Cohort: 2

Locations: Gentilly, France & Munich, Germany

Application Title: Agent-powered AI transformation engine accelerating time from discovery to patient impact

Application Subtitle: A multi-agent transformation approach that embeds advanced analytics into

everyday decision-making across biopharma R&D and operations, shortening the path from discovery to patient access

Team Members

OAO: Dr. René Eber, Dr. Julian Schirmer, Eirik Sjåholm Knudsen, Marcel Holzapfel

Sanofi: Emmanuel Frenehard, Julien Marcaut, Andrew Philips, Fabrice Collet, Joanne Beardsley

The challenge

Pharmaceutical research and development generates vast amounts of data and scientific insight.

However, in large global organisations, the processes that turn discovery into patient impact often move more slowly than the science itself.

Expertise is distributed across many functions. Discovery scientists, clinical teams, manufacturing

specialists and commercial staff all work with different tools and decision frameworks. This

fragmentation makes it difficult to share insights and coordinate action. Traditional digital

transformation programmes tend to focus on deploying new technologies or delivering isolated use

cases. While useful, these efforts often leave everyday ways of working unchanged and limit how widely AI is adopted across the organisation.

As a result, opportunities to shorten development timelines or improve operational decisions can

remain underused.

The solution

Organisation-wide AI adoption supported by agents

Sanofi, working in partnership with OAO, is addressing this gap by focusing on how AI is used across the organisation, rather than where it is deployed.

OAO has been deploying its multi-agent system across all hierarchy levels, serving as the enterprise's transformation engine. The AI agents create a bidirectional flow: capturing ground-truth insights from employees, converting their domain expertise into investable innovations, and steering leadership to place focused AI bets. This turns employees from transformation recipients into active participants.

The joint initiative aims to make AI part of daily work for employees across functions and seniority levels. Agents help teams identify opportunities where AI could add value, translate employee input into concrete use cases, and connect local experimentation with enterprise-level priorities. This organizational intelligence helps leadership focus investment on the most promising initiatives, including scaling priority use cases in commercial operations and biologics discovery.

This makes AI adoption a participatory process rather than a centrally defined rollout.

How it works

Connecting AI across research and operations

The programme spans Sanofi's full value chain, from research & development to manufacturing & supply to commercial operations. The AI agents have generated thousands of use case ideas across the organisation. Several of these have been prioritised by leadership and scaled enterprise-wide, including two flagship initiatives in biologics discovery and commercial operations.

In biologics discovery, a unified AI platform combines predictive and generative models with automated data ingestion and retraining. Scientists can build and deploy models in weeks rather than months, reducing the time required to explore hypotheses or analyse protein sequences. Processing that once took days can now be completed much faster, enabling near-real-time predictions for a large share of active projects.

In commercial operations, an AI-driven omnichannel system supports engagement with healthcare

professionals. Deployed across multiple markets and brands, the system uses a set of specialised models to recommend actions tailored to local context. These recommendations are integrated into existing workflows, allowing teams to act on insights without adopting new tools or processes.

Learning from scientific and commercial use cases feeds back into the broader system, allowing

methods developed in one area to inform others. In parallel, the AI Agents platform continuously captures barriers and opportunities from employees and leaders and translates them into prioritized initiatives – shaping where these research and commercial systems are scaled and invested in.

How it differs from conventional approaches

Turning employees into active transformation participants, not recipients

Conventional AI programmes in large enterprises often prioritise individual applications or technology platforms. Sanofi and OAO’s approach focuses instead on embedding AI into decision-making and collaboration.

Rather than requiring employees to adapt to new systems, AI agents meet employees where they are – surfacing their domain expertise and turning it into actionable innovations and initiatives. This lowers the barrier for participation and, over time, builds organisational capability and shared understanding of where AI is most useful.

The emphasis is on sustained adoption rather than one-off implementation.

Impact

·        More than 1,300 AI use cases co-created through organisation-wide participation

·        Over 1,400 employees actively engaged to unleash AI innovations

·        450 data-informed initiatives shaped by management and 10+ executive AI bets initiated by leadership

·        20% AI readiness uplift with transformation efforts and improved clarity around AI priorities

·        Single-digit commercial performance uplift, with a 10:1 return on investment across deployed use cases

·        11 AI models in production supporting biologics discovery, with model build times reduced from several months to under three

Why it matters

Reducing the gap between discovery and patient access

Sanofi and OAO’s collaboration highlights the role organisational design plays in effective AI adoption. By focusing on participation and integration into everyday work, the initiative supports more consistent use of AI across R&D and operations, ultimately for accelerating delivery to patients

This approach helps large biopharma organisations like Sanofi move insights into practice more efficiently, supporting faster decision-making without relying on narrow centres of expertise. Over time, this can contribute to further shortened paths from scientific discovery to patient benefit.