Pharmacovigilance (PV) has evolved into a strategic imperative, driven by intensified regulatory scrutiny and an increasing focus on patient safety. Pharmaceutical companies now face a rapidly evolving landscape characterized by rising adverse event volumes, fragmented real-world data sources, and increasingly complex global regulatory frameworks. Regional variations in drug safety reporting requirements further compound compliance challenges across diverse markets. At the same time, the demand for timely and accurate reporting has intensified, particularly as next-generation technologies introduce operational efficiencies while simultaneously raising regulatory concerns regarding the ethical and compliance implications of generative AI in PV. To navigate these complexities, external providers have become indispensable partners, offering deep PV expertise and adaptable support models. These providers bring proven drug safety process frameworks, highly trained PV professionals, and localized regulatory expertise, including qualified persons for PV, ensuring seamless compliance across global markets. Recognizing the need for enhanced efficiency, providers are investing in AI, automation, and advanced analytics to optimize case processing, adverse event management, and signal detection, all while reducing costs and improving operational scalability. In this report, we assess 29 PV operations providers featured on the Pharmacovigilance (PV) Operations PEAK Matrix®. Each provider profile provides a holistic picture of its service focus, solution offerings, and domain investments. The assessment is based on Everest Group’s annual RFI process for calendar year 2024, interactions with leading PV providers, client reference checks, and an ongoing analysis of the PV operations market.    

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

As life sciences enterprises navigate the clinical development’s growing complexity due to advanced therapies, increased data volumes, and the need for operational agility, outsourcing models are evolving in response. Sponsors now seek flexible, cost-efficient, and expertise-driven models to accelerate time-to-market and maintain regulatory compliance. This Viewpoint examines core characteristics, market shifts, and strategic implications of Full-service Outsourcing (FSO) and Functional Service Provider (FSP) models in clinical operations. Sponsors can benefit from tailored insights to assess and refine their clinical outsourcing strategy. The report explores the increasing adoption of hybrid models, the rising influence of AI and cloud-based infrastructures, and key decision-making factors, such as therapeutic complexity and trial geography. It highlights how enterprises can unlock greater control, scalability, and innovation in clinical trials by selecting the right outsourcing mix. It empowers decision-makers with strategic insights to navigate evolving outsourcing models, enabling them to enhance operational agility, optimize costs, and maintain greater oversight. Scope Industry: life sciences Geography: global Contents In this report, we examine FSO and FSP outsourcing models’ features and use cases Market trends influencing the shift in outsourcing approaches Key decision-making factors for selecting the right model Challenges in implementing and optimizing outsourcing strategies The growing relevance and advantages of hybrid models in clinical operations

Life sciences enterprises are increasingly turning to Real-world Evidence (RWE) as an essential input for decision-making across the product life cycle – from early-stage R&D to post-market access and safety. RWE offers validated insights into treatment effectiveness, patient outcomes, and safety, but fragmented data sources, inconsistent quality, and evolving compliance expectations often challenge its generation. With the rising volume and diversity of Real-world Data (RWD), traditional analytics approaches are no longer sufficient. AI, including technologies such as NLP, machine learning, and generative models, is redefining how RWE is produced and operationalized. AI is accelerating data curation, enabling predictive analytics, and delivering regulatory-grade evidence at scale. This Viewpoint outlines how AI is transforming the RWE landscape across six domains: drug discovery, clinical trials, manufacturing, commercialization, pharmacovigilance, and regulatory affairs. It also explores emerging models such as insights-as-a-service and autonomous evidence networks, which offer scalable, modular engagement approaches for AI-powered RWE. The report provides practical recommendations for both enterprises and providers, covering capability investments, infrastructure modernization, governance models, and partnership strategies. It aims to help stakeholders reimagine their data-to-evidence journeys and build future-ready ecosystems for continuous, AI-enabled insight generation. Scope Industry: life sciences Geography: global Contents In this report, we examine: RWE’s current landscape and growing importance in life sciences AI’s role in addressing foundational RWE challenges AI-enabled key RWD/RWE use cases across the product life cycle Emerging engagement models such as BPaaS, AaaS, and IaaS Future-forward models, including autonomous evidence networks and modular AI-enabled partnerships Strategic considerations to operationalize AI-powered RWE for enterprises and providers

Clinical Data Management (CDM) is rapidly transforming as life sciences enterprises navigate increasing trial complexity, heightened regulatory scrutiny, and the expansion of decentralized and real-world data sources. CDM now plays a strategic role in enabling faster, more reliable trial outcomes through harmonized data practices. To address this shift, providers are integrating AI, generative AI, and automation throughout the CDM lifecycle, enhancing efficiency in data ingestion, validation, medical coding, and protocol design. These advances, coupled with global delivery capabilities and deep therapeutic expertise, are empowering enterprises to scale operations and navigate patient-centric trials’ demands. In response to evolving market needs, providers are also expanding their portfolios to offer comprehensive, end-to-end solutions across the CDM value chain. In this report, Everest Group comprehensively evaluates 22 leading providers featured in the Clinical Data Management Operations PEAK Matrix® report. It highlights each provider’s service focus, key IP/solutions, domain-specific investments, and case studies.

Pharmacovigilance (PV) operations have become a strategic priority for life sciences enterprises facing heightened regulatory scrutiny, evolving safety standards, and growing complexity across global markets. This report provides an in-depth assessment of 12 leading PV service providers and their capabilities across the PV value chain, including case processing, literature surveillance, medical assessments, and regulatory reporting. The research also examines how providers are integrating automation, analytics, and platform-based solutions to drive efficiency, improve compliance, and enhance data quality. In addition, it evaluates providers’ delivery footprints, service portfolios, and investment activity, enabling enterprises to make informed sourcing decisions. The report is particularly relevant for business and procurement leaders seeking to balance cost, compliance, and quality in an evolving PV landscape. By benchmarking providers across vital dimensions, the report supports enterprises in optimizing operations and safeguarding patient safety at scale.

The surge in case volumes, more stringent regulatory demands, and therapeutic areas’ rising complexities are significantly transforming pharmacovigilance (PV). As the industry adapts to more tailored reporting requirements and longer patient monitoring periods, enterprises are under pressure to optimize operations without compromising quality. This evolution has led to a reassessment of sourcing strategies, with many exploring flexible engagement models and realigning investments to balance cost and efficiency. Providers are responding by deepening their therapeutic expertise, expanding their service offerings, and integrating automation and analytics throughout the value chain. Adopting AI-enabled tools, predictive signal detection, and real-world data is accelerating operational agility and regulatory compliance. Meanwhile, commercial models are shifting away from transactional engagements toward hybrid and value-linked constructs. These shifts are intensifying competition among CROs, BPOs, and niche players, each aiming to demonstrate adaptability, innovation, and compliance strength. The report helps business and procurement leaders understand how to evolve sourcing approaches to meet future demands while ensuring long-term PV resilience.

Clinical Data Management (CDM) operations have become a strategic priority for life sciences enterprises as they face growing trial complexities, expanding data volumes, and the integration of decentralized and real-world data sources. These dynamics are reshaping how enterprises approach data standardization, validation, and delivery, placing greater emphasis on speed, accuracy, and regulatory compliance. This report provides an in-depth assessment of leading CDM providers and their capabilities across the CDM value chain – from trial start-up to closeout. It examines how providers are leveraging AI, generative AI, and agentic AI to drive automation in data ingestion, real-time validation, medical coding, and query management. The report is particularly relevant for business and procurement leaders seeking to balance costs, compliance, and quality in an evolving CDM landscape. In this report, Everest Group comprehensively evaluates 7 leading providers featured in the CDM Operations PEAK Matrix® Compendium report. Each provider profile highlights the service focus, key IP/solutions, and domain-specific investments. By benchmarking providers across key capability dimensions, the report supports life sciences enterprises in selecting the right partners to enable next-generation data operations and accelerate clinical development outcomes.