Clinical trial complexity continues to increase with hybrid study models, diverse data sources, and growing regulatory expectations, making traditional oversight models inadequate. Conventional monitoring approaches reliant on 100% source data verification and frequent on-site visits are costly, inefficient, and reactive, often identifying issues too late in the process. Risk-based Quality Management (RBQM) offers a proactive, data-driven approach by embedding continuous risk assessment, quality-by-design, and critical-to-quality principles throughout the trial life cycle. By leveraging centralized monitoring, Key Risk Indicators (KRIs), and Quality Tolerance Limits (QTLs), RBQM enhances trial efficiency, improves data integrity, and safeguards patient safety while optimizing resources for high-priority risks. Regulatory bodies such as the FDA, EMA, and ICH have reinforced RBQM adoption through evolving guidelines, driving life sciences organizations to modernize their quality oversight frameworks. This report examines how RBQM platforms are transforming clinical trial execution and oversight. It evaluates leading RBQM technology providers across dimensions such as market adoption, platform functionality, technology maturity, and thought leadership. It highlights how RBQM has evolved from Risk-based Monitoring (RBM) to a holistic, technology-enabled quality management model integrating AI, predictive analytics, and real-time dashboards. These solutions are helping sponsors reduce operational burden, ensure compliance, and drive more efficient, patient-centric research. Despite increasing maturity, challenges such as data integration, organizational adoption, and standardization persist. This report provides insights into key selection considerations, trends shaping RBQM adoption, and the innovations enabling next-generation quality oversight in clinical trials. The report profiles nine leading RBQM technology providers, showcasing their capabilities and industry use cases.

eCOA solutions are transforming clinical trials by enabling accurate, real-time capture of patient, clinician, and caregiver-reported outcomes through smartphones, tablets, web portals, and provisioned devices. These platforms are redefining how clinical trials capture participant-reported data by replacing paper-based methods with secure and digital solutions. Sponsors and CROs increasingly demand flexibility, scalability, and multimodal support, driving providers to invest in innovations such as multimedia inputs, AI-enabled study setup, and seamless integration with other clinical systems. The report profiles 19 leading eCOA product providers, classifying them as Leaders, Major Contenders, and Aspirants on Everest Group’s PEAK Matrix®. It analyzes market share distribution, with a few dominant players and many niche innovators, and assesses buyer priorities – ease of use, integration, domain expertise, and flexible pricing. Key takeaways emphasize patient engagement, interoperability, and AI-driven efficiencies as essential for future growth.

The life sciences industry has made significant strides in digitizing operations across clinical development and commercial engagement. However, many organizations still struggle with fragmented data, manual coordination, and delayed responses, hindering real-time decision-making. Systems of Execution (SoE) offer a transformative solution by enabling real-time orchestration of tasks, detecting risks, triggering outcomes, and optimizing execution across the enterprise. Watch Everest Group experts Durga Ambati, Chunky Satija, and Nisarg Shah as they delved into the concept of SoE, exploring high-value use cases and industry considerations for successful implementation. Learn how SoE platforms can unify data across commercial, medical affairs, and patient services to deliver coordinated, stakeholder-centric engagement, and how they can act as an intelligent execution layer that bridges core systems and AI applications.

CTMS platforms centralize and streamline every aspect of a clinical trial – planning, site coordination, patient tracking, budget oversight, document management, and regulatory compliance – into one unified, cloud-based system. The increasing trial complexity, the growing data volume, and the need for real-time collaboration have driven the shift toward cloud-based, integrated CTMS platforms that provide real-time operational insights into trial performance. Modern CTMS platforms integrate with EHRs, EDCs and other clinical systems and leverage AI, automated workflows, and real-time analytics to help teams forecast enrolment issues, manage risks, and speed up trial execution. In this report, we assess 13 CTMS product providers. The report enables buyers to choose the best-fit provider based on their sourcing considerations and empowers providers to benchmark themselves against their peers.

Clinical trials are becoming more complex due to the adoption of decentralized models, increasing data volumes, and greater regulatory scrutiny. Conventional approaches centered on 100% Source Data Verification (SDV) and uniform on-site monitoring are inefficient, costly, and slow at detecting emerging risks. RBQM addresses these limitations through a comprehensive, risk-focused framework. Anchored in principles such as Quality by Design (QbD), centralized monitoring, and continuous risk assessment, RBQM empowers sponsors to identify, prioritize, and mitigate critical risks across all trial phases. The approach enhances trial quality, accelerates timelines, and ensures compliance with global regulatory standards, including FDA, EMA, and ICH E6 (R3). This report outlines the evolution from Risk-based Monitoring (RBM) to RBQM, presents the core principles and key components of RBQM, and offers guidance on how enterprises can derive success from their RBQM initiatives. It is intended for sponsors and Contract Research Organizations (CROs) seeking to modernize trial oversight and improve operational performance through data-driven quality management.

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.

The pharmaceutical industry is significantly transforming, driven by escalating R&D costs, rising competition, and a rapidly evolving global regulatory landscape. In this dynamic environment, regulatory affairs have emerged from their traditional compliance-focused roles to become strategic business agility enablers. Enterprises are increasingly recognizing the potential of AI, generative AI, and agentic AI to reduce manual workloads, accelerate time-to-market, and improve the accuracy and consistency of regulatory submissions. This report provides comprehensive insights into AI adoption’s current state in regulatory affairs, highlighting key challenges, value drivers, and investment trends. It explores prioritized strategic and operational use cases across regulatory processes and outlines how enterprises can overcome adoption hurdles through robust governance frameworks. The report also examines specialized providers’ evolving role in this transformation. Through their domain expertise, technology capabilities, and scalable solutions, these providers help reduce risk, ensure compliance, and enable faster time-to-market. Industry Life Sciences BPS Geography Global Contents In this report, we examine: Regulatory affairs’ evolving role in life sciences Current and future AI investment trends across pharma enterprises Leading AI use cases in regulatory strategy and operations A governance framework to address AI adoption challenges The strategic role of regulatory affairs specialist providers in accelerating AI adoption Memberships Life Sciences Business Process Sourcing and Vendor Management

The life sciences R&D engine is rapidly digitizing, and clinical development software now sits at its center. This report sizes the global clinical trials technology market at nearly US$6.5 billion for 2025 (8-10% CAGR) and dissects its four core segments – Electronic Data Capture (EDC), Clinical Trial Management Systems (CTMS), Decentralized Clinical Trial (DCT) platforms, and clinical data and analytics suites. This study maps demand surges in oncology, rare disease, neurology, and obesity trials and examines the contrasting pressures of build-versus-buy debates and heightened data privacy scrutiny. Beyond market numbers, the report spotlights whitespaces such as end-to-end interoperability, real-time visualization, and true low/no-code configurability, analyzes segment-specific product trends (AI-driven EDC validation, next-generation CTMS forecasting, patient-centric DCT designs, and unified analytics hubs), and details the investment priorities sponsors and CROs are setting for 2025. A forward-looking section explores generative AI and agentic AI use cases, from autonomous protocol design to real-time safety monitoring, and outlines adoption stages and success prerequisites. These insights equip stakeholders to refine technology roadmaps, close capability gaps, and accelerate drug-development timelines. Scope Industry: life sciences Geography: global Contents In this report, we examine: 2023-25 clinical technology market size, growth, and segment mix Therapeutic area demand and shifting R&D spend Growth drivers and inhibitors Persistent gaps and sponsor-sourcing criteria Product-innovation themes across EDC, CTMS, DCT, and analytics High-potential generative AI and agentic AI applications and adoption stages 2025 investment priorities for sponsors, CROs, and technology providers

Decentralized and hybrid designs add more complexity to clinical trials, generating vast data volumes from diverse sources. This complexity creates significant data management challenges. In response, sponsors are adopting unified clinical Data and Analytics (D&A) platforms to centralize and standardize clinical data for actionable insights, offering real-time monitoring, predictive analytics, and risk management. These platforms transform disparate datasets into cohesive, structured formats, helping stakeholders detect outliers, predict adverse events, and generate on-demand dashboards and reports. D&A platforms offer centralized repositories, Risk-based Quality Management (RBQM), and seamless integration with Electronic Health Records (EHRs), wearables, and medical devices. These features improve data access and interoperability, contributing to more informed decision-making and enhanced quality and risk oversight. To meet sponsor needs, clinical D&A platform providers are integrating AI and generative AI to automate repetitive tasks, generate insights, and strengthen quality and risk management. In this report, we analyze 18 clinical D&A platform providers’ capabilities and offerings. The report empowers buyers to select the right provider for their sourcing considerations and enables providers to benchmark themselves against their competition. Scope Industry: life sciences Geography: global Contents In this report, we: Examine the clinical D&A platform provider landscape Assess clinical D&A platform providers on their capabilities and market success-related dimensions Membership(s) Clinical Development Technology Sourcing and Vendor Management

The clinical trial landscape is fundamentally transforming with generative AI adoption. Historically, trials have relied on manual processes for protocol design, database lock, and regulatory documentation, often leading to inefficiencies, rising costs, and prolonged development timelines. Growing clinical data volume and increasingly stringent regulatory requirements pressure pharma companies to optimize costs and accelerate drug development. Generative AI is uniquely positioned to address these challenges by synthesizing fragmented datasets, automating routine tasks, and enabling real-time data analysis and predictive modeling. By streamlining trial workflows, supporting adaptive trial designs, and enhancing decision-making, it is helping sponsors improve operational efficiency while maintaining data integrity and compliance. Integrating generative AI into clinical trials requires redefining core processes, reimagining stakeholder roles, and implementing new workflows that allow AI-driven insights to be seamlessly embedded into trial operations. This Viewpoint explores how sponsors can effectively adopt generative AI, key implementation considerations, and the best practices to ensure successful clinical development integration. Scope Industry: life sciences Geography: global Contents In this report, we examine: How generative AI addresses inefficiencies and drives innovation in trial processes Strategic changes required to integrate generative AI, including redefining roles and optimizing workflows Key considerations and best practices for successful adoption and organizational goal alignment Membership(s) Clinical Development Technology Sourcing and Vendor Management