AI offers powerful tools for improving pharmaceutical processes, but success lies in targeted applications. By balancing technology with reliability, regulatory control and process understanding, AI can be a solution rather than a distraction to complex industry challenges.
XpertRule’s Iain Crosley explains how AI can deliver real value for patients and pharma manufacturers alike.
Pharmaceutical manufacturers face mounting pressures: an ageing population, resource constraints, high drug development costs and an increasing need for cost-effective medication. According to the Medicines Manufacturing Innovation Centre, these pressures are holding pharma companies back from adopting digital technologies like AI.
This worrying assessment highlights two critical issues:
- Increasingly complex patient needs contrast sharply with the slow, costly process of bringing new drugs to market.
- Out-dated large-batch manufacturing methods and relatively short ‘use by’ windows lead to significant drug wastage – squandering not only money and resources but also hampering the pharma industry’s efforts to achieve Net Zero.
What’s frustrating is that the very problems seemingly preventing AI adoption are precisely those AI tools are designed to address. Continuous improvement, greater precision and compliance, increased efficiencies and reduced downtime are all areas where AI is proven to deliver significant gains.
As a partner to pharma manufacturers and providers, XpertRule is helping bridge this gap. Our AI solutions can optimize processes, ensure compliance and fuel innovation, enabling pharmaceutical companies to move faster, make better decisions and treat more patients.
Yet, a cautious approach to AI persists. Despite clear advantages, the pharma industry still faces significant roadblocks to full-scale implementation.
Barriers to Pharma’s AI Revolution
This reality was a central topic at last year’s PharmaTech Integrates conference, where industry leaders, including myself, gathered to discuss AI’s future in pharmaceutical manufacturing.
Key barriers raised at the event included:
- Data – As pharma supply chains become increasingly global, complex and opaque, getting organisations to share data for mutual benefit remains a major hurdle.
- Paperwork – Much of the pharmaceutical regulatory trail remains paper-based, requiring a huge amount of manual effort, resulting in substantial cost and time inefficiencies.
- Super-Optimization – Over-optimizing processes can make them more fragile, especially in critical supply chains. Running too ‘lean’ can amplify the impact of minor disruptions, while minor changes to inputs can drastically alter end products.
While speakers at this year’s PharmaTech Integrates on September 19th will likely discuss strategies to overcome these barriers, I will focus on sharing practical AI applications in pharmaceutical research and manufacturing.
Here are three key areas where AI tools are already delivering tangible benefits:
Capacity Planning & Scheduling
Achieving a balance between too little and too much is a tricky task for any factory operation but especially so in the complex, multi-product pharma industry. Idle manufacturing lines represent lost income, while sudden demand spikes require spare capacity. On top of that, many drug ingredients are expensive and must be used within strict timeframes, so tying up capital in excess stock can be risky.
The job is made harder by traditional planning methods being largely manual and based on reactive decision-making, leading to inefficiencies and missed opportunities. AI tools, by contrast, use real-time data, heuristic knowledge, advanced analytics and optimisation models of processes to improve the scheduling of factory and laboratory operations.
A powerful example of this is XpertRule’s work with a leading UK chemical manufacturer and global distributor, where our integrated order processing and process monitoring system has transformed their operational efficiency. By linking historical stock management, cycle time analysis and ‘Golden Batch’ monitoring with ERP systems in a user-friendly, dashboard, we’ve given purchasing, manufacturing and sales teams real-time production visibility. This enables just-in-time (JIT) deliveries to customers, minimizing lead times and reducing the financial burden of excessive inventory.
This use case shows how AI-driven insights allow manufacturers to dynamically adjust capacity and scheduling in response to shifting demands, boosting operational efficiency and promoting leaner, more agile production processes.
Quality Control
The pharmaceutical industry has an uncompromising approach to quality standards due to the direct impact on patient safety and regulatory compliance. Conventional quality control methods, while rigorous, are labour-intensive and prone to human error. AI can significantly enhance this process by enabling more accurate, consistent and efficient monitoring and control.
AI tools can integrate and interrogate vast amounts of structured and unstructured data, from production processes and laboratory results to medical records and clinical trials – to detect patterns and anomalies. For instance, our XpertFactory platform can collect and analyse machine and environmental data to provide deeper insights into key control variables, thereby improving Overall Equipment Effectiveness (OEE). Engineers can now receive real-time alerts about potential issues before they escalate, leading to near-zero error processes and increased uptime.
A ‘Digital Twin’ of the production process can further optimize OEE by simulating equipment performance and identifying future maintenance needs, helping prevent costly breakdowns and maintain consistent product quality.
A ground-breaking example of how AI tools can improve quality control is XpertRule’s work with ARGUS, a project exploring how digital technologies can detect microbial contamination in real-time and in-line.
A build-up of microbes is a key challenge for manufacturers of home and personal care products, such as shampoos and washing-up liquids, as it can mean having to clean and sanitize factory equipment and may even pose a risk to consumers. To avoid costly recalls stringent checks are in place; however, current microbiology-based tests are slow and laborious.
The ARGUS project uses innovative photonic sensors to monitor microbial growth on surfaces at multiple locations across the supply chain. Data from these sensors is combined with other relevant data from supply chain partners in a new digital platform, allowing insights to be shared. Though still in its early stages, this novel approach promises to disrupt current quality practices, with potential application across several manufacturing sectors.
Drug Discovery
Bringing a new drug to market is a long and costly process, taking over a decade and up to £1bn to discover, develop and test a new medicine. Pharmaceutical companies face tremendous pressure to accelerate this timeline while keeping costs in check. AI is playing an increasingly important role in making drug discovery more precise and efficient, significantly increasing the likelihood of success.
One of the most exciting areas is AI’s huge potential to improve Target Identification and Validation, where scientists identify the specific protein or gene to target with a new drug to treat a disease. By processing vast amounts of biological and chemical data, AI can pinpoint viable drug targets and help validate them for further testing, greatly reducing the time and cost of finding new treatments.
Other areas where AI is transforming drug development include Predictive Modelling, where AI is used to analyze historical data from previous client trials to forecast outcomes; and Compound Screening, where AI is helping researchers sift through massive libraries of compounds to determine those most likely to succeed in clinical trials.
These examples illustrate how the right AI tool or combination, applied effectively, can help pharma companies manage exponentially increasing volumes of data, unlock answers contained within that data and deliver meaningful improvements in production speed, efficiency, quality and compliance, ultimately benefiting manufacturers and patients.
To explore this topic further, please register for this year’s PharmaTech Integrates on September 19th, where I will be part of a panel discussion on the practical applications of AI in pharma.
For more info and to register, please click here
This article is part of our new series, Reality Check: What AI Really Means for Manufacturing, designed to inform, inspire and help you implement AI in your manufacturing operation. Look out for our next article on how Symbolic AI could help address a ticking timebomb within manufacturing.