AI is driving a convergence of emerging tech and legacy systems. There is now an urgent need to understand how this will impact pharma R&D

In this series, we explore the implications for operating models, drug development, and efficiency. We begin with the convergence of TechBio and the pharma R&D operating model. 

The pharmaceutical industry is at a pivotal juncture, with artificial intelligence (AI) catalyzing a significant evolution in research and development. This has given rise to a new type of organization, often called “TechBio,” which approaches drug discovery with a data-first, computational mindset. This stands in contrast to the established R&D model of large pharmaceutical companies. 

At Capgemini, we see AI fundamentally reshaping how life sciences R&D is designed, operated, and scaled. In the next part of this Pharma Convergence series, “Rewiring Early Drug Development with AI and Digitization,” we examine the impact on the six pivotal phases of early drug R&D. But first, let’s explore the TechBio models now gaining momentum, challenging pharma R&D leaders to combine next-gen tech and tradition: the speed, data‑centricity, and experimentation of AI‑driven drug discovery with the rigor, governance, and execution strength of traditional pharmaceutical development.

To make a sustainable impact with artificial intelligence in drug discovery, pharma R&D teams must understand the operational differences and what it takes to bridge these two models. These distinctions are not merely technological; they span the core organizational pillars: people, process, and technology. We have identified three key differences life sciences leaders will need to navigate in the immediate future.

How traditional pharma and TechBio differ across people, process, and technology

The traditional pharma model is one of reliance on specialized silos, sequential workflows, and fragmented data systems. The emergence of TechBio is undoing that syncopation. Integrated, data-driven teams run rapid learning cycles on unified platforms. The impact on people, processes, and technology in organizations is significant.

People and culture: Specialized silos vs. integrated teams 

The way pharma R&D teams are structured and the culture they operate in represent a primary point of divergence. 

Traditional pharma 

Large pharmaceutical companies are typically organized into deep functional departments, such as chemistry, biology, and toxicology.This structure cultivates world-class expertise within each specific domain. However, the pharma R&D process often involves sequential handoffs between these siloed groups, which can slow down project timelines.[1] The organizational culture is frequently process-oriented and risk-averse, which is a rational response to a highly regulated environment where late-stage clinical failures are exceptionally costly.[2]

TechBio

These companies often adopt organizational structures from the technology sector, forming small, integrated, cross-functional teams. A single project team might consist of biologists, chemists, data scientists, and software engineers working in a tightly coupled, collaborative manner. This model is designed to break down barriers between experimental science and computational analysis, facilitating rapid, iterative problem-solving. The culture often emphasizes agility and data-driven learning, viewing the early termination of unpromising projects not as a failure, but as an efficient allocation of resources. 

Process and workflow: Hypothesis-led vs. data- and AI-driven drug discovery 

The differences in team structure enable fundamentally different R&D processes. 

Traditional pharma 

The conventional R&D process is a hypothesis-driven, sequential model often described as a “V-shaped” funnel. It typically begins with a deep, resource-intensive investigation into a single biological target. This linear process moves methodically through preclinical research and multiple phases of human clinical trials – a journey[3] that can take 10 to 12 years with a success rate of less than 10% for assets entering clinical development.

TechBio

The TechBio model is often described as “T-shaped” exploration. It inverts the traditional funnel by starting with a broad but computationally inexpensive screening of numerous biological hypotheses in parallel. Machine learning algorithms analyze the resulting data to identify the most promising therapeutic signals. Only then are significant resources invested in a deep, focused investigation of these validated targets. This process is built around rapid Design-Make-Test-Learn (DMTL) cycles, where AI-designed molecules are synthesized and tested, with the results being fed back into the system to improve the next generation[4] of models.

Technology and data: Operational tool vs. core asset 

The foundation for these new processes is a philosophical shift in the role of technology and data. 

Traditional pharma 

Historically, data has been viewed as a byproduct of experiments — essential for documentation and regulatory submissions, but often stored in disparate, siloed systems. This can make it challenging to aggregate and analyze the data at the scale required for modern AI applications.

TechBio

In the TechBio model, data is the central and most valuable R&D asset.[5] These companies are built around the intentional generation of massive, proprietary, high-quality datasets that are structured for machine learning from the moment of creation. This strategy fuels a “virtuous cycle,[6]” with more high-quality data leading to more predictive AI models, which in turn guide the generation of more valuable experimental data, creating a compounding competitive advantage. This is supported by a native technology infrastructure built on scalable cloud computing and, in some cases, dedicated supercomputers.

Measurable impact of AI in drug discovery and the path forward 

These operational differences are producing tangible results, particularly in the early stages of drug discovery. 

  • Timelines: Multiple TechBio companies (e.g., Insilico Medicine) have published case studies showing a significant reduction in preclinical timelines, compressing the industry average of four to six years to as little as 12 to 18 months.[7]
  • Success rates: As the case of Insilico Medicine indicates, In the early stages of drug discovery and development, AI-driven approaches can find the drug candidates with generation and synthesis of only 80–200 molecules, whereas traditional methods typically involve screening hundreds to thousands of molecules. However, the central question remains whether this early promise will translate into improved success rates in the more complex and costly Phase Two and Three trials, where the majority of drugs fail.
  • Innovation: By analyzing vast datasets without human bias, AI platforms[8] can identify novel biological targets, moving research beyond well-established areas and potentially unlocking new therapeutic avenues.

The following table provides a quantitative summary of the reported impact of TechBio on key R&D metrics compared to traditional industry averages. 

The rise of TechBio and R&D’s evolving operating models data table image updated v2

What’s next for pharma R&D 

The emergence of TechBio does not signal the end of the traditional pharmaceutical model. On the contrary, big pharma’s deep expertise in clinical development, regulatory affairs, and global commercialization remains indispensable.  

The future of drug development is likely to be an integrated ecosystem, where the strengths of both models are combined, often through strategic partnerships.[9] The most successful organizations will be those that effectively fuse the innovative, data-driven discovery engines of TechBio with the robust, late-stage development and commercialization power of established pharmaceutical companies. 

Capgemini supports life sciences organizations in bringing these elements together. We help our clients connect discovery and development, enable productive ecosystems, and realize sustainable impact across the pharma R&D lifecycle.

References:

1. Drug discovery; 2. MDPI; 3. APR; 4. Recursion; 5. ACP Pubs; 6. Recursion; 7. Fierce Biotech; 8. Recursion; 9. Labiotech; 10. Drug Target Review; 11. Drug Discovery Online; 12. Citeline; 13. BiopharmaTrend

Insilico Medicine

Capgemini and Insilico Medicine have formed a strategic partnership to accelerate AI‑driven drug discovery, combining Capgemini’s expertise in scaling data and AI across life sciences with Insilico’s industry‑leading generative AI platforms.

Insilico’s Pharma.AI suite enables end‑to‑end discovery — from target identification to molecule design and clinical prediction — dramatically reducing timelines and improving success rates.  Together, the two organizations aim to address persistent R&D productivity challenges by reimagining discovery as an integrated, AI‑first system. 

The partnership focuses on four key innovation areas: specialized scientific LLMs and datasets, advanced lab automation, rapid AI‑driven drug discovery sprints, and AI‑enabled biomarker identification and strategy. These initiatives converge data, models, and experimentation into a continuous learning loop — enabling faster, more scalable, and more reproducible breakthroughs across the pharmaceutical R&D lifecycle.