What is the future of AI in pharmaceutical industry?

The growing role of AI in pharma

Artificial intelligence (AI) is currently shaking up businesses, industries and entire countries. Some examples? Tech companies are investing billions to make sure they can lay the foundation for sustained business success in the near future. Other companies are implementing AI solutions, hoping to accelerate and automate processes.

Let’s have a look into how that transformation is happening for AI in pharmaceutical industry:

AI accelerates drug discovery and development

One of the most promising applications of AI in pharma is in drug discovery. Traditionally, exploring the use of new drugs is a time-consuming and costly process, taking an average of 10–15 years from initial research to regulatory approval. AI, however, is dramatically accelerating this timeline.

• Identifying potential drug candidates

AI-powered algorithms can analyse complex datasets to identify promising drug compounds much faster than traditional methods. Machine learning models can predict the effectiveness of new drugs by examining molecular structures and their interactions.

• Reducing research costs

AI-driven simulations can reduce the need for expensive lab experiments, helping pharma companies cut R&D costs while improving accuracy in drug development. 

• Repurposing existing drugs

AI is also being used to identify new uses for existing drugs, helping speed up approvals and reduce development timelines.

Companies like Insilico Medicine are leading the charge, using AI to reduce R&D costs and development timelines (Insilico Medicine).

AI in clinical trials: faster, smarter, more efficient

Clinical trials are a cornerstone of pharmaceutical innovation. Yet, they are often expensive and time-consuming. 

• Optimising patient recruitment

AI can analyse electronic health records (EHRs) to identify suitable candidates for clinical trials, improving recruitment speed and diversity.

• Improving trial design

AI models help design more effective clinical trials by predicting potential outcomes and identifying optimal dosing strategies.

• Real-time monitoring

AI-driven analytics provide real-time insights during trials, enabling researchers to adjust fast and fail less.

Pfizer, in collaboration with IBM Watson, uses AI to streamline trial design, optimise patient recruitment, and predict trial outcomes (Pfizer). This approach improves efficiency and reduces failure rates.

AI in pharma to optimise operational efficiency & regulatory compliance

The low-hanging fruit for ROI on AI is 100% in optimizing operational efficiency. Low risk, high reward. AI and GenAI are massively streamlining pharma operations by automating documentation, ensuring manufacturing quality, and predicting supply chain issues. This reduces compliance risks, cuts operational costs, and helps companies adapt quickly to regulatory changes while maintaining consistent product quality.

• Reducing cost of internal operations

AI-driven automation dramatically improves efficiency in business-critical processes, particularly where documentation and text play a key role, for example regulatory filings, packaging for distribution and other HQ operations.

• Manufacturing optimisation

AI-driven automation enhances production efficiency, reducing errors and minimising downtime in pharmaceutical manufacturing.

• Supply chain resilience

AI helps pharma companies anticipate disruptions, such as raw material shortages, and develop contingency plans to maintain stability.

For example, Merck has implemented an AI-powered digital assistant, the myGPT Suite, designed to help employees handle daily tasks more efficiently. This tool accelerates productivity by providing immediate, conversational responses powered by AI, reducing operational bottlenecks and delivering better outcomes for customers.

Be aware of the risks of AI in pharma

While AI in pharma offers immense benefits, it also comes with challenges.

Data privacy and security

AI relies on vast amounts of sensitive data, including patient information, which can be vulnerable to breaches. Therefore, it is important to implement robust data encryption, adhere to GDPR and HIPAA regulations, and ensure you have secure data storage systems in place.

Bias in algorithms

AI systems can inherit biases from training data, potentially leading to inequitable outcomes in clinical trials or patient treatment. To minimise these risks of biases in AI, it is important to use diverse datasets, conduct regular audits, and involve multidisciplinary teams to identify and address any potential bias.

High implementation costs

Implementing AI can be resource-intensive, requiring significant investments in infrastructure, training, and expertise. But it does not have to be. If you start with AI strategically, weighing both the costs and the potential cost savings through the use of AI. Start with smaller, high-impact pilot projects that truly demonstrate ROI before investing more and scaling up AI initiatives.

AI in pharmaceutical industry? Here to stay.

Artificial intelligence in pharma is one of the biggest game changers of the industry: faster innovation, improved patient outcomes, improved efficiency. Pharma companies looking to get started can best unlock its potential by addressing risks and adopting AI strategically. Find those use cases, build a proof of concept, turn them into solutions, and turn solutions into success. And we’re here to help.