AI Podcast Interviews Articles and Reports

The asset management industry is going through several challenges, like the prolonged low-interest rate environment, dwindling margins, increased cost pressures, and squeezed profitability. Meanwhile, exponential growth in data volumes has overwhelmed legacy data management systems and analytical tools, making it tiring to process and extract valuable insights from the deluge of information. 

According to research from the American Society for Biochemistry and Molecular Biology, nine out of 10 drugs fail to make it to market in the clinical trials process. Researchers in the cited studies found that 40-50% of these failures are due to a lack of clinical efficacy, meaning that the drug is not able to produce its intended effect in people, and 30% were due to unmanageable toxicity or side effects.

Business processes are defined in large part by the data within an organization. Value-driven data, by definition, represents higher-quality data. The importance of high-quality data cannot be overlooked. Research from MIT, even from before the pandemic AI boom, showed that insufficient data can cost as much as 25% of the revenue for most companies.

As a rule, AI adoption tends to take more time for legacy industries compared to digitally-native sectors. As a profile from June 2021 in Harvard Business Review explains, insurance companies are data-rich but have long relied on actuarial approaches to data and analytics.  The insurance industry has several concerns regarding the integration of AI. Insurance companies obviously have regulatory compliance as a top priority, so any AI solution implemented needs to comply with existing regulations regarding consumer protection and data security, among others. 

This article is sponsored by NLP Logix and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

This article is sponsored by Pieces and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

The applications of AI in the healthcare and life sciences industries are vast, with data-powered algorithms and analytics promising to upend the status quo. Applications, including the use of AI to interpret medical imaging, optimize drug discovery and development, analyze large-scale data sets to identify patterns among patient populations, and streamline provider workflows, are already transforming the industry. 

Over the last decade, public introductions of advanced technologies from big tech firms, such as Google Glasses and Meta's (then-Facebook) Metaverse platform, were met with infamous disappointment. In stark contrast, initial iterations of generative AI (GenAI) in large language models and other tools deployed across the global economy by smaller players like OpenAI in the last few years are having a far more lasting impact. A recent report from McKinsey noted that the staying power of GenAI could add $2.6 trillion to 4.4 trillion USD annually to the global economy.