Accenture invests in Ryght AI to speed agentic AI-driven clinical research and trial execution
Accenture Ventures has taken a strategic stake in Ryght AI, a platform built to modernize how clinical research is planned and run. The goal: shorten study startup, improve enrollment, and help get treatments to patients sooner by applying agentic AI to the most operationally painful parts of trials. Financial terms were not disclosed.
Why this matters for clinical R&D
Trials keep getting more complex. Site selection, feasibility, and enrollment stall progress and burn budget, largely due to manual workflows and fragmented data. Accenture and Ryght AI want to tackle those bottlenecks head-on, where weeks get lost: feasibility assessment, site selection, and patient recruitment.
What Ryght AI brings
Ryght AI's AI Site Twin creates dynamic digital replicas of research sites around the world. Each site model pulls from historical performance, patient demographics, regulatory context, and real-time operational signals. Sponsors and CROs can use these models to forecast enrollment, pinpoint the right sites faster, and streamline activation.
What Accenture adds
Accenture will layer its life sciences expertise, technology services, and data engineering on top of Ryght AI's domain-specific platform. The intent is to embed agentic AI into critical decision points so teams can move with more confidence and fewer handoffs, at scale.
Security and collaboration
Ryght AI is SOC 2 Type II compliant, with real-time collaboration features for sponsors, CROs, and sites-reducing friction during study startup and enabling more data-driven execution across the trial lifecycle. For context on SOC 2, see the AICPA overview here.
Broader context
This move aligns with Accenture's ongoing support for AI-enabled biopharma R&D platforms. Recent investments include 1910 Genetics, Turbine, and Earli-each focused on improving different points across discovery and development.
Practical takeaways for sponsors and CROs
- Unify your site data foundation: historical performance, screen-fail rates, patient mix, regulatory history, and operational SLAs. Agentic AI adds value only when the inputs are consistent and usable.
- Pressure-test protocols early: simulate enrollment and startup scenarios with site twins to spot avoidable amendments and staffing gaps before first patient in.
- Prioritize sites by predicted fit: segment by disease prevalence, investigator throughput, and past adherence to timelines-then align budgets and outreach accordingly.
- Tighten governance: define human-in-the-loop checkpoints, model validation, and audit trails that align with your SOPs and quality system.
- Plan integrations upfront: map connections to CTMS, EDC, eTMF, and feasibility tools; start with a pilot in one therapeutic area and scale after you see measurable lifts.
What to watch next
- Cycle time: trial feasibility duration, site selection lead time, and time-to-activation.
- Enrollment quality: forecast accuracy, screen-fail rate, and early dropout.
- Operational efficiency: protocol amendment frequency, cost per enrolled patient, and site productivity variance.
Key quotes
"Generative and agentic AI are transforming the foundation of clinical research. With our investment in Ryght AI, we're giving our life sciences clients the ability to fully leverage operational and scientific data and bring life-changing therapies to market faster, more efficiently and with greater precision." - Petra Jantzer, PhD, Senior Managing Director and Global Lead of Life Sciences, Accenture
"This partnership allows Ryght AI to extend the impact and adoption of our technology to a global scale. Together, we can help life science companies reimagine how clinical trials are planned, launched and managed-accelerating the journey from discovery to treatment." - Simon Arkell, Chief Executive Officer, Ryght AI
Level up skills for agentic AI in clinical ops
If you're building competency in AI-driven trial operations and data workflows, explore practical programs curated by role at Complete AI Training. Focus on courses that cover data engineering for clinical systems, model validation, and responsible AI in regulated environments.
Your membership also unlocks:
