Introduction

A global pharmaceutical enterprise with more than 15,000 employees and €5 billion in annual revenue had made significant investments in artificial intelligence to accelerate drug discovery, optimize clinical trials, and improve manufacturing quality. While AI innovation was advancing rapidly across research, development, and commercial teams, the lack of standardized development practices and governance increasingly limited the organization’s ability to scale solutions safely and efficiently in a highly regulated environment.

The Story

AI development across the organization evolved organically. Data science teams worked independently, experimenting with new models and techniques to solve local challenges. While this approach fostered innovation, it also led to fragmentation. Hundreds of proofs-of-concept were created, but only a small fraction reached production or delivered sustained business value.

Without shared standards or tooling, models were difficult to reproduce, reuse, or maintain. Deployed solutions lacked monitoring and ownership, and compliance requirements for traceability and validation were not consistently met. Leadership lacked visibility into the AI portfolio, making it difficult to prioritize investments or assess risk.

We partnered with the organization to introduce a structured Model Lifecycle Management (MLM) approach, embedding MLOps principles and regulatory governance across the entire AI value chain. The goal was to bring order, transparency, and control without slowing innovation

The Challenge

Several interconnected issues hindered the scalability and reliability of AI operations.

Development practices varied widely across teams, with no common standards for version control, documentation, or validation. As proofs-of-concept multiplied, models became siloed and hard-coded, often abandoned after initial success. The absence of a centralized model registry led to duplicated work and missed reuse opportunities.

Once models were deployed, there was little visibility into performance or data drift, with issues often detected only by end users. Maintenance became increasingly burdensome due to undocumented dependencies, deprecated libraries, and unclear ownership. At the same time, pharmaceutical compliance and auditability requirements were not consistently addressed, creating regulatory risk.

Highly skilled experts spent excessive time managing infrastructure and firefighting issues instead of delivering business value.

The Results

Through a nine-month Model Lifecycle Management transformation, the organization achieved significant operational and cultural improvements:

Process maturity: Transitioned from ad-hoc experimentation to disciplined, end-to-end MLOps, reducing deployment time by 60% and standardizing validation workflows.

Visibility and control: Registered more than 150 models in a centralized catalog, giving leadership real-time insight into value, risk, and portfolio health.

Operational excellence: Improved uptime to 99.9% for critical models and reduced incident resolution time by 70%.

Maintenance sustainability: Lowered infrastructure costs by 25% and reduced technical debt through consolidation and retirement of obsolete assets.

Compliance and risk management: Achieved full auditability, passed regulatory inspections with positive feedback, and embedded ethical review processes.

Reuse and productivity: Enabled cross-project model reuse, accelerating new development by 40% and reducing redundant efforts.

Business impact: Accelerated drug discovery, improved clinical trial efficiency, and enhanced manufacturing quality through predictive monitoring.

Cultural transformation: Shifted AI from a research-led mindset to a disciplined engineering practice, strengthening collaboration and stakeholder trust.