AI in Pharmaceuticals and Healthcare

There are many potential use cases for AI in the pharmaceuticals and healthcare industry, ranging from patient treatment to facilitating the R&D process. Machine learning algorithms’ ability to analyze large sets of data and discover meaningful patterns makes it a perfect match for the pharma industry.

A recent analysis by Accenture concluded that when combined, key clinical health AI applications can potentially create $150 billion in annual savings for the United States healthcare economy by 2026.

Another study revealed that 40% of respondents from the pharmaceutical industry confirmed that their organisations had already deployed AI.

Machine learning algorithms’ ability to analyze large sets of data and discover meaningful patterns makes it a perfect match for the pharma industry.

Pharma and medicine are data-rich disciplines. When it comes to analyzing medical data, patient classification, image analysis, genetic profiling and drug discovery and sales, no human agent can compete with powerful and highly intelligent machines. However, the data can be complex and easily misinterpreted.

There are many potential use cases for AI in the healthcare industry, ranging from patient treatment to facilitating the R&D process. Looking at this field, we see healthcare and pharma industry benefiting from AI in the following areas:

ai in healthcare
ai in healthcare

Artificial Intelligence in Aviation/Travel

The world’s leading airlines use artificial intelligence to improve operational efficiency, avoid costly mistakes, and increase customer satisfaction. Machine learning possibilities include fleet & operations management, development of autonomous machines and processes, and predicting the passenger behavior.

AI in the aviation industry is disrupting the way companies approach their data, operations, and revenue stream.

The world’s leading airlines are already using artificial intelligence to improve operational efficiency, avoid costly mistakes, and increase customer satisfaction.

There are many different areas where machine learning can empower the aviation business. They can mainly be broken down into three categories:

Artificial Intelligence in Finance and Banking

From algorithmic trading to customer retention, big players in finance sector are using AI to gain competitive advantage. AI can bring value across automated portfolio management, products recommendations, risk assessment, fraud detection, image recognition, and much more.

The term artificial intelligence was coined in 1955 by John McCarthy, a math professor at Dartmouth. Due to its evocative name, this field has produced a wide array of hype and claims. Nonetheless, data science is becoming increasingly recognized as the motive power steering the leading industries to the future.

Faster processor speeds, lower hardware costs, and better access to computing power have given rise to a growing number of FinTech companies. There has also been a rapid growth of high quality datasets for learning and prediction owing to increased digitisation and the adoption of web-based services.

In the highly competitive financial sector, artificial intelligence is at a rapidly evolving phase, with new use cases and algorithms uncovered in a matter of days rather than years. The availability of AI-powered systems lies heavily on the existing data and infrastructure, and the fundamental demands of financial regulation.

A recent study pointed out that the rise of data science in the finance sector is driven by five key factors: the general advancement of technology, factors particular to the financial sector, potential for increased profitability, competition on the market, and regulatory compliance.

Machine learning can help companies to reduce costs by increasing productivity and making decisions based on information unfathomable to a human agent. Intelligent algorithms are able to spot anomalies and fraudulent information in a matter of seconds.

AI use cases holding most value to the financial industry include:

ai in finance industry
ai in finance industry

Artificial Intelligence in Telecom Business

Telecom operators use machine learning to improve customer satisfaction and increase network reliability. To name a few, telecoms can benefit from predictive modelling, process analysis, fraud detection, churn prediction, and dynamic resource allocation.

Being applied across a wide array of industries, artificial intelligence has also made its way to the telecom business. Innovative telecom operators use AI and machine learning to increase network reliability, improve customer satisfaction & retention, optimize their business processes for higher profit, and much more.

We’re not far from the time where data science isn’t just a way of gaining competitive advantage. Soon, it’s a must-have for any telecom company looking to thrive in the next 20 years.

Currently, we’re seeing telecoms benefitting from data science in three main areas:

ai in telecom business
ai in telecom business