top of page

Unexpected rise of AI in The Pharmaceutical Industry

The COVID-19 pandemic highlighted the healthcare industry as usual. Pharma corporations have led the innovative Coronavirus pandemic, alongside logistics and civic legislation. The pharmaceutical market has increased tremendously, and this is projected to continue. According to research, the global pharmaceutical manufacturing industry, valued at USD 405.52 billion in 2020, would rise at a CAGR of 11.34% from 2021 to 2028.

Technological advances, an ageing population, a concentration on developing countries' healthcare requirements, a surge in chronic diseases, and more pharma R&D spending all compound the Covid-19 effect.

Every player strives to maximise market opportunities. Pharmaceutical businesses compete fiercely. Numerous factors drive this fierce industry competition:

Innovation: Infectious disease companies compete to innovate. Covid-19 created a multibillion-dollar vaccine business.

Value-driven care: Companies competing on client experience go above and beyond. Eli Lilly, an American pharmaceutical manufacturer, supports diabetes patients who lost their jobs or insurance due to Covid-19.Pharma operations have changed, according to a McKinsey analysis. Covid-19 has driven organisations to be more open-minded, transparent, and agile. There is also a larger focus on improving network risks, both within and outside the workforce.

Increased transparency: The epidemic has forced the traditionally-regulated pharma industry to adopt new technology. Pharma brands are increasingly using digital and AI-driven analytical tools to increase transparency in drug development, marketing, manufacturing, and other areas. 75% of major organisations—those with more than US$10 billion in revenue—invested over US$50 million on AI projects/technologies, according to Deloitte. 95% of mid-sized organisations invested up to US$50 million in 2020.

Increased medication spending: By 2026, 300 new drugs are likely to be marketed. This exceeds the last decade's new medicine launches. Global medication spending is predicted to climb by 3-6% CAGR. By 2026, spending is estimated to exceed $1.8 trillion. This is on top of Covid-19 vaccinations and boosters.If competition is the future of growth, artificial intelligence will determine which pharma brands stay ahead.

  • How AI Helps Pharmaceutical Companies Compete?

AI improves medication discovery:
Pharma brands are embracing the power of AI to aid the relatively expensive and competitive drug discovery process. AI systems may successfully discover disease patterns in massive datasets and help determine which drug compositions would be best suited for treating particular ailments. The MIT-industry cooperation "Machine Learning for Pharmaceutical Discovery and Synthesis," which includes Bayer, Lilly, Novartis, Pfizer, and others, leverages MIT's powerful machine learning capabilities to improve medication recoveries. It can:

  • Access and analyse massive chemical data to improve business operations and outcomes.

  • Improve drug design, optimization, and synthesis with essential insights.

  • GSK and Vir Biotechnology recently used artificial intelligence to find coronavirus-treating antivirals, including Covid-19.

  • By implementing AI technologies, pharma businesses are actively focusing on cutting R&D expenses, eliminating human errors, and expediting the research timeline—leading to affordable-yet-profitable medications.

AI improves medicine development and production:
According to MIT study, just 14% of clinical trial medications are FDA-approved. That's bad for clinical trials that cost $1 billion per new drug. This means the drug approval process can turn out to be a costly affair if the drug does not get approved. AI can help optimise drug development here. By:

  • Performing quality control and establishing high-quality standards

  • Automating daily core workflows

  • Fixing production line supply chain issues and reducing material waste

  • Enhancing the manufacturing reuse value

  • Predictive maintenance and cost-cutting

  • Forecasting demand and supply changes

  • AI can give diagnostic aid and empower clinicians to deliver individualised treatment

  • Real-time patient data powers personalised treatment.


AI-powered solutions and machine learning technology can empower healthcare brands to:

  • Analyze large patient data sets.

  • Create an electronic medical record system to securely store patient data on cloud platforms.

  • Enable clinicians to view historical patient data, diagnostic tests, etc. to customise treatment in real time.

  • Identify issues. For instance, recently, the FDA allowed the marketing of an AI-drive platform—GI Genius—which can assist physicians in spotting indications of colon cancer. This colonoscopy equipment uses machine learning and AI algorithms to detect lesions in real time.

  • In a pandemic, use AI-powered wearables for remote patient monitoring. By 2025, the global remote patient monitoring (RPM) systems market is expected to reach $117.1 billion.Real-life example: Tencent and Medopad, a London-based healthcare company, developed an AI software to diagnose Parkinson's Disease in three minutes. Video analysis of patient footage promotes early diagnosis, successful screening, and accurate daily function evaluation.

AI can predict pandemics.
Machine learning models predict and prevent malaria epidemics. A data mining classification system called Support Vector Machine (SVM) was employed in a Maharashtra study to predict early malaria start with a lower error rate. This type of AI/ML solution can equip businesses to engage in early preventative care and put the proper measures in place to tackle it. 
Step 1: AI/ML tools use real-time data from many web sources.

Step 2: The predictive tool compares environmental, biological, and other factors to previous epidemic outbreaks to identify patterns, trends, and solutions.

Another outstanding example of predictive forecasting is GlaxoSmithKline’s use of AI to sell their seasonal drugs in the Allergy Cold and Flu category. The company employs a predictive algorithm that helps depict how the future season for allergy or cold and flu will evolve across diverse geographical locations; including where it may peak and where it may lag. The brand can:

  • Share flu season information on their website.

  • Improve national and regional media delivery

  • Inform stores on seasonal distribution, stocking, display, etc.

  • Pharma businesses can "sell and market" smarter with AI.

Pharma businesses may use AI to target and personalise their marketing campaigns because the industry is sales-driven. AI can:

  • Collect customer data in real-time, map out the customer journey and understand the customer’s needs, preferences, behavior, etc. better

  • Create creative marketing tactics that meet customer needs and company goals.

  • Measure the efficacy of marketing efforts and analyse key performance metrics conversion rates, retention rates, etc.

  • To minimise inefficiencies, compare prior marketing campaigns. It also predicts marketing campaign success.

  • AI can also study in-store/online customer behaviour. GlaxoSmithKline tracks consumer and merchant eye movements with AI. (with their consent). In its shopper's science lab, the company analyses data from eye-tracking glasses worn by shoppers in shops or online to see how end-users view the products. The AI-powered application captures critical images during shopping and analyses Areas of Interest (AOI) parameters including time to first fixation, heatmaps, gaze plots, video replays, etc. This vital data can be utilised to optimise product placement, artwork, and labeling, and understand consumer behaviour.

Get in Touch - We can help you grow your Healthcare and Pharma Business

Office 404,Tower 1,
World Trade Centre,
Kharadi, Pune


  • Facebook
  • LinkedIn

Thanks for submitting!

bottom of page