When AI Changes The Deal: Seven AI-related Considerations for Life Sciences M&A Dealmakers

Sally Wagner Partin, Sharon R. Flanagan, and Torrey Cope consider how AI is reshaping drug development, particularly at the stages of target and compound identification, and set out the implications of those changes for valuation, diligence, risk, milestone design, and timing in life sciences transactions.

Artificial intelligence (AI) is increasingly embedded in drug discovery and clinical development and also shows promise when it comes to reducing the costs of later-stage development. In a limited study of AI-native companies, Phase I success rates reached 80–90% for AI-enabled assets, compared to historical averages of 40–65%, while Phase II outcomes remained broadly consistent with historical norms at approximately 40%, according to a Boston Consulting Group analysis. These changes have direct implications for how assets are evaluated, when parties transact, and how transactions are structured and priced. AI does not eliminate development risk, but it is changing where that risk sits in the development timeline. Corporate development teams would therefore be well-advised to carefully consider the following:

1. AI is currently redistributing, not eliminating, development risk. AI-driven improvements increase the likelihood that programs clear early clinical milestones. As a result, later-stage validation may become more central to value.
2. AI provides sellers increased optionality on deal timing. If AI reduces the cost and time required to reach key clinical milestones, companies may be able to extend their cash runway by advancing fewer, more promising programs further before pursuing a sale. AI can also reduce the number of months a seller must operate at its burn rate before reaching a value-inflection point. Together, these effects can extend runway. At the same time, faster early progress may allow companies to bring assets to licensors or buyers earlier.
3. Legal due diligence expands from clinical risk to data and model considerations. Where a drug candidate has been identified or optimized using AI, the core diligence questions extend to how the asset was generated, and whether the FDA will find the output of AI credible. There is likely to be a need for more intensive front-end diligence and more detailed representations and warranties.
4. Early-stage milestones, pricing, and value may shift as Phase I becomes more predictable. The value associated with the Phase I milestone may increasingly be priced earlier and reflected in upfront consideration rather than contingent milestone payments. Milestone achievement triggers may also shift to the successful completion of Phase I. The number of Phase I-ready assets may increase, with downward pressure on pricing. To the extent AI reduces the cost and risk of later-stage development, sellers may argue that milestone structures should allocate a greater share of value to later-stage payments.
5. Milestone timing may compress. If earlier-stage milestones are reached more quickly, the time between signing and achievement may shorten. Assumptions regarding milestone timing reflected in valuation models and contractual sunset provisions may therefore require reassessment.
6. Commercially reasonable efforts (CRE) may evolve as AI becomes embedded in development decision-making. If AI reduces the cost and time required to advance early-stage programs, the baseline for what constitutes “reasonable” conduct may shift. Also, if AI increases the number of viable programs competing for internal resources, parties may have greater flexibility to reallocate efforts toward higher-priority assets. Additionally, the increasing availability of AI-enabled approaches may influence how efforts are evaluated, and questions may arise as to whether a party’s failure to use them is consistent with commercially reasonable efforts.
7. Phase I and other early-stage failures may attract increased scrutiny and disputes. If AI increases the probability that programs successfully complete Phase I, where a program fails here the outcome may attract increased scrutiny from counterparties and increase the likelihood of disputes. Specifically, missed early-stage milestones may be more likely to be contested.

This blog post is a shortened version of an article which was published by Life Science Leader. To read the full article, click When AI Changes A Deal: Rethinking Risk, Milestones, And Timing In Life Sciences M&A.

This post is as of the posting date stated above. Sidley Austin LLP assumes no duty to update this post or post about any subsequent developments having a bearing on this post.