AI Articles and Analysis about Predictive analytics and forecasting

The OECD.AI Policy Observatory is an inclusive platform that brings together resources and expertise from the OECD and its partners to facilitate dialogue and provide evidence-based policy analysis on the impact of AI. It is built upon the foundation of the OECD AI Principles, the first intergovernmental standard on AI adopted in 2019, endorsed by OECD countries and partner economies.

This article is sponsored by Riskified, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines.
The advent of the COVID-19 pandemic is leaving retail and eCommerce enterprises awash in numerous, often chaotic trends, many of which are proving critical for driving AI adoption throughout the industry. Perhaps most prominent among these market dynamics is an enormous shift to online retail.

Accurately projecting and communicating shipment details in freight and logistics is paramount for successful, safe delivery across the vast supply chain. 

As AI becomes increasingly interwoven in the fabric of our everyday lives, the insurance sector is finding new business challenges and opportunities in all of the ways these emerging technologies bring increased risk - and security - to our lives.

As a business practice, model development aims to create a dataset, tailored through machine learning, that can accurately predict outcomes or classify data based on input variables. By following a structured approach, developers can ensure that the model development process is efficient, effective, and reproducible.

DHL is a German logistics company that offers parcel delivery, express mail, freight forwarding, and third-party logistics. Its parent company is Deutsche Post (‘DPDHL’), the world’s largest logistics enterprise, operating in over 220 countries and employing more than 510,000 people worldwide. 

Although not often regarded as a technological first-mover, the insurance industry has recently seen robust, even rapid, adoption and deployment of AI capabilities, particularly in those related to natural language processing (NLP). 

While the steel industry may not be synonymous with AI adoption, steel manufacturers are not different from any other business in ensuring efficiency in their output and throughput. There are almost certainly opportunities for AI implementation across an industry worth hundreds of billions of dollars that currently lags behind in adopting AI technology.