It is time for AI to Gloat — AI Daily | #tinder | #pof

Introduction

Earlier this week I had the opportunity to talk to Yoni Friedman a sales engineer at Gloat who talked to me about all the work that Gloat is up to. In this article, I will cover how this AI company works, the implications of their work and finally what future opportunities this presents.

Who is Gloat?

Gloat is an AI-based company which aims to create an agile workforce by removing barriers for workers to easily move between jobs within an organisation. This both helps increase company retention rates and help find the perfect roles for employees within the company.  Gloat has been described as the Tinder of the workplace. So essentially, from the business needs sides, Gloat creates perfect matches to match the needs of projects and business goals with employees looking for business exposure and skills development in different areas of the business. By finding these perfect matches, Gloat creates this overall view of the talent management environment where an organisation can organically identify needs the business to quickly allocate, find the right talent for it and allocate. However, they don’t just stop there as they also provide other opportunities such as mentorship and professional skill development for the employees.

How the AI work?

The AI, fundamentally, works through via different layers of; collecting data, modelling and applications. In the first layer of collecting data, Gloat actually takes a rather unorthodox approach because unlike a traditional AI approach where you would input data in large volumes, the algorithm at Gloat only needs around two pieces of information in the system – employee information and company opportunities which are then updated by the users. The next layer, modelling, takes all the data previously collected in the data stage and creates its own AI modules. Yoni excellently summarised this process in our conversation by describing it as “Lego bricks” that layer together to create an all-inclusive framework which then places all the data together. And finally, there is the application layer which is when these Lego pieces come together to compare and match employees to opportunities as well as match employees to other employees for mentoring opportunities. Furthermore, the AI modelling can help create a continuously evolving portfolio of applications focused either on employees with opportunities including a career development roadmap where the AI itself recommends a potential career path of projects and other opportunities based on an employee’s actual path set within the organisation and focused on leaders by providing organisational level insights from the data.


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