To AI or Not To AI: Critical Business Functions in Biopharma and Biotech

Introduction

In total, pharma companies could gain an additional $254bn in operating profits worldwide by 2030, assuming a high degree of industrialization of AI use cases.
– PWC, 2023

Rapid advancements in AI and digital technology are arriving by the day at a pace inherently at odds with the slow deliberations of the biotechnology (biotech) and biopharmaceutical (biopharma) industries. Both sectors, characterized by complex research, stringent regulations, and high-stakes outcomes, are increasingly leveraging AI to navigate challenges, accelerate innovation, and unlock new avenues for value creation. This white paper explores the current and future impact of AI on critical business functions within these vital industries, drawing upon insights from leading authorities to provide a strategic perspective for leaders in the field.

Industry-agnostic: AI’s broad adoption

The adoption of AI, encompassing both analytical AI and the rapidly advancing field of generative AI (GenAI), continues its upward trajectory across industries worldwide. Recent surveys indicate a significant acceleration, with more than 75% of organizations globally now reporting the use of AI in at least one business function, a marked increase from previous years (see Figure 1, e.g., 55% in 2023).¹

Notably, GenAI adoption is surging, particularly among larger companies, with a majority of organizations regularly using it in functions like marketing, sales, product development, and IT.¹

Organizations that use AI in at least 1 business function, % of respondents

Life Sciences: Unique Opportunities and AI’s Growing Relevance

The potential economic impact is substantial. Analysis by PwC suggests AI could boost global economic output by up to 15 percentage points over the next decade, adding significant momentum to global growth rates.² However, realizing this potential hinges not just on technological prowess but on responsible deployment, clear governance structures, and the establishment of public and organizational trust.² This underscores that AI implementation is driving fundamental business reinvention across sectors.²

The life sciences industry presents a fertile ground for AI application, offering unique opportunities alongside distinct challenges.

The sheer volume and complexity of biological, clinical, and operational data generated across the value chain are well-suited for AI’s power to detect (whether rightly or wrongly) even whispers of possible signals.

The McKinsey Global Institute estimates that AI, particularly GenAI, could unlock between $60 billion and $110 billion in annual economic value for the pharmaceutical and medical products industries alone, driven by productivity gains and innovation.6 Other analyses project the AI market in pharmaceuticals to reach similar orders of magnitude, e.g., $16.5 billion by 2034 according to EY, underscoring the perceived value and growth potential.⁸

AI is rapidly transitioning from isolated experiments to a core enabling technology across the entire life sciences value chain. Its influence is rapidly increasing in:

Research & Development (R&D): Accelerating target identification, predicting molecular interactions, and designing novel compounds.⁸
Clinical Trials: optimizing protocol design, enhancing patient recruitment and stratification, enabling real-time monitoring, and predicting trial success rates.¹⁰ AI is projected to significantly reduce trial costs and timelines.¹⁴
Manufacturing & Supply Chain: Improving process control, enabling predictive maintenance, optimizing inventory, and enhancing quality assurance.¹¹

Regulatory Affairs: Assisting with document generation (e.g., Clinical Study Reports), managing health authority queries, and automating compliance monitoring.¹⁰
Commercial Operations: Enabling personalized marketing content generation, optimizing sales strategies, and improving patient engagement.⁷
Personalized Medicine: Analyzing genetic profiles and clinical data to tailor treatments to individual patients.⁸

While much attention has focused on AI’s role in accelerating the scientific and operational aspects of drug discovery and development, its potential impact extends significantly into the strategic business functions that underpin corporate growth and market positioning.

Leading consultancies like McKinsey and BCG are increasingly targeting and recruiting these deep technical and scientific experts, recognizing that combining profound industry insight with AI proficiency is the new benchmark for driving transformative success

What About AI’s Potential Across Key Biopharma/ Biotech Business Functions?

Beyond what the majority of white papers on AI’s enormous potential in biotech/biopharma have focused on to date, AI is equally poised to reshape critical business functions. These strategic areas, while perhaps less mature in AI adoption compared to R&D, represent a crucial frontier for gaining competitive advantage, optimizing resource allocation, and driving long-term value.

This white paper will explore AI’s transformative potential in the following key areas:

Due Diligence (DD) & Competitive Market Analysis (CI): AI offers the capability to rapidly analyze vast datasets, including scientific literature, patents, clinical trial results, financial reports, and real-time market signals, far exceeding the speed and scope of traditional manual methods. This can lead to deeper insights, faster evaluations, and more comprehensive risk assessments for M&A, investments, and strategic planning.²⁰

Strategic Partnerships: AI can facilitate the identification of synergistic partners by analyzing research alignment, pipeline complementarity, and technological fit. It can also aid in evaluating the potential value and risks associated with collaborations and potentially support the modeling of expected outcomes.⁶

Capital Raising: AI tools show promise in helping companies identify and target the most relevant investors based on sophis-ticated analysis of investment history and portfolio focus. Furthermore, AI may assist in refining investment narratives and supporting the financial valuations presented during fundraising efforts.¹⁹

Licensing: AI can streamline the identification of both in-licensing and out-licensing opportunities. Its analytical capabilities can support deal valuation, comparable analysis, and revenue forecasting, while Large Language Models can assist in analyzing complex legal agreements.²⁷

The subsequent chapters will delve into the practical considerations for building AI capabilities before examining the specific applications, advantages, and challenges of AI within each of these critical business functions.

The Role of Consulting Deep Domain Experts

As the AI revolution reshapes the biopharma and biotech landscape, navigating the sheer complexity of integrating these powerful technologies—from R&D and clinical trials to strategic commercial decisions as highlighted in Chapter 1—demands more than just technological know-how; it requires true strategic partnership. This is where today’s AI-trained management consultants will shine, evolving far beyond generalist advice.

The clear trend is to contract or recruit deep domain experts—individuals with significant R&D and strategic experience, often holding PhDs and specialized scientific credentials—who are not only AI-enabled but can also dissect nuanced industry-specific challenges and tailor client-specific AI solutions that deliver real-world value.

Cross-disciplinary conversations between highly specialized technical and more business strategy-focused personnel are essential in biopharma and biotech. There is enormous value in what expert consultants armed with Large Language Models (LLMs) can bring by dramatically enhancing cross-disciplinary conversations. Reflecting this need, leading consultancies like McKinsey and BCG are increasingly targeting and recruiting these deep technical and scientific experts, recognizing that combining profound industry insight with AI proficiency is the new benchmark for driving transformative success in the life sciences sector.