AI Articles and Analysis about Enterprise resource planning

In just the last year, rapid advancements in AI technologies, particularly in natural language processing (NLP), have dramatically impacted virtually every industry. Most recently, large language models (LLMs) have become front and center – driven by the overwhelming popularity of OpenAI's ChatGPT. 

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.

Implementing responsible AI in the financial sector is crucial for ethical practices, fairness, and transparency. Financial institutions must prioritize data privacy, address biases, ensure explainability, and practice ongoing monitoring. By doing so, they build trust, mitigate risks, and foster sustainable growth. 

As nearly every American household has realized over the last year, large language models combined with generative AI abilities pose tremendous challenges and opportunities for enterprises of every shape and size. Just ask anyone who has heard of ChatGPT.

MetLife is a leading global insurance company headquartered in New York City. It provides its customers various insurance and financial services, including life insurance, health insurance, retirement plans, and investment management.

Critical to the definition of robotic process automation (RPA) is the notion that the tasks a 'robotic' software automates are repetitive by nature, with exceptions in rare instances. While RPA cannot independently learn from and adapt to new contexts and workflow problems, it can if the RPA system is imbued with the correct AI capabilities. 

It is exceptionally difficult to get started, much less succeed, in AI adoption if one does not have at least a foundational education, particularly in those “modules” pertaining to AI capabilities, requirements, and organizational readiness. A business wanting to implement AI must understand the current state of adoption and how this current state matches up against AI readiness requirements.

There’s more to successful AI adoption than picking the right technology. Business leaders should be aware of the technical requirements of the initiative they’re undertaking, and few of those requirements are as important as data.