Teams, engagement and profitability

Update: We’ve launched. Yay! Head to Pep.ai to get it.

In 2012 Google kickstarted an initiative to understand the science behind wide variations in performances across teams. Code named project Aristotle, this data heavy exercise was supposed to yield the secret sauce(s) of top performing teams. The idea was simple, observe the various skills, personality types and backgrounds of people from over 180 teams and look for patterns. The result was that there were no patterns. The who part of the equation just didn’t seem to matter. Eventually it was understood that there are certain ‘group norms’ that efficient teams just follow (unknowingly of course) that play a major role in creating a much higher shared collective IQ than teams that did not follow said norms, even though the latter may have had individuals with much higher IQs individually.

The who part of the equation just didn’t seem to matter.

We recommend you read the linked article above but here is a summary of five key dynamics that set successful teams apart from others at Google.

  1. Psychological safety: Can we take risks on this team without feeling insecure or embarrassed?
  2. Dependability: Can we count on each other to do high quality work on time?
  3. Structure & clarity: Are goals, roles, and execution plans on our team clear?
  4. Meaning of work: Are we working on something that is personally important for each of us?
  5. Impact of work: Do we fundamentally believe that the work we’re doing matters?

Our interest in understanding teams and their dynamics led us to its macro extension, employee engagement. It turns out only about 32% of the American workforce is engaged with their jobs. Worldwide this number is at 13%. In trying to dig deeper into this, we spoke with over 20 CEOs from an array of industries, all of whom recognised disengaged employees as one of the key problems of their business. Looking back at our combined experience of over 20 years of working in the knowledge economy, we too now could clearly recognise instances of disengagement manifesting itself as low productivity, regular absenteeism, office timings being all over the place, excessive use of work-from-home’s, people becoming apathetic towards others in the same or different team and in cases people consciously undermining the efforts of others around them.

Its interesting to note that employee engagement is not a new construct or a problem we’ve discovered today. Research on it has been around for over two decades. There are papers that look at it from an antecedent and consequence angle. Maslach et al 2001 defined some of early predictors of burnout that plays a significant role in productivity. Schaufeli et al 2007 extended the burnout theory to define engagement as its positive antithesis.

Some common themes that appear regularly and consistently across all researches are feelings of choice and control, appropriate recognition and reward, a supportive work community, opportunities for feeding views upwards, and meaningful work.

Advances in machine learning and conversational interfaces have brought us to a place where a unique employee engagement solution, looking at all the themes defined above, is now possible. Next up, we get into the details of how Pep AI aims to create happier, more satisfied workforces by deep diving into each of the key themes.