[cajobportal Insights] AI and ML to measure Employee Engagement

Good Afternoon

The annual spend by companies on Employee Engagement is USD 720 mn+. Yet the efforts have struck a chord with just 13% of the workforce.

Has your organisation experimented with Organizational Network Analysis (ONA)

There are today AI and ML driven start-ups, like  KeenCorp, that analyse patterns in an organization’s (anonymized) email traffic to assess whether there have been changes in levels of tension experienced by a team, department or entire organization

Using AI along with psycholinguistic analysis, tension variations in written communications is computed. This number would encompass the impact of employee engagement, governance, change and investment issues.

Case in point was when the ~500,000 of the bankrupt Enron’s corporate emails – was used to back-trace the “key moments in the company’s tumultuous collapse”. The software predicted engagement score was lowest in the year 2001 when Enron filed for bankruptcy.

The AI system had identified a significant “inflection point” in Enron’s history on June 28, 1999. This was the day the Board had discussed a plan called “LJM”, involving a group of questionable transactions. The same would cover up the company’s badly under-performing assets while improving its financials. Eventually, LJM added to Enron’s demise. At that time, everyone at the company, including employees and board members, was reluctant to challenge this dubious plan. No one went to the board and said, “This is wrong; we shouldn’t do it.” But KeenCorp says its algorithm detected tension at the company starting with the first LJM deals.

The software can chart how employees react when a leader is hired or promoted.

In another case, the client investigated a branch office after its heat map suddenly started glowing and found that the head of the office had begun an affair with a subordinate.

Take the case of Vibe, a program that searches through keywords and emoji in messages sent on Slack, the workplace-communication app. The algorithm reports in real time on whether a team is feeling disappointed, disapproving, happy, irritated, or stressed.

In a Utopian world, employees would be honest with their bosses, and come clean about all the problems they observe at work. But in the real world, many employees worry that the messenger will be shot; their worst fears stay bottled up.

Text analytics might allow firms to gain insights from their employees while intruding only minimally on their privacy

What do you think?