How pharmaceutical leaders are operationalizing AI

Microsoft is positioning its "AI for Better Health" initiative and its new Microsoft Discovery platform as the centerpiece of pharmaceutical AI adoption, with Novo Nordisk building a governed AI reasoning agent on Microsoft Azure to help researchers analyze clinical data, validate hypotheses, and improve R&D decision-making. Microsoft said three shifts are emerging among leading pharmaceutical organizations: AI moving from productivity gains to workflow transformation, competitive advantage increasingly determined by data readiness and governance rather than model access, and a move from isolated copilots to AI embedded across the value chain. Drug development can take more than a decade and billions of dollars before a medicine reaches a patient, the company noted.

Bringing a new therapy to market remains one of the most challenging endeavors in healthcare. Drug development can take more than a decade, with billions of dollars invested before medicine ever reaches a patient. At the same time, pharmaceutical organizations face increasing pressure to accelerate research, strengthen manufacturing operations, improve supply chain resilience, and deliver more personalized patient experiences. 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. Reshaping manufacturing and supply chain 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 https://azure.microsoft.com/en-us/blog/announcing-microsoft-discovery-general-availability-and-microsoft-discovery-app-preview/ , 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 https://www.microsoft.com/en/customers/story/26569-novo-nordisk-as-azure built a governed AI reasoning agent on Microsoft Azure http://azure.microsoft.com 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 https://www.microsoft.com/en/customers/story/23550-amgen-microsoft-copilot-studio built its Catalyst Copilot on Microsoft Copilot Studio https://www.microsoft.com/en-us/microsoft-365-copilot/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 https://www.microsoft.com/en/customers/story/25322-almirall-azure-openai/ 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 https://www.microsoft.com/en/customers/story/25625-ucb-azure built its SKAI platform on Azure and Microsoft Foundry https://azure.microsoft.com/en-us/products/ai-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 https://www.microsoft.com/en/customers/story/25323-heathrow-scientific-dynamics-365-business-central modernized finance and manufacturing operations with Dynamics 365 Business Central https://www.microsoft.com/en-us/dynamics-365/products/business-central and Power BI https://www.microsoft.com/en-us/power-platform/products/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 https://aka.ms/koerber gxp is helping pharmaceutical manufacturers modernize one of the industry’s most complex operational challenges: managing manufacturing recipes at scale. Powered by Azure OpenAI https://azure.microsoft.com/en-us/products/ai-foundry/models/openai/?ef id= k CjwKCAjwqonVBhA4EiwA9wYJ3cRdCx47dEFgIkB6uVZwbhFWlnZjpZK8I8 YuGAw8d1zjHsXb9BbThoC4hUQAvD BwE k &OCID=AIDcmm2gz5ejpc SEM k CjwKCAjwqonVBhA4EiwA9wYJ3cRdCx47dEFgIkB6uVZwbhFWlnZjpZK8I8 YuGAw8d1zjHsXb9BbThoC4hUQAvD BwE k &gad source=1&gad campaignid=21496728177&gbraid=0AAAAADcJh vMPPniT2QGBT5DlRJNBX3ee&gclid=CjwKCAjwqonVBhA4EiwA9wYJ3cRdCx47dEFgIkB6uVZwbhFWlnZjpZK8I8 YuGAw8d1zjHsXb9BbThoC4hUQAvD BwE 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 https://aka.ms/PharmaMedTechManuf , 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 https://www.microsoft.com/en/customers/story/25534-rohto-dynamics-365 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 https://www.microsoft.com/en/customers/story/24804-astellas-azure-data-box 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 https://aka.ms/PharmaMedTechManuf , 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