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Job Growth Surges in September, Signaling Economic Resilience.

The September jobs report paints a picture of a labor market that is not only resilient but also surprisingly strong. This positive news, coupled with upward revisions for previous months and a continued upward trend in average hourly earnings, offers a reassuring signal about the underlying strength of the U.S. economy. However, the slight decrease in the average workweek serves as a reminder that some uncertainty persists, and the Federal Reserve will need to carefully weigh all available indicators when making decisions about future monetary policy actions.

Why employee recognition matters and how to effectively deliver it.

In my two previous columns (Creating a Great Employee Experience and Why RESPECT Matters), I introduced the RESPECT taxonomy of what employees most want from their employer and shared research revealing the customer loyalty and financial gains to organizations for delivering RESPECT (Recognition, Exciting Work, Security, Pay, Education and Career Growth, Conditions, and Truth).

Managers with highly engaged employees deliver improved business outcomes, including increased retention, higher productivity rates and lower absenteeism.

With many leading HR organizations successfully integrating artificial intelligence (AI) technologies into recruiting processes, AI and machine learning are improving recognition experiences. Yet there are essential differences between the effective use of AI in recruiting and recognition. In recruiting, AI has proven most successful in helping decision-makers source, screen and evaluate large numbers of candidates. For managers, recognition is not a volume process but a personal expression of appreciation.

Artificial Intelligence (AI) is transforming how organizations deliver products and services, manage customer experiences and orchestrate operations.

Early versions (1990s) of AI in applicant tracking systems (ATS) were used in keyword matching to help recruiters sift through large volumes of applications. As computational processing power improved exponentially one decade later, machine learning algorithms and natural language processing led to predictive analytics for candidate assessment and sentiment analysis, enabling recruiters to gain deeper insights into candidates’ personalities and cultural fits.