Posted on
November 17, 2023

Balancing Learning and Performance

Change is continuous, despite what we wish.

We’d really, really like to just catch our breath, and coast along for a while. We’ve worked so hard to build all this! Let’s just ride with it, as-is? We just want to execute! Can’t we just put the blinders on, and hit pause on learning for a bit…?

Unfortunately, the answer is usually no.

So we ask:

  • Are our internal changes keeping up with the environmental changes we face?
  • When must we emphasize change, and when can we get away with avoiding it?

Patterns of change in nature can serve as great metaphors for organizational change, offering guidance. Let’s explore one such metaphor and check whether it can help us find answers to the questions above.

David Hurst and Brenda Zimmerman thoroughly explore a pattern of ecological renewal in "From Life Cycle to Ecocycle: A New Perspective on the Growth, Maturity, Destruction, and Renewal of Complex Systems" (1994).

They draw parallels between the growth-cycle of a forest, culminating in a forest fire (with the subsequent renewal), and the experiences within a maturing organization.

They explain that, in a forest, the cycle passes through distinct stages:

  1. Exploitation - Seeds take root in “open patches”
  2. Conservation - Growth produces structure that limits variety
  3. Creative Destruction - Age, vulnerability, homogeneity lead to crisis
  4. Renewal - Energy (rain, sun) returns and supports recolonization

In an evolving organization, the patterns of human behavior, interactions, and focus often transition through similar phases of change. The entrepreneurial seeds find energy for growth, and this growth leads to structure and size and rigidity. Adaptability is sacrificed for repeatability. Change becomes something to avoid.

“He who rejects change is the architect of decay.” - Harold Wilson

But what if decay is part of the cycle, to create a more fertile soil for change? This is the argument that Hurst and Zimmerman make. They model the cycle as an infinity loop, with renewal leading back to the next pursuit of potential.

Credit: Hurst and Zimmerman

The axes highlight two changing factors, across the cycle:

  1. Connectedness - “refers in a general sense to properties of networks such as density, connectivity, and hierarchy”
  2. Potential - the sum of the possibilities of a system, good or bad

High connectedness can lead to rigidity, which creates a ripe setting for the (eventual) crisis, when the organization fails to adapt to its changing environment.

Let’s walk through the full loop:

In the exploitation phase, patterns similar to the r-selection dynamics of population biologists can be seen. Organisms emphasize productivity, and a wide variety of forms take shape. Resources are plentiful and competition is weak.

As the system transitions to the conservation phase, resources become more scarce for new entrants and competition is strong. The system is nearing the “carrying capacity” of the environment. This yields the K-selection dynamics where efficiency is emphasized, and rigid structures take shape to produce a steady-state.

This rigidity and structure makes the system vulnerable to catastrophe, when the environment changes rapidly. Although the system has reached a high potential, the structure of its connectedness has constrained its adaptability. In this phase, the system is constructed of dissipative structures that are always consuming energy. It will take a spark, and a massive conflagration, to release that energy to change.

On the “back loop” after the crisis, the potential drops first, consumed by the fire. Then the connectedness retreats, as rigid structures and hierarchies are toppled, and looser networks take shape in the void, re-establishing connection and rebuilding potential.

Notice that while the right side of the ecocycle is primarily focused on performance, the left side shifts that focus to learning. As the forest maximizes growth, it consolidates resources and prizes efficiency. After the fire, renewal seeks to adapt to the changed environment, and “find the right seeds”.

But do we have to burn it down?

The path across the exploitation and conservation phases is the familiar S-curve we talk about with innovation. Once the system starts to get big (i.e. with profits) and emphasize efficiency, there is little energy (or patience) for seeds to grow.

The Innovator’s Dilemma calls this out - it’s difficult to set up a greenhouse in a mature forest.

In Geoffrey Moore’s “Zone to Win”, he outlines an approach that tries to build a greenhouse in the forest. While the mature businesses keep their focus on performance (in the Performance Zone), new ventures are spun up in a protected environment (called the Incubation Zone).

Source: “Zone To Win”, Geoffrey Moore

Performance is tracked with a Performance Matrix, which aggressively seeks efficiencies at the intersection of each source and channel for their offers (e.g. usually referencing goals for revenue or value-delivery).

Simultaneously, and independently, the Incubation Zone nurtures a set of “new seeds” for future growth. These are managed with different systems, policies, and leadership, keeping it outside of the Performance Matrix (for now). It seeks high potential, with a looser connectivity to the rest of the organization.

Moore emphasizes a distinction between “sustaining” and “disruptive” innovation. While sustaining innovation can be driven from the needs of performance (see: Productivity Zone), disruptive innovation should not have to compete with the rest of the forest - so it carves out a “open patch” with enough energy to thrive.

So if we set up the distinct zones, we can manage a healthier forest, but how big should the zones be? How much learning should we drive?

Finding a Balance

In the ecocycle, the conditions at the boundary between conservation and creative destruction are hard to recognize. The status quo bias reigns supreme. Can you sense that the environment is changing around you?

Yes - by observing the changes and then challenging the long-standing beliefs and structure that led to your growth and success.

At the peak, the organization has created an illusion of certainty with conservative structure that serves its needs for efficiency. These dissipative structures consume all the energy out of the organization. Constraint Maps can be used to better recognize these energy sinks, and highlight that change requires energy, too. If this internal reflection can be balanced with keen observations of the changing external environment, including honest acknowledgements of uncertainty, then we can get a “fire tower view” of our landscape.

When smoke is detected on the horizon, it might just be a campfire, but it also might be a signal to shift some energy to the greenhouse. The uncertainty we sense in our observations can drive learning efforts that support parallel renewal.

Another signal to watch for is when the targets in our performance matrix (e.g. OKRs) start to consistently show risk. A changing environment puts stresses on the rigid structures and policies that fueled our growth. When the recipes start to fail, the goals start to wobble. Should we just lean into better execution? Or revisit the goals and performance models themselves?

The concept of double-loop learning highlights this distinction. When the conservation phase has reached its peak, it is better to start challenging the original beliefs and assumptions that produced the strategic goals. The environment is changing. The old ways are struggling. New outlooks are needed.

Source: Systems Thinker

Goals demand performance, but renewal requires learning. How can we shift some of our attention from performance to learning, and maintain the discipline offered by something like the performance matrix?

Learning Matrix

It starts with managing expectations for learning.

Some popular approaches from the past, like the Balanced Scorecard, treated learning has just another capability or perspective, with its own set of performance goals:

Source: Balanced Scorecard Framework

But to support healthy inquiries into uncertainty, we can’t pretend we have the “known-ends” and “known-means” that drive goal management systems. Explorations are needed, not execution.

We need a matrix that outlines our space of exploration, and highlights where we should focus (and how these efforts are going).

If we model each learning opportunity as a loop, our efforts proceed through something like this:

Learning Loops

In a learning loop, we lead with a question, instead of an objective. Similar to how goals can be clarified with SMART attributes or objectives can be specified with Key Results, we can clarify our questions by adding Key Assumptions:

  • Question
  • Key Assumption #1
  • Key Assumption #2

The key assumptions can be drawn from the active beliefs and mental models that have supported the conservative structures that define the current organization. Loops support active learning through discovery and assumption testing, and can be managed.

We can structure a Learning Matrix to provide a parallel management tool to the Performance Matrix. While the performance matrix is organized into rows for sources of value-creation, and columns for channels of value-delivery, we can build the rows and columns of a learning matrix around dimensions that harbor uncertainty: internal capabilities (for possible solutions) and problems (for specific customer segments).

Learning Matrix

Owners can be associated with each row and column, to provide accountability for the exploration of the uncertainty in that area. This can be applied at any altitude in an organization, from the executive suite to a product team. The “local CTO” can identify a meaningful set of rows, and the “local CMO” can identify a meaningful set of columns, to highlight learning needs. These learning needs are driven by the current risk profile and overall uncertainty facing the local context.

As a bonus, if the questions and assumptions are scored by relative importance and relative uncertainty (i.e. lack of evidence), then Assumption Mapping can be used to help prioritize the subsequent discovery work.

Common innovation strategies can be investigated across a learning matrix:

  • Seeking new market, customers, and problems for new technologies (e.g. green spaces)
  • Seeking adjacent market segments for current solutions and tech (e.g. adjacencies)
  • Applying new technologies into current problem spaces (e.g. AI)

The learning matrix can offer a management tool that is complementary to a performance matrix, and that helps leaders put the two on equal footing, and strike the right balance.

For the overall organization, use of learning matrices could support a drive to improve the systemic capability around learning, taking steps towards becoming a learning organization.

“The goal of organizational learning is to successfully adapt to changing environments, to adjust under uncertain conditions, and to increase efficiency.” - Mark Dodgson

Measuring Learning Organizations

Most discussions about organizational learning happen closer to the HR function, where employees are encouraged to set learning goals for individual skills development. But an organization’s ability to learn is not just the aggregation of the individual learning plans, it’s something that leverages the whole system.

In “Organizational Learning Capability: A Proposal of Measurement”, Pilar Jerez-Gomez, Jose Cespedes-Lorente, and Ramon Valle (2005) proposed four dimensions that contribute to success in organizational learning (and constructed a survey assessment around it (see Appendix 1).

The four dimensions are:

  • Managerial commitment - Have managers seen the smoke signals and set expectations around learning needs?
  • Systems perspective - Is the full picture (like the ecocycle) across performance and learning, up and down the organization, clearly understood and communicated?
  • Openness and experimentation - Is the organization managing a learning matrix?
  • Knowledge transfer and integration - Are communities of practice helping disseminate learning across contexts, including outside the organization?

With better structural support for learning, something akin to Key Performance Indicators (KPIs) can emerge.

Could we imagine some measurement systems to use as Key Learning Indicators (KLIs)?

Key Learning Indicators: (proposed)

Hurst and Zimmerman said, “renewal demands constructive damage to the status quo” and that leaders should focus on managing changeability, instead of directly managing change. Creating an environment where performance is not the sole focus is one way to improve changeability of the system in the organization. An environment that regularly challenges beliefs, stays curious, does active sense-making with healthy dialog… these are the habits that navigate around the ecocycle without burning down the house.

Appendix 1: Survey/Assessment of Learning Organization Capability

Source: “Organizational Learning Capability: A Proposal of Measurement”