Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Titolo articolo panel discussion on test
AI-driven strategies, which have emerged in diverse hightech fields over the past few years, are now reshaping supply chains in pharmaceuticals, healthcare, chemicals, and biotechnology by placing sustainability and human well-being at the forefront. Over the course of the early twenty-first century, rapid strides in data-centric technologies and refined metrics for environmental, social, and governance (ESG) considerations have opened up new possibilities for optimizing everything from R&D processes to large-scale
manufacturing and global distribution. The pages that follow offer cross-industry perspectives through different panel discussions on how AI and ESG principles are evolving, and how they might define the future of sustainable operations across various sectors.
A key advantage of AI-enhanced supply chain management lies in its ability to allocate resources more intelligently while reducing carbon footprints. Many experts agree that intelligent automation can be particularly valuable for navigating complex regulatory frameworks and temperatureor time-sensitive logistics—common challenges in heavily regulated or specialized industries. Traditional methods often struggle with sudden changes, whether they stem from fluctuating market demands or unexpected supply bottlenecks. In contrast, AI-driven platforms can adapt to these fluctuations by deploying predictive analytics and simulation
models. This adaptability not only boosts real-time responsiveness but also aligns with ESG targets by minimizing waste, reducing energy consumption, and promoting ethical sourcing strategies.
Stakeholders also highlight the economic benefits of introducing AI and ESG considerations at earlier stages in the supply chain. By identifying potential production slowdowns or operational inefficiencies before they escalate, AI-powered solutions help manufacturers and healthcare providers avoid costly emergency measures. Such proactive approaches reduce reliance on less sustainable, shortterm fixes, ensuring that both environmental protection and financial stability remain central goals. Moreover, achieving a careful balance of ethical obligations and profit motives simplifies the path to scalability, allowing organizations to
rapidly adjust to changing global demands without compromising quality or sustainability targets.
Commentary article
Antonio Pesquiera
Researcher, INESC-INOV, ISCTE-IUL – University Institute of Lisbon, Portugal
Antonio Pesqueira is a senior professional in healthcare and pharmaceuticals, focusing on integrating technology to optimize commercial and supply chain processes. He specializes in digital transformation, sales force effectiveness, and data-driven strategies to enhance operational efficiency. Antonio contributes to ISCTE/IUL’s Ph.D. program, researching healthcare data governance and innovation. He has co-chaired the Pharmaceutical Supply Chain & Security World Forum and presented at global industry events. Antonio has published over 20 articles on healthcare digital transformation and agile project management and serves on the editorial board of IJAIMH. He is a certified Scrum Master and IBM Data Scientist
Over the last two decades, a broad spectrum of AI-driven solutions has appeared, catering to laboratories, pilot plants, and full-scale facilities across multiple industries. Many practitioners appreciate the versatility that arises from modular data architectures and flexible analytics platforms, which can be quickly repurposed for different product lines or global markets. Although smaller-scale tools initially received the most attention, the trend is shifting toward large, integrated systems capable of processing massive datasets and seamlessly interfacing with international networks. In some cases—especially with complex or proprietary workflows—fully customized frameworks are developed to address specific ESG mandates and reinforce responsible product stewardship.
A more recent development in AI-guided supply chains is the incorporation of process analytical technologies (PAT) and automated decision-making protocols. Panelists noted that AI-driven analytics, when combined with real-time data, can significantly accelerate R&D milestones—ranging from the design of greener packaging solutions to the reduction of harmful byproducts. Once these optimized processes are standardized, basic operational monitoring may suffice to preserve consistent quality. Nevertheless, the dynamic nature of AI allows continuous data streams to yield vital, up-to-date insights, enabling supply chain managers to fine-tune operations to minimize environmental and social impacts. In addition, automated feedback loops are particularly powerful in mitigating out-of-specification outputs by responding quickly to any fluctuations that might compromise safety, quality, or compliance.
Despite these advantages, many professionals still question why AI and ESG frameworks are not yet embedded in every aspect of manufacturing and supply chain operations. Their skepticism often reflects the cultural and financial obstacles that arise from longstanding corporate habits and infrastructures. Some experts argue that educational initiatives, beginning as
early as undergraduate curricula in life sciences, engineering, and supply chain management, are needed to cultivate a generation of professionals fully conversant in AI-based sustainability methods. Others note that cost barriers, especially for smaller organizations or academic labs, can be substantial. These hurdles, however, may be mitigated by growing awareness that forward- looking companies who invest in ESG and AI not only gain a competitive edge but also secure long-term risk mitigation. Indeed, industry-wide regulations appear to be heading in a direction that favours early adoption of responsible technologies.
Classical challenges associated with fragmented data systems and siloed legacy platforms are gradually resolving, thanks in part to the advent of cloud-based architectures capable of handling large and diverse datasets. A renewed focus on data interoperability is making it easier for organizations to adopt a holistic approach, in which information regarding climate impact, resource utilization, and fair labour practices all feed into the same AI-driven analytics engine. Moreover, many software vendors now provide user-friendly interfaces that allow supply chain operators to track ESG goals in real
time, bridging the gap between purely algorithmic models and the practical requirements of manufacturing facilities. Looking
ahead, it seems likely that AI and ESG considerations will become increasingly intertwined in many regulated industries.
Recent global disruptions—most visibly during the COVID-19 pandemic—have underscored both the vulnerabilities in multinational supply chains and the importance of local resilience. These events have spurred renewed emphasis on safe, resource- efficient, and socially responsible operations, including reshoring initiatives in multiple regions.
The potential for automation and advanced analytics to expedite process development is immense, especially when weighed against the pressing need to minimize waste and ensure ethical compliance. In response, emerging guidelines are
expected to offer clearer incentives for integrating AI and ESG within supply chains, helping to harmonize global standards
on safety, sustainability, and cost-effectiveness. Ultimately, the fusion of AI-driven analytics with ESG objectives signals
a transformation in manufacturing and service delivery that goes well beyond incremental improvements. From reducing
environmental footprints to ensuring transparent labour practices, and from enhancing patient outcomes to safeguarding
community well-being, these emerging strategies mark the start of a promising new era. As data scientists, engineers,
healthcare professionals, and policymakers converge, the goal is to forge a supply chain that is not only more efficient and
adaptable, but also respects the planet and the people who rely on essential products and services.
A total of 19 companies participated in the panel discussions. Their insights—detailing AI and ESG implementation strategies, case studies, and best practices—are featured in the sections that follow this commentary. These contributions provide a wide-ranging view of how organizations are adapting to new AI capabilities and sustainability demands across multiple industries.
Head of Manufacturing Data Science, Pharma Group Applied Materials
Head of Manufacturing Data Science, Pharma Group Applied Materials
What are the emerging trends in the integration of AI and ESG in global supply chains, particularly in industries like healthcare and pharmaceuticals?
Artificial Intelligence has been used in various forms for many decades but has recently gained attention from a broader audience as it enters mainstream use. In fact, techniques like Multivariate Analysis, Chemometrics, Advanced Process Control, self-optimizing algorithms and real-time optimization could be considered types of AI and already play a role in pharmaceutical manufacturing supply chains. The broad change we are seeing is in interest in AI from a senior level within organizations, who are excited about the potential for algorithmic approaches to improve supply chain performance. This senior support – along with hiring of internal data science teams that understand and can make use of AI-type approaches – has resulted in a real ‘sea-change’ in how supply chains are being
managed in leading pharmaceutical companies. Particularly, today we are seeing an accelerating evolution towards advanced levels of digital maturity as pharma companies recognize the need for connected, data-rich manufacturing and supply chains. Specific applications include the areas of advanced scheduling, predictive maintenance and alarm management, delivering advanced asset effectiveness. While these were always priorities, the momentum behind taking an AI approach is accelerating the adoption of these types of solutions with and assigned budgets and teams that understand the tools needed.
We also see changes in standards across the industry to systematize the approach towards AI, which is creating a framework for the adoption of these technologies. For example, the second edition of GAMP 5 includes specific consideration of AI/ML and is founded on taking a risk-based approach, including determining the accuracy of training data and clear model validation. This GAMP5 second edition now contains a common regulatory framework for general ML modelling.
To enable improved accessibility to medicines, whether that be mass vaccines or personalized medicine approaches, vast quantities of data collection, interpretation and sharing is critical. Real-time data monitoring, modelling and prediction, timely access to process status and the availability of predictive or prescriptive actions underpinned by a real-time, data-driven approach to scheduling are all contributing to an intelligent, smart manufacturing landscape.
Head of Manufacturing Data Science, Pharma Group Applied Materials
What are the emerging trends in the integration of AI and ESG in global supply chains, particularly in industries like healthcare and pharmaceuticals?
Artificial Intelligence has been used in various forms for many decades but has recently gained attention from a broader audience as it enters mainstream use. In fact, techniques like Multivariate Analysis, Chemometrics, Advanced Process Control, self-optimizing algorithms and real-time optimization could be considered types of AI and already play a role in pharmaceutical manufacturing supply chains. The broad change we are seeing is in interest in AI from a senior level within organizations, who are excited about the potential for algorithmic approaches to improve supply chain performance. This senior support – along with hiring of internal data science teams that understand and can make use of AI-type approaches – has resulted in a real ‘sea-change’ in how supply chains are being
managed in leading pharmaceutical companies. Particularly, today we are seeing an accelerating evolution towards advanced levels of digital maturity as pharma companies recognize the need for connected, data-rich manufacturing and supply chains. Specific applications include the areas of advanced scheduling, predictive maintenance and alarm management, delivering advanced asset effectiveness. While these were always priorities, the momentum behind taking an AI approach is accelerating the adoption of these types of solutions with and assigned budgets and teams that understand the tools needed.
We also see changes in standards across the industry to systematize the approach towards AI, which is creating a framework for the adoption of these technologies. For example, the second edition of GAMP 5 includes specific consideration of AI/ML and is founded on taking a risk-based approach, including determining the accuracy of training data and clear model validation. This GAMP5 second edition now contains a common regulatory framework for general ML modelling.
To enable improved accessibility to medicines, whether that be mass vaccines or personalized medicine approaches, vast quantities of data collection, interpretation and sharing is critical. Real-time data monitoring, modelling and prediction, timely access to process status and the availability of predictive or prescriptive actions underpinned by a real-time, data-driven approach to scheduling are all contributing to an intelligent, smart manufacturing landscape.
40 years connecting the world of science for industry
Our journals:
