Artificial intelligence (AI) and machine learning (ML) are shifting from being business buzzwords toward wider enterprise adoption. The efforts around strategies and adoption are reminiscent of the cycle and tipping point for enterprise cloud strategies, when companies no longer had the option to move to the cloud and it only became a question of when, and how. AI and ML implementation strategies are in the same evolution mode as companies build their approaches. Below are some thoughts around the how. Forrester reported that almost two-thirds of enterprise technology decision-makers have either implemented, are currently implementing, or are expanding their use of AI.

Improving employee engagement is at the top of many human resource leaders' to-do lists. But combing through an ever-growing amount of engagement survey data to extract actionable insights can be overwhelming. So industry vendors have created artificial intelligence (AI) tools designed to automatically analyze survey data to pinpoint themes and characterize the meaning of words or phrases. Tools like natural language processing (NLP) can save HR time and generate more-useful data along the way. SHRM Online spoke with Armen Berjikly, senior director of growth strategy for Ultimate Software in Weston, Fla., during the HR Technology Conference & Exposition for his thoughts on the state of NLP technology today, the pros and cons of using AI to analyze engagement survey data, and the importance of developing a code of ethics for using AI in HR. Prior to working at Ultimate, Berjikly was the founder and CEO of Kanjoya Inc., a workforce intelligence company that pioneered advancements in NLP technology dedicated to understanding human emotion.

Performance management has been changing over these years from traditional annual reviews to periodic and continuous feedback. We, being a software tool provider, are able to experience the changes in customer (managers, HR and employees) expectations on their performance management software. Initially, it was mere, year-end review automation to categorize the employees into various ratings; then organizations started moving away from bell curves, ratings and showing interest on goals and achievement. Now more focus is shown on quarterly and monthly reviews before the annual appraisals are conducted. It is fact that highly engaged employees will perform better; feedback and developmental inputs will help employees work better and be more productive than mere rating them on their skills and categorizing.

Artificial intelligence (AI) and machine learning (ML) are shifting from being business buzzwords toward wider enterprise adoption. The efforts around strategies and adoption are reminiscent of the cycle and tipping point for enterprise cloud strategies four years ago when companies no longer had the option to move to the cloud and it only became a question of when? And how? AI and ML strategies are in the same evolution mode as companies build their approaches. Below are some thoughts around the how. Forrester recently reported that almost two-thirds of enterprise technology decision-makers have either implemented, are currently implementing, or are expanding their use of AI.