How AI is Reshaping the Pharmaceutical Industry

 

 

Introduction

With the advent of artificial intelligence (AI), every aspect of humans and business is facing the impact. AI is steadily changing how industries work.  The pharmaceutical sector is no different; AI is bringing new ways to solve old problems. From speeding up drug discovery to making clinical trials more efficient, AI is creating real improvements. But it's not all smooth. There are issues around data safety, regulations, and the role of human workers. In this blog, I will try to explain how AI is affecting pharma today, with insights from experts.

Pharma Is Slow, Expensive, and Risky

One of the biggest issues with the pharma industry, which is also the core also is that developing a new drug is not easy. On average, it takes around 10-15 years and costs more than billions to bring one drug to market. Secondly, most drugs fail during the clinical trial phase. Only about 10% of drugs that enter clinical trials actually get approved.

Other operational issues include:

• Delays in patient recruitment for trials
• Long and manual processes in research and manufacturing
• Difficulty in predicting how patients will respond to treatments
• Rising costs with little room for error

These challenges make the industry slow to innovate and unable to meet urgent health needs quickly. One of the biggest examples we have seen is during the COVID-19 pandemic. Where countries faced shortages of the COVID-19 vaccine.  

The Consequences

When drug development is slow, patients suffer. During the COVID-19 pandemic, traditional methods wouldn't have delivered vaccines in time. Quick turnaround was possible only because companies used AI tools to speed things up.

• Patients with rare diseases often go untreated for years due to limited research.
• Pharmaceutical companies spend millions on failed trials.
• Delays in drug approval can lead to higher mortality and public mistrust.

On top of that, the amount of data in healthcare is growing fast. But making sense of it without AI is nearly impossible. Manual analysis isn't enough when you're dealing with millions of patient records, genetic data, and clinical notes.

 AI Brings Practical Tools to the Table

AI helps make sense of large data sets, find patterns, and make predictions. It doesn't replace scientists or doctors, but it supports them in making better, faster decisions.

Let's explore where AI is already making a difference.

1. Drug Discovery

What AI Does?

• Analyses data from past research to predict which compounds might work
• Simulates how new drugs will behave in the body
• Reduces the need for trial-and-error in labs

Example:

• Benevolent AI helped identify baricitinib, an arthritis drug, as a potential COVID-19 treatment. This was achieved in just a few days using AI algorithms.
• DeepMind's AlphaFold predicted the 3D structure of over 200 million proteins, a task that would take humans years.

Impact: Drug discovery timelines are cut by years, and costs are reduced by up to 70% in some cases.

2. Clinical Trials

What AI Does?

• Matches eligible patients to clinical trials using electronic health records (EHRs)
• Predicts which patients are more likely to respond to a drug
• Flag potential safety issues early

Example:

• Pfizer used IBM Watson to match patients to oncology trials. This reduced the time taken for recruitment significantly.

Data Point: Around 80% of clinical trials are delayed due to problems with recruitment. AI can cut this delay by up to 30%.

3. Personalised Treatment

What AI Does?

• Analyses genetic, lifestyle, and environmental data to recommend treatments
• Helps doctors make more informed decisions

Example:

• Tempus uses AI to provide oncologists with data-driven insights for treating cancer patients based on molecular and clinical data.

Impact: Improves treatment effectiveness and reduces the risk of adverse reactions.

4. Manufacturing and Supply Chain

What AI Does:

• Predicts equipment failures before they happen
• Optimises supply and demand planning

Example:

• Novartis has been using AI to monitor its production lines, reducing waste and improving efficiency.

Data Point: Predictive maintenance using AI can reduce unplanned outages by up to 50%.

The Advantages: Why AI Makes Sense

• Faster time to market: AI tools cut months or years from R&D timelines.
• Better decision-making: AI helps identify patterns that humans might miss.
• Improved accuracy: Reduces human error in data analysis and prediction.
• Cost savings: Automates tasks that would require large teams.
• Scalability: AI systems can handle growing amounts of data without losing speed or accuracy.

It's Not All Smooth Sailing

Despite the progress, using AI in pharma isn’t without its issues.

1. Data Privacy and Security

Patient data is sensitive. AI needs large datasets to work well, but this increases the risk of breaches.

Concerns:

• Compliance with HIPAA, GDPR, and other data protection laws
• Need for anonymisation and encryption
• Who owns the data? Patients or companies?

2. Regulatory Hurdles

AI tools must be approved by regulators before use. But current frameworks were built for traditional methods, not algorithms.

Challenges:

• Lack of clear guidelines
• Need for "explainable AI" that regulators can understand

3. Trust and Human Expertise

Doctors and researchers may hesitate to rely on AI, especially when they don’t understand how it works.

Issues:

• Black-box models offer predictions but not explanations
• Need for training to help staff use AI tools properly

4. Bias in AI Systems

If the data used to train AI is biased, the results will be too. This can lead to wrong or harmful outcomes.

Fix: Regular audits and diverse data sets are necessary to avoid these issues.

Expert Insights: What the Leaders Say

Eric Topol, MD, says, "AI won't replace doctors, but doctors who use AI will replace those who don't."

Vas Narasimhan, CEO of Novartis, calls AI "essential for the future of medicine."

FDA Commissioner Robert Califf has spoken about the need to modernize regulation to keep up with AI's growth in healthcare.

What's Next for AI in Pharma?

1. Smarter, Explainable AI

Future AI tools will be designed to explain how they work, building trust among users and regulators.

2. Wider Use in Developing Countries

AI could help bring modern medicine to areas with fewer resources by speeding up diagnosis and treatment.

3. Closer Collaboration with Regulators

Regulatory bodies are starting to work more closely with companies to create safe and effective AI tools.

4. AI-First Biotech Startups

New companies are being built around AI from day one, giving them an edge in speed and innovation.

Conclusion

AI is not going to solve all the problems that industry is facing; However, it's a strong tool that can help fix some of the biggest problems in pharma. By accelerating drug discovery, enhancing trials, and delivering personalised care, AI yields tangible, measurable benefits. However, it also brings new responsibilities, including protecting data and ensuring fair use. Companies that embrace AI thoughtfully and responsibly will be the ones that lead the way.

The future of pharma isn't just digital. It's smarter, faster, and more connected. And AI is at the center of that change.