to action in the flow of work. 

What agentic AI for plant operations means in practice

In process manufacturing, agentic AI cannot mean black-box autonomy. Plants run on physics, safety standards, and regulatory requirements that do not bend. In practice, agentic AI means human-agent teams: systems that observe, reason, and recommend—and in some cases initiate workflow steps—with the right approvals and guardrails in place. 

• Bring operational and engineering context together: Connect OT data, engineering documents, maintenance history, alarms, work orders, and shift logs so teams can see what is happening in the right plant context. 
• Guide decisions within real plant constraints: Recommendations need to reflect safety, quality, process limits, and operating conditions so they can be trusted in production. 
• Help the right teams act faster: The value is not just insight—it is helping operations, maintenance, reliability, and quality teams move on the next step with less delay and less guesswork. 
• Capture what the best operators already know: Over time, agentic systems should help turn tribal knowledge into repeatable practices that scale across shifts and sites. 

This matters most in high-pressure moments—such as troubleshooting, abnormal situation management, reliability planning, outage recovery, energy optimization, and quality response—where minutes matter and context is often scattered across systems. In plant operations, AI only matters when it helps teams act faster, safer, and with more confidence. 

What manufacturing leaders are prioritizing in agentic AI

That is why the conversation with manufacturers is changing. At Hannover Messe and ManuChem, leaders were no longer asking for definitions of generative AI. They were asking a harder question: where can this create measurable value in a live operating environment? Across those discussions, six market signals came through consistently. 

1. Focus on outcomes and guardrails. The strongest conversations were not about abstract AI potential. They were about measurable operational value and a governance model where humans remain accountable for judgment, escalation, and approvals. 
2. Start with operational friction, not architecture. The best use cases begin with real pain points—lost time finding the right drawing, inconsistent troubleshooting across shifts, slow outage recovery, or low trust in legacy system data. Technology matters, but the entry point should be a business problem that teams already feel. 
3. Brownfield reality is the default. Most process manufacturers are not starting from scratch. Winning approaches respect legacy systems, edge constraints, and the need to modernize without stopping the plant—making a flexible operating model across edge, cloud, and AI critical. 
4. Trust is the adoption lever. In industrial environments, trust goes beyond compliance—teams need identity, access control, auditability, lineage, and explainability. If a recommendation cannot be traced and bounded, it will not be used in production. 
5. Design for scale across shifts and sites. The most valuable scenarios are often not the flashiest demos—they are the ones that reduce variability, capture institutional knowledge, and help teams work more consistently from one shift, line, or site to the next. 
6. Sovereignty and ontologies are rising priorities. Customers increasingly care where data resides, who can access it, and how policy applies across environments. At the same time, semantic models and ontologies are becoming critical for connecting operational, engineering, and business context—enabling AI to reason more effectively. 

One story that captures these themes well is the work Yara and Kongsberg Digital have done together. 

Real-world agentic AI: How Yara and Kongsberg Digital built an operational intelligence layer

Yara’s Porsgrunn fertilizer plant offers a good example of what this looks like in practice. The challenge was familiar: critical plant data existed, but it was spread across systems and difficult to use in real time. Engineering documents, 3D models, maintenance history, and live operational information were not coming together fast enough to support troubleshooting and day-to-day decision-making. The result was slower response times and more field work than necessary. 

Working with Kongsberg Digital, Yara built an Azure-based operational digital twin experience that brought these sources together into a usable operating environment. That matters because, in process manufacturing, better decisions rarely come from more data alone—they come from putting the right context in front of the right people at the right moment. The goal is not more intelligence in the system—it is better decisions on the plant floor. Once that foundation is in place, copilots and agents can do something practical: help teams understand situations faster, guide the next action, and reduce the time lost moving between disconnected tools. 

The business impact is what matters—and Yara’s results are a strong signal of what becomes possible when you remove operational friction: 

• Up to 70% faster shutdown recovery by consolidating operations onto a unified, actionable platform. 
• 60% fewer field trips through improved remote visibility and diagnostics. 
• 50% efficiency gains in targeted engineering tasks—helping teams resolve issues faster and operate with greater precision. 

Access to the right data at the right time is how we define success, and our digital twin gets us there. We’re building something that can scale safely and deliver value over time.

Roar Nilsen, Program Manager, Digital Engineering, Yara 

The broader lesson is important. Before AI can guide work in a plant, teams need a trusted operational foundation that connects data, documents, assets, and process knowledge in the flow of work. Yara’s results show what happens when that foundation is in place: operational knowledge becomes easier to access, decisions become faster, and expertise scales more effectively across the organization. For manufacturers asking what to do next, the implication is straightforward. 

A pragmatic playbook for agentic plant operations 

For teams asking where to begin, the next step is not to copy every emerging use case—it is to build the conditions that allow a few high-value use cases to succeed, earn trust, and scale. In other words, the opportunity is not to experiment more—it is to operationalize what works. In my view, that starts with six practical moves. 

1. Build the context layer. Unify operational, engineering, and maintenance knowledge so systems can answer basic but essential questions about asset state, process context, constraints, and history. 
2. Use ontologies where they add business meaning. Unified data alone is not enough. Manufacturers increasingly need semantic models that connect systems, assets, documents, and work patterns in ways AI can understand. 
3. Prioritize a high-friction use case. Start where value is clearly being lost—such as troubleshooting, shutdown planning, work preparation, reliability triage, or quality response—and pilot with the expectation that successful patterns will scale. 
4. Design the guardrails early. Be explicit about permissions, approvals, explainability, and which actions an agent can recommend versus execute. 
5. Embed intelligence in the flow of work. The goal is not to force people into another portal—it is to support productivity and decision-making where teams already operate. 
6. Scale through repeatable playbooks. Capture what the best teams do, standardize it across shifts and sites, and improve it through feedback over time. 

The north star is not how many models are deployed—it is fewer hours lost to data hunting, faster recovery after disruptions, shorter resolution times, fewer unnecessary site visits, lower variability across shifts, and a safer environment where teams can focus on judgment instead of searching for information. 

What comes next for manufacturers

Across customer conversations this season, one point has become clear: manufacturers will not realize the full value of AI through pilots alone. The real opportunity is to build an operating model where intelligence is trusted, governed, and embedded in the workflows that keep plants safe, efficient, and resilient. 

If you are exploring agentic AI for plant operations, start where value is being lost. Choose a workflow, unify the operational context around it, establish the right guardrails in place, and scale what works. That is how process manufacturers move from experimentation to execution—and from dashboards to decision-making. 

The post Agentic AI for plant operations: From dashboards to decisions appeared first on The Microsoft Cloud Blog.

Yet a gap remains. While leaders have embraced AI’s potential, much of that energy is still concentrated in isolated use cases. The result is progress that feels real but rarely scales. Efficiency improves in pockets. Insights surface in moments. But enterprise impact remains limited. Frontier Transformation begins where this pattern breaks.

It signals a shift in how organizations think about AI—from something applied to specific tasks to something embedded across the business. AI moves into the flow of work, shaping decisions, powering processes, and enabling entirely new ways of operating. This shift is not about doing the same work faster. It is about redefining what work can be.

The organizations leading this transition are distinguished not by how much AI they deploy, but by how deliberately they align it to outcomes that matter. They focus on where AI can unlock new forms of value—across employees, customers, operations, and innovation. And they build the conditions for that value to scale, grounded in both intelligence and trust.

For business decision makers, this moment requires a different lens. The question is no longer where AI can save time. It is where AI can change the trajectory of the business.

The four paths to business value

While every organization’s journey looks different, leading organizations are converging on four core areas where AI drives meaningful impact.

These four paths define how AI moves from experimentation to enterprise value.

1. Enrich employee experiences

AI is transforming how work happens.

When intelligence is embedded into the tools employees already use, it reduces friction and elevates contribution. People spend less time searching for information or coordinating tasks and more time applying judgment, creativity, and expertise.

For example, organizations are using AI-powered knowledge hubs to surface institutional knowledge from documents, meetings, and media—making it easier for employees to access and apply critical information in real time.

This shift improves not only productivity but also decision quality, enabling employees to act faster and with greater confidence.

2. Reinvent customer engagement

Customer expectations continue to rise, but traditional engagement models struggle to keep pace.

AI enables organizations to deliver faster, more relevant, and more personalized interactions across channels. It can respond instantly to routine inquiries, connect customers to specialized expertise, and generate tailored recommendations in context.

In practice, this shift is already reshaping front-line experiences. AI-powered systems can eliminate wait times for common requests while routing more complex issues to the right experts with full context, improving both customer satisfaction and employee efficiency.

For example, Alaska Airlines created a natural language destination discovery experience that helps travelers find and book trips more intuitively. The result was 90% user satisfaction and 75% less planning time, showing how AI can make customer engagement both more personal and more efficient.

3. Reshape business processes

AI’s greatest potential lies in rethinking how work gets done.

Instead of optimizing individual steps, organizations can redesign entire workflows, accelerating execution and improving outcomes. Companies applying AI in this way are already seeing measurable gains in speed, efficiency, and scalability.

In some cases, organizations have reported dramatic results, such as significant reductions in time spent searching for business-critical information and the ability to scale complex analysis without increasing headcount.

These kinds of gains illustrate how AI enables new operating models rather than incremental improvements.

4. Bend the curve on innovation

AI expands what organizations can create and achieve.

Consider how organizations are using AI to integrate vast, distributed datasets or analyze unstructured content—such as interviews and videos—to unlock new insights. This capability is accelerating how quickly teams can experiment, learn, and bring new ideas to market.

For example, Space Intelligence used Microsoft AI capabilities to accelerate large-scale forest mapping—reducing the time required to map global forests by 75% while scaling coverage to billions of hectares.

When innovation becomes faster, more accessible, and more repeatable, it begins to compound across the organization.

Together, these four paths show how AI evolves from isolated initiatives into a driver of sustained business growth.

Why intelligence and trust matter

As organizations scale AI, a familiar challenge emerges: complexity increases, data becomes fragmented, and systems grow increasingly disconnected. As a result, early gains become harder to sustain.

The difference between organizations that stall and those that scale comes down to how they build their foundation.

At Microsoft, we see two elements as essential.

Intelligence ensures AI is grounded in real work—connecting data, workflows, and business context so outputs are relevant and actionable.

Trust ensures AI can scale safely—embedding security, governance, and responsible AI practices from the start so organizations can innovate with confidence.

These elements reinforce each other: intelligence drives value, and trust enables that value to scale. Together, they transform AI from a set of tools into a durable enterprise capability.

What BDMs should do next

For business decision makers, the priority is not adopting more AI. It is realizing more value from it.

Leaders seeing the greatest impact focus on a few consistent moves:

• Start with clear business outcomes where AI can deliver measurable impact.
• Demonstrate value early through focused deployments that build confidence.
• Scale through repeatable systems that extend success across the organization.

This approach helps organizations move from pilots to platforms—and from isolated results to enterprise impact.

Moving forward

Frontier Transformation is already underway. The opportunity now is to move beyond isolated gains and use AI to reshape how the business creates value.

To learn more, read the e-book Four Paths to Business Value with AI and explore how these paths can accelerate your organization’s journey.

The post 4 paths to Frontier Transformation: From AI experimentation to real business value appeared first on The Microsoft Cloud Blog.