A worker in a pharmaceutical facility placing sealed boxes onto a conveyor belt for further distribution

By Steve Brownett-Gale, Marketing Lead, Origin

The pharmaceutical industry is consistently pursuing innovation and now, with the rise of innovation, the industry has pursued leveraging this technology to revolutionise supply chain management by increasing efficiency, improving quality and accelerating innovative supply chain solutions.

If successful, the global economic impact of AI could boost GDP by $7-10 trillion by enhancing productivity and streamlining processes. This could translate to an estimated $60 – $110 billion in annual economic value in pharmaceuticals.

While the concept of AI has been around for over a decade, AI with human-like intelligence has transformed industries worldwide, providing endless possibilities.

In this article, I explore whether or not AI has achieved the promising potential it offers the pharmaceutical industry and how this could be enhanced.

Sophisticated quality control

Pharmaceutical packaging plays a crucial role in safeguarding critical medicines making quality control a vital component of the supply chain. UK regulations require that all packaging includes clear, understandable patient informational leaflets (PILs) and detailed information including the product name, dosage, chemical composition and safety information.

Innovations such as natural language processing (NLP) and machine vision, exemplified by technologies like the Cognex In-Sight system, are transforming quality control by ensuring accuracy and clarity while identifying packaging errors.

This system also verifies compliance with safety standards, checking for unique identifiers to prevent counterfeiting and anti-tampering devices to prevent contamination and misuse, especially by children.

AI has notably improved quality control for advanced packaging types like pre-filled syringes and auto-injectors. Amgen’s integration of AI in their quality control procedures detects air bubbles in viscous injectables, leading to a 70% increase in particle detection and a 60% decrease in false positives.

Overall, the integration of AI technologies like NLP and machine vision is significantly improving the quality control processes in pharmaceutical packaging, thereby enhancing efficiency and productivity.

Responsiveness and transparency

In a global market, transparency significantly enhances supply chain risk management and improves access to safe, quality healthcare. Blockchain technology provides a secure, immutable ledger that tracks every transaction of pharmaceutical products, boosting transparency and safeguarding against counterfeit medicines. Meanwhile, AI enables real-time monitoring of the supply chain, helping to prevent disruptions, predict market shifts, and detect counterfeit products through smart packaging and anomaly detection.

Combining blockchain with AI amplifies these benefits. Blockchain records transactions securely, while AI analyses this data to identify risks, forecast trends, and enhance decision-making. This integration not only maintains supply chain integrity but also helps stakeholders address inefficiencies, prevent drug shortages, comply with regulations, and ensure consistent quality control.

Although this integration is a relatively novel approach and faces challenges like data silos and interoperability issues, continuous learning and improvement are key to overcoming these obstacles and achieving comprehensive supply chain transparency.

Drug discovery and innovation

The COVID-19 pandemic underscores the crucial need for swift and ongoing drug discovery and development. Over the past decade, the digitisation of vast global medical data, including molecular screening profiles and health records, has fuelled AI-driven advancements in drug research, accelerating the creation of new treatments.

AI methods like de novo molecular design, structure-based design, and deep learning are revolutionising the drug discovery process, enabling the rapid analysis of biomedical data to speed up clinical developments. A notable instance is Pfizer’s use of AI in developing PAXLOVID, which cut computation times by 80-90%, significantly hastening research timelines.

In the UK, the MHRA’s introduction of the AI-Airlock reflects a commitment to integrating AI in healthcare regulation, allowing for the testing and refinement of medical technologies in NHS settings safely and effectively.

With the looming threat of future pandemics, AI’s ability to optimize logistics, predict demand, and ensure the timely distribution of essential medicines is becoming increasingly vital. AI-enhanced supply chains are essential for quickly delivering new, effective treatments during health crises, ensuring global access to life-saving drugs.


Genetic differences significantly influence how individuals respond to medications, from common antibiotics to complex chemotherapy treatments. These differences can cause adverse reactions (ADRs), which may be severe or life-threatening. For example, certain people lack the genes necessary to activate medications like codeine.

To combat ADRs, the NHS is prioritizing pharmacogenomics (personalised medicine) aiming to fully implement it by the end of the decade to improve drug and medical device safety.

The vast amount of complex genetic data presents challenges but AI is poised to address this by decoding, analysing, and organising large datasets, making it particularly effective in identifying genetic markers that predict treatment efficacy and safety as well as identifying anomalies.

These capabilities extend to optimising logistics in personalised medicine by forecasting demand, managing inventory, and ensuring timely delivery.

AI-enhanced supply chain management is vital for delivering personalised treatments efficiently, thereby improving patient outcomes and minimising the risk of adverse reactions.