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.

Agile Team Composition: Generalists versus Specialists

Estimating levels of effort for a set of tasks by a group of individuals well qualified to complete those tasks can efficiently and reliable be determined with a collaborative estimation process like planning poker. Such teams have a good measure of skill overlap. In the context of the problem set, each of the team members are generalist in the sense  it’s possible for any one team member to work on a variety of cross functional tasks during a sprint. Differences in preferred coding language among team members, for example, is less an issue when everyone understands advanced coding practices and the underlying architecture for the solution.

With a set of complimentary technical skills it’s is easier agree on work estimates. There are other benefits that flow from well-matched teams. A stable sprint velocity emerges much sooner. There is greater cross functional participation. And re-balancing the work load when “disruptors” occur – like vacations, illness, uncommon feature requests, etc. – is easier to coordinate.

Once the set of tasks starts to include items that fall outside the expertise of the group and the group begins to include cross functional team members, a process like planning poker becomes increasingly less reliable. The issue is the mismatch between relative scales of expertise. A content editor is likely to have very little insight into the effort required to modify a production database schema. Their estimation may be little more than a guess based on what they think it “should” be. Similarly for a coder faced with estimating the effort needed to translate 5,000 words of text from English to Latvian. Unless, of course, you have an English speaking coder on your team who speaks fluent Latvian.

These distinctions are easy to spot in project work. When knowledge and solution domains have a great deal of overlap, generalization allows for a lot of high quality collaboration. However, when an Agile team is formed to solve problems that do not have a purely technical solution, specialization rather than generalization has a greater influence on overall success. The risk is that with very little overlap specialized team expertise can result in either shallow solutions or wasteful speculation – waste that isn’t discovered until much later. Moreover, re-balancing the team becomes problematic and most often results in delays and missed commitments due to the limited ability for cross functional participation among team mates.

The challenge for teams where knowledge and solution domains have minimal overlap is to manage the specialized expertise domains in a way that is optimally useful, That is, reliable, predictable, and actionable. Success becomes increasingly dependent on how good an organization is at estimating levels of effort when the team is composed of specialists.

One approach I experimented with was to add a second dimension to the estimation: a weight factor to the estimator’s level of expertise relative to the nature of the card being considered. The idea is that with a weighted expertise factor calibrated to the problem and solution contexts, a more reliable velocity emerges over time. In practice, was difficult to implement. Teams spent valuable time challenging what the weighted factor should be and less experienced team members felt their opinion had been, quite literally, discounted.

The approach I’ve had the most success with on teams with diverse expertise is to have story cards sized by the individual assigned to complete the work. This still happens in a collaborative refinement or planning session so that other team members can contribute information that is often outside the perspective of the work assignee. Dependencies, past experience with similar work on other projects, missing acceptance criteria, or a refinement to the story card’s minimum viable product (MVP) definition are all examples of the kind of information team members have contributed. This invariably results in an adjustment to the overall level of effort estimate on the story card. It also has made details about the story card more explicit to the team in a way that a conversation focused on story point values doesn’t seem to achieve. The conversation shifts from “What are the points?” to “What’s the work needed to complete this story card?”

I’ve also observed that by focusing ownership of the estimate on the work assignee, accountability and transparency tend to increase. Potential blockers are surfaced sooner and team members communicate issues and dependencies more freely with each other. Of course, this isn’t always the case and in a future post we’ll explore aspects of team composition and dynamics that facilitate or prevent quality collaboration.

The Value of “Good Enough”

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.

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.)

Relative Team Expertise and Story Sizing

In Parkinson’s Law of Triviality and Story Sizing, I touched on the issue of relative expertise among team members during collaborative efforts to size story cards. I’d like to expand on that idea by considering several types of team compositions.

Team 1 is a tight knit band of four software developers represented in Figure 1.

Figure 1 - Team 1
Figure 1 – Team 1

Their preferred domain and depth of experience is represented by the color and area of their respective circles. While they each have their own area of expertise, there is a significant overlap in common knowledge. All four of them understand the underlying architecture, common coding practices, and fundamental coding principles. Furthermore, there is a robust amount of inter-domain expertise. When needed, the HTML5/CSS developer can probably help out with JavaScript issues, for example. The probability of this team successfully working together to size the stories in the product backlog is high.

Team 1 represents a near-ideal team composition for a typical software related project. However, the real world isn’t so generous in it’s allocation of near-ideal, let alone ideal, teams. A typical team for a software related project is more likely to resemble Team 2, as represented in Figure 2.

Figure 2 - Team 2
Figure 2 – Team 2

In Team 2, the JavaScript developer is fresh out of college,  new to the company and new to the business. His real-world experience is limited so his circle of expertise is smaller relative to his teammates. The HTML5/CSS developer has been working for the company for 10 years and knows the business like the back of her hand. So she has a much wider view of how her work impacts the company and product development. As a team, there is much less overlap and options for helping each other through a sprint is diminished.  As for collaborative story sizing efforts, the HTML5/CSS and C# developers are likely to dominate the conversation while the JavaScript developer agrees with just about anything not JavaScript related.

As Agile practices become more ubiquitous in the business world, team composition beings to resemble Team 3, as shown in Figure 3.

Figure 3 - Team 3
Figure 3 – Team 3

The mix now includes non-technical people – content developers and editors, strategists, and designers. Even assuming an equal level of experience in their respective domains, the company, and the business environment, there is very little overlap. Arriving at a consensus during a story sizing exercise now becomes a significant challenge. But again, the real world isn’t even so kind as this. We are increasingly more likely to encounter teams that resemble Team 4 as shown in Figure 4.

Figure 4 - Team 4
Figure 4 – Team 4

As before, the relative circle of expertise among team members can vary quite a bit. When a team resembles the composition of Team 4, the software developers (HTML5/CSS and C#) will have trouble understanding what the Learning Strategist is asking for while the Learning Strategist may not understand why what he wants the software developers to deliver isn’t possible.

When I’ve attempted to facilitate story sizing sessions with teams that resemble Team 4 they either become quite contentious (and therefore time consuming) or team members that don’t have the expertise to understand a particular card simply accept the opinion of the stronger voices. Neither one of these situations is desirable.

To counteract these possibilities, I’ve found it much more effective to have the card assignee determine the card size (points and time estimate) and work to have the other team members ask questions about the work described on the card such that the assignee and the team better understand the context in which the card is positioned. The team members that lack domain expertise, it turns out, are in a good position to help craft good acceptance criteria.

  • Who will consume the work product that results from the card? (dependencies)
  • What cards need to be completed before a particular card can be worked on? (dependencies)
  • Is everything known about what a particular card needs before it can be completed? (dependencies, discovery, exploration)

At the end of a brief conversation where the entire team is working to evaluate the card for anything other than level of effort (time) and complexity (points), it is not uncommon for the assignee to reconsider their sizing, break the card into multiple cards, or determine the card shouldn’t be included in the sprint backlog. In short, it ends up being a much more productive conversation if teammates aren’t haggling over point distinctions or passively accepting what more experienced teammates are advocating. The benefit to the product owner is that they now have additional information that will undoubtedly influence the product backlog prioritization.

False Barriers to Implementing Scrum – II

In a previous post, I described several barriers to implementing scrum. Recently, an additional example came to light similar to the mistake of elevating scrum or Agile to a philosophy.

In a conversation with a colleague, we were exploring ways on how we might drive interest for browsing the growing wealth of Agile related information being added to the company wiki.  It’s an impressive collection of experiences of how other teams have solved a wide array of interesting problems using Agile principles and practices. Knowing that we cannot personally attend to every need on every project team, we were talking through various ways to develop the capacity for exploration and self-education. I think I must have used the phrase “the information is out there and readily available” a couple of times to many because my colleague reacted to where I put the bar by comparing learning Agile to surgery.

Using the surgery metaphor, she pressed the comparison that all the information she needs about surgery is “out there and readily available” but even if she knew all that information she likely wouldn’t be a good surgeon. Fair point that experience and practice are important. And if that is the case, then everyone should be taking every opportunity they can to practice good agile rather than regressing to old habits.

More importantly, perhaps, is the misapplied metaphor. Practicing agile isn’t as complicated as surgery or rocket science or any other such endeavor that requires years of deep study and practice. Comparing it to something like that places the prospects of doing well in a short amount of time mentally beyond the reach of any potential practitioners.

Perhaps a better metaphor is the opening of a new rail line in the city. A good measure of effort needs to be expended to educate the public on the line’s availability, the schedules, how to purchase fares, where the connections are, what are the safety features, etc. Having done that, having “put the information out there where it is generally available,” it is a reasonable expectation that the public will make the effort to find it when they need it. It is unreasonable, and unscaleable, to build such a system and then expect that every passenger will be personally escorted from their front door to their seat on the train.

It is also interesting to consider what this does to the “empathy scale” when such an overextended metaphor is applied to efforts such as learning to practice Agile. If we frame learning Agile as similar to surgery then as people work to implement Agile are we more inclined to have an excessive amount of empathy for their struggles and be more accepting or accommodating of their short comings?

“Not to worry that you still don’t have a well formed product backlog. This is like surgery, after all.”

Are we as an organization and each of our employees better served by the application of a more appropriate metaphor, one that matches the skill and expectations of the task?

“We’ve provided instruction as to what a product backlog is and how to create one. We’ve guided you as you’ve practiced refining a product backlog. You know where to find suggestions for improving your skills for product backlog stewardship (wiki, books, colleagues, etc). Now role up your sleeves and do the work.”

Successful coaching for developing the ability in team members for actively seeking answers requires skillfully letting them struggle and fail in recoverable ways. It is possible to hold their hand too long. To use another metaphor, provide whatever guidance and instruction you need to so they know how to fish, then let them alone to practice casting their own line.


Photo credit: langll

Agile Interns

I had the privilege of presenting to a group of potential interns from the Colorado School of Mines interested in agile project management. Action shot…

The slide shows what we can offer them as interns: Failure, chaos, and confusion. I unpacked that as follows…

Failure

It’s important interns have the opportunity to learn how to fail in small, deliberate and safe increments along with the opportunity to learn how to extract every possible lesson from failures and how those failures lead to eventual success. Much of our business is driven by experimentation and hypothesis testing. Most of those experiments will fail, at least initially.

Chaos

We strive to be anti-fragile. One way to accomplish this is to be good at working under chaotic and ambiguous conditions. When involved with highly evolutionary design sessions, shifting sands can seem like the most solid ground around.

Confusion

One of the values for bringing interns into the organization is the fresh perspective they offer.  Why waste that on having them fetch coffee? However, interns can often be intimidated by working with people who have decades of experience under their belt. So it’s important they know they have the opportunity to work in an environment that expects questions and recognizes no one knows it all. They are in an environment that  seeks alternative points of view. In this organization, everyone gets their own coffee.

Practicing Agile – Building Mastery One Day At A Time

Old joke: A young couple visiting New York City for the first time has lost their way. They spot a street musician, just the person to help them get reoriented. “Excuse us, but can you tell us how to get to Carnegie Hall?” The musician stopped playing and thought for a moment before replying: “Practice.”

The prevailing wisdom is that it takes 10,000 hours of practice to achieve the level of mastery in any particular field of endeavor. Turns out, this is true for fields with well-defined measures for excellence like chess and music. In each of these areas, the rules are relatively simple, but mastering them isn’t easy. It’s pretty easy to tell when someone is playing an instrument out of tune or off-beat. And yet, a pawn shop guitar in the hands of Joe Satriani or Liona Boyd will likely result in that instrument expressing a voice no one knew it had. As for chess masters, they’re the ones who win against all challengers regardless the time or place of the match.

For areas of human endeavor where the edges are less well defined, like business or coaching, there may be no marker for how much practice it takes to reach a stable mastery. Having successfully started and built one business does not guarantee the next venture will be equally successful. A coach with a winning system for one team may end up at the bottom of the ranks when the same system is used with a different team.

Developing expertise with scrum is a blend of both of these. The rules are simple, but they are not easy to master. At the same time the territory isn’t well defined and frequently changes. A new client, a new team, or a new project and the edges for what is possible change. Misunderstanding this has been at the root of much of the frustration I’ve observed among people new to agile. They come from a world with well-defined edges – traditional project management practices filled with Gantt charts, milestones, functional specifications, use cases, deployment requirements, and a plethora of other artifacts that “must” be in place before work can begin. As many unknowns as possible must be made known, risks pounded down to trivial annoyances, and all traces of ambiguity squeezed out of the project plan. Learning how to let go of deeply rooted practices like this is no small thing. We like the comfort of well-defined rules. And when there’s work to be done with scarce resources to make it happen, we reach for the rules most familiar to us.

So how can we update the tried-and-true, super comfy confines of past practices and rule sets?

Practice.

Research following on the “10,000 hours of practice” generalization has shown that it isn’t just that someone has completed 10,000 hours of practice. The critical factor was how they practiced. Was each hour spent completing the same motions and behaviors from the previous hour or were they spent building on successive experiences, seeking greater challenges, and developing a deeper understanding of their craft? Following the latter path leads to the incremental improvements required to build mastery. And once obtained, the same attitude toward practice helps sustain a level of mastery. There will always be something more to learn, a fresh perspective to experience, or a more satisfying way to experience success.

There is a great deal of neuroscience at the foundation of practice and few would dispute the value of learning how to learn. And yet as our experience grows and we master a particular field, it’s deceptively easy to fall into a complacency of thought whereby we convince ourselves there isn’t anything else to learn. That is until some seismic paradigm shift makes it clear the rules have changed and we’d let our mastery go stale. The consequences of this are captured by Greene (2012) in his book, Mastery:

“We prefer to live with familiar ideas and habits of thinking, but we pay a steep price for this: our minds go dead from the lack of challenge and novelty; we reach a limit in our field and lose control over our fate because we become replaceable.” (pg. 176)

If this happens, learning how to learn may not be enough. Learning how to unlearn may be equally valuable for regaining mastery.

In classic hacker culture, you aren’t a hacker until other recognized hackers call you a hacker. It’s a title to be earned, not claimed. The unfortunate title of “scrum master” aside, it is useful to take this credentialing tradition to heart with scrum as well. Consider yourself an apprentice scrum practitioner until other recognized scrum masters recognize your mastery. Holding such a frame keeps us humble, curious, and open to constant and never ending improvement.

I’ve been practicing, leading, or coaching scrum in one capacity or another for over 10 years and based on my billable hours over the past several years, I’m quite confident I’ve passed the 10,000 hour mark for practicing scrum. Even so, I’m not a master scrum master…yet. The reason is simple and is expressed by the great cellist Pablo Casals’ response to filmmaker Robert Snyder’s query as to why Casals continues to practice five hours a day at 80 years of age, “Because I think I am making progress.” I keep building upon my practice because each day I discover new ways to enhance team performance and improve my skills. Perhaps more telling, any time I think I’ve heard every excuse for not following the scrum framework, someone on one of my teams surprises me.

If you’re interested in staying on the path toward scrum mastery, you need to get out of the books and into the world. There are a variety of ready opportunities to mark and gauge your progress.

  • Frequently review the framework for scrum and compare what’s there with your current projects. If there are mismatches, find out why. Is there really a good reason for straying from the framework? If so, open a dialog about these differences during your retrospectives.
  • If possible, ask your fellow agile practitioners when they are holding their next review, backlog refinement, or sprint planning session and get yourself invited as an observer.
  • There are probably a number of excellent agile related meet-ups in your area. Speaking from personal experience, these are incredibly valuable communities of support and new ideas.

References

Greene, R. (2012). Mastery. New York, NY: Viking Penguin

This article was originally published by the Scrum Alliance under Member Articles.

Moving Past “I Don’t Know”

Recently I had the opportunity to attend the Mile High Agile 2015 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?”

This article was originally published by the Scrum Alliance under Member Articles.