AI Podcast Interviews Articles and Reports

This interview analysis 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.

This interview analysis is sponsored by CLARA Analytics 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 Software-as-a-Service (SaaS) model has experienced remarkable growth in the fintech sector over the past decade. According to research from Boston Consulting Group, fintech revenues are projected to increase sixfold by 2030, reaching $1.5 trillion.

In managing public sector projects, success hinges on strategic planning and innovative practices. Challenges such as budget constraints, communication issues, and resource limitations must be addressed through effective communication, precise resource allocation, and the strategic use of technology. Implementing digital upskilling programs can bridge the skills gap, while cloud-based solutions boost efficiency. 

This interview analysis 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.

One of the key strategies for future-proofing an organization is recognizing the limits of AI models and filtering out the misconceptions that AI is a one-solution-fixes-all narrative for businesses. Enterprises build an AI strategy supported by use cases and how LLMs can change the workforce going forward.

Explainable AI models are essential in pharmaceutical R&D because they provide transparency and understanding of how AI-driven predictions are made. In drug discovery and development, stakeholders, including researchers, regulatory bodies, and healthcare professionals, need to trust and understand AI models' outputs to make informed decisions. Without explainability, AI models can be seen as "black boxes," leading to skepticism and reluctance to adopt these technologies in critical decision-making processes. 

Like much of the financial services space broadly, the insurance industry is growing interested in adopting AI. The technology holds immense potential to revolutionize insurance, enhancing efficiency and accuracy across various processes such as claims and underwriting. However, concerns surrounding data privacy and integration complexities have prompted a measured approach among industry leaders.