Artificial Intelligence in Pharma: The Benefits and Challenges Linked With the Tech

Artificial intelligence (AI) has had an enormous impact across myriad industries, and the pharmaceutical industry is no exception.

AI has become an important catalyst for the pharmaceutical sector with its enormous potential to transform everything from manufacturing optimization to research and development.

However, companies in this sector face several challenges, including privacy concerns and a shortage of skilled professionals.

Understanding the integration of AI in the pharmaceutical sector

What is AI in pharma?

AI is designed to undertake specific tasks that would normally require human cognitive abilities such as recognizing patterns, analyzing large datasets, and supporting decision-making by providing faster and more accurate data.

AI has the potential double profits

The latest PwC study indicates that if pharmaceutical companies integrate AI today with the aim of working faster, reducing costs, and developing drugs more efficiently, they increase the chances of doubling profits from about 20 cents per dollar to 40 cents per dollar by 2030.

Within the next few years, AI-driven improvements could add more than US$250 billion in value to the pharma industry, the report estimates with 40% of this coming from manufacturing and supply chains.

However, achieving this will involve having a well-defined plan for the implementation of AI, which only a few companies currently have in place, so a huge opportunity is still being missed.

Key benefits of AI adoption in pharmaceuticals

1. Data analytics in pharma quality control

With the help of the latest analytical tools, companies can generate large amounts of data, much of which humans would find difficult to evaluate.

With the help of AI, and in particular machine learning models, it is possible to process the collected data, spot hidden connections, and generate valuable insights.

In a nutshell, analytical tools enriched by AI can:

• Spot deviations in expected outcomes, highlighting possible problems with raw materials or finished goods.

• Predict trends such as potential equipment issues, and help to avoid these using previously collected data.

• Determine the best production and testing procedures by analyzing process characteristics and how these could affect quality.

• Provide real-time quality checks during production for quality assurance and quality control, minimizing lab testing, delays, and manual labor, and ensuring that medications always meet the required quality standards and can be distributed immediately.

2. Enhancing visual quality control

With the help of computer vision and deep learning, AI technology can undertake high-quality automatic inspections of final products.

In a continuous cycle, vision systems equipped with AI can:

• Detect minor defects such as particulate matter, cracks, and discoloration

• Count and evaluate particles in fluids (necessary for sterile products)

• Examine the integrity of packages including seal integrity.

Check out how AI-powered visual quality inspection systems work

https://www.youtube.com/watch?v=ppQEYI4F7vE

3. Speeding up analytical methods

In order to achieve the best results, developing and validating analytical methods involves a great deal of experimentation, trial-and-error, and continual improvement.

AI can significantly accelerate and simplify this process, which means quicker time-to-market for new medicines.

Here’s how AI can help in method development:

• AI algorithms can suggest the optimum experimental designs, minimizing the number of runs necessary to identify important variables and how these interact

• AI can potentially anticipate how chemicals will behave during tests, for instance how long they must remain in the machine or how well they separate, which minimizes the time scientists spend on trial-and-error experiments.

• AI can help to spot the best parameters for special light-based tests such as near-infrared and Fourier transform infrared to check, for example, if a medicine has been mixed evenly or to identify raw materials.

• AI can read and understand complex test results automatically and can suggest ways to improve testing.

AI's role in drug discovery and development

AI speeds up drug discovery by understanding proteins and improving diagnoses much more quickly.

It forecasts protein structures, aids in the early detection of diseases, and "partners" with researchers by managing repetitive jobs, allowing humans to focus on key decisions and innovative ideas, according to Dr Jennifer Bradford, Head of Data Science at Coronado Research.

Case study of AI implementation in pharma

In January 2025, Insilico Medicine (a biotech firm actively using AI) and Qilu Pharmaceutical (a large Chinese pharma company) announced their intention to create a strategic partnership worth US$120 million to work on new small-molecule drugs for cardiometabolic diseases by engaging Insilico's generative AI platform, Pharma.AI.

Insilico is working on AI-powered drug design and optimization for metabolic targets while Qilu is responsible for further development of the drug and its commercialization.

Strengthening compliance and risk management with AI in pharma labs

Because AI can analyze complex regulatory content as well as evaluate internal documentation, it can considerably improve risk management and compliance by spotting potential weak points.

By examining previous non-conformance and audit data, and any deviations, it is able to anticipate any potential compliance risks and advise the steps necessary to avoid this. AI algorithms can also ensure adherence to GxP* guidelines after examining large volumes of regulatory data, standard operating procedures, and batch records**.

*GxP (short for ‘good practice’) is a set of international quality standards and regulations intended to ensure quality and compliance within the life sciences industry.

**A batch record is a document that features the entire manufacturing history of a pharmaceutical product.

AI can also spot areas of possible non-compliance prior to an audit, helping laboratories to promptly address these as well as monitoring data inputs and outputs 24/7 for any inconsistencies or changes.

Streamlining supply chain management and drug delivery

It is vital but nevertheless challenging to ensure high-quality raw materials and parts are used across the extremely complex supply chain.

AI can reduce the risks related to counterfeit medicinal products and poor material quality by offering improved visibility and supervision, from supplier certification to final product delivery.

AI can therefore play an important role in the industry’s supply chain by:

1. Predicting a supplier’s level of reliability by evaluating data regarding supplier performance and audit outcomes, as well as any history of quality issues.

2. Spotting products and packaging that fail to meet the original product standards by applying AI-powered image recognition together with data analytics.

3. Improving storage conditions and shipping routes using real-time data including temperature, humidity, or delays so that products remain safe to use and are delivered more quickly.

Navigating the challenges and risks of AI in pharma

Organizations face various risks when using patient data to train AI models, such as privacy issues, data misuse, and difficulties with regulatory compliance.

Let’s explore some of the key challenges and risks involved in using AI in the pharma industry.

1. Data privacy and security concerns

Adhering to data privacy laws such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) is one of the biggest obstacles that pharmaceutical companies face when implementing AI.

These laws provide strict guidelines for the collection, storage, processing, and sharing of sensitive personal and health-related data.

GDPR was developed by the EU to protect people' basic rights and freedoms in relation to the handling of their personal data, while the HIPAA is a U.S. federal law that sets national standards to ensure people's protected health information remains secure.

Data breaches

AI in pharma depends on patient data, which obviously raises concerns regarding the misuse of health data, or potential data breaches.

According to the Cost of a Data Breach Report 2025 from IBM:

• The average cost of a data breach in 2025 was US$4.4 million, which represents a 9% decrease compared to 2024 as a result of more rapid identification and containment.

• Organizations that widely used AI for security saved about US$1.9 million, while those that had not implemented AI spent much more dealing with security problems.

2. Slow AI Adoption

Outdated technology, ineffective teamwork, and opposition to change can slow AI adoption, which is why effective change management is essential.

3. Lack of skilled professionals

In an environment where 80% of organizations use AI in at least one area of their business, and more than 90% intend to boost AI investments, the demand for experts in the design, deployment, maintenance, and operation of AI systems is higher than the supply, according to a McKinsey report.

The same report indicates that 46% of business leaders have experienced skill gaps that have led to challenges in the adoption of AI.

4. Regulatory hurdles for AI implementation

The pharmaceutical industry operates under strict oversight by agencies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA), both of which have issued guidance on AI (e.g., the FDA Guidance Document on AI and the joint FDA-EMA principles).

AI systems must be thoroughly examined to ensure they are safe, particularly as some models will evolve over time. Regulators also require detailed clarification of how AI makes judgments, which can be verified during audits.

5. Quick ROI expectations

Because this tech has such great potential, it also leads to very high expectations so pharmaceutical companies are under pressure to achieve quick returns on their AI investments.

• Companies pour huge investments into AI to assist with developing new drugs, improving research, and speeding up processes, and they are keen to see the benefit of these investments.

• The ultimate goal is to save money and bring new medicines to the market more quickly.

• The situation is made worse by the rising cost of drugs and the pressure from some governments for lower prices.

• At the same time, technology does not stand still so to prevent systems becoming obsolete, organizations must continually update their AI tools or collaborate with technology vendors.

Final word

It’s an undeniable fact that AI is already pushing the pharma industry to a new level but, at the same time, companies must be constantly alert to any biases, hallucinations (i.e., false data), and other issues that AI systems could deliver.

Adopting AI is not an easy task, with further pressure being placed on companies in terms of regulations and a shortage of skilled experts.