R&D productivity has been stagnant for the past decade, with our R&D ROI analysis revealing flat industry-level productivity since 2012. 1 Looking ahead, the landscape poses significant challenges, such as heightened competition for fewer targets and shifting geopolitical and regulatory environments.
As they work to combat these converging challenges, pharmaceutical companies could consider the role of the R&D operating model, encompassing governance, processes, and organizational structure. In our experience, five elements of an R&D operating model can greatly improve efficiency and effectiveness in pharmaceutical development (Exhibit 1).2 While some of these elements may be considered fundamental, others represent emerging and differentiating models that set new standards in the industry.
Dramatically streamlined governance
At many pharmaceutical companies, committees often include members whose expertise or oversight may not directly relate to the committee’s focus. This reality, combined with overlapping decision rights, can slow down the decision-making process—including in terms of portfolio reviews, investment decisions, protocol reviews, and science and technical reviews.
Pharmaceutical companies can design the shortest possible path to critical decisions by consolidating decision-making bodies and integrating advisory discussions directly into asset teams. Under this structure, the top committee makes decisions on portfolio priorities, milestone decisions, and funding levels, while scientific and technical reviews remain with the asset team or as informal or offline reviews.
During the height of the COVID-19 pandemic, Pfizer leaders adjusted processes so stop decisions weren’t subjected to multiple layers of approval. Instead, decision makers gathered twice a week to make all critical decisions, contributing to the rapid development of vaccines.3 Companies could apply this approach to high-priority programs that deserve focus, such as transformative therapies addressing significant unmet medical needs.
Dynamic, at-risk, and parallel resource deployment
Rigid governance structures hinder the ability to pivot strategies and reallocate resources effectively based on factors such as clinical trial data and market and competitive intelligence. Contrarily, pharmaceutical companies could consider adopting a dynamic approach to allocating resources that identifies promising assets and intensifies focus and resources to maximize blockbuster potential.
For example, COVID-19 vaccine developers accelerated timelines by conducting process steps in parallel, preparing clinical-grade material before completing preclinical tests, and making significant investments in manufacturing capacity based on limited information.4 Similarly, biotechnology company Flagship Pioneering employs rigorous criteria to enable rapid resource reallocation. The company creates small teams working on one dedicated project with a budget of $1 million to $2 million. If proof of concept isn't achieved within six to 12 months, the team is disbanded. Successful teams advance to the next phase with larger budgets and expanded teams. This approach could inform how pharmaceutical companies allocate resources.
Ownership of activities that generate disproportionate value
Many companies lack a clear top-down thesis on how and where they can uniquely create value. Consequently, decisions related to trial delivery are often made on a program-by-program or study-by-study basis, leading to inconsistencies in delivery approach, organizational and governance complexities, and higher overall costs.
Instead, companies could determine where they have distinctive capabilities (such as an AI-driven discovery platform with a proprietary data library) and align the operating model accordingly. For example, they could decide which capabilities to own versus outsource and could approach vendor management strategically to ensure a consistent delivery model. The goal would be to own the activities that generate disproportional value and form partnerships for other activities to maintain flexibility and adaptability in response to portfolio changes.
One leading company faced organizational challenges, including complexity, functional silos, shadow capacity, role duplications, and unclear accountability. To address these issues, it decided to enhance internal delivery for strategic activities (such as protocol design) and customer-facing tasks (such as interactions with investigators) while implementing various operational streamlining measures. These measures included establishing a dedicated organization with end-to-end asset delivery accountability, centralizing capabilities that were previously distributed across development functions, and realigning the organizational structure and size to support these changes.
Rewiring a domain to operate as an AI-native company
Despite advancements and investments in AI and digital analytics, biopharma companies often struggle to achieve scalable business impact, frequently due to niche use case applications or isolated lighthouse projects that fail to deliver significant results. To combat this problem, companies could choose a specific domain—for example, a therapeutic area or technology platform—to run like an AI-native company, with integration back into the broader R&D engine. That is, they could seamlessly integrate data-driven and AI-based inputs by designing an operating model based on five key elements: understanding patients, identifying targets, discovering and optimizing leads, optimizing clinical trials, and maximizing asset impact (Exhibit 2).
To put this concept into practice, they may organize business units, as well as governance committees, around each of these decisions. They may also have a central AI-enabled engine that integrates multiple data sources relevant for all decision points, creating a continuously reinforcing feedback loop.
Optimization of global geographic footprint
Companies have to make strategic trade-offs between maintaining a widespread network and managing operational complexity and costs. For example, overinvestment in maximizing global footprints can provide access to specialized capabilities but could lead to inefficiencies and limit potential cross-pollination of ideas. Conversely, an overly centralized approach may fail to make the most of innovation across diverse ecosystems.
A potential path forward would be to build a few integrated R&D hubs that house various strategic functions in locations that provide access to talent and innovation while hosting more-operational activities—such as pharmacovigilance, data management, and programming—in lower-cost geographies. Offshoring presents opportunities beyond optimizing costs: companies can consider factors such as how to accelerate critical-path activities by having dispersed teams use time zones to their advantage and grow the global workforce to match the shift toward new, global markets.
Some companies are starting to explore new locations as traditional central hubs (such as Boston) and offshore locations (such as India) become crowded. For example, some companies have started to offshore functions such as regulatory and clinical development to countries such as Poland.
Given the complex, cross-functional nature of the operating model, tackling all elements at once can introduce significant operational risks. We have observed that organizations achieve better outcomes by starting with one or two key areas, with conviction and commitment from senior R&D leaders. These initial areas will depend on the organization’s starting point, pipeline context, and aspirations. But one place to start is establishing a robust baseline of the company’s current spending and performance and pair this with a qualitative understanding of the operating model’s challenges—such as decision-making bottlenecks and accountability gaps.
By taking this focused and phased approach, companies can not only address immediate issues but also build the organizational muscle required for sustainable transformation. The lessons learned along the way will become invaluable assets, enabling the company to continuously evolve its operating model and ultimately enhance R&D productivity.
