But how can you figure out which functions within your business can actually be transformed by AI? What are the quality limitations? How can you evaluate which business service companies are using AI effectively while others could be selling hyped up linear algebra? The best way to know if an AI product is right for your business is by asking the right questions.
Any job seeker or talent acquisition professional will tell you about the challenges in the digital candidate experience. On one side, exasperated candidates blanket job websites with resumes and cover letters.
Vettd, the leader in deep learning for Human Capital Management, launches new eBook for enterprise HR leaders
Talent classification is a simple concept to grasp but can be a difficult practice to adopt without the right tools. We refer to talent classification as the process of categorizing human capital according to shared qualities or characteristics. This process helps you recognize, differentiate, and understand the talent you have at your disposal. Decision-making in talent acquisition and strategic workforce planning becomes much more straightforward with this level of insight.
Humans will always play a critical role in the hiring process, but the consistency vs efficiency challenge exemplifies why reviewing resumes is a task better suited for modern machines than modern humans.
As part of the candidate screening process, some organizations task their recruiters with manual tagging of the resumes they review. The idea here is to increase the usability of talent databases by generating metadata manually.
The college and university admissions process has plenty of room for improvement based on experiences of two Vettd interns who spent the summer gathering data for a large project. In this blog, the interns suggest several ways to make the admissions process more user friendly.
Vettd.ai college interns are gaining a unique perspective about the difference between classroom learning and real-world experience. They are being challenged to use open-source and low-cost software components to create prototype applications to compliment or compete with alternative approaches to natural language processing (NLP) solutions.
You've probably read about how AI is being used in data security, financial trading, healthcare, fraud detection, or elsewhere. AI has taken hold in these industries, in part, because there is “big data” associated with each and ready-made AI solutions. But, what about the industries or use cases that don’t involve mountains of numerical data?
