Digital reengineering in health & life sciences: From transformation ambition to measurable results

Health and life sciences organizations are not lacking transformation plans. Most already have ambitious digital roadmaps, cloud strategies, AI initiatives and modernization programs in place. The challenge today is turning those ambitions into measurable outcomes.

For many organizations, the next phase of transformation is being driven by AI. CGI's 2026 Voice of Our Clients research shows that AI cannot scale on fragmented legacy environments. Enterprise AI requires modern systems, interoperable data and redesigned workflows as its foundation.

While 73% of health and life sciences leaders identify technology and digital acceleration as a high-impact priority, many continue to face barriers that prevent transformation investments from delivering results at scale.

Across the industry, leaders are balancing rising demand, cost pressures, regulatory complexity, workforce shortages and aging technology. This is why digital reengineering has become a strategic priority. Rather than pursuing large-scale replacement programs, organizations are increasingly focused on selectively modernizing the capabilities that unlock agility, resilience and long-term value.

Closing the execution gap

The health and life sciences sector has made significant progress in defining its digital ambitions, with 63% of industry leaders reporting that digital transformation is having a high impact on their business model. Yet only 20% describe their organizations as highly agile. In addition, only 42% report strong alignment between business and IT operations to support digital strategy, compared with a global average of 51%. These findings highlight a persistent gap between transformation ambition and execution.

Many organizations have established modernization objectives, cloud strategies and plans for AI-enabled innovation. AI maturity remains uneven because legacy environments, disconnected data and outdated operating models limit enterprise-scale deployment. Execution, not ambition, is now the challenge.

Three interconnected barriers continue to stand in the way.

1. Legacy technology is limiting agility

Legacy technology is becoming harder to adapt to the changing needs of administration and patients.

Legacy systems can limit interoperability, slow innovation and consume resources that could otherwise be invested in transformation. Replacing entire technology estates is rarely practical or necessary. Leading organizations are taking a more targeted approach, modernizing the systems and processes that constrain performance first.

VOC findings identify legacy systems as the primary barrier to executing digital strategy, with nearly half (49%) of health and life sciences leaders citing them as a significant challenge. Selective modernization improves interoperability, streamlines data movement, and enables automation, creating a secure foundation for scaling AI.

2. Talent challenges are becoming technology challenges

Technology transformation is increasingly tied to workforce transformation.

Many organizations depend on legacy skills that are becoming harder to replace as experienced talent retires. Leading organizations are partnering strategically so internal teams can focus on modernization and innovation.

3. Operating models must evolve

Technology investments alone cannot deliver transformation outcomes.

Scaling AI requires an enterprise operating model with governance, measurable outcomes and human oversight, not isolated pilots. Traditional delivery models are too slow for today's pace of change and require closer alignment between business and IT.

Digital reengineering aligns technology, talent and operating models around measurable outcomes. This foundation enables AI across high-value journeys such as patient access, claims and R&D.

For example, CGI worked with Hywel Dda University Health Board to improve operational efficiency, clinical outcomes and financial performance through streamlined processes, optimized technology investments and a roadmap for long-term transformation.

CGI also supported the NHS's largest cloud migration to date, moving the e-Referral Service (e-RS) to a scalable cloud environment capable of supporting more than 70,000 daily referrals while strengthening security, reducing costs and improving access to services.

These examples demonstrate how targeted modernization can help translate transformation ambitions into measurable outcomes.

Designing the foundations for trusted transformation

As organizations modernize, they must strengthen the foundational capabilities needed to scale transformation securely and sustainably while maintaining the agility to adapt to AI's rapid evolution.

Cybersecurity, privacy, compliance and data sovereignty must be designed into platforms from the outset. Trust by design has become a business imperative, particularly in an industry responsible for some of the world's most sensitive data. Our VOC research shows that 75% of health and life sciences leaders are pursuing security and cyber risk mitigation initiatives, compared with a cross-industry average of 65%.

Cloud strategy now emphasizes resilience, cybersecurity, regulatory confidence and data sovereignty as much as modernization. Underpinning these priorities is trusted, accessible data.

Organizations cannot govern AI, protect privacy, or respond to cyber threats without clear visibility into where data resides, how it is used, and who has access to it. Combined with secure architectures and robust AI governance, this foundation enables organizations to scale AI while meeting privacy, security and regulatory requirements.

The real imperative: Building capacity to execute

Health and life sciences organizations need greater capacity to execute the initiatives they have already prioritized.

Digital reengineering provides a practical path forward by focusing on selective modernization, trusted foundations and the capabilities required to deliver sustained change. As AI adoption accelerates, this foundation becomes even more important.

Success will come not from continued experimentation, but from disciplined scaling, measurable value creation and prioritizing use cases that address real business and workforce needs. Organizations that align governance, measurement, workflow redesign and data readiness will be best positioned to scale AI and broader digital transformation efforts.

The organizations that succeed will not necessarily be those investing the most in new technologies. They will recognize that AI accelerates, not replaces, digital modernization, enabling them to scale AI responsibly and turn digital ambition into measurable outcomes.

In Part 2 of this series, we will explore how Health and Life Sciences organizations can move beyond AI pilots and experimentation to create sustainable, enterprise-wide impact.