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

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

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

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