Parkinson’s Law of Triviality and Story Sizing

From  Wikipedia: Parkinson’s law of triviality

Law of triviality is C. Northcote Parkinson’s 1957 argument that people within an organization commonly or typically give disproportionate weight to trivial issues. Parkinson provides the example of a fictional committee whose job was to approve the plans for a nuclear power plant spending the majority of its time on discussions about relatively minor but easy-to-grasp issues, such as what materials to use for the staff bike shed, while neglecting the proposed design of the plant itself, which is far more important and a far more difficult and complex task.

I see this phenomenon in play during team story sizing exercises in the following scenarios.

  1. In the context of the story being sized, the relative expertise of each of the team members is close to equal in terms of experience and depth of knowledge. The assumption is that if everyone on the team is equally qualified to estimate the effort and complexity of a particular story then the estimation process should move along quickly. With a skilled team, this does, indeed, occur. If it is a newly formed team or if the team is new to agile principles and practices, Parkinson’s Law of Triviality can come into play as the effort quickly gets lost in the weeds.
  2. In the context of the story being sized, the relative expertise of the team members is not near parity and yet each of the individual team members has a great deal of expertise in the context of their respective functional areas. What I’ve observed happening is that the team members least qualified to evaluate the particular story feel the need to assert their expertise and express an opinion. I recall an instance where a software developer estimated it would take 8 hours of coding work to place a “Print This” button on a particular screen. The credentialed learning strategist (who asked for the print button and has no coding experience) seemed incredulous that such an effort would require so much time. A lengthy and unproductive argument ensued.

To prevent this I focus my coaching efforts primarily on the product owner as they will be interacting with the team on this effort during product backlog refinement session more frequently than I. They need to watch for:

  1. Strong emotional response by team members when a size or time estimate is proposed.
  2. Conversations that drop further and further into design details.
  3. Conversations that begin to explore multiple “what if” scenarios.

The point isn’t to prevent each of these behaviors from occurring. Rather manage them. If there is a strong emotional response, quickly get to the “why” behind that response. Does the team member have a legitimate objection or does their response lack foundation?

Every team meeting is an opportunity to clarify the bigger picture for the team so a little bit of conversation around design and risks is a good thing. It’s important to time box those conversations and agree to take the conversation off-line from the backlog refinement session.

When coaching the team, I focus primarily on the skills needed to effectively size an effort. Within this context I can also address the issue of relative expertise and how to leverage and value the opinions expressed by team member who may not entirely understand the skill needed to complete a particular story.

(John Cook cites another interesting example of Parkinson’s Law of Triviality (a.k.a. the bike shed principle) from Michael Beirut’s book “How to” involving the design of several logos.)

 

Image by Arek Socha from Pixabay

Moving Past “I Don’t Know”

In 2015 I attended the Mile High Agile conference in Denver where Mike Cohn delivered the morning keynote address: “Let Go of Knowing: How Holding onto Views May Be Holding You Back.” As you might expect from a seasoned professional, it was an excellent presentation and very well received. A collection of 250+ scrum masters, product owners, and agile coaches is no stranger to mistakes, failures, and terrifying moments of doubt.

As valuable as the ideas in Cohn’s presentation are, I want to take them further. Not further into the value of keeping our sense of sureness somewhat relaxed, rather onto some thoughts about what’s next. After we’ve reached a place of acknowledging we don’t know something and are less sure then we were just a moment before, where do we go from there? It’s an important question, because if you don’t have an answer, you’re open to trouble.

The “I Don’t Know” Vacuum

Humans are wired to find meaning in almost every pattern they experience. The cognitive vacuum created by doubt and uncertainty is so strong it will cause seemingly rational people to grasp at the most untenable of straws. It’s a difficult path, but developing the skill for being comfortable with moments of doubt and uncertainty can lead to new insights and deeper understanding if we give our brains a little time to search and explore. Hanging out in a space of doubt and uncertainty may be fine for a little while, but it isn’t a wise place to build a home.

After acknowledging we don’t know something or that we’ve  been wrong in our thinking, it’s important to make sure the question “What’s next?” doesn’t go begging. I’d wager we’ve all had the dubious pleasure of discovering what we don’t know in full view of others and in those situations the answer to this question becomes critical. It may not need an immediate answer, but it does need an answer. If you don’t work to fill the vacuum left by “I don’t know” or “I was wrong,” someone else surely will and it may not move the conversation in the direction you intended.

The phenomenon works like this. Bob, a capable scrum master, ends up in a situation that reveals a lack of experience or understanding with the scrum framework and doesn’t know what to do. Alice, maybe immediately or maybe later, moves into the ambiguity, assumes control, and tells the team what should be done. If Alice is wise in the ways of agile, this could end well. If command-and-control is her modus operandi in the defence of silos and waterfall, it probably won’t.

So how can an agile practitioner prepare themselves to respond effectively in situations of doubt and uncertainty? Here are a few things that have worked for me.

Feynman-ize the Conversation

In his book “Surely You’re Joking , Mr. Feynman!,” Nobel physicist Richard Feynman tells a story from his early career where several building engineers started reviewing blueprints with him, thinking he knew how to read them. He didn’t. Having been surprised by being placed in a position of assumed expertise, Feynman improvised by pointing at a mysterious but ubiquitous symbol on the blueprint and asking, “What if that sticks?” The engineers studied the blueprint in light of Feynman’s question and realized the plans had a critical flaw in a system of safety valves.

That’s how to Feynman-ize a conversation. Start asking questions about things you don’t understand in a manner that challenges those around you to seek the answer you need. In essence, it expands the sphere of doubt and uncertainty to include others in the situation. This tactic is particularly effective in situations where corporate politics are strong. Bringing the whole team into the uncertainty space helps neutralize unhelpful behaviors and increase the probability the best answer for the moment will be found. It is no longer just you who doesn’t know. It’s us that that don’t know. That’s a bigger vacuum in search of an answer. In short order, it’s likely one will be pulled in.

The Solution Menu

Thinking of the agile practitioners in my professional circle, they are all adept at generating possibilities and searching their experience reservoir for answers based on similar circumstances. When the creative juices or flow of answers from the past are somewhat parched by the current challenge, it is natural to project the appearance of not knowing. Unless you’ve drawn a complete blank, you can still use the less-than-ideal options that came to mind.

“I can think of several possible solutions,” you might say. “But I’m not yet sure how they can be adapted to this challenge.” Then offer your short list of items for consideration. One of those menu choices might be the spark that inspires a team members to think of a better idea. Someone else may find an innovative combination of menu choices that gets to the heart of the issue. I’ve even had someone mishear one of my menu choices such that what they thought they heard turned out to be the more viable solution. This is just another way to leverage the power of everyone’s innate drive for finding meaning.

Design an Experiment

If there is a glove that fits the “I don’t know” hand, it’s experimentation. I suppose you could work to stretch the guessing glove over “I don’t know.” But if your team is aware that you don’t know something, it’s worse if they know you’re pretending that you do. Challenges and problems are the situation’s way of asking you questions. If the answers aren’t apparent, form a solution hypothesis, set up a simple test, and evaluate the results. And as the shampoo bottle says: lather, rinse, repeat until the problem is washed away. It’s another way to expand the sphere of uncertainty to include the whole team and increase the creative power brought to bear on the problem. If your shampoo bottle is this agile, I’ve every confidence you can be, too.

Now I’m curious. What has helped you move past “I don’t know?”

 

Image by Gerd Altmann from Pixabay

Deliberate Practice and Coding

Deliberate practice applied to coding offers some unique opportunities. Unlike other skills, like learning to play the cello (to pick one that I have some experience with), you can go very far without a personal mentor. The feedback from the computer is about as objective as it gets. It will let you know exactly how good your code is.

This also helps remove the emotional component – positive and negative – that can sometimes impede progress with an in-person mentor. This doesn’t remove all emotion, however. Just about everyone who’s worked in a professional coding shop has witnessed the rare occurrence of a coder cursing at or even physically attacking their computer because their code isn’t working as expected. Those are surreal moments when an avalanche of cognitive biases and unconscious behavior become visible to all but the coder. That’s a topic for for a different post. Suffice it to say, learning how to control your emotions, channel frustration, and ignite curiosity is part of what distinguishes good coders from great coders.

Which gets met to finding quality feedback. While I’ve made a good living writing mountains of proprietary code for various business and corporations, I earned my coding chops by working on or authoring open source projects. This was the best source I found for getting feedback on my code. It also taught me another important lesson: Do not attach your identity to the code you write. Like any noob, I had a lot of pride in my early code that was pretty much untested outside my little work environment. In the open source world, the feedback was often swift and harsh. Or, at least is was when my identity was attached to it. Learning to separate work product from identity revealed just how much emotional spin I was putting on what was in hindsight reasonable feedback. I have concerns that the current climate in the coding world is opting for soft feedback and good feelings over legitimate and reasonable feedback. This, too, is for another post.

It’s worth giving some thought about the the pros and cons of working with an actual person for mentorship. Along with good instruction, a single mentor will pass along their own limitations and biases. Not necessarily a bad thing, just something to be aware of. So multiple mentors are better than just one, which starts to move down the path of actively participating in open source projects. By “actively” I mean not just contributing code, but studying the code (and it’s history) of existing successful projects. There are usually many ways to solve a problem with software. Work to understand why one approach is better than another. Insights like this are best gained, in my experience, by studying good code.

Somewhat related, if you are working from a book or a training program, actually type in the examples – character by character. Don’t cheat yourself by copy-pasting code examples. This is the muscle memory component to coding that you will find when learning other more physical skills (like playing the cello.) If you really want to experience the gnarly edge, ditch the IDE and code with at text editor. I still do all my coding in vim and this keyboard.

Another approach to deliberate practice is the idea of coding “katas.” This never clicked with me. I attribute this to having studied martial arts for 25 years, most of that time at the black belt level. Mapping the human psycho/physical world and the purpose of katas in the dojo to the machine world is too much of a mis-match. Much is lost in the translation, in my experience. The katas in the dojo, regardless the art form, translated easily to other styles and practices. The coding “katas” are more tightly coupled to the coding language in which they are written. In my view, it’s yet another example of swiping a cool sounding word and concept and force-fitting it to another domain. A software version of cargo cults – expecting form to create function. “Black belt” or “Ninja” coder are other force-fits. Yet again, something for another post.

But those are my limitations. Your experience will no doubt be different. As learning exercises and proficiency tracks, many of the coding “katas” look to be very good.

(For related thoughts on how building your own tools can deepen your understanding of a skill, see “Tools for Practice.” The examples in the article combine software development and cello practice.)

 

Image by Robert Pastryk from Pixabay

The Path to Mastery: Begin with the Fundamentals

Somewhere along the path of studying Aikido for 25  years I found a useful perspective on the art that applies to a lot of skills in life.  Aikido is easy to understand. It’s a way of living that leaves behind it a trail of techniques. What’s hard is overcoming the unending stream of little frustrations and often self-imposed limitations. What’s hard is learning how to make getting up part of falling down. What’s hard is healing after getting hurt. What’s hard is learning the importance of recognizing when a white belt is more of a master than you are. In short, what’s hard is mastering the art.

The same can be said about practicing Agile. Agile is easy to understand. It is four fundamental values and twelve principles. The rest is just a trail of techniques and supporting tools – rapid application development, XP, scrum, Kanban, Lean, SAFe, TDD, BDD, stories, sprints, stand-ups – all just variations from a very simple foundation and adapted to meet the prevailing circumstances. Learning how to apply the best technique for a given situation is learned by walking the path toward mastery – working through the endless stream of frustrations and limitations, learning how to make failing part of succeeding, recognizing when you’re not the smartest person in the room, and learning how to heal after getting hurt.

If an Aikidoka is attempting to apply a particular technique to an opponent  and it isn’t working, their choices are to change how they’re performing the technique, change the technique, or invent a new technique based on the fundamentals. Expecting the world to adapt to how you think it should go is a fool’s path. Opponents in life – whether real people, ideas, or situations – are notoriously uncompromising in this regard.  The laws of physics, as they say, don’t much care about what’s going on inside your skull. They stubbornly refuse to accommodate your beliefs about how things “should” go.

The same applies to Agile practices. If something doesn’t seem to be working, it’s time to step in front of the Agile mirror and ask yourself a few questions. What is it about the fundamentals you’re not paying attention to? Which of the values are out of balance? What technique is being misapplied? What different technique will better serve? If your team or organization needs to practice Lean ScrumXPban SAFe-ly than do that. Be bold in your quest to find what works best for your team. The hue and cry you hear won’t be from the gods, only those who think they are – mere mortals more intent on ossifying Agile as policy, preserving their status, or preventing the perceived corruption of their legacy.

But I’m getting ahead of things. Before you can competently discern which practices a situation needs and how to best structure them you must know the fundamentals.

There are no shortcuts.

In this series of posts I hope to open a dialog about mastering Agile practices. We’ll begin by studying several maps that have been created over time that describe the path toward mastery, discuss the benefits and shortcomings of each of these maps, and explore the reasons why many people have a difficult time following these maps. From there we’ll move into the fundamentals of Agile practices and see how a solid understanding of these fundamentals can be used to respond to a wide variety of situations and contexts. Along the way we’ll discover how to develop an Agile mindset.

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How to Know You Have a Well Defined Minimum Viable Product

Conceptually, the idea of a minimum viable product (MVP) is easy to grasp. Early in a project, it’s a deliverable that reflects some semblance to the final product such that it’s barely able to stand on it’s own without lots of hand-holding and explanation for the customer’s benefit. In short, it’s terrible, buggy, and unstable. By design, MVPs lack features that may eventually prove to be essential to the final product. And we deliberately show the MVP to the customer!

We do this because the MVP is the engine that turns the build-measure-learn feedback loop. The key here is the “learn” phase. The essential features to the final product are often unclear or even unknown early in a project. Furthermore, they are largely undefinable or unknowable without multiple iterations through the build-measure-learn feedback cycle with the customer early in the process.

So early MVPs aren’t very good. They’re also not very expensive. This, too, is by design because an MVP’s very raison d’être is to test the assumptions we make early on in a project. They are low budget experiments that follow from a simple strategy:

  1. State the good faith assumptions about what the customer wants and needs.
  2. Describe the tests the MVP will satisfy that are capable of measuring the MVP’s impact on the stated assumptions.
  3. Build an MVP that tests the assumptions.
  4. Evaluate the results.

If the assumptions are not stated and the tests are vague, the MVP will fail to achieve it’s purpose and will likely result in wasted effort.

The “product” in “minimum viable product” can be almost anything: a partial or early design flow, a wireframe, a collection of simulated email exchanges, the outline to a user guide, a static screen mock-up, a shell of screen panels with placeholder text that can nonetheless be navigated – anything that can be placed in front of a customer for feedback qualifies as an MVP. In other words, a sprint can contain multiple MVPs depending on the functional groups involved with the sprint and the maturity of the project. As the project progresses, the individual functional group MVPs will begin to integrate and converge on larger and more refined MVPs, each gaining in stability and quality.

MVPs are not an end unto themselves. They are tangible evidence of the development process in action. The practice of iteratively developing MVPs helps develop to skill of rapid evaluation and learning among product owners and agile delivery team members. A buggy, unstable, ugly, bloated, or poorly worded MVP is only a problem if it’s put forward as the final product. The driving goal behind iterative MVPs is not perfection, rather it is to support the process of learning what needs to be developed for the optimal solution that solves the customer’s problems.

“Unlike a prototype or concept test, an MVP is designed not just to answer product design or technical questions. Its goal is to test fundamental business hypotheses.” – Eric Ries, The Lean Startup

So how might product owners and Agile teams begin to get a handle on defining an MVP? There are several questions the product owner and team can ask of themselves, in light of the product backlog, that may help guide their focus and decisions. (Use of the following term “stakeholders” can mean company executives or external customers.)

  • Identify the likely set of stakeholders who will be attending the sprint review. What will these stakeholders need to see so that they can offer valuable feedback? What does the team need to show in order to spark the most valuable feedback from the stakeholders?
  • What expectations have been set for the stakeholders?
  • Is the distinction clear between what the stakeholders want vs what they need?
  • Is the distinction clear between high and low value? Is the design cart before the value horse?
  • What are the top two features or functions the stakeholders  will be expecting to see? What value – to the stakeholders – will these features or functions deliver?
  • Will the identified features or functions provide long term value or do they risk generating significant rework down the road?
  • Are the identified features or functions leveraging code, content, or UI/UX reuse?

Recognizing an MVP – Less is More

Since an MVP can be almost anything,  it is perhaps easier to begin any conversation about MVPs by touching on the elements missing from an MVP.

An MVP is not a quality product. Using any generally accepted definition of “quality” in the marketplace, an MVP will fail on all accounts. Well, on most accounts. The key is to consider relative quality. At the beginning of a sprint, the standards of quality for an MVP are framed by the sprint goals and objectives. If it meets those goals, the team has successfully created a quality MVP. If measured against the external marketplace or the quality expectations of the customer, the MVP will almost assuredly fail inspection.

Your MVPs will probably be ugly, especially at first. They will be missing features. They will be unstable. Build them anyway. Put them in front of the customer for feedback. Learn. And move on to the next MVP. Progressively, they will begin to converge on the final product that is of high quality in the eyes of the customer. MVPs are the stepping stones that get you across the development stream and to the other side where all is sunny, beautiful, and stable. (For more information on avoiding the trap of presupposing what a customer means by quality and value, see “The Value of ‘Good Enough…For Now’“)

An MVP is not permanent. Agile teams should expect to throw away several, maybe even many, MVPs on their way to the final product. If they aren’t, then it is probable they are not learning what they need to about what the customer actually wants. In this respect, waste can be a good, even important thing. The driving purpose of the MVP is to rapidly develop the team’s understanding of what the customer needs, the problems they are expecting to have solved, and the level of quality necessary to satisfy each of these goals.

MVPs are not the truth. They are experiments meant to get the team to the truth. By virtue of their low-quality, low-cost nature, MVPs quickly shake out the attributes to the solution the customer cares about and wants. The solid empirical foundation they provide is orders of magnitude more valuable to the Agile team than any amount of speculative strategy planning or theoretical posturing.

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The Value of “Good Enough…for Now”

Any company interested in being successful, whether offering a product or service, promises quality to its customers. Those that don’t deliver, die away. Those that do, survive. Those that deliver quality consistently, thrive. Seems like easy math. But then, 1 + 1 = 2 seems like easy math until you struggle through the 350+ pages Whitehead and Russell1 spent on setting up the proof for this very equation. Add the subjective filters for evaluating “quality” and one is left with a measure that can be a challenge to define in any practical way.

Math aside, when it comes to quality, everyone “knows it when they see it,” usually in counterpoint to a decidedly non-quality experience with a product or service. The nature of quality is indeed chameleonic – durability, materials, style, engineering, timeliness, customer service, utility, aesthetics – the list of measures is nearly endless. Reading customer reviews can reveal a surprising array of criteria used to evaluate the quality for a single product.

The view from within the company, however, is even less clear. Businesses often believe they know quality when they see it. Yet that belief is often predicate on how the organization defines quality, not how their customers define quality. It is a definition that is frequently biased in ways that accentuate what the organization values, not necessarily what the customer values.

Organization leaders may define quality too high, such that their product or service can’t be priced competitively or delivered to the market in a timely manner. If the high quality niche is there, the business might succeed. If not, the business loses out to lower priced competitors that deliver products sooner and satisfy the customer’s criteria for quality (see Figure 1).

Figure 1. Quality Mismatch I
Figure 1. Quality Mismatch I

Certainly, there is a case that can be made for providing the highest quality possible and developing the business around that niche. For startups and new product development, this may not be be best place to start.

On the other end of the spectrum, businesses that fall short of customer expectations for quality suffer incremental, or in some cases catastrophic, reputation erosion. Repairing or rebuilding a reputation for quality in a competitive market is difficult, maybe even impossible (see Figure 2).

Figure 2. Quality Mismatch II
Figure 2. Quality Mismatch II

The process for defining quality on the company side of the equation, while difficult, is more or less deliberate. Not so on the customer side. Customers often don’t know what they mean by “quality” until they have an experience that fails to meet their unstated, or even unknown, expectations. Quality savvy companies, therefore, invest in understanding what their customers mean by “quality” and plan accordingly. Less guess work, more effort toward actual understanding.

Furthermore, looking to what the competition is doing may not be the best strategy. They may be guessing as well. It may very well be that the successful quality strategy isn’t down the path of adding more bells and whistles that market research and focus groups suggest customers want. Rather, it may be that improvements in existing features and services are more desirable.

Focus on being clear about whether or not potential customers value the offered solution and how they define value. When following an Agile approach to product development, leveraging minimum viable product definitions can help bring clarity to the effort. With customer-centric benchmarks for quality in hand, companies are better served by first defining quality in terms of “good enough” in the eyes of their customers and then setting the internal goal a little higher. This will maximize internal resources (usually time and money) and deliver a product or service that satisfies the customer’s idea of “quality.”

Case in point: Several months back, I was assembling several bar clamps and needed a set of cutting tools used to put the thread on the end of metal pipes – a somewhat exotic tool for a woodworker’s shop. Shopping around, I could easily drop $300 for a five star “professional” set or $35 for a set that was rated to be somewhat mediocre. I’ve gone high end on many of the tools in my shop, but in this case the $35 set was the best solution for my needs. Most of the negative reviews revolved around issues with durability after repeated use. My need was extremely limited and the “valuable and good enough” threshold was crossed at $35. The tool set performed perfectly and more than paid for itself when compared with the alternatives, whether that be a more expensive tool or my time to find a local shop to thread the pipes for me. This would not have been the case for a pipefitter or someone working in a machine shop.

By understanding where the “good enough and valuable” line is, project and organization leaders are in a better position to evaluate the benefits of incremental improvements to core products and services that don’t break the bank or burn out the people tasked with delivering the goods. Of course, determining what is “good enough” depends on the end goal. Sending a rover to Mars, “good enough” had better be as near to perfection as possible. Threading a dozen pipes for bar clamps used in a wood shop can be completed quite successful with low quality tools that are “good enough” to get the job done.

Addendum

I’ve been giving some more thought to the idea of “good enough” as one of the criteria for defining minimum viable/valuable products. What’s different is that I’ve started to use the phrase “good enough for now.” Reason being, the phrase “good enough” seems to imply an end state. “Good enough” is an outcome. If it is early in a project, people generally have a problem with that. They have some version of an end state that is a significant mismatch with the “good enough” product today. The idea of settling for “good enough” at this point makes it difficult for them to know when to stop work on an interim phase and collect feedback.

“Good enough for now” implies there is more work to be done and the product isn’t in some sort of finished state that they’ll have to settle for. “Good enough for now” is a transitory state in the process. I’m finding that I can more easily gain agreement that a story is finished and get people to move forward to the next “good enough for now” by including the time qualifier.

References

1Volume 1 of Principia Mathematica by Alfred North Whitehead and Bertrand Russell (Cambridge University Press, page 379). The proof was actually not completed until Volume 2. (This article cross-posted at LinkedIn.)

The Perfect System in an Imperfect World

With apologies to Winston Churchill,

Many forms of project management have been tried, and will be tried in this world of sin and woe. No one pretends that Scrum is perfect or all-wise. Indeed, it has been said that Scrum is the worst form of project management except all those other forms that have been tried from time to time.

Agile in general, and scrum in particular, has suffered their share of hard yet deserved knocks. But many of these complaints come from people who are expecting perfection, some panacea or magic remedy to what ails their project management world. Often they want this perfection out of the box and miss the hard work needed to implement a relatively simple set of rules and guidelines while shifting from the “old ways” of getting work done.

Consider a flock of geese.

Over the course of hundreds of thousands of years they have worked out an efficient way to migrate. Not perfect, but well adapted to the world in which they live. At the heart of this behavior are several important principles: Shared responsibility, clear communication, and coordinated effort.

Consider Agile similarly. It is a perfect system for an imperfect world. The principles found in the formation of a flock of geese can be found within the Agile Manifesto. Its foundation of assuming the need for experimentation, learning, and adaptation is central to it’s enduring success. If these values are absent from or poorly represented in an organization’s culture, the chances for sustainable success using any methodology are diminished.

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Drive for Teams

I recently re-read Daniel Pink’s book, “Drive: The Surprising Truth About What Motivates Us.” I read it when it was first published and I was still managing technical teams. Super brief summary: The central idea of the book is that people are mostly driven by intrinsic motivation based on three aspects:

  • Autonomy — The desire to be self directed.
  • Mastery — The urge to improve skills.
  • Purpose — The desire to engage with work that has meaning and purpose.

I find this holds true for individuals. However, when applied to teams optimizing for these three aspects can be problematic. If an individual on a team seeks to maximize autonomy, they are likely to come into conflict with the objectives of the team. For example, a software team that is tasked with developing a component that is expected to interact with several other components developed by other teams. If a single developer, in the interests of maximizing their individual autonomy, has decided to develop the component according to standards, design principles, and tools that are different from those of teammates and other teams (essentially, a local optimization,) then the result is likely to be sub-optimal overall.

Some individual autonomy must necessarily be sacrificed in the interests of effective collaboration. It’s possible, even desirable, that individual pursuits of mastery and purpose can be maintained. However, it may be necessary for an individual to focus on mundane tasks and the objectives of the team for periods of time. Finding ways to maintain a healthy balance between the intrinsic motivators and the purpose of the team is no small task and, when found, requires constant attention to maintain.

Perhaps it is possible to attach the team’s or organization’s purpose to the interests of the individual. Or sort for hiring people who have a personal purpose that is in-line with the organization’s purpose.

Image by M. Maggs from Pixabay