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  • Provider Compendium

    June 27, 2025
    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.
  • Viewpoint

    June 27, 2025
    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.
  • Viewpoint

    June 25, 2025
    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.
  • State of the Market

    May 07, 2025
    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
  • Viewpoint

    May 07, 2025
    Increasing trial complexity, growing data volumes, and the rise of decentralized and real-world data sources are fundamentally transforming the clinical trial landscape. Traditional Clinical Data Management (CDM) processes are largely manual and rely on siloed data systems, making them inefficient and error-prone. In response, AI is emerging as an essential force in reshaping CDM. This Viewpoint analyzes the transformative roles of AI, generative AI and agentic AI, in modernizing CDM operations. It details how AI is enabling intelligent automation across the trial lifecycle, from protocol design and CRF setup to real-time data validation, anomaly detection, and regulatory documentation. Agentic AI is further pushing boundaries by enabling adaptive, autonomous decision-making with minimal human intervention. These capabilities not only reduce cycle times and improve data quality but also fundamentally shift how clinical teams manage, interact with, and derive insights from data. The report also offers a landscape view of AI-powered solutions across provider types, including global CROs, specialist CDM firms, and IT/BPO players. It outlines key cases, priority capabilities, and practical considerations for life sciences enterprises looking to integrate AI into their CDM strategies. Industry Life Sciences BPS Geography Global Contents In this report, we examine: The limitations of traditional CDM models in an increasingly complex data environment The role of AI, generative AI, and agentic AI in transforming CDM operations Key use cases across the CDM value chain The provider landscape, including differentiated value propositions across CROs, IT/BPO firms, and niche players Future outlook on multi-agent collaboration, autonomous compliance, and intelligent patient engagement through agentic AI Strategic considerations for evaluating and implementing AI-powered CDM solutions Memberships Clinical Development Technology Life Sciences Business Process Sourcing and Vendor Management
  • Tech Launch Perspective

    March 12, 2025
    Telecom-specific AI-powered solutions will be vital in optimizing telecom infrastructure and delivering next-generation connectivity as 5G, Open RAN, and network disaggregation gain momentum. The industry is significantly transforming, with Large Language Models (LLMs) and Small Language Models (SLMs) being increasingly adopted to automate, optimize, and enhance network operations, customer service, and infrastructure planning. Unlike generic AI models, industry-specific LLMs and SLMs are designed to understand, process, and generate telecom-relevant insights, making them more efficient and accurate for domain-specific applications. SLMs are particularly valuable for low-power, real-time AI applications, making AI-driven network optimization and diagnostics more scalable. This LLM integration offers numerous benefits, including improved efficiency, personalized customer interactions, and advanced network optimization. In this report, we explore NetoAI’s SLM product TSLAM 1.5B's transformative potential, examining its key features and impact on network management, customer support, and security assistance. We also provide insights into its market positioning, key benefits, and broader implications for telecom enterprises as they cost-effectively adapt to sustainable standards. Scope All industries and geographies Contents In this report, we: Provide insights into the global telecommunications industry Examine NetoAI’s current positioning and alignment among telecommunication enterprises Explore the features, benefits, and challenges of NetoAI’s product TSLAM 1.5B Membership(s) Application Services Artificial Intelligence (AI) Banking and Financial Services Information Technology Cloud and Infrastructure Services Clinical Development Technology Cybersecurity Data & Analytics Digital Services Digital Workplace Enterprise Platform Services (EPS) Healthcare Payer and Provider Information Technology Insurance Information Technology Marketing and Interactive Experience Life Sciences Information Technology Retail and CPG Sustainability Technology and Services Sourcing and Vendor Management
  • Provider Compendium

    Feb. 28, 2025
    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
  • Viewpoint

    Feb. 21, 2025
    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
  • Provider Compendium

    Feb. 14, 2025
    Electronic Data Capture (EDC) systems are integral to clinical research, enabling clinical data collection, storage, and management. EDCs mark a significant shift from paper-based case report forms to digital and web-based trial data collection. The COVID-19 pandemic further emphasized EDC systems’ importance as the need for remote and decentralized clinical trials surged. The pandemic highlighted the need for robust, flexible, and secure data capture solutions to support treatments’ and vaccines’ rapid development and approval. These platforms have evolved to centralize data collection, improve data quality, and enable real-time clinical trial monitoring. They now integrate complex clinical data sources, such as electronic health records, laboratory data, clinical trial management systems, connected devices, and real-world data. Overall, EDC systems have significant benefits in clinical data collection, storage, and management. In this report, we assess 20 EDC 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. Scope Industry: life sciences Geography: global Contents In this report, we: Examine the EDC product provider landscape Assess clinical EDC product providers on their capabilities and market success-related dimensions Membership(s) Clinical Development Technology Sourcing and Vendor Management
  • PEAK Matrix®

    Nov. 12, 2024
    Due to decentralized and hybrid designs, the growing clinical trial complexity generates vast data volumes from diverse sources and creates significant data management challenges. In response, sponsors are increasingly employing unified clinical data and analytics (D&A) platforms to centralize and standardize clinical data to gain actionable insights, offering real-time monitoring, predictive analytics, and risk management. These platforms transform disparate datasets into cohesive, structured formats, allowing stakeholders to 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. Clinical D&A platform providers are incorporating cutting-edge AI and generative AI capabilities to automate repetitive tasks, generate insights, and strengthen quality and risk management to address sponsor needs. In this report, we analyze 18 clinical D&A platform providers featured on the Everest Group’s PEAK Matrix® based on their capabilities, offerings, and market impact. The report will empower buyers to choose the right provider for their sourcing considerations and enable providers to benchmark themselves against their competition. Scope Industry: life sciences Geography: global Contents In this report, we: Examine the provider landscape for clinical D&A platforms Assess clinical D&A platform providers on capabilities and market success-related dimensions Position the providers on Everest Group’s PEAK Matrix® framework as Leaders, Major Contenders, and Aspirants Compare providers’ key strengths and limitations Membership(s) Clinical Development Technology Sourcing and Vendor Management