The Satir Change Curve

How much change can your organization or group take on with ease and grace?

That’s one of the fundamental questions to ask on your agile journey. On the one hand, big changes can produce big results but they are risky. On the other hand, small changes are low risk but they are slow. In the 1960s, family therapist Virginia Satir introduced a mental model that helps navigate this issue. The “J Curve” or “Change Curve” as it is often called looks like the image below. The width of the curve is the ‘time to value’ and the depth of the curve is the ‘cost of value.’ The Y axis is a metric (such as time to market) and the X axis is time.

 Image result for satir curve
The curve suggests asking two questions:
  • The Patience Question: How quickly does the change need to show benefits?
  • The Money Question: How much is the organization willing to invest in the change?

Let’s do an example. You listen to your organization and learn that the next change needs to show a benefit within one month and cost no more than $100K. You are considering moving from manual builds to CI/CD. A quick survey of the literature shows that this change will take many months and cost hundreds of thousands of dollars. Hence, this exceeds the ability of the organization to change and will probably be unsuccessful if attempted. Not respecting the change curve can lead to trouble. If a change takes too much time or costs too much money, then the change will fail. Respecting the change curve will help you guide your organization on its agile journey by choosing appropriately sized changes.

Everyone is Agile


The list of companies touting agile is long. Some of the software companies might be familiar. Spotify, Salesforce, Google, Apple, Amazon, Yahoo, Red Hat, Adobe, and Facebook are all agile. Smaller, lesser-known software-development companies such as Atlassian, Paycor, Pivotal Labs, BNA Software,, and DevSpark are agile.

Companies we don’t typically think of as agile are working to be agile. Microsoft claims to be agile. General Electric, Hewlett-Packard, and Bank of America are agile. The United States Department of Defense is agile.

Game developers are agile. Financial companies and media companies are agile. Banks and universities are agile.

InThe Agile Mind-Set, Gil Broza asks an intriguing question: What noun typically follows agile?

Broza writes:

“People talk about agile development, agile project management, agile processes, agile methods, and agile best practices. Some speak about the agile methodology or the agile framework. Others refer to pairings like Scrum/agile and lean/agile.”

The language of agile is everywhere.

Consultants talk about becoming agile to avoid disruption. Terms like extreme programming, Scrum, and kanban are tossed around as ways to become agile whether people know what they mean or not. “Sprint”, “iteration”, “backlog”, and “burn down” are all entering the lexicon.

Forbes describes what agile leaders look like:

“Agile leaders are not only fast and effective problem solvers when dealing with situations they’ve never dealt with before, but they are also laser-focused on results and excellent at reshaping plans and priorities when faced with unexpected changes in the environment. They are resourceful and competitive. And, they get it done fast.”


Crossing the chasm

As agile has spread, the backlash has been fierce.

A number of people have written about the ubiquity of agile and its subsequent loss of meaning. Dave Thomas, one of the original developers of the “Manifesto for Agile Software Development” or Agile Manifesto, has declared, “Agile is dead.” Thomas suggests that agile “has been subverted to the point where it is effectively meaningless, and what passes for an agile community seems to be largely an arena for consultants and vendors to hawk services and products.” He suggests the word has been co-opted to boost sales in the same way that “green“ has been used.

A great rant from Tom Elders on Hacker News starts with “I can’t take this agile crap any longer. It’s lunacy. It has all the hallmarks of a religion.”

What is happening with agile?

According to the most recent “state of agile” survey from InfoQ, agile has gone mainstream and the majority of organizations use agile techniques for at least some software development projects.

We can use Geoffrey Moore’s chasm model for technology adoption to get a sense of what’s happened in the marketplace with agile. Moore’s model for disruptive technologies is useful because it looks at innovations that require people to do things differently — innovations that require behavior changes.

Looking at Moore’s model, innovators and early adopters are visionaries with a high willingness for change, high risk tolerance, and strong support from management. Early adopters understand the benefits and are willing to experiment in order to gain a competitive edge.

There is a large gap or chasm between these innovators and early adopters and the largest segments of the market: the early and late majorities.

Pragmatists and conservatives on the other side of the chasm are far more likely to approach agile from a completely different perspective. They are risk averse. They have heard of agile but likely think it is a process change that they can easily roll out to their IT organizations. Their risk tolerance is low, they want quick results, and they’re expecting relatively easy-to-implement process changes.

In other words, they are driven by practicality and want an out-of-the-box solution. The early majority wants technologies that are simple to implement.

As a result, many vendors and consultants have figured out that they can take advantage of the industry buzz and the early majority’s desire for practicality to sell agile tools and processes to convince these customers they are becoming more agile. As William Pietri wrote on the Agile Focus blog, “An idea that provides strong benefits to early adopters gets watered down to near uselessness by mainstream consumers and too-accommodating vendors.”

Much of this has happened in the agile marketplace as early adopters sought out-of-the-box tools and processes.

Coaches and consultants with experience in making the transition are spread thin and many new consulting organizations look to take advantage of the situation and sell their services.

The early majority also sees agile as a process to enhance productivity rather than a potentially disruptive culture change. Agile can (depending on existing culture) be a significant cultural change. Crossing the chasm is more difficult with agile than with other innovative technologies because organizations might not have a culture that is ready for agile and either don’t understand or underestimate the cultural change inherent in agile.

The Agile Manifesto


The Agile Manifesto reads:

We are uncovering better ways of developing software by doing it and helping others do it.

Through this work we have come to value:

• Individuals and interactions over processes and tools

• Working software over comprehensive documentation

• Customer collaboration over contract negotiation

• Responding to change over following a plan

That is, while there is value in the items on the right, we value the items on the left more.

The Agile Manifesto describes a change in beliefs, a cultural change.

Tobias Mayer described it this way in The People’s Scrum:

“Scrum is a framework for organizational change and personal freedom. It is not a methodology, it is not a process, and it is much more than a tool.”

Agile is a set of beliefs, a set of ideas. Are executives and leaders willing to adopt and champion these ideas? Or are they merely looking to “optimize” employees because employees are seen as the constraining element of the system?

Moore’s ideas about crossing the chasm help us understand that what is happening is normal for innovations that impact behavior.

We don’t believe agile is dying or jumping the shark, but rather is experiencing growing pains as it reaches new markets. In many cases, however, what this means to organizations on the other side of the chasm is that what they’re doing or attempting to do is not really agile.


Adoption vs. transformation

One of the more common mistakes made when implementing agile is not seeing it as a framework for organizational change. This typically looks like adopting sprints and the artifacts associated with sprints and ignoring other components of the change framework, most often agile values.

When asked why agile projects fail, the number two reason cited in VersionOne’s 2014 “State of Agile Survey” after “None of our projects failed” was “Company philosophy or culture at odds with core agile values.”

Henrik Kniberg tells the story of one of his most successful projects — a system built for the Swedish police that allowed them to use laptops in the field — and what happened afterwards [Kni13]. Because the project was extremely urgent, the group was allowed to use an agile approach and break out of the traditional organizational culture. Everything went well, the police organization viewed it as a success, and the project even won a “project of the year” award.

What came next, however, was even more interesting. A high-level decision was made to rebuild from scratch that same system police had used in the field, using Siebel. This was part of a standardization effort to reduce the complexity and number of systems. Not only was the decision made to use a technology that the development team didn’t agree with, but it was decided to use a more traditional, sequential project-management approach to development. Development took a couple years and when it finally rolled out, it was a disaster because the police found it to be slow and clumsy and basically unusable. Making the change even more difficult was that the police preferred their existing system, which worked. Kniberg estimates that this cost the Swedish police more than £1 billion.

Adopting agile practices is likely to lead to marginal improvements at best if current values and culture are out of alignment with agile beliefs and the organization doesn’t change.

As Mike Cottmeyer wrote in “Untangling Adoption and Transformation”: :

• Transformation is about changing the “agile being” side of the equation.

• Adoption is about changing the “agile doing” side of the equation.

Some symptoms that might indicate that transformation has not yet fully happened and agile culture and values have not yet been adopted are:

• Agile teams have defined dates and scopes.

• A manager assigns tasks to team members.

• Impediments to development are not addressed.

• Team members don’t point out problems when they see them.

• Testing is not allowed because it highlights shortcomings.

• Burn-down charts are altered to present a rosy picture.

• Management plans rather than teams.

• All features are seen as high priority.

• Communication is one way, from leaders to employees through broadcasts.

• Agile is seen as something “the technology people do”.

• Teams are not developing working software.

• Teams are reporting rather than discussing progress.

• Superstars are valued over team.

• No changes affect how things are done.

• There is a reluctance to hire qualified outside experts.

• Leadership demands results without providing direction.

• Knowledge is hoarded.

To realize the full benefits of agile requires the values or the “being” part of agile. Michael Sahota and others have discussed how agile processes and methods can be adapted to different cultures. We would like to take a different approach. We believe that if organizations adopt agile as a set of beliefs, they will develop an agile culture and that this agile culture is what leads to continuous adaptation and innovation. The focus of the change effort must be on the heart, not the head or the hands.

Processes and methods can become stale and rote, and can stifle innovation — even processes that were initially developed to be agile. An agile culture, however, will continuously improve and adapt without the need for periodic change initiatives.

Numerous books and best practices exist to help organizations with implementing agile practices, or the “doing” side of the equation. Our reason for writing this series is to examine the values and culture that make organizations agile.


Coming Up In This Series

This post is based on our book, Why Agile Works: The Values Behind the Results and is the first of a series of blog posts coming soon, all taken from the book . To be notified when the next blog post appears, subscribe to our newsletter & blog updates.


Emotion and Cognition

 Article Originally Posted on

How our theory of the mind has changed over time

Agile values “individuals and interactions over processes and tools.”

Understanding individuals and how they interact takes an understanding of how and why people make decisions. As it turns out, our understanding of how people think is often off the mark.

Throughout history, people have used the most complex piece of technology we’ve created to date to describe how our minds work. Over the last thousand years we have gone from thinking of the mind as a hydraulic system to a mechanical system to an electrical system to a computer system.

The Greeks viewed the mind as a hydraulic system similar to an aqueduct. Hippocrates spoke of the four basic humours and their relationship to personality types and moods: sanguine, choleric, melancholic, and phlegmatic.

Figure 1: Moods related to the four humours.

This view of the mind persisted until we developed mechanical devices. Descartes advocated mechanical view of the universe and living organisms1:

the reception of light, sounds, odors, tastes, warmth, and other like qualities into the exterior organs of sensation; the impression of the corresponding ideas upon a common sensorium and on the imagination; the retention or imprint of these ideas in the Memory; the internal movements of the Appetites and Passions; and finally, the external motions of all the members of the body … I wish that you would consider all of these as following altogether naturally in this Machine from the disposition of its organs alone, neither more nor less than do the movements of a clock or other automaton from that of its counterweight and wheels

While Descartes believed the mind was ephemeral and the body mechanical (dualism), many mechanical metaphors also arose for the mind. Clocks were likely one of the first. You can still hear this thinking in metaphors we use today such as “you can see the wheels turning” or “she has a mind like a clock” or “like clockwork.”

John Locke used the idea of the Gutenberg printing press in his in his view of memory as a tabula rasa, or blank piece of paper, upon which experience left an imprint. You can hear the impact of the Gutenberg printing press on Locke’s thinking about memory2: “The other way of retention is, the power to revive again in our minds those ideas which, after imprinting, have disappeared, or have been as it were laid aside out of sight.” Contrary to many before him, he believed we weren’t born with innate ideas but rather that everything we know was shaped by our experience.

Freud spoke about the mind metaphorically as a steam engine.

Desires could be suppressed or repressed, but like steam in a steam engine, psychological pressure would lead to an explosion unless it found an outlet.

In the 1790s, Italian scientist Luigi Galvani discovered that a spark caused the leg muscles of dead frogs to twitch. This discovery lead to the idea of bioelectricity.

Figure 2: Galvani’s experiment with frogs’ legs.

With Galvani’s discovery, electrical impulses began to replace water or mechanical means as a metaphor for communication between the senses and the mind. In 1849, the German scientist Hermann von

Helmholtz measured the speed at which electrical signals were carried through nerve fiber. At the time, people believe the signal was instantaneous.

The telegraph, invented in the 1830s, provided Helmholtz a conceptual model for understanding how sensory signals reached the brain. Other electrical components we’ve related to the human mind at one point or another throughout the 20th century include vacuum tubes, transistors, electrical switches, resistors, capacitors, amplifiers, relays, tape recorders, and memory banks.

The mind as a computer

Ask people today how they think the mind works and odds are good that they will describe a computer. They will talk about accessing memory or they will talk about processing data. They will talk about sending off a request and computing results. Or they will talk about acting on input to produce a specific output.

We think of our brains as having a separate processing unit and separate memory. Data is input, stored in memory, and when processed appropriately correct answers should appear. For example, if we understand the operation called multiplication and are given two different numbers such as 7 and 6 we should return a correct result, 42.

The concept of short-term memory and long-term memory is also very similar to the computer model of long-term disk drive storage and short-term cache/RAM memory.

Figure 3: Computer memory hierarchy.

John Daugman, Professor of Computer Vision and Pattern Recognition at Cambridge, writes about the pervasiveness of this metaphor3:

Today’s embrace of the computational metaphor in the cognitive and neural sciences is so widespread and automatic that it begins to appear less like an innovative leap than like a bandwagon phenomenon … There is a tendency to rephrase every assertion about mind or brain in computational terms, even if it strains the vocabulary or requires the suspension of disbelief.

The mind doesn’t work the way we think it does

Here’s a simple puzzle4. Listen to your intuition.

A bat and ball cost $1.10. The bat costs one dollar more than the ball. How much does the ball cost?

A number came to your mind: 10 cents. The puzzle evokes an appealing, intuitive answer that is, of course, quite wrong. If you work this out in a quick algebraic formula, you’ll find the answer is 5 cents. On average, students tested at MIT, Harvard, and Princeton gave the intuitive answer 50% of the time. At less selective universities, the rate was much higher.

As much as we may sometimes think that our minds perform like computers and that we make rational decisions, science says otherwise. We’re brash, we make snap judgments, we ignore what we don’t want to see, and we have tendencies to like things that we like. Ask any salesman and they will tell you people don’t make decisions rationally (though they’ll often rationalize them after the fact!).

Example: The mere exposure effect

In 1969, Robert Zajonc, a psychologist at the University of Michigan conducted an experiment where he printed a silly sounding word on the front page of the student newspaper in Michigan every day for several weeks5. He used words like: kardirga, saricik, biwonjini, and nansoma.

He then sent questionnaires to readers and asked them to categorize the word as ‘good’ or ‘bad’. Words that appeared in print many times were judged to be more positive than those that didn’t or only appeared once or twice.

The sheer appearance of these words in a trusted source on a number of occasions caused people to think more favorably of the words. He called this the mere exposure effect (sometimes referred to as the familiarity principle), a psychological trait where people develop a preference for something simply because they’re familiar with it.

The effect has been demonstrated across cultures and with multiple types of stimuli including: faces, Chinese characters, language, and sounds.

It explains decisions by stock traders, distortions in academic journal rankings and quite likely much of the success of chain restaurants and hotels.

Zajonc argued that “affect,” his term for the unconscious mind, is always present as a companion to cognition where the opposite is not always true. Many decisions are made completely without the rational mind.

Zajonc’s research, controversial for the day because it challenged the prevailing view that rationality guides our decisions, helped found the field of social cognition and opened up studies into emotion and cognition.

Example: Mirror neurons

In 1995, the mirror neuron was discovered in the primate brain. Giacomo Rizzolati at the University of Parma discovered that mirror neurons in the brain light up when we see other people do things on purpose6. If you see someone pick up a piece of fruit to eat, mirror neurons in your brain light up. This was, finally, the anatomy of empathy.

Soon, new mirror-neuron studies were underway, and they led to new insights. A key insight was that the mirror neurons not only pick up on intentional actions like grasping a pencil, they also pick up on emotional actions such as facial expressions.

Figure 4: Illustration of mirror neurons firing during the action of eating fruit and observing.

David Rock writes7:

When we see others’ facial expressions, we activate the same in our own motor cortex, but we also transmit this information to the insula, involved in our emotions. When I see your facial expression, I get the movement of your face, which drives the same motor response on my face, so a smile gets a smile. The motor resonance is also sent on to your own emotional centers, so you share the emotion of the person in front of you.

If empathy had an anatomical location in the brain—a place where specific nerves were dedicated to empathic connection with another—then what about other aspects of social connection? What about like and dislike? What about respect, inclusion and ostracism? Where were they located?

As an Agile coach or manager, you can see why it’s important to model the behavior desired of your team.

Rizzolati’s discovery of the mirror neuron has had a profound impact on the field of social cognition and ignited an explosion of research. Not only do certain potential neurons in our brain fire when we perform tasks, they fire when others perform similar tasks. Though we’ve still only conducted research in primates, we may have found the biological roots of empathy.

How does the mind actually work?

Recent research into the mind and discoveries in the field of artificial neural networks suggest that the metaphor of the mind as a computer is just as flawed as the metaphor of the mind as a steam engine.

From medical research, we know our brain consists of millions of biological neurons. How do these neurons function together to produce thought? How do these neurons work together as a single unit, the human brain?

The field of artificial neural networks offers some insights into how these millions of neurons turn input into decisions8. While still a relatively new science, it’s important (and fairly easy) to understand this model metaphorically.

Figure 5: A simple artificial neural network, a back propagation network, consisting of an input layer (sensors), any number of hidden layers (where the “thinking” occurs through the weighted connections wij and wjk)), and an output layer (representing the decision).

Artificial neural networks (ANNs), like the above, contain a learning rule, which modifies the weights of the connections according to the input and desired recognition of that input.

During this “learning” or training algorithm, information is stored in the weights and connections that allows the network to respond to a given set of inputs.

If you wanted to train an ANN to recognize the number 1, you would present it with a series of numbers over and over again while you’re training the neural network. Each time a number is presented, the network makes a guess whether it is a 1 or not. You provide the network with positive or negative (right or wrong) feedback about its guess and this feedback is used to adjust the levels in the hidden layer of neurons.

Figure 6: A Hopfield network recognizing the number ’1’.

In this manner, the ANN “learns” to recognize the number 1 (or another number or image that the ANN has been trained to recognize).

One of the important things to note for ANNs is that you typically have to expose them to thousands of training runs with feedback before the hidden layer weights adjust. Simulated on a computer, depending on the complexity of the ANN, this can take a bit of time.

Though we still don’t know how all the associations in our mind exactly work, an associative network of neurons makes a much better metaphor for thinking about the mind than a computer. When we think of a concept—a car, for example—car is defined by its associations: driving, vehicle, motor, wheels, passengers, speed, roads, traffic, wheels, dashboard, transmission, etc.

Two systems of thinking

If you think of our minds as having evolved over thousands of years, the mind can be viewed as different layers of neural networks in different areas of the brain like the prefrontal cortex (responsible for higher-level reasoning) and older, more primitive areas of the brain, such as the limbic system (responsible for emotion).

Figure 7: Components of the limbic system.

Daniel Kahneman, winner of the Nobel Prize in economics for his groundbreaking research on the mind, conceptualizes this as System 1 (“fast”) and System 2 (“slow”).4

System 1 operates automatically and quickly with very little effort. System 1 governs our rapid pre “fight or flight” emotional responses. Some activities driven by System 1: Driving a car on an empty highway, answering 2+2 = ?, doing the dishes, orienting to a sudden sound, snap judgments based on appearance, and detecting hostility in a voice.

System 2 is responsible for our slower, more conscious cognitive processes. System 2 is responsible for what we often think of as efforts that require more concentration. Some activities driven by System 2: Counting the occurrence of the letter ‘T’ in this document, writing a term paper, making a social appearance, identifying all the women with white hair, preparing for a speech, and validating a logical argument.

It’s important to note that the two systems are not functionally separate. In many circumstances, System 1’s emotional response may drive the direction of System 2. They are also not physically separate in the brain as there are many connections and relationships we simply don’t understand yet from a biological perspective. However, it helps to think of these systems as System 1 (‘fast, emotional”) and

System 2 (“slow, cognitive”).


Most people have a general idea that emotional thinking is different from rational thinking. We talk about people making emotional decisions, decisions that are influenced by anger or sadness, for example.

But where do our emotions come from? Are they learned, ingrained, or both?

Paul Ekman, a University of California psychologist, studied emotions across cultures and what he found was that there is a common core set of emotions9. Ekman’s studies showed that there was wide agreement across various Eastern and Western cultures when it came to the emotional meaning of certain facial expressions.

Anger, fear, disgust, surprise, happiness, and sadness were found to be universally recognized, even by a tribe in Papua, New Guinea who could not possibly have learned of their meaning through the influence of television or other cultural influences.

Figure 8: Anger, fear, disgust, surprise, happiness, sadness.

System 2’s emotions can be thought of in terms of categories such as anger, sadness, fear, enjoyment, love, surprise, disgust, and shame.

Each category has various shades of grey. Sadness, for example, could be broken down further into: grief, melancholy, sorrow, loneliness, self-pity, gloom, and despair.

Ekman’s research suggests some level of biological basis for emotions—with culture taken out of the equation, people still share many of the same emotions.

Fast often overrides slow

One of the most interesting questions in contemporary cognitive studies is: how is emotion related to cognition?

Joseph LeDoux’s research on fear suggests that the amygdala may be in a position to emotionally hijack the brain10.

LeDoux tracked the signals in rat brains as they responded to a loud sound. First, the signals traveled from the eye or ear to the thalamus and then across a single synapse to the amygdala. A second signal from the senses was also routed to the neo-cortex, the thinking part of the brain. This allowed the amygdala to respond quickly while the neo-cortex pondered the information.

Figure 9: Testing of auditory stimulus in rats.

In crucial fight-or-flight situations, this dual wiring allowed the amygdala to respond quickly while the neo-cortex formulated a plan. LeDoux’s research overturned the conventional thinking of the day that signals were sent to the neo-cortex for processing and recognition and then down to the limbic system. This explains why we can act in certain emergency situations seemingly without thinking.

The brain also uses a very ingenious method of making sure we remember these critical emotional experiences. Under stress or other intense emotion, our bodies secrete epinephrine and norepinephrine that prime the body for an emergency. These hormones activate receptors on the vagus nerve carrying messages from the brain to the heart and back. These signals trigger neurons in the amygdala that signal other regions of the brain to strengthen memory. This is why we tend to remember emotionally charged situations, such as a first date or where we were when we heard John Lennon was killed, better.

If you rear-end another car, your hippocampus will retain the context of the situation—where you were, how fast you were driving, and what the other car looked like. But it’s the amygdala that triggers anxiety whenever you get too close to a car in similar circumstances.

To paraphrase LeDoux, the hippocampus is key in recognizing a face of your co-worker, but it’s your amygdala that adds you don’t really like him.

The emotional mind (System 1) is much quicker than the rational mind (System 2) and emotional decisions carry a particularly strong sense of certainty. In hindsight, we may find ourselves wondering why we bought that particular stock or got into that heated argument.

Emotional intelligence

Historically, emotion and cognition have been viewed as separate entities with cognition supposedly in control. Research suggests, however, that System 1 may drive far more decisions than we typically think possible.

Linda Rising, a patterns and Agile expert, has said she used to think that the conscious part of our brain was the tip of the iceberg, about 10 percent, and the unconscious was the 90 percent hidden from view. Now, she says, it’s clear that the conscious mind is even tinier.

While there’s much we still don’t know about how the mind works, it’s clear that emotions influence cognition. Perhaps even more than we think. Science tells us that we are more like Kirk, than

Spock. To be smart, we need to be smart about our emotions.

In 1995, Daniel Goleman coined the phrase emotional intelligence11. This term may sound familiar as, unfortunately, it’s become a bit of a buzzword. The fundamental question Goleman was trying to answer though remains relevant: Why do people with average IQs outperform those with the highest IQs 70% of the time? Why doesn’t IQ determine success more often?

Goleman proved that it was emotional intelligence or EQ that was far more likely to determine success. His work has been subsequently confirmed and expanded on in numerous studies. For example, TalentSmart tested EQ with 33 other workplace skills and found EQ to be strongest predictor of performance, leading to 58% of success in a wide variety of jobs.

A few other stats from TalentSmart12:

  • 90% of top performers are high in EQ while just 20% of bottom performers have a high EQ
  • People with high emotional intelligence make on average $29,000 more per year
  • A 1-point increase in EQ equals a $1,300 increase in salary

Emotional intelligence skills relate to how effectively people work with others, specifically around:

  • Self-awareness
  • Self management
  • Social awareness
  • Relationship management

Self-awareness is understanding your own emotions, skills, strengths, weaknesses, and capabilities while social awareness is the ability to empathize with others and understand their verbal and non-verbal signals. Self management is the ability to overcome our emotions, especially negative emotions such as frustration or anxiety. And relationship management brings together all three of these skills to lead, manage conflict, influence others, and build teams.

Agile depends on emotional intelligence

How do emotional intelligence skills relate to the Agile principles?

Self-awareness plays a huge role in forming self-organizing teams. Individuals that know their strengths as well as their weaknesses are much more likely to seek out pro-active help for the sake of developing the best architectures, requirements, and designs. Someone that is afraid to seek out help for fear of appearing “less intelligent” may wait too long.


Self management is key to building projects around motivated individuals. Are developer estimates achievable and can developers meet commitments on time? If requirements change, are members of the team going to be frustrated or will they be able to negotiate through to a mutually desirable conclusion?

In order to work together and communicate effectively, business leaders and the software development team must have the social awareness to understand each other and interpret subtle clues. Social awareness is particularly invaluable for software development managers and Agile coaches. If someone seems frustrated and has not met a commitment, understanding how to best communicate with this person can be critical in resolving the situation.

Relationship management brings together all of these skills in the formation and ongoing performance of teams. Agile principles founded on relationship management include:

  • Business people and developers must work together daily throughout the project.
  • The most efficient and effective method of conveying information to and within a development team is face-to-face conversation.
  • The best architectures, requirements, and designs emerge from self-organizing teams.
  • At regular intervals, the team reflects on how to become more effective, then tunes and adjusts its behavior accordingly.

The good news is that research shows that not only can emotional intelligence skills be learned but they can be retained for many years, especially in the right environment where skills can be practiced on the job and reinforced.

The Hay Group found that workshops can improve EQ.

After implementing an EQ training program with 100K+ employees, Starwood Hotels moved “from relying on scripted interactions to having associates who are able to make thoughtful decisions about how to most effectively respond to the needs and requests of our customers.”

Johnson & Johnson, Boeing, American Express, PSEG, Roche Pharmaceuticals, and L’Oreal are a few of the leaders incorporating EQ into their best practices13.

FedEx has implemented an EQ training program14 called Six Seconds with the motto: “Emotions drive people, people drive performance.”


The mind doesn’t work the way we think it does. It’s not a computer and emotion influences cognition—frequently from the driver’s seat.

We justify car purchases by saying we got a good deal when we really liked how the car made us feel. We make decisions at work based on gut feelings rather than detailed analyses because we simply don’t have time to do a cost-benefit analysis on every decision. Nothing would ever get done. These shortcuts that we’ve developed based on our experiences and emotional intuition have a great deal of value to us. However, sometimes they can steer us wrong.

Whether you manage people on Agile teams, are a team member, or interact with Agile team members, it’s important to understand that most decisions are made emotionally rather than rationally. This is why, to be successful, we need to be smart about our emotions and develop our emotional intelligence.