AI in healthcare and pharma workflow automation: efficiency gains and adoption barriers

Artificial Intelligence (AI) is poised to re-shape clinical and pharmaceutical workflows, yet evidence about its net efficiency and the obstacles to its real-world uptake remain fragmented. This thesis aims to systematically assess the efficiency gains and barriers associated with AI-enabled workflow automation in healthcare and pharmaceutical settings. It investigates the transformative potential of Artificial Intelligence (AI) in healthcare and pharmaceutical (pharma) workflow automation, focusing on both the efficiency gains and the barriers hindering its widespread adoption. To explore this, a systematic approach was employed, beginning with a comprehensive query on Scopus, followed by rigorous validation and iterative refinement to ensure the quality and relevance of the selected articles. This study synthesizes findings from a diverse range of literature to evaluate the impact of AI on various processes, including patient triage, imaging workflows, administrative tasks, and drug discovery. Findings show measurable efficiency gains—such as reduced triage times, decreased workflow latency, and automation of repetitive tasks. However, key barriers persist, including lack of model transparency, data quality issues, integration challenges, and regulatory uncertainty. An empirical field study was also conducted, mapping the major players (startups) in the industry and the way they bring from their part efficiency to the pharma-healthcare ecosystem as well as the main barriers they face. In conclusion, this thesis not only consolidates the fragmented evidence on AI’s efficiency gains and adoption barriers in healthcare and pharma but also enriches the discourse with empirical insights from industry practice. The findings underscore that while AI holds immense promise for workflow automation and operational excellence, its true value can only be realized through a holistic approach, one that combines technological innovation with organizational readiness, regulatory clarity, and a commitment to ethical, human-centered implementation. By integrating systematic literature analysis with industry insights, this work provides a robust foundation for future research and offers practical guidance for stakeholders seeking to harness AI’s transformative potential in the healthcare and pharmaceutical sectors.

L’Intelligenza Artificiale (IA) sta rivoluzionando i flussi di lavoro nei settori sanitario e farmaceutico, ma le evidenze sui reali guadagni di efficienza e sulle barriere all’adozione rimangono frammentarie. Questa tesi offre una sintesi sistematica e integrata della letteratura, focalizzandosi esclusivamente sull’automazione dei processi abilitata dall’IA lungo tutta la catena del valore in sanità e pharma. Attraverso una revisione sistematica della letteratura, sono stati analizzati 489 articoli pubblicati tra il 2019 e il 2024, selezionando 35 studi di alta qualità che documentano i principali benefici dell’IA: riduzione dei tempi di triage, diminuzione delle latenze nei flussi di lavoro, automazione delle attività amministrative e supporto alla scoperta di nuovi farmaci. Tuttavia, emergono anche barriere significative, tra cui la scarsa spiegabilità dei modelli, la qualità e l’interoperabilità dei dati, le difficoltà di integrazione nei sistemi esistenti e le incertezze normative. Un’analisi empirica del mercato, basata su 261 startup e interviste a professionisti del settore, conferma che la collaborazione uomo-macchina e la presenza di solidi framework di governance sono essenziali per un’implementazione efficace ed etica dell’IA. In conclusione, la tesi sottolinea che il vero valore dell’IA in sanità e pharma può essere realizzato solo attraverso un approccio olistico che combini innovazione tecnologica, preparazione organizzativa, chiarezza regolatoria e centralità della componente umana. I risultati offrono una base solida per future ricerche e raccomandazioni pratiche per stakeholder interessati a sfruttare il potenziale trasformativo dell’IA nei settori sanitario e farmaceutico.