Tracking leader development to optimize learning, engagement, & productivity (part 1)
Since 2002, my colleagues and I have studied and measured the developmental progress of thousands of leaders—with a specific focus on VUCA skills. In part 1 of this article, I lay out what we’ve learned about the developmental trajectories of individual leaders and how development can be tracked, optimized, and leveraged.
In part 2, I show how organizations can use growth trajectories to evaluate and increase the effectiveness of talent development programs.
The graph below shows the growth trajectories of three fictional leaders—Tony, Maya, and Jihan—all of whom are employed by a fictional high tech multinational called T-Rex.
Although T-Rex and its leaders aren’t real, they provide a good (although somewhat tidied up) representation of patterns observed in real-world performances.
The assessment taken by Tony, Maya, and Jihan was the LDMA, an assessment of leadership decision-making skills. Its primary focus is on VUCA skills. When taking the LDMA, test-takers are asked to explain how they would address a complex (thorny) real-world leadership issue, and why they think their approach would be effective. The scores represented in the graphic above are Lectical Scores, which can be thought of as complexity level scores. They tell us how much complexity an individual is likely to be able to work with in everyday workplace contexts. Scores (Y-axis) were awarded by Lectica’s electronic scoring system, CLAS, with human oversight.
Tony, Maya, and Jihan are software engineers, all initially hired when they applied for roles on the same development team in 2011. All three took their first LDMA in 2012 after being recruited into T-Rex’s talent development program. After taking the LDMA, they were given access to their LDMA reports and encouraged to follow the learning suggestions in the reports. No additional support was provided at the time because T-Rex wanted to see how much participants would grow during the following year without further intervention.
As you can see, Maya and Jihan performed in the 1100 range in 2012, while Tony performed in the 1130 range. These scores were different enough to place Tony in the upper-level management range at T-Rex (lime green band), while Maya’s and Jihan scores were on the border of the mid-level management range (border of the red-yellow band).
The management level bands in this graphic represent the fit ranges for typical roles in the T-Rex management hierarchy. These would be determined with a General Role Complexity Analysis.
The 2012 Lectical Scores strongly suggested that Tony was ahead of the pack. This observation was supported by Tony’s line manager, Lisa, who reported that Tony’s engineering skills were considerably more advanced than those of his teammates. In fact, T-Rex was tempted to offer Tony a leadership role at this point, but before doing so, they asked us to upgrade Tony’s LDMA to the human scored version, which includes an analysis of 4 VUCA skills plus Clarity of argumentation. The upgrade revealed that although Tony grasped the complexity of the scenario presented in the LDMA at a relatively high level, his VUCA and Clarity skills, which are required for effective action in complex situations, were on the low side.
It was decided that for the time being, Tony would stay in his current role, where he could further develop VUCA and Clarity skills prior to assuming a formal leadership role. Because Tony’s engineering skills were more advanced than those of his colleagues, we advised Lisa to do her best to provide Tony with engineering challenges that would keep him in his Goldilocks Zone while he was working on his VUCA skills.
By 2013, Jihan’s Lectical Score had increased by 10 points, whereas Maya and Tony provided little or no evidence of growth. Because we had discovered low VUCA and Clarity skills when we upgraded Tony’s first LDMA, we were unsurprised by his 2013 Lectical Score. Before the complexity of Tony’s thinking about workplace issues was likely to increase, he needed more reflective real-world practice. Neither were we surprised by Maya’s score. We often see little of evidence of growth during a single year in the absence of some kind of deliberate ongoing learning activity, and Maya had reported having little time to follow up on the suggestions in her first LDMA.
Jihan’s 10 point growth was more of a surprise until we took into account that along with two of his teammates, Jihan had undertaken every single learning activity in his LDMA report and had used the materials on our website to learn how to VCoL his way through everyday learning opportunities. At this point, even with only two data points, we were pretty comfortable predicting a steeper long-term growth trajectory for Jihan than for his colleagues Maya and Tony, and advised T-Rex to begin fast-tracking as long as his performance in other areas was acceptable. The biggest challenge with working with outliers like Jihan, whose growth outpaces the growth of most other employees, is providing them with a series of true peers and line managers who can keep up. Interpersonal dynamics can fray rapidly when an outlier joins a team. Coaching is likely to be necessary.
VCoL (the Virtuous Cycle of Learning) is both a model of learning and a practice that helps people learn skills faster and better while increasing engagement and satisfaction. We estimate that 5%–10% of the individuals who take the LDMA teach themselves to VCoL by following up on report recommendations. (This, by itself, is an indicator of growth potential.) Most adults learn to VCoL by taking a course that helps them habituate the practice.
After being tested in 2013, everyone who entered the talent development program in 2012 participated in a 4-month practice-based course in which they all learned to maximize their ability to learn from everyday experience with VCoL. About 70% of participants reported becoming habitual VCoLers, and over 90% believed they were learning better and faster by the end of the program.
By 2015, a clear pattern seemed to be emerging. All three of our participants were showing evidence of growth, with Jihan making the most progress, Maya coming in second, and Tony progressing more slowly.
At this point, T-Rex asked us to upgrade Tony’s 2015 assessment to get a more precise idea of what might be slowing him down. We discovered little evidence of change in his VUCA skills, though his Clarity scores were high. We also learned that Tony, who still occupied his initial role in the organization, had not integrated well into the team. His 360 peer ratings had been problematic in 2014, leading to the decision to keep him in his current role for an additional year. They were worse in 2015. At this point, he was viewed as arrogant and judgmental by most of his teammates and was finding it increasingly difficult to conceal his frustration.
Lisa had noticed Tony’s increasingly visible frustration with his teammates and was having trouble providing him with extra challenges without fueling their perception that he was being rewarded for what they saw as bad behavior. She argued that T-Rex could lose Tony if he was not moved to a team composed of similarly talented engineers. We agreed that he was also more likely to build VUCA and engineering skills in a group of well-matched peers. A few weeks later, Tony was moved to the “genius pool,” a group of T-Rex engineers who are not connected to a single project, but whose expertise is made available on demand.
Maya’s Lectical Score increased by 10 points between 2013 and 2015. She had been a dedicated participant in the VCoL course and was now a VCoL evangelist—even training to teach the VCoL course to new hires. During 2013, she volunteered for a lead role on several projects. Because T-Rex teams use a collaborative, distributed decision-making process that requires good VUCA skills, these were great opportunities to put her growing VUCA skills to work. After taking the 2015 LDMA, Maya applied for a management role. To support the decision-making process, we upgraded her 2015 LDMA with VUCA and Clarity scores. Her Clarity score was very good, and her VUCA scores were compatible with the requirements of an early mid-level management role at T-Rex. She got the job.
The complexity level of Jihan’s LDMA performance in 2015 was 20 points greater than in 2013. This amount of growth is exceptional. Because Jihan was being considered for advancement to an upper-level management role at this time, we upgraded the 2015 LDMA to provide VUCA and Clarity scores. Both exceeded the requirements of the new role. Moreover, Jihan’s 360 results were positive, with only a few areas for improvement, and his technical engineering capabilities were growing along with his other skills. Overall, Jihan was maintaining balance across skill-sets and was widely respected. The decision to fast-track him was working out well.
By 2017, we had enough data to make provisional growth projections for Tony, Maya, and Jihan. They turned out to be pretty close to the growth projections shown here. These are based on all seven test times.
Despite a leveling-out of Maya’s growth during a prolonged family crisis between 2017 and 2019, her growth trajectory shows that she is likely to be ready for the complexity of a senior leadership position in 6–7 years.
Tony is thriving in his genius pool role, and is finally showing evidence of growth in the complexity of his leadership decision-making skills. At this point, we would have projected a steeper growth trajectory for Tony than shown here, but his growth between 2017 and 2021 slowed a bit earlier than we would have expected in 2017. Even though he’ll tell anyone who listens that he isn’t interested in leadership, it looks like he’s going to have the skills to lead a genius pool one day. (It’s a senior role in T-Rex.)
Jihan continues on a steep growth trajectory through the next several years. In 2017 he already had his eye on his line manager’s role. If T-Rex holds on to him, he’ll be eyeing the C-suite in a few more years.
Growth tracking can play in a valuable role in developing, engaging, and retaining talent. Here, I’ve illustrated how growth tracking can be used alongside an organization’s existing practices to support the growth of essential skills and enhance decision-making around promotion and role assignment. Next, you’ll see how T-Rex stands up against other fictional, but reality-based organizations who measured the growth of leadership decision-making skills from 2012 to 2021.
You may be wondering how T-Rex 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.