The age of digital electronics have brought around incredible changes in the talent industry, much of it focusing on connecting the right candidates to the right facilities and creating a corporate advantage.
According to a recent IBM study, which surveyed over 2,100 Chief Human Resource Officers, thirty-three percent of CHROs believe AI will revolutionize the way they do business over the next few years. In that same study, 65 percent of CEOs expect that people skills will have a strong impact on their businesses over the next several years. At IBM, AI is seen as a tremendous untapped opportunity to transform the way companies attract, develop and build the workforce for the decades ahead.
Consider this: The average hiring manager has hundreds of applicants a day for key positions and spends approximately 6 seconds on each resume. The ability to make the right decision without analytics and AI’s predictive abilities is limited and has the potential to create unconscious bias in hiring.
IBM announces the rollout of Watson Recruitment’s Adverse Impact Analysis capability, which identifies instances of bias related to age, gender, race, education or previous employer by assessing an organization’s historical hiring data and highlighting potential unconscious biases. This capability empowers HR professionals to take action against potentially biased hiring trends – and in the future, choose the most promising candidate based on the merit of their skills and experience alone. This announcement is part of IBM’s largest ever AI toolset release, tailor-made for nine industries and professions where AI will play a transformational role.
AI is a catalyst for HR transformation with clients.
AI is already providing a significant advantage in client's industries and has enabled companies to refocus recruiters’ time on attracting and hiring the candidates who are most likely to succeed in a given role, in an efficient and inclusive manner.
The goal for developing these tools is simple: to ensure organizations select candidates based on ability, not bias.
Read more here.
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