Biopharma has never been more innovative—or more at risk of costly missteps. In a world of gene therapies, engineered antibodies, and AI-powered drug discovery, a single strategic decision can make or break a billion-dollar investment. Yet, most key biopharma strategic decisions – from M&A to pipeline management – still rely on fragmented data, legacy decision-making frameworks, and subjective expert judgment. This approach fundamentally fails to keep pace with today’s complexity.
One of the greatest opportunities for our industry is leveraging artificial intelligence to bridge the gaps between traditionally siloed datasets and power integrative, cross-disciplinary decision-support tools. Such AI-driven solutions will significantly enhance strategic judgment at the highest levels of corporate and public institutions and help us accelerate our mission of bringing safe and effective medicines to patients faster.
Complex programmes defined as genetic therapies, or therapies involving structural engineering at the protein or cellular level
Source: Cortellis Competitive Intelligence, 2025
The Rise of Complex Therapeutics
The past decade has seen biopharma leave the days of unaugmented natural products or simple monoclonal antibodies behind. Complex therapeutics—such as cell therapies, fusion proteins, and component-level engineered antibodies—have dramatically risen in prominence. Cortellis Competitive Intelligence (CCI) shows us that, from being virtually unheard of at the turn of the millennium, these sophisticated treatments comprised 28% of the drug pipeline in 2015 and constituted approximately 42% of the pipeline in 2024. Notably, research into cell therapies and fusion proteins has more than doubled since 2016, marking a rapid acceleration in innovation and investment in these cutting-edge areas. Meanwhile, CCI tells us that while traditional small molecules, which dominated drug development historically, became the plurality rather than most new drug programs by 2019, reflecting a fundamental transformation in therapeutic priorities.
The Challenges of Advanced Therapeutics
While the rapid rise of these advanced therapeutics promises groundbreaking advances in patient care, their inherent complexity also introduces significant challenges. Developing, validating, and producing therapies such as engineered antibodies or cell therapies requires a level of cross-disciplinary collaboration that pushes an already interdisciplinary industry to its limit. Should we in-license a novel editing vector to augment our CAR-T programme? Does our Contract Manufacturing Organization (CMO) really have the capacity to support this first-in-class technology through Phase 2?
Traditional frameworks struggle, often relying on exceptional key leaders to remember all the details. AI-driven strategic support tools that can capture internal expertise and fuse it with relevant external opportunities offer a transformative opportunity for leaders to make better decisions for their companies, and for our industry as a whole.
The Globalization of Biotech Innovation
Parallel to this scientific evolution is the shift in biotech innovation’s geographic and cultural landscape. Once predominantly a North Atlantic phenomenon, biotech innovation is now a global endeavor, thriving particularly in dynamic hubs like China and India. 50% of all global vaccines, and 30% of all global pharmaceuticals are manufactured in India, building remarkable skill and capacity within the country. Meanwhile, according to Cortellis Deals Intelligence (CDI), approximately 30% of all major licensed molecules in 2024 came from China, highlighting the country’s accession to a major innovative biopharma powerhouse. These regions are rapidly emerging as vibrant epicenters for biotechnological breakthroughs, enriching the global dialogue with diverse perspectives and novel approaches.
However, each of these regions brings its own language, culture, regulatory frameworks and documents, and conventions. Cross-regional coordination is yet another layer of complexity in the modern biotechnology business. AI systems that are able to integrate multiple cultures – from languages to document conventions to regulatory considerations – will materially help our whole industry bridge this gap.
A Cross-Continental Breakthrough
Nothing exemplifies these trends better than Akeso & Summit’s development of ivonescimab. Ivonescimab, engineered in China by Akeso, is a highly engineered antibody. It is both “bispecific” (it targets PD-1 and VEGF) as well as tetravalent (it can bind these targets in 4 different locations). Not only is this antibody exquisitely designed at its binding sites, but the backbone itself is a custom “Fc-null” structure, designed to not trigger undesirable immune effects. According to CDI, Akeso is on track to make $5B from this asset, Summit – the US owner, is now valued at $14B based on this asset, and the drug is showing early potential to defeat Keytruda as the best-selling drug in the world.
Developing, identifying, and translating science like this currently depends on singular, visionary leaders who can navigate complexity and seize opportunities—often with a degree of luck. AI decision-support tools will enable more leaders to operate at this level, allowing our industry to more consistently and systematically identify, fund, and translate high-impact innovations
The Key to Addressing Biotech Complexity
AI is already becoming central to addressing the challenges of increased complexity for our industry.
Across the industry, we’re seeing well-designed AI systems adopted for sensitive purposes. Novo Nordisk has begun to use AI—in conjunction with a smaller, higher-leveraging human team—to generate clinical study reports. Beyond this foundational layer, AI will become an indispensable tool for searching, synthesizing, and interpreting the vast, complex biotech information landscape, transforming fragmented data into actionable insights.
We anticipate that the best AI-powered strategic decision-support tools will leverage integrated real-time market analytics, competitive intelligence, scientific results, clinical trial data, and regulatory updates in a unified framework. It will then combine this data with the differentiated internal expertise that our industry has spent decades building.
Executives and decision-makers will then be able to ask questions, model outcomes, and ultimately build confidence in strategic decisions that consider all the relevant contexts in one place.
Could AI Have Prevented Costly Biopharma Missteps?
Could a decision-support tool aware of the scientific landscape and newly acquired internal expertise have helped Schering and Merck avoid deprioritizing and nearly divesting pembrolizumab ($30B annual sales, according to CCI) between 2007 and 2010? Corporate disruption caused by a series of acquisitions muddied the waters around what had previously been a priority asset.
Could a decision support tool aware of the clinical trial protocols have helped AbbVie properly contextualize the risks of Stemcentrx ($5.8B, according to CDI, on a failed drug) in 2016? The emergence of ADCs was correctly seen as very exciting, but unusual secrecy around (single arm) clinical results is the kind of “cold water” context AI would find hard to ignore.
Could a support tool aware of the clinical success of semaglutide and the market opportunity in metabolic diseases help Roche properly value OWL-833 in 2018 (sold for $50M according to CDI, with projected $15B annual sales)? GLP-1 has been associated with reduced food intake since the late ’90s, and obesity is a huge market.
The answer to each of these is almost certainly yes. The potential impact of AI-driven strategic decision-making in biopharma is immense. By harnessing AI effectively, we can improve decision-making, reduce costly missteps, and accelerate the development of life-saving treatments. The industry’s future will belong to those who master this transformation.
