As the Great Resignation continues and employers everywhere wrestle with hiring and retaining great employees, they'd do well to consider one of the more timeless drivers of why people leave their jobs: roughly half the time, they're 'quitting their boss' more than they are quitting their company. And yet the number of organizations that really invest in helping their frontline managers be great coaches-- by which I mean the kind of managers that can genuinely inspire and coach employees to greater job satisfaction and performance-- is precious few.
But the advent of AI-powered employee coaching technologies in the enterprise-- which are already making an impact in the hot and emerging category of Revenue Operations and Intelligence (RO&I)-- presents a real opportunity for companies to change all of that. The ones who focus and invest in making line managers 'super coaches' will put themselves in a far stronger position to win over the long term.
The High Cost of Bad Management
Unfortunately, most workers have experience working for a bad manager. And every company has felt the effects of poor management in the form of higher attrition, poor morale, declining productivity, and the like-- all of which negatively impact the bottom line.
At the same time, many of us have enjoyed the experience of working for a great manager and coach, someone who inspired us and found ways to make us dramatically better at what we do. The challenge for the modern enterprise is to make those great coaching experiences commonplace, and the bad coaching experiences the rare exceptions. AI-powered performance management presents a huge opportunity to do just that, helping more frontline managers become great coaches, faster.
What the Sports World Teaches Us
Managers are often compared to sports coaches, but today there is a key difference between the two roles that most people don't think about: the availability of data.
Sports is an area of human activity with a high number of repetitions and maximum instrumentation and telemetry. Hundreds of cameras can be placed on every tennis court, recording every breath and every stroke of not just one, but hundreds or thousands of games and players. Coaches can then use that information to determine the tailored coaching plan that can contribute to an individual athlete's success.
Two decades ago, humans would watch the athletes and write down telemetry in Excel spreadsheets, creating the perception of turning content into data. Now with machine learning, image and pattern recognition, sports videos are turned into data in real-time, providing immediate actionable insights. Coaching and managing employees, in contrast, have been highly manual processes, driven by anecdotes, because we've lacked the telemetry and data to describe the behavior of employees. No longer.
The opportunity to use data similarly in the workplace is being accelerated because of the COVID-19 pandemic, which drove the digitization of a much larger number of work activities for a majority of industries. Most workplace communications are now digital, and activity data is collected from sources like employee emails and calendars. This produces a huge amount of data-- about the activity of, say, salespeople-- that sports coaches have long had about athletes. As a result, managers can coach salespeople similarly to athletes, based on data, rather than anecdotes.
In practice, an AI-based coaching system can determine that a salesperson named Joe has not done enough prospecting with the right personas, and so will likely not meet his quota in the next quarter and the quarter after. Armed with this insight about prospecting activity and others, Joe's manager coaches Joe on how to increase the volume and improve the quality of his prospecting to close out the quarter hitting or exceeding his goal.
Part of what makes this so powerful is that AI is not generating insights for Joe's manager based solely on observations of Joe's work activity. Rather, the system is analyzing the activities of thousands of salespeople. It's similar to what sports coaches do now. They leverage the data generated by countless professional skiers going down a hill, tracking and analyzing every nuance of every angle, every turn and every pole plant. That aggregate data surfaces patterns that allow the performance of any skier to be compared and coached to what the Olympic skiers do on their best runs.
At the end of the day, which coach is better-- the one that worked with a few athletes over a long time and slowly acquired good coaching skills? Or the one that has learned from thousands of the best athletes? Bottom line: If you compare your best performers to your worst performers, you can coach people to perfection and at scale.
We still have a lot to learn about the emerging field of AI-based coaching. But some of the early learnings are fascinating. For example, people typically do not want to be coached by machines directly. They don't want an algorithm dictating their to-do list and managing their activities, rendering "next best action" technologies more of a gimmick. That's why it's so critical that front-line managers be the ones interacting with the technology, assessing insights, and developing coaching plans for their employees and go on a journey of implementing those coaching plans together with their teams.
The coaching technology will help managers in other ways. For all that professional sports coaches are used as a metaphor for managers, front-line managers have generally received little training on being good coaches. They lack the data skills, structure, discipline and workflow to be great coaches. There is no school to get a frontline manager MBA. And typically top individual contributors from last year get promoted to be managers this year, which is a completely different job. The opportunity exists now for managers to learn from other managers who are great coaches using the same AI training systems that assist their salespeople. And that kind of knowledge-based teamwork can really change the ball game.