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» Products & Services » » Market Research, Analytics and Forecasting » Analytics

AI-Enhanced Insights and Analytics: Driving Customer Value and Market Performance

ID: PSM-405


Features:

13 Info Graphics

15 Data Graphics

410+ Metrics

21 Narratives


Pages: 36


Published: 2026


Delivery Format: Shipped


 

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919-403-0251

  • STUDY OVERVIEW
  • BENCHMARK CLASS
  • STUDY SNAPSHOT
  • KEY FINDINGS
  • VIEW TOC AND LIST OF EXHIBITS
AI is rapidly shifting the competitive landscape in biopharma, redefining how organizations generate insights, allocate resources, and outperform competitors. Insights & Analytics (I&A) and market research functions are under increasing expectations to deliver not just analysis, but decision advantage. However, for many companies, AI adoption remains constrained by data readiness, governance complexity, and gaps in operational integration.

This report examines how leading biopharma organizations are translating AI ambition into new SOPs, capabilities, and measurably improved performance. This critical report identifies where AI is driving the greatest business impact within biopharma market research, how top teams scale adoption responsibly, and which operating model shifts enable sustained value creation. This research provides executives with benchmarks and strategic clarity to ensure AI investments strengthen decision quality, accelerate market performance, increase efficiency, and build durable competitive edge.

Industries Profiled:
Pharmaceutical; Biotech; Manufacturing; Health Care; Logistics; Transportation; Consumer Products; Laboratories; Diagnostic; Biopharmaceutical; Clinical Research


Companies Profiled:
AstraZeneca; Biogen; Braeburn; Eisai; Gilead; Jazz Pharmaceuticals; LifeScience Logistics; Merck; Novartis; Otsuka; Pierre Fabre; Polpharma; Qiagen; Sanofi; Savara; Scilex Pharmaceuticals; Sobi; Théa; Valneva; Zydus Cadila

Study Snapshot

Best Practices, LLC engaged 23 Insights & Analytics leaders from 20 biopharma companies through a structured benchmark survey focused on AI tool usage, integration, and operating models.

Key topics covered in this report include:

  • I&A scope, core responsibilities, and internal partners supported
  • Value pathways: projects, capabilities, and decision enablement
  • Advanced analytics methodologies and AI/ML techniques in practice
  • Operating model enablers: InsightOps, skills, governance, and platforms

Key Findings

Select key insights uncovered from this report are noted below. Detailed findings are available in the full report.

  • AI’s near-term value = speed + scale of synthesis: Teams are seeing the biggest positive impact from AI in project speed, employee productivity, automation of manual steps, and better resource utilization—exactly the levers that free bandwidth for higher-order thinking.
  • Skill and adoption gap is real: On average, only ~18% of team members have formal AI/ML training; many organizations are still in pilot/nascent stages. This limits scale unless processes and enablement catch up.
Table of Contents

Sr. No.
Topic
Page No.
I.
Executive SummaryPg. 3
II.
Insights & Analytics Group: Core Responsibilities and Internal Partners SupportedPg. 11
III.
How a Strong Insights & Analytics Function Delivers ValuePg. 15
IV.
Optimizing Advanced Analytics CapabilitiesPg. 24
V.
Participant DemographicsPg. 33
VI.
About Best Practices, LLCPg. 36

    List of Charts & Exhibits

    I. Executive Summary

    • Executive brief: Strategic imperatives for accelerating AI-enabled Insights & Analytics
    • One-page executive summary framing AI-enabled value creation in I&A
    • Participating benchmark organizations overview
    • Executive takeaways summarizing operating model, techniques, and enablement themes
    • Key recommendations to maximize directional insight and socialize capabilities

    II. Insights & Analytics Group: Core Responsibilities and Internal Partners Supported

    • Internal partner support frequency of I&A groups
    • Responsibility matrix for core Insights & Analytics group capabilities
    • Factors distinguishing “strategic” analytics and insights from “tactical” analytics and insights

    III. How a Strong Insights & Analytics Function Delivers Value

    • Critical specialist roles within an Insights & Analytics team that drive maximum value for internal partners
    • Top value drivers delivered by high-performing insights & analytics teams
    • Key barriers limiting the future success of Insights & Analytics
    • How successfully AI-driven practices are elevating I&A performance
    • Where AI tools are strengthening value delivery to stakeholders
    • Formal AI/ML training levels within I&A teams
    • The most costly pitfalls for Insights & Analytics functions
    • How I&A teams are driving innovation and scaling AI solutions

    IV. Optimizing Advanced Analytics Capabilities

    • Analytic methodologies in use and the business questions they address
    • AI/ML techniques most commonly used by I&A teams
    • AI/ML techniques that deliver the greatest workflow and value gains
    • High-value analytics projects across key functions
    • Insource vs. outsource approach for core analytics activities
    • Replacing traditional market research approaches with AI/ML
    • Analytics and reporting platforms used by I&A teams to support data-driven decisions
    • Evaluating AI tools: Strengths, limitations, and practical experience

    V. Participant Demographics

    • Industries represented by participating organizations
    • Geographic span of responsibility of benchmarked Insights & Analytics groups