1 (page number not for citation purpose) ORIGINAL RESEARCH Mapping the AI Landscape in Life Sciences: A Framework for Evaluation and Adoption Jennifer Hinkel, MSc1,2 ; Fraser Peck, MBBCh3 and Cory Kidd, PhD1 1Sigla Sciences, Incline Village, NV, USA; 2Kellogg College, University of Oxford, Oxford, UK; 3Pharmacy Consulting Limited, Farnborough, UK Corresponding Author: Jennifer Hinkel, Email: jennifer@siglasciences.com DOI: https://doi.org/10.30953/bhty.v8.398 Keywords: artificial intelligence, clinical development, commercialization, ethical challenges, life sciences, regulatory, strategic model Abstract Background: The adoption of Artificial Intelligence (AI) is accelerating in the life sciences sector, offering opportunities to enhance biopharmaceutical research, development, and commercialization. However, the sector lacks structured tools to prioritize AI initiatives across varied business domains. Objective: This study presents a framework to categorize and evaluate potential AI applications in the life sciences industry, organizing use cases along two critical dimensions: Phase of Product Lifecycle and Opera- tional Domain. Methods: A structured mixed-methods approach was employed, including a modified Delphi consensus pro- cess with industry experts, a qualitative case study review, and iterative framework refinement between August 2023 and August 2024. Results: The resulting matrix framework enables life sciences professionals to assess AI opportunities across research, clinical development, commercialization, and post-marketing activities. Key findings highlight the pervasive nature of AI impact, the emphasis on data-driven strategies, and the regulatory and ethical chal- lenges facing biopharma firms. Conclusions: This framework provides a practical model for strategic AI adoption decisions within the life sci- ences sector and lays the groundwork for future research, policy development, and enterprise transformation efforts. Plain Language Summary Artificial Intelligence (AI) is starting to reshape how pharmaceutical companies discover new drugs, test them in clinical trials, and bring them to patients. However, with so many possible uses for AI, it can be hard for companies to decide where to begin and how to prioritize. This article presents a simple framework that organizes potential AI projects by the stage of drug development, such as early research or post-market mon- itoring, and by how much the project involves people outside the company, like doctors or patients. The goal is to help life sciences companies think more clearly about where AI could add value and what risks to mon- itor. With thoughtful planning, AI can make biopharma companies and the healthcare system they support, smarter, and more focused on patient needs. Submitted: April 27, 2015; Accepted: September 2, 2025; Published: October 10, 2025 Recent advances in Artificial Intelligence (AI) are prompting discussions across various scientific and business sectors regarding where, how, and why AI technologies might be deployed, and what the implications will be. The medical and life sciences fields are exploring how AI might be incorporated throughout Blockchain in Healthcare Today ISSN 2573-8240 https://orcid.org/0000-0002-8461-7037 https://orcid.org/0000-0002-6849-2764 https://orcid.org/0000-0002-8129-7234 mailto:jennifer@siglasciences.com https://doi.org/10.30953/bhty.v8.398 Citation: Blockchain in Healthcare Today 2025, 8: 398 - https://doi.org/10.30953/bhty.v8.3982 (page number not for citation purpose) J. Hinkel et al. the drug development process, from discovery to patient care, including through commercialization, marketing, and market access strategies.1 As of mid-2024, the AI adoption in the life sciences industry has progressed from theoretical discussions to practical implementations, albeit many in the ‘pilot phase’. stage. Clinicians, pharmaceutical manufacturers, and policymakers are currently evaluating whether these technologies offer substantive opportunities for innova- tion, enhanced efficiency, and improved patient outcomes or if they are subject to inflated expectations characteris- tic of a ‘hype cycle’.2 AI represents a range of technologies, including large language models, machine learning, natural language processing, and computer vision.3 Some researchers pro- pose that AI and related technologies represent significant shifts in how researchers and clinicians approach complex problems in life sciences and healthcare.4 Some potential AI applications hold particular interest to biopharmaceutical manufacturers because they prom- ise to reduce the time and/or investment involved in bring- ing new therapies to market. In the earliest stages of drug discovery, AI algorithms aim to speed the identification of new therapeutic targets and potential drug candidates.5 In clinical development, AI might optimize study designs, improve patient recruitment, or enhance data analysis.6 Post-commercialization, AI-driven tools may be deployed to enhance patient engagement or medication adherence or to automate the collection of real-world evidence (RWE).7 While AI technologies might be promising, they also present several challenges to the industry, ranging from ethical, legal, regulatory, and data security issues to more extensive concerns that organizations might need to change long-standing operating procedures as technolo- gies advance.8 Compounding these issues is the pace of advancement. Examples include the rapidity of AI devel- opment far outpacing the ability of regulatory and legal frameworks to keep up.9 This asymmetry adds significant complexities in a highly regulated industry that is notably averse to legal risk. Despite the risks, the potential benefits of ‘leading the pack’ on AI adoption might be significant. Companies that successfully integrate AI into their operations might gain competitive advantages, from accelerated research and development to more optimized market access strate- gies. Conversely, those who fail to bring AI on board may increasingly miss out on growth opportunities.10 In the light of these challenges and the rapid pace of advancement, the authors aim to provide a framework that professionals across various functions within life sci- ences companies can use to understand and evaluate the place of AI technologies in their workflows and firms. By aligning AI applications along two key axes that we refer to as Operational Domain and Phase of Product Lifecycle, we offer a structured approach to evaluating and priori- tizing AI initiatives. This framework is designed to assist those in positions of leadership with making informed decisions about AI adoption, balancing potential benefits against risks and challenges. Methods for Framework Development This study employs a structured mixed-methods approach, including a modified Delphi Method. It draws from the authors’ industry experience, consultations with external biopharmaceutical experts, a qualitative review of case studies from literature and business reports, and an iter- ative process of elicitation and consensus-building. The research was conducted between August 2023 and August 2024, allowing for the incorporation of recent develop- ments. It is expected to continue iteratively, incorporating elements of realist synthesis to further expand the frame- work and refine example use cases. An initial framework was developed based on the collective expertise of the research team and additional consulting experts. These included professionals with extensive experience in pharmaceutical commercial- ization, clinical development, and AI applications in research and patient-facing settings. This sector expertise guided the creation of the two axes that form the founda- tion of the framework: Phase of Product Lifecycle and Operational Domain. Once the axes were established, examples of AI applied to typical tasks within the life sciences industry were iden- tified and mapped onto the framework based on expert discussion and consensus. The identification process involved (1) consulting industry case studies, publications, and expert knowledge, (2) selecting biopharma industry activities indicative of various phases and operational domains with existing or proposed AI use cases, and (3) highlighting examples that could highlight potential ben- efits and challenges of AI adoption in each section of the framework matrix, given that these examples are most likely to spark discussions on the requirements, risks, ben- efits, and objectives of AI as applied to specific industry use cases. The initial framework and use case examples under- went iterative refinement based on the incorporation of additional feedback from industry experts. In summary, we followed these steps (Table 1). This methodology allowed us to create a framework that is grounded in real-world industry understanding and reflects the current state and potential future direc- tions of AI applications in life sciences. Ethical Considerations This study did not require formal ethics review, as it did not involve human subjects research, clinical trials, or the https://doi.org/10.30953/bhty.v8.398 Citation: Blockchain in Healthcare Today 2022, 5: xxx - https://doi.org/10.30953/bhty.v5.xxx 3 (page number not for citation purpose) Mapping the AI landscape in life sciences collection of personal data. The research was based on publicly available information, the expertise of the core research team, and consultations with field experts. All experts consulted in this study provided consent to share their professional insights and expertise in an anonymous fashion as a contribution to the framework development. Results Our research resulted in the development of a proposed framework for understanding and implementing AI tech- nologies in the life sciences sector. This framework aligns AI applications along two key axes: Phase of Product Lifecycle and Operational Domain. Phase of Product Lifecycle (Horizontal Axis) This axis aligns approximately with the traditional stages of drug development and commercialization in the life sciences industry. It recognizes that AI might have differ- ential impact at various stages of the drug product jour- ney from concept to market. Research & Discovery Research and discovery represent the early stages of drug development before human testing and encompass a range of computational and biological research methods. Clinical Trials and Regulatory Approval Clinical trials and regulatory approval represent the clin- ical development phase of activities that typically start with first-in-human testing and culminate with the com- pletion of registrational clinical trials and regulatory/mar- keting approval. Commercial Launch This phase overlaps here as a parallel process, with planning starting well prior to Regulatory Approval and continuing through that launch date and beyond, and including a range of commercial, medical, and access-related activities. Post-Market Activities Post-marketing commercial and scientific activities sup- port pharmacovigilance, the collection of RWE, ongoing communication with healthcare professionals, and con- tinued patient support. Operational Domain (Vertical Axis) This axis represents a continuum from internal opera- tions to increasingly external interactions, reflecting not only the diverse operational areas where AI technologies are applied in the life sciences sector but also the growing complexity of regulatory, data, and ethical considerations as we move from internal operations toward patient-fac- ing applications. Internal Operations include activities within the company’s boundaries, including those that deal with proprietary data and processes. While still sub- ject to regulations, these activities carry the least exter- nal exposure and thus lower regulatory and ethical risks. B2B Engagements represent the first step from internal activities into external interactions, involving partner- ships, suppliers, and other businesses, either scientific or commercial in nature. Here, collaborative processes and data sharing introduce additional regulatory consider- ations and data protection needs, but with less regulatory overhead than engaging with healthcare professionals or patients. Healthcare Professional Engagements These cover interactions with healthcare professionals, introducing significant regulatory oversight due to data protection, transparency laws, ethics, and the impact to patient care. Patient Engagements As the last operational domain, patient operational domains represent direct patient interactions and the highest level of external engagement. Patient-facing activities face the most stringent regulatory require- ments, data privacy concerns, and ethical considerations given the impact on patient care and individual health outcomes. Table 2 illustrates this matrix and provides representa- tive examples of AI applications at each intersection of Lifecycle Phase and Operational Domain, showcasing potential use cases for AI technologies across common biopharma business areas. Key Findings In parallel with our development of this framework, we identified the following themes as applicable to biophar- maceutical manufacturers considering AI adoption: Pervasive Impact AI use cases span across all phases of the product lifecycle and all addressed operational domains. The breadth of potential applications suggests a fundamental shift in how the industry will operate in the future. Table 1. Initial framework and use cases based on incorporation of additional feedback from industry experts Use case Framework Step 1 Conceptualization and axis development Step 2 Expert consultation Step 3 Framework refinement Step 4 Identify business activities and map AI use cases to the framework. Step 5 Iterative improvement as the field advances https://doi.org/10.30953/bhty.v5.xxx Citation: Blockchain in Healthcare Today 2025, 8: 398 - https://doi.org/10.30953/bhty.v8.3984 (page number not for citation purpose) J. Hinkel et al. T ab le 2 . M at ri x fr am ew or k fo r un de rs ta nd in g A I us e ca se s an d ap pl ic at io ns in li fe s ci en ce s Ph as e of p ro du ct li fe cy cl e O pe ra tio na l d om ai n In cr ea si ng le ve ls o f r eg ul at io n an d ri sk w ith m or e ex te rn al -fa ci ng a ct iv iti es t ow ar ds t he r ig ht In te rn al o pe ra tio ns B2 B en ga ge m en ts H C P en ga ge m en ts Pa tie nt e ng ag em en ts Ph as es o f d ru g di sc ov er y, de ve lo pm en t, an d la un ch D is co ve ry a nd P re -C lin ic al D ev el op m en t Al -d riv en ta rg et id en tifi ca tio n an d va lid at io n M ac hi ne le ar ni ng fo r in s ilic o dr ug d es ig n Al -p ow er ed p re di ct ive to xi co lo gy m od el s Fe de ra te d le ar ni ng p la tfo rm s fo r co lla bo ra - tiv e re se ar ch Al -o pt im iz ed p ar tn er in g se le ct io n fo r CR O , in ve st m en t, an d BD Al -d riv en c om po un d so ur cin g an d sy nt he sis pr ed ict io n Al -c ur at ed r es ea rc h in sig ht s fo r KO L en ga ge m en t N at ur al la ng ua ge p ro ce ss in g fo r cl in ica l ex pe rt ise m ap pi ng Al -p ow er ed s cie nt ifi c co nf er en ce a nd K O L m at ch m ak in g Al a na lys is of p at ie nt -g en er at ed h ea lth d at a fo r bi om ar ke r di sc ov er y M ac hi ne le ar ni ng m od el s fo r pa tie nt s tra tifi - ca tio n in ra re d ise as es Al -d riv en p at ie nt a dv oc ac y ne tw or k an al ys is C lin ic al D ev el op m en t an d R eg ul a- to ry S ub m is si on Al fo r ad ap tiv e tr ia l d es ig n an d pr ot oc ol op tim iz at io n M ac hi ne le ar ni ng fo r pa tie nt r ec ru itm en t fo re ca st in g N LP -p ow er ed a ut om at ed r eg ul at or y su b- m iss io n pr ep ar at io n Al -d riv en s ite s el ec tio n an d pe rfo rm an ce pr ed ict io n M ac hi ne le ar ni ng fo r CR O p er fo rm an ce op tim iz at io n Al fo r op tim iz in g m ul ti- re gi on al c lin ica l t ria l de sig ns Al -p ow er ed in ve st ig at or r el at io ns hi p m an ag em en t Vi rt ua l a ss ist an ts fo r pr ot oc ol c la rifi ca tio n an d su pp or t M ac hi ne le ar ni ng fo r in ve st ig at or s ite fe as ib ilit y as se ss m en t Al c ha tb ot s fo r pa tie nt s cr ee ni ng a nd en ro llm en t M ac hi ne le ar ni ng fo r pe rs on al iz ed p at ie nt re te nt io n st ra te gi es Al fo r re al -ti m e pa tie nt m on ito rin g an d ad ve rs e ev en t p re di ct io n in tr ia ls C om m er ci al La un ch a nd M ar ke t A cc es s Al fo r dy na m ic pr ici ng m od el s an d m ar ke t ac ce ss s tra te gi es M ac hi ne le ar ni ng fo r sa le s fo rc e ef fe ct ive - ne ss o pt im iz at io n N LP fo r co m pe tit ive in te llig en ce a nd m ar ke t s en tim en t a na lys is Al -d riv en p ay er n eg ot ia tio n sim ul at or s M ac hi ne le ar ni ng fo r di st rib ut io n ch an ne l op tim iz at io n Al fo r au to m at in g an d op tim iz in g RF I a nd te nd er p ro ce ss es Al -p ow er ed p er so na liz ed H CP e ng ag em en t pl at fo rm s N LP fo r re al -ti m e H CP s en tim en t a na lys is du rin g in te ra ct io ns Al fo r pr ed ict in g an d op tim iz in g H CP pr es cr ip tio n be ha vio rs Al fo r pe rs on al iz ed p at ie nt s up po rt p ro gr am de sig n M ac hi ne le ar ni ng fo r m ed ica tio n ad he re nc e pr ed ict io n an d in te rv en tio n Al -d riv en c ha tb ot s fo r pa tie nt e du ca tio n on co m pl ex th er ap ie s Li fe cy cl e M an ag em en t an d Po st -M ar ke tin g A ct iv iti es Al fo r sig na l d et ec tio n in p ha rm ac ov ig ila nc e M ac hi ne le ar ni ng fo r pr od uc t l ife cy cl e op tim iz at io n st ra te gi es N LP fo r au to m at ed m ed ica l l ite ra tu re m on ito rin g Al fo r re al -w or ld e vid en ce s tu dy d es ig n an d pa rt ne r se le ct io n M ac hi ne le ar ni ng fo r su pp ly ch ai n ris k pr ed ict io n an d m iti ga tio n Al -p ow er ed fo re ca st in g fo r lo ng -te rm pr od uc t p er fo rm an ce Al -d riv en p er so na liz ed m ed ica l i nf or m at io n se rv ice s M ac hi ne le ar ni ng fo r pr ed ict in g an d ad dr es sin g H CP in fo rm at io n ne ed s N LP fo r pr oc es sin g an d an al yz in g H CP fe ed ba ck a t s ca le Al fo r de te ct in g an d pr ed ict in g m ed ica tio n no n- ad he re nc e M ac hi ne le ar ni ng fo r pe rs on al iz in g pa tie nt su pp or t i nt er ve nt io ns N LP fo r an al yz in g pa tie nt -re po rt ed o ut co m es an d so cia l m ed ia d at a A I: A rt ifi ci al In te lli ge nc e; B 2B : b us in es s- to -b us in es s; C RO : c on tr ac t re se ar ch o rg an iz at io n; H C P: h ea lth ca re p ro fe ss io na l; KO L: k ey o pi ni on le ad er ; N LP : n at ur al la ng ua ge p ro ce ss in g. https://doi.org/10.30953/bhty.v8.398 Citation: Blockchain in Healthcare Today 2022, 5: xxx - https://doi.org/10.30953/bhty.v5.xxx 5 (page number not for citation purpose) Mapping the AI landscape in life sciences Efficiency and Innovation Many AI applications focus on improving efficiency (e.g. automation of regulatory submissions) or driving inno- vation (e.g. AI-powered drug discovery). AI applications are frequently framed as tools that will reduce effort, time, and/or cost. Enhanced Customer Engagement AI technologies may be leveraged by both commercial and medical field teams, particularly in digital or ‘omnichan- nel’ marketing initiatives or in medical communications. Data-Driven Decision Making Across the matrix, there is a strong emphasis on data sources and data access as inputs to AI models. These data may be internally or externally derived, speaking to the value of high-quality data assets. Access to data assets is likely a competitive advantage or differentiator. Patient-Centricity The framework reveals numerous AI applications focused on improving patient experiences and outcomes, align- ing with a general shift toward more patient-centric approaches and the need to communicate patient-centric value, especially in the commercial phase. Patient-centricity aligns with value-based care models and evolving regu- latory expectations around the incorporation of patient reported outcomes (PROs) in drug development and post-marketing phases. Interconnectedness Several AI applications bridge multiple domains or phases, hinting at the potential for these technologies to change industry structures or processes over time. This interconnectedness suggests the need for integrated AI strategies that consider the full spectrum of pharmaceuti- cal operations and stakeholder interactions. Ethical and Regulatory Considerations AI uses cases in biopharma that raise numerous ethical and regulatory questions. These include questions around data privacy and security, algorithmic bias, data owner- ship and consent, and the interpretability or generalizabil- ity of AI-driven decisions. Limitations Our methodology has limitations, including an acknowl- edgment that the rapid pace of AI development is likely to outpace even expert knowledge. Another limitation in expert consensus methods is the introduction of poten- tial bias based on the team’s collective experiences and perspectives. We mitigated these limitations to the extent possible through engaging diverse experts and iterating through the framework development process. Furthermore, this framework is not likely to be exhaus- tive, given the challenge of comprehensively capturing all possible AI applications in a rapidly evolving field. For example, biopharmaceutical manufacturing companies also perform a number of corporate functions in finance, legal, human resources, training, and similar operational domains considered out of scope in this project. How- ever, there may be areas where AI innovation can also be applied. Conclusion This article presents a new framework for understanding and categorizing AI technologies across the life sciences product lifecycle and operational domain. By mapping along two axes representing paradigms well-understood by professionals in the biopharmaceutical industry, we provide a structured approach for such professionals to evaluate and prioritize AI technology initiatives. Our findings demonstrate the pervasive potential of AI to transform various aspects of the life sciences sector, but not without risks and challenges to navigate, including data quality and availability, algorithmic biases, privacy and security, ethical and regulatory considerations, and the need for organizational change management. As the field continues to evolve rapidly, ongoing research and industry collaboration will be crucial to fully harness the potential of AI while addressing its associated challenges. The framework presented in this article pro- vides a foundation for these future efforts, offering a com- mon language and structure for discussing and analyzing AI applications in the sector. Funding This study did not receive any funding. The authors have not received any payments or services from third par- ties directly related to the submitted work in the past 36 months. Conflicts of Interest Ms. Hinkel is co-Editor-in-Chief of Blockchain in Health- care Today. The authors declare no competing interests related to the subject of this article. For transparency, all authors are employed by or have formerly worked in the life sciences and adjacent sectors, including working for and/or consulting for biophar- maceutical manufacturers. Miss. Hinkel and Dr. Kidd have worked for and/or consulted for health informatics firms. While this research was not funded by any industry entity, it was undertaken independently based on the authors’ academic and research interests. It is neither promotional nor does it reference any specific products. 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