Rethinking Workplace Learning and Development Catalyzed by Complexity

Abstract

Keywords

workplace learning complexity democratization of HRD

Why Complexity?

Workplace learning models were historically developed in a relatively stable, predictable, somewhat compartmentalized world. But today we live in radical uncertainty and contingency. The world is a real-time, real-consequences, multiplayer, high stakes gaming table. No one living through the Global Reset (Schwab & Malleret, 2020) of COVID-19 would have any trouble agreeing that we are in an age of such complexity that it is likely to require a rethinking of all that we do. Complexity has changed the workplace learning playing field. Complexity derives from the Latin word complexus—meaning “surrounding, encompassing”; the past participle of complecti—meaning “to encircle, embrace”; and plectere—meaning “to weave, braid, twine, entwine”. The word complex is “a whole comprised of interconnected parts” (Online Etymology Dictionary) that are also interdependent.

Scholars (e.g., Lee, 2003; Weick & Sutcliffe, 2001) began examining complexity’s impact on work and organizations decades ago. What brings it to the fore today? And how in particular is complexity changing workplace learning and development? What features of complexity are impacting their design and implementation? These are the questions we sought to answer in Rethinking Workplace Learning and Development (Watkins & Marsick, 2023).

Our book features responses to these questions by a purposeful sample of L&D leaders, who joined us for remote coffee and conversations. We subsequently invited a purposeful sample of leading Workplace Learning & Development practitioners and Chief Learning Officers (CLOs) to discuss how they see Learning & Development adapting to support change in organizations and systems. In this article, we share some of their stories, views and predictions about learning in organizations of different sizes, purposes, and cultures.

Their stories shed light on decentralization and democratization of learning, aided by intelligent technologies. Learning leaders are questioning long-held assumptions and experimenting with strategies suited to complex interdependent systems, networks and ecologies rather than traditional hierarchies. Leadership is being redefined as shared. Solutions are multiple to prepare for emerging eventualities that increasingly cannot be predicted. Small moves may lead to big changes. Expertise and innovation may emerge from the periphery. Through these stories, we use the lens of complexity and interdependent complex adaptive systems to explore what it means to wire learning into the culture and practices of learning-rich environments to meet critical learning challenges in today’s workplace.

We capture their voices and review the literature here to shed light on how complexity is understood and measured. We highlight key trends and demonstrate that learning is relational, entangled in complex, boundary-crossing systems, and ever changing. Our final conclusions, questions and reflections mirror the twists, turns, and surprises that emergence foregrounds and speak to the value of a continuous state of necessary ready-ing for change (Bateson, 2022).

How Does Complexity Influence Learning Today?

Many scholars have defined complexity. We have drawn on several of these, with a focus on Complex Adaptive Systems. Our thinking has been influenced by Pendleton-Jullian and Brown (2018) and organizational management thinkers such as Weick and Sutcliffe (2001). But we started with Holland (1992) and others affiliated with the Sante Fe Institute. What distinguishes complexity science is the focus on the whole (Gleick, 2008). Miller (2015) wrote that “We inhabit a world, where even the simplest parts can interact in complex ways, and in so doing, create an emerging whole that exhibits behavior seemingly disconnected from its humble origins” (p. xviii). Earlier, Mitchell (2009) defined a complex system as “a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behaviors, sophisticated information processing, and adaptation via learning or evolution” (p. 13). He added a focus on emergence: “…an alternative definition of a complex system: a system that exhibits nontrivial emergent and self-organizing behaviors. The central question of the sciences of complexity is how this emergent self-organizing comes about” (Mitchell, 2009, p. 13). Finally, Mitchell (2009) stressed that the adaptive behavior of complex systems when faced with environmental disruptions is self-organizing through learning or an evolutionary process. This adaptive behavior is what enables emergence. Central features of a complex system then are interdependence of parts, adaptation, non-linearity, and emergence.

Brown and Thomas (2009) caution that, amidst “infinite complexity, endless possibility, and near constant change … our approach to education and learning needs to be as rich and complex as the challenges and opportunities we face” (p. 335). As Ashby’s (1962) law of requisite variety states, we need to match the variety in the system with the variety of our responses. This then is what workplace learning and development must do in the face of complexity—diversify our responses, promote and work with the interdependence within the system, and create the conditions for new possibilities to emerge.

How do we know what direction to take in a state of ambiguity? Or when our actions affect other units in the organization? Every change in a complex system introduces change elsewhere through its connective tissues. Emergence of new thinking and approaches happen at the boundaries. Pendleton-Jullian and Brown (2018) note that “Complex systems are found in the transitional space between order and chaos; they are charged by their potential to slip into chaos while human nature works desperately to tame them into order. Poised between ordered and chaotic systems, complex systems are brimming with potential at all times” (p. 45). Human resource developers can better help employees recognize and respond to complexity if they can identify what it comprises and the implications complexity holds for how learning occurs in these circumstances.

Measuring Complexity

L&D leaders monitor and adjust to both unexpected, as well as hoped for, outcomes of change. What needs to be measured to understand complexity? Rebout et al. (2021) describe the complexity of a system as the sum of diversity, combinability, and flexibility. The number of different units, how flexibly they may be structured or restructured, and the extent to which they can be combined— together these factors create exponentially increasing complexity. Schwab and Malleret (2020) emphasize the non-linearity as a further complicating factor where, in complexity, actions taken in one part of the organization can have a disproportionate impact in another area. Earlier measures of complexity considered other factors. For example, Jacobs (2013) looked at multiplicity, diversity, and interconnectedness as dimensions of complexity. Vesterby (2008) considered what you would be able to quantify to determine the degree of complexity in a system and identified these variables: # of components, # of different kinds of components, # of elements of each kind, # of relations, # of diverse relations, and the # of each type of relation.

Schwandt’s (2009) view of measuring complexity is closer to more contemporary views. Like other scholars, Schwandt also considered the degree of diversity and interdependence in the system as contributing to the level of complexity but noted that the greater the ambiguity and fast flux, the greater the complexity. These factors significantly impact the system’s ability to act.

Complexity and Learning

Mitchell (2009) notes that the adaptive behavior of complex systems is centered on their ability to self-organize through learning or evolutionary processes when faced with environmental disruptions. What does research tell us about complexity and learning? In a study of learning in an emergency department, Goldman et al. (2009) found that: “learning in a chaotic environment does not involve substantially different learning facilitators than learning in a non-chaotic [environment]: observation, practice, mistakes, feedback, questioning, and reflection …. What is different about how learning occurs is the relationship of the episodes and the amount of motivation and self-direction required by the learner” (p. 568). On the other hand, Taber et al.’s (2008) study of paramedics and firefighters concluded that incidental learning influences:

The ability to respond to emergent situations, adapt policy into practice, and navigate through the grey areas and organized chaos of their professions. However, in these terrible moments, when all things anticipated or thought known dissolve, and when a firefighter or paramedic stands alone or with a comrade needing to make just the right decision, another kind of active, creative, fast-as-lightening learning must be deployed (p. 284).

Incidental learning in complex contexts is only sometimes accurate. As Harner’s (2013) study in a healthcare context noted:

Insufficiencies in knowledge will force incidental learning to occur as employees look for solutions to unanticipated problems. Rapid decisions and instant responses required by time constraints result in incidental learning and subsequent unintended behavior that may or may not be desirable (p. 43).

Jinks’ (2022) study also sought to understand the complexity in learning. She used Minecraft to create a complex learning context and studied users over time to identify an index of the roughness of learning (patent pending). This measure looks at the interaction between a measure of effectiveness and the adaptive capacity of the individual at increasing levels of complexity to identify the points at which the task is easy enough or too complex to be solved without intervention. She hypothesized that in extreme levels of complexity, system-level interventions are needed.

Recurring L&D Practices Amidst Complexity

How, then, do learning and development leaders support learning amidst this level of complexity? Here we single out three recurring trends. First, at the individual level, organizations support learner-driven preferences by democratizing learning options and delivery, often by capitalizing on intelligent technologies. Second, boundary-crossing capabilities and practices to enable new learning stand out. Third, In the face of escalating interdependence, organizations are merging learning and development with organization development functions to embed core practices in the culture.

Democratizing Learning -- Where Purpose and Pull Meet

L&D leaders differentiate purpose-driven learning from initiatives pushed down from the top, based on organization-driven vision and strategy. Purpose is motivated by curiosity, growth, and intrinsic motivation. Organizations seeking purpose are guided more by “pull” (Hagel et al., 2010). Pull recognizes that small moves can lead to big impact because of the amplifying effects possible in interdependent, interacting complex systems.

L&D leaders increasingly capitalize on purpose by democratizing learning initiatives in organizations. They have pushed choices down to learners regarding what, when, why, and how they wish to learn. Learning is personalized and customized. The environment can be seeded with options for what Bersin (2018) calls learning-in-the workflow so that employees can easily access what they need to know in different modalities. On-demand learning and digital coaches provide structured guidance, and peers join one another to learn in communities of practice. Thus, everyone can access what was once the province of professionals. There is a shadow side to self-driven learning. Employee ability to learn is not uniform; and organizational norms, mechanisms and practices might not support critical thinking (Jimenez, 2019) Some organizations do scaffold capacity building. For example, both Unilever and IBM provide learning experience platforms that tailor learning plans for upskilling and/or reskilling to self-identified goals. They help learners to then switch jobs to use acquired new capabilities within or outside the organization. They provide rewards and recognition for learning.

Democratizing learning can enhance collaborative peer learning in support of common interests and innovation. Hagel et al. (2010), for example, identify creation spaces that build on pull to create new knowledge. How do organizations help people from diverse backgrounds cross their boundaries of difference in order to generate new solutions to complex, ever-changing circumstances? We turn for answers to boundary crossing practices shared by L&D leaders that moved our lens on complexity learning to the group/team unit of analysis.

Boundary Crossing

Edmondson and Harvey (2018) recognize that much collaboration involving boundary-crossing happens outside of formal teams. They emphasize processes of teaming to produce new ideas, products or services that no one individual or group could achieve on their own—which unveils the challenge of how to cross deep boundaries of semantic, interpersonal and technical knowledge. Citing Dechant et al. (1993, p. 9), Decuyper et al. (2010) define boundaries as “the intangible but very real lines which separate person from person, group from group, and group from organization” (p. 118). Boundary crossing is especially important in response to complexity because, as Pendleton-Jullian and Brown (2018) emphasize, change at the boundary is a gateway to interventions designed to innovate for system learning.

Research-based models of team learning (Dechant et al., 1993; Decuyper et al., 2010) draw attention to learning-intensive practices for boundary crossing, as well as psychological safety as a prerequisite for good teaming (Edmondson, 1999). Decuyper et al. (2010) identify examples of these learning practices, as in: “dialogue, feedback, sharing of information, framing, reframing, confrontation, negotiation” (p. 116). Robbins, 2020, for example, in a study of five seed-phase entrepreneurial teams, found that successful teams used “tools like turn taking, probing, and repair (error correction or gradual refinement—by self or other) … for dealing with conflict” (p. 15). Gherardi (2000) further noted that “in everyday practices, learning takes place in the flow of experience, with or without our awareness of it” (p. 214). Practices encode norms derived by unconscious socialization, but as Gherardi (2000) also notes, they can be altered to disrupt norms that are no longer functional.

A literature review by Ungureanu et al. (2020) noted that boundary crossing for internal or external innovation did not always achieve their goals, in part due to inadequate relational capabilities. They further identified three types of relational capabilities—structuring, alignment, and communication. Research also showed that there was no one right way to intervene at the boundary to reach desired outcomes. However, research did suggest that how interventions were used could either break or defend boundaries. Ungureanu et al. further found that organizations could improve success of cross-functional innovation teams by providing “support and facilitation… by creating collaboration structures …, project brokers, collaboration contracts or appropriate climates for learning and innovation” (p. 988).

L&D leaders we talked with described organizational practices that supported effective boundary crossing. When working as an internal consultant in the technology industry, Bill Gardner (2023)—currently Founder and Managing Partner, Noetic Outcomes Consulting, LLC—used FlowTeams, a kind of rapid prototyping, for “achieving shared purpose” via “exploration of new ways of thinking …[that] goes beyond reliance on existing knowledge” and draws on “whole brain learning, sensory knowing, visualization, metaphor, and imagination” (Watkins & Marsick, 2023, p. 100). FlowTeams resemble the kinds of creation spaces that Hagel et al. (2010) advocate to tap into divergent thinking of edge players who share common purpose and operate on self-organizing principles.

Agile technologies provide an array of boundary objects designed to facilitate iterative experimentation and boundary crossing that are built into structures and sequences to both tap into divergent thinking as well as resolve differences across boundaries to converge on product decisions. Sleeva (2021, 2023) researched three different companies adopting Agile to adjust to change and respond to uncertainty. Boundary crossing in Agile called for new practices such as swarming—assistance from many team members when one member cannot complete a task on their own; and pair programming—when two equally skilled developers work together in roles of navigator and driver using one computer. Sleeva’s study showed how practices such as these encourage and support boundary-crossing and experimentation. Sleeva, Head of New Ventures, Digital Network Services (in Watkins & Marsick, 2023) also noted the need for changes in organizational cultures and practices in order to scale Agile. Sleeva’s study underscores a significant trend that L&D leaders described, that is, a merging of L&D with OD and other human change interventions. We turn to that next.

Merging Functions

While many organizations remain quite siloed, some acknowledge that learning and development is often no longer the province of the unit responsible for it and that enacting changes that add value to the organization requires collaboration with other units, often the organization development function. When we talked to these practitioners, they commented that leaders often initiate and lead major learning initiatives with little or no consultation with learning and development. Managers are also often evaluated on their ability to mentor and coach those they lead. Indeed, in our research on the learning organization culture, this item is the single most predictive of changes in organizational performance (Watkins & Kim, 2018). Human resource developers can better help employees recognize and respond to complexity if they can identify what it comprises and the implications complexity holds for how learning occurs in these circumstances

Creating Inclusive Conversations

At ESPN/Disney, integrating a learning and organization development approach to developing skills in conducting more inclusive conversations has led to these conversations simply being “how we do things here” (Cornileus, 2023, in Watkins & Marsick, 2023). Cornileus, Senior Vice President for Learning and Talent Solutions at Disney, noted that as they were planning how to roll out this approach as part of a diversity initiative, they considered the organization’s culture of meeting over food or coffee in the company café and tapped into this norm in the design of inclusive conversations. Initially, they held dinners where people addressed an opening question, then were free to discuss whatever was on their minds. Then COVID-19 happened and they moved the conversations online. Over time, the conversations evolved and were prompted by current concerns of employees—for example, health and wellbeing during the pandemic which also enabled them to capture concerns and quickly funnel resources to employees. After the George Floyd incident, employees were able to share their concerns through inclusive conversations. During the “Don’t Say Gay” challenge in Florida, the employee resource group led an inclusive conversation and over 500 people joined the conversation from senior to entry level employees. The conversations allowed the organization to hear about and respond to needs rapidly and accurately.

We take a learning approach to change; one that, as in complexity science, is emergent and continuously adapting to the context and what is learned along the way. Central to our vision of rethinking learning and development is that the organization’s culture itself needs to be wired for learning and change. This moves organization development beyond short term initiatives to embedding adaptive capacities in the organization. This is what ESPN/Disney has done. They have enhanced the organization’s capacity to have difficult, essential conversations.

Learning for Future Fit

Changing L&D’s capacity along with the long-term career potential of employees is what Patrick Hull (2023), Vice President of Global Learning and Future of Work, and colleagues are doing at Unilever. At Unilever, Hull (in Watkins & Marsick, 2023) described how the organization is developing employee skills for future fit. Given that robotics and other technological advances could replace many of the jobs at Unilever, the organization undertook an initiative to create opportunities for employees to reskill to enable them to apply for jobs that require skills similar to those they currently possess. Unilever partnered with the World Economic Forum and SkyHive (https://www.skyhive.ai/). This technology partner collates and reviews labor market data, statistics, CVs, and jobs to identify declining and emerging jobs at a regional level. While analyzing jobs that automation could eliminate, they learned that skills were applicable across a more extensive range of jobs than they had thought. The shift for L&D was to move from an exclusive focus on skilling for a current position to include reskilling for a future job.

Hull contended that L&D has a responsibility to create a renewable workforce. He emphasized that reskilling begins with purpose: what employees bring to the organization, where they get energy, and their unique fit. Using an AI-matching tool, employees identify three skills they would need to attain to move to the future job they hope to obtain and then develop a future fit plan. They may use Degreed, coaching, flex experiences where they try out the future job, or similar learning strategies to develop these skills. One other consequence of this initiative is that L&D professionals may also see new career options. They work more across the organization, and as Hull commented, “It is also an increasingly integrated role with other parts of the organization and a blurring of boundaries—for example, working hand-in-glove with other HR/People specialists such as reward, talent, OD, learning grouping. I partner constantly with my colleagues to make this happen” (p. 38).

This partnering and cross-functional work is essential in complex situations. Nora Bateson (2022) calls for trans-contextual mutual learning to allow creativity to emerge. By working across functions and contexts, L&D can help disseminate new ideas from one context to another, bridge disparate approaches, and foster collective meaning making.

Implications for HRD

Complexity has been a player in the workplace for some time, but it is now a widespread determinative factor. An early indicator of what this means for HRD was the discovery of the pervasiveness of informal and incidental learning for continuous learning on the job in the knowledge era. Work and jobs called increasingly for non-routine, adaptive learning linked to work demands. HRD moved from work-based learning to learning-based work (Marsick et al., 2021). The unit of analysis shifted—as signaled by the pervasiveness of the learning organization as an explanatory framework for learning and change. Instructional systems design, focused on individual competence building and self-directed learning, has not disappeared. But training is on demand, byte sized and focused on skill or compliance. Sole reliance on provable, predictable ground truth has moved over to accommodate learner-centered design thinking and creative world building. Molly Nagler (2023), Chief Learning Officer at Pepsico (in Watkins & Marsick, 2023), emphasized that the first thing that had to happen to pivot to new forms of L&D is that L&D practitioners needed to change some of their assumptions about learner control, the power of online learning, and the potential of creating cultural change at scale using digital technologies. We heard this again and again from everyone with whom we spoke.

We ended our conversations about complexity with many questions and wonderings, and a healthy dose of questioning of our assumptions. We found many patterns, but we are also aware that complexity, by its very nature, precludes predictability, even though it does open the door to decisions based on propensities or “tendencies to act in a particular way given an entity’s capacities” (Pendleton-Jullian & Brown, 2018, p. 37). Interventions at the boundaries need to be made and monitored, which means that analytics are essential. And as we found in talking with Rob Perrone (2023), who has managed learning analytics at a major professional services firm, we need to ask the right questions. In the past, L&D looked to prove value via Return on Investment. Those answers do not necessarily speak to what is important for adding value to the organization. Perrone underscores: “We need to be clear about what problem we’re trying to solve and how learning is going to contribute to that solution.” Asking the right questions can be political and it typically calls for buy-in from senior leaders. (Perrone, in Watkins & Marsick, 2023, p. 131).

The right questions, we think, have to do with what Nora Bateson (2022) describes as readying: “a process prior to the ‘change’ that allows organisms to become ‘ready’ to respond in new ways.” We need to notice small moves that might have big impact, and put practices, structures, processes, and cultures in place that enable ready-ing and what Bateson describes as “side-by-side-ing” or holding multiple, sometimes conflicting possibilities, side by side to which we remain sensitized and open in our thinking. This echoes the earlier point in this article that Pendleton-Jullian seeks cognitive equity that allows for collaboration, dialogue, boundary-crossing, and the opening of neural networks that help us generate new ideas. We need to adopt an idea from a medical educator and colleague, Dr. Dimitri Papanagnou, who—together with other faculty—has introduced an uncertainty curriculum for preparing medical doctors in training for the inevitable uncertainty of clinical environments (Papanagnou et al., 2022).

In the industrial and immediate post-industrial eras, our search in L&D for the holy grail often ended in best practices. In the age of complexity, it is still a good idea to learn all that we can about what colleague Nancy Dixon colloquially called “pretty good practices” from which we can borrow, adapt and learn. But we also need to be ready-ing for new ways to address complexity. Instead of finding a perfect answer that takes a long time to create and test, we may need to engage in non-linear decision making, creative what-if-ing, cognitive equity, and multiple rapid prototyping. We need to enhance our environmental scanning, hone our intuition, check more often with edge employees who come at the problem from vantage points different to ours, collect and monitor new kinds of data, grow the imagination, and ask frequent questions. We need to exchange being right for courageous comfort with ambiguity when there are no right answers. And we need to check our assumptions at the door of complexity!

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Karen E. Watkins

Victoria J. Marsick

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