How can biopharma overcome digital transformation failure?

The biopharma industry doesn’t have a technology problem, it has an implementation problem

Over the last few years, digital transformation has become one of the most widely discussed topics in biopharma manufacturing. Automation, advanced analytics, digital twins, the use of artificial intelligence (AI), and connected manufacturing processes have all featured prominently in transformation roadmaps designed to improve operational performance and accelerate the delivery of therapeutics to patients.

On the surface, industry is aligned on the direction of travel. Most organizations understand the potential value of digital transformation and can articulate a clear vision for how improved connectivity, automation, and data utilization could enhance process understanding, improve decision-making, and strengthen manufacturing performance.

Yet despite this ambition, progress is falling short of expectations—some might say, biopharma is experiencing a digital transformation failure. Ashley Howard, Senior Product Director for Automation and Digital at Cytiva, observes:

“The value of digital transformation is understood. The challenge is bridging the gap between where we are today and what it takes to realize that value. That requires more than technology. It requires changes to organization structures, incentives, day-to-day ways of working, and decision-making across all those functions involved in manufacturing. For many organizations, the gap is so wide that knowing where to start can feel overwhelming.”

In other words, biopharma doesn’t have a digital strategy problem. It has an implementation problem. The industry understands where it must go. What it continues to struggle with is how to best get there and how they scale across the organization while minimizing risk.

Digital ambition is no longer the barrier

Few organizations today would argue against the need for digital transformation. Better data can improve process understanding, automation can reduce variability and increase consistency and connected manufacturing systems can accelerate decision-making. At the same time, manufacturing environments are becoming increasingly complex. New therapeutic modalities, accelerated development timelines, growing expectations around productivity, increasing labor costs, and pressure on facility utilization are changing the economics of biopharma manufacturing.

Other industries have faced similar inflection points. As semiconductor manufacturing became increasingly complex and capital intensive, organizations could no longer rely on simply building additional capacity to remain competitive. Instead, they were forced to improve yield, process capability, automation, predictive maintenance, and digital integration. Digital maturity became a business necessity rather than a technology initiative.

For many years, biopharma has been able to absorb inefficiencies through strong margins and capacity expansion. However, the industry is increasingly approaching the same inflection point as the semiconductor industry. As organizations look to scale operations more efficiently and effectively, digital transformation is becoming an operational requirement rather than a strategic aspiration. Recognizing the value of transformation and successfully delivering it are two very different things, however. Even though biopharma has never had more scientific potential, digital execution is the bottleneck at the heart of modern bioprocessing.

Why digital transformation fails in bioprocessing: it’s not lack of commitment, it’s implementation

It is easy to assume that digital transformation programs fail because biopharma is resistant to change. In reality, the issue is far more nuanced. Implementing change in a regulated manufacturing environment introduces practical challenges that can be difficult to overcome. There are many reasons digital transformation fails in bioprocessing. Digital fragmentation in biomanufacturing often pits departments against each other, and overcoming silos is key to speeding transition. Legacy infrastructure must be considered. There are validation and compliance barriers to digital technologies in GMP manufacturing, and above all, validation requirements need to be maintained. Resources are constrained, which is particularly challenging when new systems must integrate with existing workflows and technologies, while IT and cybersecurity requirements often introduce additional complexity and approval steps. Because companies often face a skills gap in digital bioprocessing, this makes matters worse.

In a risk-averse industry, it’s easy to see how potential disruption can quickly outweigh the perceived reward. What might have appeared straightforward in principle can very quickly become significantly more complicated in practice. As Ashley observed, “The cost and risk of change often outweigh the perceived benefit.”

This is where transformative initiatives begin to slow. The challenge is not understanding what needs to be done, but determining whether the value justifies the effort, disruption, and organizational commitment required to deliver it.

Digital fragmentation in biomanufacturing: the problem with starting at the destination

One of the most common causes of failure is the belief that transformation needs to be comprehensive from the outset. Many organizations begin with ambitious future-state visions that attempt to connect every system, integrate every workflow, and solve multiple challenges simultaneously. The intention is understandable. Teams want to create a solution that is scalable, robust, and capable of meeting future needs.

In practice, however, organizations can become trapped in extended planning cycles, spending months aligning stakeholders and refining requirements before implementation has even started. Ashley highlighted a challenge that many organizations will recognize: “We often start with a big vision and spend so much time gathering requirements from multiple stakeholders that, by the time we’re ready to act, the problem or priority has already changed. We end up back where we started.”

The irony is that successful transformation rarely begins with a perfect strategy or a fully defined set of requirements. More often, it starts by solving a specific operational problem, proving value, and building momentum from there. The organizations making the greatest progress focus on practical implementation rather than perfection and recognize that digital transformation is a journey of incremental improvements, not a single large-scale event.

AI is exposing weak foundations for digital transformation

Few technologies have generated more excitement than artificial intelligence (AI). Across the industry, organizations are exploring how AI might accelerate process development, improve manufacturing performance, strengthen decision-making, and unlock competitive advantage. However, the growing interest in AI has also exposed existing challenges that were often tolerated or overlooked. Advanced analytics, machine learning, and AI are dependent upon accessible, trusted, and contextualized data. Without strong foundations, these technologies struggle to deliver meaningful value.

As Ashley put it: “You can't just throw AI at it, you have to build the foundations first.”

Fragmented systems, disconnected data, and inconsistent levels of digital maturity have always limited the industry’s ability to fully utilize data. The difference today is that AI is making those limitations impossible to ignore.

For many organizations, the starting point is not AI itself, it’s integrating disparate systems and data for AI to use more effectively. Most bioprocesses produce too much data relative to too little insight. That’s because data is collected across multiple systems and process stages but isn’t accessible, contextualized, or connected in a way that supports real-time data-driven insights and decision-making. Establishing trusted data foundations, improving contextualization, and ensuring data can move effectively across systems will go a long way toward overcoming silos in pharma digital transformation.

When successful pilot projects stop moving forward

One of the most common frustrations in biopharma is that successful pilot projects often struggle to scale. A proof-of-concept may demonstrate clear value within a specific process, department, or site, but extending that success across an organization is significantly more difficult.

The challenge is that pilots are designed to prove technology, while scaling requires proving an operating model. What works in a controlled environment must integrate with existing infrastructure, governance processes, and ways of working. At that point, the challenge extends well beyond technology. As a result, many organizations discover that proving value and scaling value are two very different challenges. The technology may have proven itself, but the organization is often not ready to scale it.

The organizations that scale most effectively often treat pilots as the first step of a broader operating model change. Rather than proving a technology in isolation, they focus on creating repeatable processes, securing stakeholder alignment, and demonstrating how value can be replicated across multiple sites and functions.

Regulators acknowledge and address validation and compliance barriers

Biomanufacturers often rely on legacy systems with siloed databases. Connecting modern digital tools to aging infrastructure is rarely straightforward, and integrating these tools can mean uncertain performance and the added burden of risking validation and compliance failures. Changes can require extensive documentation and testing, making it easy to understand why an organization might hesitate to adopt new digital technologies.

Today, that environment is evolving. Regulators are acknowledging these challenges and encouraging a more flexible, risk-based approach. For example, the US Food and Drug Administration (FDA) recently finalized Computer Software Assurance (CSA) guidance that recommends focusing validation activities on higher-risk software functions and reducing documentation for lower-risk changes (1). This shift shows a growing regulatory openness to removing unnecessary barriers to innovation—provided product quality remains robust.

Minding the skills gap in digital bioprocessing

Biopharma often sees the potential in advanced process control, data analytics, and automation, yet struggles to find people with the right background in data science, software, or statistics. Traditional biomanufacturing roles weren’t always designed with these capabilities in mind. In fact, according to the Cytiva Biopharma Resilience Index, more than one-third of executives surveyed reported challenges in finding skilled staff to support digital and AI efforts (29%) (2). By investing in people—through upskilling or recruiting digital talent—as much as digital technology, organizations can start to bridge this gap.

From ambition to execution: overcoming digital transformation failures

The biopharma industry does not lack digital ambition or technology. What it continues to struggle with is implementation—or taking that first step. Until the industry becomes more confident on how to close that gap, digital transformation will continue to remain aspiration for a little longer as organizations rise to meet the challenges.