Pharma R&D Pipeline Productivity: Three Ways To Enhance Bandwidth And Efficiency

By Naveed Panjwani, pharma R&D consulting lead, PA Consulting

The global pharmaceutical industry faces persistent challenges to the productivity of its R&D pipelines. Recent years have seen a stabilization of a decade-long decline, with R&D leadership teams investing in transformation and digitalization of R&D processes. But while return on clinical-stage assets has improved, the industry recognizes that the productivity bar is ever-rising.

R&D productivity is a function of pipeline volume, pipeline value, and the efficiency with which that pipeline is delivered. The bandwidth of major pharma late-stage pipelines has remained stagnant or even reduced relative to pre-pandemic years, as companies have focused expertise on fewer therapeutic areas and fewer, higher-value, first-in-class new molecular entities or selected indication expansion programs.¹ The value potential of pipeline assets has long been under pressure, as healthcare payers continue to squeeze pricing. This has been true for single-payer systems in Europe for two decades but is now increasingly a factor in the U.S.^2,3 — a market that has historically contributed just under half of industry revenue and the majority of industry margin — as “Most Favored Nation” drug pricing policies and direct-to-consumer markets take shape. Value is further threatened as major markets move to reduce IP exclusivity timelines, for example, via the U.S. Inflation Reduction Act and similar legislation being contemplated by the EU.⁴

While the industry battles to improve probability of success in complex areas of disease biology, achieving safety and efficacy endpoints remains subject to high uncertainty at pivotal moments of investment. The efficiency of pipeline delivery, however, is more directly within a company’s control, and the industry has invested heavily in that domain.

The long-standing challenges of lengthy development cycle times and high costs have not been easy to address. Risk-based approaches to data gathering, systemic issue detection analytics, decentralization of trials, and data-driven site selection have all had a positive impact. Long clinical trial and inter-phase cycle times are beginning to benefit from at-risk front loading to reduce white space between clinical phases, improved protocol design, predictive patient matching, and automated pre-screening.

The efficiency bar, however, keeps rising. Economic shocks from trade and geopolitical friction are fueling the already substantial cost of talent, raw materials, and services on which drug development relies. Development cycle times continue to creep up as data volumes explode, protocols incorporate ever more endpoints, and narrower inclusion/exclusion criteria constrict an already small trial population.

As asset value and probability of success stagnate, the industry must address the sheer quantity of people and financial resources needed to plan, collect, and analyze data across a clinical program. It must also accelerate adoption of AI-fueled efficiency use cases and grasp the opportunity created by growing confidence in generative and agentic AI.

PA Consulting sees three major themes for continued progress in improving the efficiency of pipeline delivery:

1. Optimizing Resource Utilization

As R&D budgets and specialist talent remain under pressure, leading companies are treating resource utilization as a core productivity lever, redesigning how expertise is deployed, governed, and supplemented across the portfolio. We see companies deploying the following core tactics to get more value from scarce R&D resources:

• Refining resource deployment models: Companies have made varying choices in how they structure drug development resources. While some organize functional resources into therapeutic area- and/or development stage-based operating models, others integrate end-to-end R&D by asset, platform, or biological mechanism of action. Each choice carries trade-offs that require careful design and ongoing curation to deliver efficiently.
• De-bossing and de-layering: Reducing R&D management layers decentralizes trial decision-making by moving accountability toward operational doers, dramatically altering traditional management spans of control. This frees up individual and collective time, cuts bureaucratic planning, and minimizes unproductive consensus-driven decision-making. However, implementation requires careful anticipation of unintended consequences — gaps in skills and role responsibilities, and a managed shift toward greater self-reliance.
• Strategic and selective outsourcing: Rather than an all-in or all-out model, companies are selectively outsourcing specific study tasks, types, geographies, or entire therapeutic areas, while retaining critical competencies in-house. As pipelines evolve, an outsourcing strategy must be frequently revisited to test the original value thesis — and companies must not shy away from changing tack when it is no longer sound.

2. Driving Technology-Enabled Operational Innovation

As clinical development grows more data-intensive and operationally complex, technology-enabled innovation is becoming essential to improving speed, quality, and efficiency. Companies are leveraging the following tactics to translate digital investment into measurable productivity gains:

• Digitalization and workflow automation: Structured digital workflows are most effective when they span trial tasks end-to-end, reducing repetitive manual effort and cutting the FTEs needed in trial start-up, conduct, and reporting. Digital tools can automate document creation and propagation, or real-time data cleaning. Sound, cross-functional implementation choices are crucial, however, to avoid becoming lost in a forest of isolated point solutions that lack productivity-critical interoperability.
• Data integration and analytics: Consolidating tools and establishing a single source of truth from the start of a clinical program improves downstream document creation, data quality, and amendment speed. Automation of data flow enables faster electronic data capture (EDC) and system setup, requires fewer resources, and lowers the risk of error.
• Technology-enhanced recruitment: Use of electronic health records (EHRs), AI-driven outreach, and digital communication platforms enables more precise and efficient patient recruitment and retention, broadening population access and reducing dependence on traditional site-based methods.

3. Executing With Operational Discipline

As clinical programs grow more complex, execution discipline is key to driving measurable productivity gains. Companies are focusing on the following tactics to reduce avoidable complexity, compress cycle times, and embed new ways of working:

• Reduction in study complexity: The temptation to cram protocols with non-critical and exploratory endpoints has long fed cycle time inflation. Systematic, data-driven removal of unnecessary procedures and data points is beginning to counter this, reducing costs, easing patient and site burden, and accelerating endpoints. Advanced analytics, AI tools, and industry benchmarking now offer frameworks to continually optimize study design — but impact depends on clinical development teams making a mindset shift that balances scientific curiosity with genuine operational discipline.
• Critical path management: Trials and entire clinical programs are strewn with sequential activities and unproductive waiting periods that consume months of avoidable cycle time. A play-by-play rethinking of processes has revealed opportunities to front-load activities into waiting periods and convert selected sequential steps into parallel ones. Disciplined curation of the critical path can liberate months — even years — of avoidable cycle time but requires teams to step outside their process comfort zone.
• Process curation: Dropping a new process or technology into clinical trial work is insufficient on its own and often leads to poor adoption or workarounds. The move from writing traditional long-form documents to creating biomedical concept data that is automatically propagated contextually into various outputs, for example, demands a considerable shift for clinical science, safety science, and medical writing roles. New workflows — whether technology-enabled or not — require deliberate consideration of how roles and process norms must adapt alongside thoughtful people engagement to ensure success.

Turning Priorities Into Measurable Progress

Improving pharma R&D pipeline productivity will require disciplined progress across all three connected priorities: deploying scarce resources more intentionally, translating digital and AI-enabled innovation into practical operational gains, and embedding the execution discipline needed to reduce avoidable complexity and cycle time. For R&D leaders, the next step is to assess where their organizations are losing the most bandwidth today — in structure, systems, or ways of working — and prioritize measurable, scalable, and sustainable interventions.

References:

1. Citeline Pharma R&D Annual Review, April 2026
2. Goldman D & Lakdawalla D. White paper: The Global Burden of Medical Innovation, USC Schaeffer Institute for Public Policy & Government Service 
3. A value-based approach to pricing, an EFPIA position paper, April 2023
4. Deal on comprehensive reform of EU pharmaceutical legislation, European Parliament Press Release, December 2025

About The Author:

Naveed Panjwani is a partner in PA Consulting’s Health and Life Sciences practice, specializing in the transformation of clinical trials and R&D productivity. He brings over 25 years of experience spanning  life sciences industry and consulting roles, with deep expertise spanning the full pharmaceutical R&D lifecycle - from drug discovery through to late-stage clinical development.