Clinical trials are becoming increasingly digital yet remain decisively manual. Sponsors and CROs have invested heavily in digital platforms, but without orchestration across these systems, operational blind spots persist. Data is collected, monitored, and reported – but rarely acted upon in real time. Trial execution still relies on manual coordination, disconnected processes, and delayed responses. What is missing is not visibility, but adaptability. The question is no longer whether we have enough data or tools, but whether clinical systems can sense what is happening, decide what to do next, and act accordingly. Systems that wait for human input are increasingly misaligned with modern clinical development’s speed, scale, and complexity. What is needed is a shift from fragmented enablement to connected intelligence. This is where Systems of Execution (SoE) come in. By embedding agentic AI across the clinical value chain, SoE enable real-time orchestration of trial activities – detecting risks, triggering actions, and optimizing execution. These systems do not just support clinical operations, they transform them. In this Viewpoint, we share our insights and perspective on the SoE approach to applying agentic AI in clinical trials.

With technology advances, changing customer behaviors, and evolving regulatory landscapes driving rapid transformations across industries, it has become critical for business leaders to anticipate future technology trends and act on them. The Top 10 Game-changing Technologies in the Pharma Industry report spotlights 10 key technologies poised to revolutionize the pharmaceutical sector, highlighting their applications, benefits, and potential challenges. Scope Industry: healthcare, pharmaceutical All geographies Contents Technologies covered in the report include: Engineered antibodies Advanced RNA therapeutics and vaccines Intelligent drug discovery Obesity therapeutics In-vitro and virtual disease modeling Next-generation cell therapies Microbiome therapeutics Advanced omics platforms Biomanufacturing 5.0 Microbiome therapeutics Membership(s) Advanced SciTech

The life science industry’s focus on driving improved customer experiences has steadily grown. Investments to improve customer experiences rank among the top priorities of life sciences enterprises. These enterprises must align customer engagement strategies with the preferences of all target customers to foster improved experiences. Before the pandemic, traditional methods dominated Healthcare Provider (HCP) engagement, but the pandemic accelerated digital channels’ role. Post-pandemic, the importance of digital channels persists alongside traditional approaches, as HCPs now prefer digital channels for specific activities, demanding a balanced engagement model. This evolution presents an opportunity for accessible and flexible engagement, requiring efficient resource allocation. A uniform approach does not suit all HCPs, highlighting the importance of the Hybrid Commercial Model (HCM). HCM optimizes resource allocation, improves insights, and elevates customer lifetime value by delivering tailored messages through suitable channels at optimal times. Addressing existing gaps in CRM technology stacks is vital for successful HCM adoption, as life sciences enterprises prioritize customer-centric approaches. This shift underscores HCM’s rapid adoption and its potential to enhance customer engagement in the life sciences sector. This report examines the rise of HCM in commercial HCP engagement, provides insights into associated challenges and benefits, explore the key tenets of HCM, discuss the significance of functionality enablers for HCM, and analyze the implications for commercial technology providers. Scope Industry: life sciences Geography: global Contents In this report, we examine: The rise of HCM Drivers, challenges, and benefits of HCM adoption Key tenets of HCM Key implications for commercial technology providers Membership(s) Life Sciences Information Technology Outsourcing Excellence

Executive Summary Pre-competitive collaboration is a novel research model in biopharmaceuticals, with the intention of accelerating collaboration and productivity, ultimately leading to more new medicines and therapies for patients. The research model exists in multiple forms including public-private partnerships. It aims to aggregate research prowess in the biopharmaceutical industry, establish an open culture of shared innovation & expertise, and ultimately incentivize collaboration. Various issues such as pipeline stagnation, patent cliffs, shrinking R&D budgets, uncertainty in the regulatory space, declining investment by venture capitalists, difficulties in clinical trials, evolving business mix, and tough scientific barriers have led to rethinking of the strategic priorities in biopharma. The loss of patent exclusivity and consequent replacement of blockbuster drugs by generics has resulted in tapering margins for life sciences firms. The stifling drug approval norms (typically, one in five drugs making the cut) and rising development costs (an average outlay of ~US$2.8 billion for development, approval, and post-approval R&D of a prescription drug) are further adding to the woes of pharma firms. This has prompted an examination of all aspects of the biopharma R&D process in recent years to try to cut costs and improve efficiency and productivity. Although this has led to mergers and reorganization in the industry, it has not resulted in the radical shift required. An approach that has become common of late (and comparable to other industries) to solve similar problems in terms of lack of productivity and innovation is to build strategies around pre-competitive collaboration and open innovation. Many large pharmaceutical companies are now getting behind this movement, which goes beyond competitive dynamics. In light of recent heightened activity in this space, this viewpoint explores the theme along the following dimensions: R&D crisis in biopharma Clinical and R&D trends Existing collaborative platforms/alliances Successful implementation and use-case Challenges to adoption Best practices