Sep 29, 2019
Tracking leader development to optimize learning, engagement, & productivity (part 2)
What we’ve learned about organizational impacts on the development of leaders, and what it really takes to foster a learning culture.
In part 1 of this two-part article, I told a story that laid out some of what my colleagues and I have learned about the developmental trajectories of individual leaders and how development can be tracked, optimized, and leveraged. In part 2, I illustrate how organizations can create conditions that support learning and foster the emergence of a dynamic learning culture.
The story I’m about to tell involves three fictional high-tech organizations—T-Rex, ICON, and TNG. Early in 2012, each organization selected 200 potential future leaders, all hired within the previous 24 months, to participate in their talent development program. (Up to this point, each company had used its own hiring and selection procedures, none of which involved Lectica’s products or services.) That summer, participants began taking the LDMA at regular intervals.
BTW: Although this story is fictional, it is based on empirical evidence from dozens of projects in dozens of organizations.
The organizations
In 2012, T-Rex, ICON, and TNG were different in a number of ways that were likely to impact learning. Here I’m going to focus primarily on differences between their decision-making processes because decision-making plays a profound role in shaping organizational culture, especially when it comes to learning.
ICON was the oldest organization in the group, founded in the second decade of the 20th century. It was also the most conventional of the three organizations. At ICON, most team-level decisions were made by line managers who had received instructions from their line managers. Communication was primarily top-down, and buy-in, when it was seen as necessary, was obtained through persuasion or incentives. Team members were sometimes asked for their perspectives on decisions, but these perspectives were rarely sought to improve decisions. They were primarily used to determine what kind of persuasion or incentives would be required to obtain buy-in. Decisions (below the C-suite) were governed by rules, and rarely involved more than choices regarding the design of an implementation plan. Engineers worked in compliance with strict timelines.
T-Rex and TNG can be loosely characterized as Agile organizations. Although “Agile” means different things in different organizations, T-Rex and TNG were similar in a number of ways. Their engineers worked in teams, using processes like Agile, Scrum, Lean, and Design Thinking that were team-focused, iterative, and open-ended. Strict predetermined timelines were rare. Instead, products were built incrementally, through frequent, iterative cycles of goal setting, development, and testing. Over time, these iterative processes were adapted for use in team-level decision-making, and the flow of communication morphed from top-down to multidirectional—line manager to team members, team members to line manager, team to team. Team member perspectives often informed decisions, and many decisions were made by consent. (i.e., a mutual agreement to try a given approach. Not the same as consensus or buy-in.) To participate, team members had to learn to express and explain their perspectives—and to take into account the perspectives of others.
Neither organization had adopted a formal approach to decision-making. Their processes had evolved organically out of a general sense that everyone should have a voice, and were more or less informed by current engineering and design processes. I don’t want to romanticize decision-making in these organizations. Decision processes were often messy and sometimes dysfunctional. Both organizations desperately needed something like “Scrum for decision-making” to help them make decisions more efficiently. But by bringing multiple stakeholders from multiple layers or functions into more iterative decision-making processes, these organizations had created opportunities for learning that were missing at ICON. They had inadvertently created some of the conditions required to support the emergence of a learning culture.
The most notable difference between T-Rex and TNG during this project was that in the second year, we spent 4 months helping T-Rex participants build skills for learning optimally from everyday experience. (In other words, we taught them to Micro-VCol.) TNG chose to opt-out of this training.
The management level bands on the graphic above represent the fit ranges for typical roles in the management hierarchies of these organizations. (Conveniently, these fit ranges are the same for all three organizations.)
The graph above tells most of the story. ICON employees, on average, demonstrated little growth. Their organization’s strict hierarchy of decision-making, governed primarily by rules, edicts, and strict timelines, discouraged thinking, learning, and experimentation. We now have several examples of organizations like ICON in our database. They are often in the process of implementing culture change that features collaboration and learning, while at the same time, their most basic structures and processes systematically prohibit behaviors associated with collaboration and learning.
The employees of T-Rex and TNG developed nicely. The average TNG participant was likely to develop leadership decision-making skills in the 1140 range within 17 years (40 points of growth on the Lectical Scale). But T-Rex did even better. Its leaders were projected to gain an average of 70 points over the same time period. The size of the effect (averaging an additional 1.75 points per year over TNG) is in line with what we observe when VCoLing is practiced consistently in a range of learning contexts. As I have pointed out elsewhere, small increases in Lectical Scores add up, over time, to big differences in growth trajectories.
Summing up
Leaders who can work effectively with the complexity of their roles make better decisions. They are also likely to be more engaged and productive. Organizations that support learning are more likely to develop leaders who can work effectively with the complexity of their roles. Learning is best supported by creating opportunities for employees to engage in iterative decision-making processes that require perspective sharing, argumentation, and the ability to respond creatively to feedback from the environment. Adding VCoL to an environment loaded with learning opportunities supercharges learning and development.
There is a great deal of room for growth in most organizations. With the support of (1) the right metrics, (2) more iterative, distributed work and decision-making processes, and (3) VCoL, organizations can be reshaped to support the development of leaders who are clear, complex thinkers with excellent VUCA skills — leaders who can stand up to the challenges of this moment.
The growth projections for T-Rex are based on the assumption that most leaders can learn to VCoL effectively. We are not yet certain that this the case. I risked a slight exaggeration because I wanted to portray the full range of variations in growth we’ve documented over the years.
You may be wondering how T-Rex, ICON, and TNG obtained the information described in this article. It’s from Lectica’s latest offering, Lectica Inside. We’re having a launch celebration — with benefits — and your organization is invited to participate. Info is available on our website.
