Introduction
“Growth”… a revered tenet for mainstream economists and a feared term for cancer patients. Be it applied to economy, business or sociological, physical, or biological systems, growth is inherently linked to complexity, rendering growing systems increasingly challenging to comprehend and/or deliver. Growth and complexity are intrinsically linked, yet they are not boundless: the constraints of economic expansion are delineated by the finite resources of our planet (Earth beyond six of nine planetary boundaries) and the limits of complexity are set by our cognitive abilities to grasp it.
Life sciences, biotech and pharmaceutical organisations are no exception to these foundational principles. This is especially true for large pharmaceutical companies that have reached a considerable complexity, leading to various inefficiencies that hinder their ability to generate more value. This issue is particularly apparent in their attempts at digital transformation, especially in adopting data-centric ways of working, which hinders their ability to fully leverage the vast data accumulated over the past 30 years.
This article outlines the key challenges and underlying complexities faced by life sciences, biotech, and pharmaceutical companies. With a particular emphasis on the digital dimension of these complexities, the final section urges managers to equip their organizations with the resources needed to achieve digital transformation goals. Companies that can navigate and master digital complexities will secure a competitive advantage. The race is underway, and it is poised to introduce disruptive new contenders to the field…
Inventory of Complexities
Complex biology: The treatment of easily targetable diseases has significantly progressed, leveraging insights into the bio-mechanistic functions of approximately 1,600 proteins (Wishart, D. S., et al. 2018). However, many unmet medical needs persist due to the intricate, multifactorial nature of their underlying mechanisms, often involving multiple proteins and complex biological pathways. These complexities render traditional R&D approaches less effective, necessitating the adoption of more advanced methodologies. Additionally, the limited understanding of disease heterogeneity, interplay between genetic and environmental factors, and dynamic molecular interactions further complicates drug discovery.
Reliable scientific findings: As the pharmaceutical industry always relied on public research findings, it is nowadays increasingly challenged by a decline in the reliability and quality of foundational research outcomes, exacerbated by the recent “replication crisis“. This issue is partly fuelled by shortcomings in the peer-review system, which prioritises mainstream topics and impactful results, driven by publishing practices that emphasise impact factors while often overlooking negative or inconclusive findings and transparency in data sharing. As a result, companies must invest additional resources to validate and complement research findings, prolonging timelines and inflating R&D costs.
Twisted patent logic: The patent system, originally designed to safeguard R&D investments and encourage innovation, has increasingly been leveraged as a tool to stifle competition rather than advance scientific progress. Many companies file patents not with the intent to develop the protected molecules or biochemical processes but to strategically block competitors from pursuing those avenues. This practice (“patent thickets” or “evergreening”) slows the pace of innovation, contributes to inflated drug costs and often diverts resources from potentially fruitful collaborations.
Regulatory hurdles: Drug safety incidents have historically acted as catalysts for regulatory changes, prompting authorities to implement more stringent monitoring systems. Driven by an increasingly complex biology to address (as described above), evaluating the safety and efficacy of modern therapies requires an increased amount of tests yielding numerous and complex digital artefacts to review. In addition, as new markets emerge, the variability in regulatory standards increases across different countries and drug providers must satisfy growing and more diverse regulatory requirements.
Heterogeneous pricing and reimbursement strategies pose a significant challenge for the pharmaceutical industry. Outcome-based pricing models tailored to a country’s income levels present opportunities for broader access but also create operational complexities in implementation and evaluation. The emergence of personalized medicines, such as CAR T-cell therapies, highlights sustainability concerns, as these high-cost treatments are designed for small patient populations, straining healthcare budgets. Additionally, escalating drug prices have, in some cases, turned essential medicines into “luxury products,” increasing risks of theft, counterfeit production, and cross-border trafficking. The growing use of generics and biosimilars further complicates this landscape, as these alternatives introduce competitive pricing pressures while raising questions about market access and the sustainability of original therapies. These challenges are exacerbated by stark differences in pricing and reimbursement policies across countries, complicating global market strategies.
Vulnerabilities in supply chains: Globalisation has exposed supply chains to heightened risks, including geopolitical tensions and pandemics, which can cause significant disruptions. Initially considered a cost-saving strategy, relocating key operations such as safety monitoring and production to low-income, low-regulation countries has shown its limitations. Rising wages in these regions, coupled with the growing complexity of supply chains involving an increasing number of intermediate players and channels, threaten the sustainability of this model. Furthermore, the heavy reliance on active pharmaceutical ingredients (APIs) from specific countries, has highlighted the fragility of the system. Also, the emergence of new therapeutic modalities like cell and gene therapies has added additional pressure, as these require specialised manufacturing facilities not always available in existing production plants, making it harder to scale and meet demand.
Mindset, talents and business practices: With the rise of digital natives in generation Z and Alpha, employees increasingly seek jobs that align with their values and offer flexibility (e.g. working from home). This shift often leads to misalignment with legacy line management and other non-agile practices. Furthermore, there is heightened competition for skilled talent, particularly within the finance, tech and life science sectors offering attractive opportunities for employees who combined both a strong business acumen and digital skills. In addition, there is mounting pressure to improve diversity and inclusion, not only in the workforce but also in clinical trials, to ensure equitable access and representation in the development of new therapies.
Challenges or Opportunities?
While the above-mentioned complexities apply to specific business domains (fundamental research, clinical development, drug manufacturing, marketing and sales…), they all result in an overarching major challenge: digital complexity. Since the advent of personal computing in the 1990s, experts in various business domains have increasingly relied on specialized IT systems to manage the growing complexities of their operations in a siloed manner. While these systems are designed to address specific tasks within their respective domains, they often fail to provide cross-domain solutions, and in many cases, they even complicate the integration process further. For instance, pharmaceutical business being by essence a multi-domain business, this approach has resulted in the use of thousands of different IT systems, each with its own vendor-specific internal logic. Consequently, millions of datasets are managed in diverse and inconsistent ways, leading to a fragmented, disorganised data landscape of varying quality. Business data is often locked within proprietary structures and logic dictated by the technical constraints of proprietary systems. This fragmentation has created a situation where no single individual is likely to have a comprehensive, end-to-end understanding of the data landscape, hence the business it is underpinning.
Companies, regardless of their size, generate internal data and utilize external data. The level of digital complexity increases with the size of the organization. However, with growing complexity comes inefficiency: how much can a system or organization complexify before it loses its ability to perform the tasks it was originally designed for? Historical industry leaders, which thrived in an era characterized by one-durg/one-target biology, simpler regulations, uncomplicated global trade, prosperous economies, relatively low geopolitical tensions and analogical (i.e. non-digital, paper-based) workflows, are now confronting unprecedented challenges. These challenges have culminated in overwhelming digital complexity.
Once this situation is acknowledged, what should be the next step? There are two options: do nothing, or develop a plan. With the rise of AI (Artificial Intelligence), companies that have fallen behind in digital transformation now have a unique opportunity to ask themselves difficult questions, make strategic decisions, and get equiped with the resources necessary to first harness and then leverage the complexity of their digital ecosystem. In 2017, L. DalleMule and T.H. Davenport concluded their HBR article with this statement: “Companies that have not yet built a data strategy and a strong data-management function need to catch up very fast or start planning for their exit.” Given that the added-value of AI mostly relies on high-quality data, this statement is more relevant than ever. The time to exit will depend on the combination of companies’ financial inertia and their reluctance to commit to and undertake a long-term, data-centric digital transformation.
