From legacy to results: How life sciences leaders deliver ROI with two-speed transformation

The challenge of innovation in legacy-driven environments

Digital transformation in life sciences rarely unfolds at a single pace. In the same organization, one team might deploy advanced AI-driven analytics to predict outcomes in seconds, while another relies on production systems installed decades ago. The real challenge isn’t choosing between stability and innovation. It’s learning how to advance both at once, otherwise known as two-speed transformation.

Modernization often highlights the uneven pace of digital transformation across life sciences manufacturing, including the challenges posed by legacy systems and the importance of integrating new technologies with existing IT and operational infrastructure. There is value in a balanced approach that threads innovation through existing operations without replacing them. While developing a measured modernization strategy, it is equally important to recognize signs of organizational stagnation and to emphasize collaboration, operational alignment, and practical, real-world application to achieve success.

What is two-speed transformation in life sciences?

Two-speed digital transformation means modernizing at two distinct rates: one focused on stabilizing core systems for reliability and compliance, and another accelerating digital initiatives such as cloud, AI and advanced analytics. The most effective approaches treat these two speeds not as competing forces, but as complementary. Stability provides the foundation, while innovation builds the future.

It’s useful to keep in mind that an uneven pace of digital transformation within life sciences is not necessarily a sign of failure. In many cases, it reflects a practical reality: organizations must simultaneously protect critical operations while pursuing innovation.

Transformations led solely from an IT perspective often struggle if they are disconnected from revenue-generating teams. Instead, leading organizations design around real operational needs, avoid one-size-fits-all solutions, and treat change management as an essential part of modernization.

The role of legacy systems in modernization

Age alone doesn't classify a system as legacy. In manufacturing, longevity can signal reliability rather than obsolescence. Critical shop floor systems such as supervisory control and data acquisition (SCADA) and distributed control system (DC) are built to last for decades, often as long as the facilities themselves. Despite some of these systems being decades old, they are frequently maintained, patched and updated to run smoothly.

A system truly becomes legacy when it reaches end of life or end of support, forcing organizations to make decisions about replacement, modernization or integration. With the right investment and a clear strategy, legacy systems can continue to deliver operational value while supporting innovation.

Rather than discarding stable core systems, organizations have successfully layered new digital capabilities onto them, turning proven platforms into foundations for innovation.

One example is PROCOS, a CGI control system that remains one of the most stable environments across clients' manufacturing sites. Over time, this system has been continuously updated to support early simulation tools and digital twins and, more recently, it’s served as the backbone for manufacturing execution system (MES) initiatives, advanced analytics and AI-driven enhancements. Through consistent reinvestment and modernization, while preserving core reliability, it is positioned to remain in place for decades.

How can life sciences companies modernize manufacturing across multiple sites?

Successful transformation across multiple manufacturing sites requires both structure and sensitivity to local operational needs. A strong governance backbone, supported by processes within CGI’s Management Foundation, provides cohesion and control at the enterprise level. At the same time, industry-specific frameworks such as CGI Manufacturing Atlas help guide clients’ transformation journeys in a practical, grounded way.

Conversely, a top-down, one-size-fits-all mandate is unlikely to succeed. Organizations can successfully modernize across sites by combining enterprise standards with local input and clearly defined business value from the outset.

Identifying signals of stagnation

One of the clearest signals that a system or organization is slowing down is rising tension between teams. A practical way to address this is to step back and start with a blank page. Instead of leading with predefined solutions or preferred vendors, shift to understanding day-to-day challenges and desired outcomes before technology enters the conversation.

Another indicator is unnecessary complexity. Overcomplicating digital transformation with layers of presentations and excessive analysis can obscure the real issue. Clear dialogue, shared understanding and straightforward solutions often unlock progress faster than elaborate plans.

Advice for life science CIOs navigating transformation

For CIOs addressing legacy technology, distributed manufacturing, and mounting pressure to modernize, the first step is to start with the people who run production. Before launching large-scale modernization programs, engage site leaders and production teams to understand their challenges across brownfield and greenfield environments. The most valuable insights often come from those who interact with the systems every day.

Success depends on maintaining a strong connection between technology initiatives and operational outcomes. Organizations need to align on transformation initiatives with production goals, improving output, minimizing disruption and delivering ROI. When change is rooted in how factories and labs operate, it delivers tangible results. In the end, demonstrated business value is the true differentiator between effort and success.

Key takeaways for successful two-speed modernizations

• Life sciences digital transformation often operates at two speeds that can complement each other: stable core operations and faster innovation initiatives.
• Older systems are not automatically obsolete. With the right maintenance and updates, many can remain reliable platforms for modernization.
• Transformation strategies are most effective when they begin with operational needs and involve production teams early.
• Integrating new technologies with existing systems can deliver faster results than complete system replacement.
• Simplicity and collaboration are often more effective than overly complex transformation plans.

Balancing innovation with operational stability remains one of the defining challenges for life sciences organizations. Those that successfully integrate new technologies while preserving reliable production environments are best positioned to achieve sustainable digital transformation.

Read more about the importance of modernizing production systems to power the future of pharmaceuticals.