Accenture Healthcare Global

Bristol Myers Squibb

Real-time Analytics

Bristol Myers Squibb needed to shorten expensive, operationally complex Phase III clinical trials and give research teams faster, clearer visibility into trial performance so they could react to issues earlier and bring therapies to patients more quickly.

median costs ranging from $19 million to over $100 million per trial
more than 30 generative AI solutions
over 30 of BMS’ top priority clinical trial teams
unique users of the platform surging from under 100 to nearly 900 in just 3 months

Reported impact

Business outcome

Bristol Myers Squibb accelerated priority clinical trials, expanded use of a common trial-operations platform, improved visibility into key execution risks, and gave teams a faster way to learn from past studies and act before operational problems delay development.

  • Cost Reduction
  • Productivity Improvement
  • Speed to Market

Solution

Bristol Myers Squibb and Accenture built Workbench as a clinical-trial accelerator that combines generative AI, automation, and real-time operational data for drug-development teams. The broader partnership implemented more than 30 generative AI solutions across R&D, product development and supply, and commercialization, but Workbench became a focal system for trial operations. It gathers structured and unstructured trial data, organizes it into decision-ready views, and presents actionable insights to project teams. A recommendation feature tailors guidance by user role, study type, and trial context, while analysis of current and historical data helps anticipate emerging issues and propose interventions before delays grow. By integrating insights across transactional systems into one trusted operating layer, Workbench shifts trial execution from reactive coordination to proactive management and standardizes best practices across priority clinical programs.

Data flow

Structured and unstructured clinical-trial data is gathered into Workbench, where automation and generative AI organize it into decision-ready views. The recommendation feature analyzes current and historical signals by user role, study type, and trial context, then returns actionable guidance and issue-prevention insights to project teams through the Workbench interface.

Solution architecture

4 components · 3 layers
  1. Compute
    • generative AI Supports insight generation across trial workflows and contributes to smarter, faster decision-making in drug development.
    • recommendation feature Delivers role- and study-specific recommendations based on current and historical trial context.
  2. Serving
    • Workbench Serves as the clinical-trial accelerator that gives teams visibility into key metrics, insights, and actionable follow-up items.
  3. Orchestration
    • automation Brings data and operational signals together across trial processes so teams can execute and respond faster.

Architecture clues

  • Institutional knowledge from team experiences is captured and shared forward.
  • The platform integrates across transactional systems to create a single source of truth.
  • The recommendation feature uses current and historical data plus role and study context.
  • Workbench fuses AI-driven insights with real-time operational data.

Evidence from the source

"A key outcome of the BMS-Accenture partnership is 'Workbench', a clinical trial accelerator that fuses AI-driven insights with real-time operational data."
"Now adopted by over 30 of BMS’ top priority clinical trial teams, with unique users of the platform surging from under 100 to nearly 900 in just 3 months"
"Workbench includes a recommendation feature delivering insights based on user role, study type, and trial context"
"the company successfully implemented more than 30 generative AI solutions across its Research & Development, Global Product Development & Supply, and Commercialization divisions."
"the tool also anticipates issues and suggests interventions."
Transparency Evidence & methodology 85/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Accenture

    Bristol Myers Squibb customer story

    First-party
    https://www.accenture.com/en/case-studies/health/bristol-myers-squibb-accelerates-drug-development-genai
    Source type
    Vendor-published customer story
    Published
    May 18, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page May 18, 2026 · Accenture · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.