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AI tools for fundraising, business development and finance are now widely available. However, there appears to be a gap between larger pharma companies which actively use these tools already in their business development (BD) and partnering efforts, and smaller biotechs which typically have not yet embedded them into their core business processes due to constraints around budget, risk and governance. Here we report the takeaways from an expert panel convened at the 2026 Swiss Biotech Day earlier this month to discuss the dynamics in the use of AI in pharma/biotech fundraising, partnering and finance. By Philipp Gutzwiller and Dr. Thomas Meier
Addressing the gap in the use of AI between biotech and large pharma in their business development efforts
The overarching trend in 2026 is that large pharma companies are no longer treating AI as an experimental add-on. Instead, they are embedding it across their entire business development (BD) operating model from early-stage asset scouting and partner identification, through deal structuring, to commercial execution. This systemic embedding of AI across BD functions is gaining industry-wide momentum. Only weeks ago, in April 2026, Novo Nordisk announced a strategic partnership with OpenAI, explicitly spanning drug discovery through commercial operations and global workforce upskilling, illustrating how AI is no longer siloed within R&D but has become an integral BD and go-to-market capability. According to industry surveys, over 70% of life-science executives now recognize generative AI’s strategic promise, reflecting the competitive pressure on large pharma to embed AI in BD workflows at scale.
This view was shared by panelist Avaleigh Milne (Global Head of Business Development Strategy & Practice at Roche) who confirmed that Roche routinely uses AI to funnel the massive inbound partnering interest and to identify fundamentally good science that has the potential to help patients. For large pharma the question is how to quickly engage in a meaningful conversation with companies whose R&D projects best fit pharma’s need. Ms. Milne clearly emphasized, however, that despite all AI-driven processes, good personal relationships are needed to finally get the deal done.
The biotech view on AI support in BD processes was represented by Urs Breitenstein (Partner at Hoffmann & Partner) who is advising biotech companies in BD transactions with pharma. He sees that the proliferation of AI-native biotech companies is simultaneously increasing the volume and complexity of potential partnering assets on the market, making AI-assisted triage and differentiated positioning even more critical for smaller biotechs seeking to stand out amid the noise. On the operational side, AI can support smaller biotech companies by helping manage virtual data room content during due diligence and by assisting with business modelling and valuation analysis when engaging with pharmaceutical companies.
David and Goliath – does size matter?
According to a survey by Boston Consulting Group in 2024, 46% of large companies (not limited to biotech/pharma) reported mid-to-high levels of AI maturity, compared to just 28% of mid-sized firms. Access to talent, data, and capital may favor larger companies in adopting resource-intensive AI initiatives giving them an advantage over smaller firms. But the picture is changing. A 2025 report in Fortune stressed that mid-sized companies may be ‘just right’ for AI adoption. The report finds that mid-sized companies, particularly growth-focused firms, are more agile and well placed to overcome adoption barriers, making these companies prime candidates to leverage the technology and unlock their potential. We believe that the gap between giants and dwarfs is closing and that AI will enable smaller firms to see eye-to-eye with their larger peers, offering valuable business development opportunities for both.
The compliance and governance constraints
Extensive use of AI-tools in pharma/biotech BD are not subject to specific rules or regulations. However, Luca dal Molin (Partner at Homburger) reminded the audience that during BD discussions special precautions should be taken to avoid AI-driven yet unintentional disclosure of confidential information that could potentially impact the patentability of inventions. Here it is important that AI-tools used during the due diligence (DD) process are prevented from using undisclosed data for AI training and effective governance of intellectual property is essential for partnership success. Formal governance mechanisms covering confidentiality, IP ownership, and deliverables provide a clear framework for collaboration. Unintentional disclosure of personal patient data and patentable innovation are clear legal red lines.
Another important consideration arises from unresolved inventorship questions: both the US and European Patent Offices currently require inventors to be natural persons, meaning that companies must maintain documented human oversight of AI-assisted discovery workflows to ensure that resulting patents can withstand post-grant inventorship challenges. In the end, it is human oversight of AI-supported BD processes and proper governance documentation that are highly recommended from a legal point of view.
The due diligence process at scale
An integral part of the BD and partnering process as well as financing efforts is a thorough DD process. Dedicated and specialized AI tools enable unbiased aggregation, cataloguing and assessment of scientific evidence, overcoming expert biases for a reliable first validation of asset quality, as explained by Matti Kesti (CSO, co-founder and board member at Floatz.ai).
The efficiency gains from AI-assisted DD can be substantial. Based on research published in 2025, deploying LLM-based agents for competitive landscape mapping in a biotech VC investment fund reduced analyst turnaround time from 2.5 days to approximately 3 hours, a roughly 20-fold acceleration. AI tools can automate up to 70% of literature screening decisions in evidence synthesis workflows, though a hybrid approach combining AI output with human expert review remains the recommended standard. Biotechs interested in successful partnering with large pharma should be receptive to AI-supported DD processes.
AI in venture financing
Finding the right investor to support the ambitious business plan can be quite a challenge for an emerging biotech company. A Shotgun-approach outreach to an unfiltered list of investors (institutional, corporate or family offices) is a set-up for failure. Rather, AI-based profiling of the investor landscape according to the biotech’s needs and funding stage and considering specific investors’ domain expertise, their typical size and type of investments, their regional preferences and successful exits in a particular field should be applied. This is particularly true for the European biotech landscape: of 67 EU-based biotech companies that went public over the past six years, 66 did so outside the EU, indicating the importance of AI-assisted identification of the right cross-border and domain-specific investor pool.
Panelist Damian Kemper (Associate Partner & Head of Engineering at Werchota.ai) demonstrated such an AI-tool, specifically designed to identify optimal investor candidates according to a predefined list of criteria and allowing customized proposals on how to approach an investor, which according to the panel will become the standard financing approach.
Take-away learnings from the expert panel
AI is reshaping BD, due diligence, and investor targeting in life sciences faster than most biotechs are adapting. Large pharma already screens partners and structures deals using AI; biotechs that cannot engage on the same terms risk being filtered out before the conversation begins. Precision matters on both sides in how scientific assets are positioned for AI-driven screening, and in how investor outreach is targeted. Governance aspects such as protecting IP, controlling data disclosure, and maintaining human oversight are the non-negotiable foundation that makes all of it defensible.
Further education
For biotech leaders interested in this topic, further information is available here: AI Tools for Biotech Leaders | Swiss Biotech Academy
Autor/Autorin
Philipp Gutzwiller
Philipp Gutzwiller is a finance and corporate advisory professional with extensive experience in the healthcare and life sciences industry. He holds an MSc in Finance and Economics from the University of Basel. During a 24-year banking career, he served as Managing Director at several international banks, advising corporate clients on M&A and financing transactions. Prior to banking, Philipp worked in the Corporate Finance team of F. Hoffmann-La Roche AG in both operational and transactional roles. At CFS Advisors LLP he currently supports life sciences companies with strategic and financial advice and serves as a Board member
Dr Thomas Meier
Dr Thomas Meier is managing partner at Viopas Venture Consulting. With offices in Basel, Zürich, Munich and Vienna the firm provides strategic, operational, business and finance advice to life sciences companies and investors. Dr Meier has an academic background in neurosciences and an extensive track record as entrepreneur, CEO and Board member.
