For most of the last decade, AI in pharmaceutical research has been positioned as a productivity story. It has helped scientists analyze faster, automate repetitive work, and reach decisions with more evidence behind them. That has been useful, but broadly incremental. What is happening across the industry now is a different kind of shift, and one that leadership teams need to engage with directly. AI is moving from a tool that supports the scientist to an active participant in the scientific process itself, which changes both the nature of the work and the operating model around it.

This matters because it directly addresses the pressures the industry is trying to solve. R&D costs continue to rise, timelines remain long, late-stage success rates are still too low, and demand for new therapies is not slowing. The question has moved on from whether AI can help. It now hinges on how quickly organizations can embed AI into the fabric of R&D and scale it without sacrificing scientific rigor or trust.

From incremental gains to exponential scale

Traditional AI made individual tasks faster, but agentic AI changes the shape of the work itself because agents can operate across the scientific method, contributing to observation, hypothesis generation, experiment design, analysis, and learning from results in ways that were previously impossible. The scientist is no longer supported step by step and is instead orchestrating multiple streams of discovery in parallel, with agents handling reasoning, retrieval, and simulation alongside them, fundamentally changing the economics of research.

Organizations can now run more hypotheses concurrently, keep more programs in flight, expand the search space for viable molecules, and improve the odds of identifying strong candidates earlier in the cycle. That means the leadership conversation should move beyond productivity and focus on throughput, portfolio breadth, and the ability to compress cycle time at scale.

Introducing the Microsoft Discovery platform

The clearest expression of this new operating model has arrived with Microsoft Discovery, an enterprise agentic AI platform that Microsoft announced at Build 2025 and moved into general availability on June 2, 2026, at Build 2026, designed specifically to accelerate research and development across industries, including pharmaceuticals, chemistry, materials science and semiconductor design. At the heart of the platform sits the Microsoft Discovery Engine, a multi-agent system that mirrors the scientific method by supporting evidence-to-hypothesis loops, experiment design, analysis and iterative learning, alongside a graph-based knowledge engine that connects proprietary research data with external scientific information so agents can reason across complex relationships with full transparency and traceability.

Discovery is built on Azure and integrates with the wider Microsoft stack, including Microsoft Foundry and Azure HPC. The platform provides the compute, simulation and scientific workflow layer that agentic research at scale actually requires. Because the platform is extensible, organizations can bring their own models, tools and datasets, integrate partner and open source solutions and keep human oversight, security and reproducibility embedded throughout, which is what makes it credible as a production environment rather than a research toy.

Early evidence of what the platform can do already exists inside Microsoft itself, where researchers used Discovery to identify a novel coolant prototype for data center immersion cooling in around 200 hours, a process that would traditionally have taken months or years, and this is precisely the kind of compression that pharmaceutical R&D leaders are looking to replicate across drug discovery and repurposing.

HCLTech as a partner in the Discovery ecosystem

HCLTech joined the Microsoft Discovery platform in December 2025 as part of a select group of technology innovators and research institutions helping to shape the next phase of AI-led R&D, with an initial focus on chemistry and materials science, drug discovery and semiconductor design. The collaboration combines HCLTech's deep domain expertise in life sciences, healthcare and engineering R&D services with Microsoft's agentic AI, cloud infrastructure and high-performance computing, and it is structured to move Discovery from research capability to enterprise-scale adoption through collaborative proofs of concept, co-innovation labs and industry-focused implementations.

HCLTech's role sits deliberately at the industrialization layer. That means helping clients connect proprietary scientific knowledge, build domain-specific agents, embed governance and auditability, integrate with regulated workflows, and move promising pilots into repeatable production capability. For pharmaceutical clients, this means activating Discovery inside their existing R&D landscape, connecting it to proprietary knowledge and experimental data, aligning it with scientific and regulatory frameworks, and scaling it into continuous operating capability rather than a series of isolated pilots.

Drug repositioning is a logical first use case because it links directly to commercial urgency, scientific evidence, and patient impact.

One of the first solutions HCLTech is building on Microsoft Discovery is a Drug Repositioning Platform, designed to turn the search for a medicine’s second use from a fortunate accident into a repeatable, evidence-driven method. Because an already-approved drug carries a known safety and manufacturing profile, finding a new indication for it can reach patients far faster, and at a fraction of the cost of starting from a blank sheet. The platform is built to surface those opportunities systematically rather than by chance.

In the solution, a digital twin, informed by public biomedical data, tests each hypothesis for real-world responsiveness before any laboratory spend. At the same time, a governance layer provides role-based access, approval workflows, a complete audit trail and intellectual-property masking, so that promising compounds can be explored without exposing sensitive information. The result is a solution that pairs the speed of Microsoft Discovery with the rigor, explainability and patient relevance that regulated research demands.

The power of enterprise knowledge

One of the clearest lessons from early implementations is that AI becomes materially more valuable when it is grounded in an enterprise context. Pharmaceutical organizations hold decades of proprietary knowledge across historical experiments, internal research, negative results, and deep domain expertise that generic models cannot access. When agents are connected to that knowledge through a platform such as Microsoft Discovery, they can reason in ways generic models cannot. Previously hidden patterns start to surface, hypotheses become more relevant, and confidence in decisions increases. Enterprise knowledge, activated correctly, is what turns AI capability into advantage, and it is the reason two organizations using the same underlying models and platform can still produce very different outcomes.

A critical moment for the industry

The timing of this shift is not incidental, as a significant number of high-revenue drugs are approaching loss of exclusivity, and organizations are looking closely at how they protect value, extend product lifecycles, and refresh their pipelines in response. Agentic scientific discovery can provide leadership teams with a credible way to respond, enabling large-scale repurposing of existing compounds, rebalancing and expanding pipelines, accelerating the transition from discovery to development, and supporting sharper prioritization of investment across research programs.

For a CEO or R&D leader, this is both an efficiency lever and a growth strategy, and it is one of the few investments in the current environment that credibly addresses both at the same time.

From linear processes to continuous discovery

Pharma R&D has traditionally been linear, with research moving through discrete stages, each carrying its own timelines, budgets, and risks, and each handing off to the next with limited feedback into the stages that came before. Microsoft Discovery changes that shape, because research becomes a continuous loop in which evidence informs hypotheses and hypotheses drive experiments. Experiments generate data that feeds back into the system, so learning stops being episodic and becomes ongoing. The same model creates a much stronger connection between real-world outcomes and early-stage discovery, because insights from patient data and clinical experience can flow back into research programs to sharpen targets, improve safety, and reduce risk earlier in the cycle, which is where a large portion of the traditional cost of failure actually sits.

Turning platform capability into enterprise impact

Technology alone will not deliver this transformation, and leaders should be cautious of anyone who suggests otherwise, because organizations still need to rethink how R&D operates, define how scientists and agents collaborate day-to-day and put governance in place that ensures transparency, reproducibility and trust. New capabilities also need to integrate with existing scientific and regulatory frameworks rather than sit alongside them, which is where most transformation programs lose momentum if the operating model work has not been done properly.

This is where the partnership model becomes decisive, because Microsoft brings the foundational platform in the form of Discovery, Azure, Foundry and HPC. HCLTech brings the industrialization capability that turns that platform into repeatable enterprise outcomes, combining engineering R&D services, life sciences domain depth and delivery scale to move Discovery from onboarding into production adoption at client sites.

What leaders should do next

For leaders in pharmaceutical organizations, the priorities are clear. Move beyond isolated pilots and set the ambition at transformation. Define the target operating model for how scientists and AI will work together. Activate enterprise data and knowledge as a core asset rather than a support function. Establish governance strong enough to sustain quality and trust at scale. Choose partners who can industrialize capability across the organization rather than deliver point solutions.

Closing perspective

The industry is at the start of a new phase in scientific discovery, where scientists, agents, and advanced computation work together in ways that were not possible even two years ago. Discovery becomes continuous, adaptive, and scalable, allowing innovation to move faster without compromising safety or rigor. Microsoft Discovery provides the platform, HCLTech provides the industrialization, and together they give pharmaceutical organizations a credible route from ambition to operating capability. The organizations that act now will do more than improve R&D performance. They will help define the reference model for how innovation happens in pharma over the next decade.