Automatically assess candidates, including emotions, from automated video interviews.

Proactive sourcing of potential candidates for roles using social media, industry databases and other sources like news feeds

Inadvertent wording of job listings can alienate potential candidates who may feel that the employer is not looking for employees with their profile. Using NLP to improve job listings can help remove one barrier to more diverse applications.

Leverage AI to automate the onboarding process of new employees - e.g. data extraction, and capture, form auto-generation, process management

Scan resumes to improve diversity and quality of candidate pool for recruitment. This will both challenge unconscious bias but also help challenge key gaps in candidate sourcing practice.

Automating responses to questions candidates have regarding the position and company during the application process to help assessment of position and candidate fit potential earlier on

When job seeking there is difficulty in reflecting on and improving a CV after having worked on it for a lot of time: candidates can use technology to do that for them before they apply for jobs. The technology will have been trained on successful CVs and resumes to identify how the content has been articulated. Therefore, such a system can compare the input with its database and identify areas for improvement. Increasingly this is effectively about reverse engineering recruitment algorithms.

Identify the characteristics and behaviours of the most successful and effective employees. This is an area of potential algorithmic bias risk.

Delivery of relevant training materials, reinforced with feedback loops to make sure that progress is appropriate. Feedback loop can then drive subject matter iteration, progress to new content or a structured focus to reinforce skills gaps - likely with additional human intervention. Clarity on what is being measured and captured is a critical definitional challenge - as is the extent to which educational professionals are in the loop.

Use 3D sensors to support the decision making process for judges in sports that require scoring or judgement calls. An example of this would be gymnastics where the tool can be trained on routine peformances. These tools may also be used to support individual's althletic training regimes.

Recommends specific training based on performance review data