Teaming

why do some organizations thrive while others get trapped in outdated patterns? the difference had nothing to do with individual talent but rather how people work together.

in this chapter, you’ll discover how successful organizations are moving beyond traditional teams and departments to create dynamic, adaptive collaboration systems, and why industrial-era management approaches fall short in today’s complex environment. through real-world examples from medicine, manufacturing, and technology, you’ll see how different contexts demand different approaches to collaboration. additionally, you’ll gain insights into why rigid standardization holds companies back, and how the most successful ones are building fluid, adaptable ways of working together. let’s begin.

watch any great sports team and you’ll see something remarkable: players who seem to read each other’s minds, anticipating moves before they happen. where does this kind of seamless coordination come from?

it comes from two sources. first, the thousands of hours of practice together, building trust and understanding through experience together. and second, from a stable set of rules and objectives – the same game played again and again.

but in today's fast-moving organizations we have neither of these luxuries. instead, we have an ever-changing, dynamic set of complex problems, which requires teams coming together dynamically, again and again.

in the early days of industrial management, frederick taylor and henry ford revolutionized manufacturing by breaking work into simple, repeatable tasks. their approach worked brilliantly for making model t’s, but it created deeply rooted habits that now hold organizations back. for example, many organizations still treat failure as mistakes to be avoided rather than opportunities for learning. they track individual performance meticulously while neglecting to measure team collaboration. and they reward people for following procedures rather than for improving them. these habits aren’t just outdated – they actively hinder experimentation and collective learning.

what makes these shifts even more important is the complexity of modern knowledge. take medicine. in the 1960s, a doctor could stay up-to-date by following a few key journals. today, medical knowledge doubles every few months, with thousands of new clinical trials published annually. no single person can keep up. so success requires not just continuous learning but collaboration across disciplines.

this new reality creates different demands at each level of work. some tasks remain relatively routine and benefit from standardization – think basic manufacturing or transaction processing. other tasks involve complex operations where established procedures meet unexpected challenges, like managing a hospital emergency department. still others push into entirely new territory, requiring discovery and fundamental innovation.

most large organizations today handle all three types of work, often simultaneously. a pharmaceutical company, for instance, needs simple efficiency in its manufacturing plants, adaptability in its clinical trials, and pure innovation in its research labs.

in this environment, the key to success isn’t working harder or being smarter but learning faster. organizations need to build their collective capacity to experiment and continuously share knowledge. this means creating environments where people feel safe to take risks, where mistakes are treated as learning opportunities, and where collaboration across boundaries is the norm rather than the exception.

at pixar animation studios, for example, artists regularly share unfinished work – rough sketches, half-formed ideas, incomplete animations. this is encouraged and even built into their creative process. the view is that early feedback, even when it means showing imperfect work, leads to better final results. this stands in stark contrast to traditional organizations where people often hide their work until it’s “perfect,” fearing criticism or negative consequences.

the challenge organizations face is changing mindsets from one of individual expertise to one of collective learning and adaptation.

when a surgical team faces an unprecedented complication mid-operation, what separates success from catastrophe has nothing to do with individual brilliance but the ability of the team to adapt and learn together in real-time. this kind of dynamic collaboration, where expertise must be rapidly shared and synthesized, represents the essence of modern organizational effectiveness. the author amy edmonson calls this teaming.

unlike traditional static teams, teaming represents a fluid, adaptive process where individuals must rapidly form, collaborate, and often disband as circumstances demand. think of it as organizational jazz rather than classical music – skilled individuals improvising together rather than following a preset score. this approach has become essential in an era where problems are too complex for any single expert to solve, and where the pace of change renders traditional hierarchies increasingly cumbersome.

the power of teaming lies in its iterative nature. each cycle of communication, action, and reflection builds upon the last, creating an upward spiral of organizational capability. when a product development team shares rough prototypes for early feedback, or when a marketing group tests experimental campaigns with clear learning metrics, they’re completing tasks and at the same time building collective intelligence. this process transforms individual insights into organizational wisdom, allowing companies to adapt and innovate at a pace otherwise impossible.

the benefits of successful teaming extend far beyond immediate performance metrics. organizations that master this approach experience profound cultural transformations. employees develop broader perspectives about how their work connects to the larger whole. innovation accelerates as ideas cross-pollinate across traditional siloes. perhaps most importantly, work becomes more engaging and meaningful as people recognize how their expertise contributes to larger collective achievements.

but implementing teaming requires a deliberate leadership approach. it requires creating what might be called a “learning architecture” – a comprehensive approach that includes framing uncertainty positively, establishing psychological safety, and building robust mechanisms for knowledge sharing across traditional boundaries. teaming is more than just a series of discrete activities; it’s a fundamental operating system for collective learning and adaptation.

when a $50 million drug trial fails, what separates the company that crumbles from one that emerges stronger? one drug company faced this exact scenario. instead of burying the failure, their research team conducted a thorough analysis that revealed fundamental flaws in their testing methodology. this discovery led to improved protocols that ultimately contributed to the successful development of new drugs; a costly setback transformed into a powerful asset.

not all failure is created equal; it can range from blameworthy to praiseworthy. the former includes wilfully or negligently violating rules, while the latter might consist of attempts at innovation that simply didn’t achieve the desired outcomes.

when a software development team deliberately ignored security protocols and released untested code, causing a system breach, that represents blameworthy failure requiring swift correction. in contrast, when pixar's animation teams share rough, unfinished work for feedback – knowing full-well it might fail to meet standards – they're engaging in praiseworthy failure that drives innovation. most failures fall somewhere between these extremes, stemming from complex interactions between systems, information deficits, and unforeseen circumstances.

the most sophisticated organizations treat failure as a rich data source. take google. its much-vaunted “20% time” policy has led to both numerous failures as well as massive hits like gmail. they maintain detailed records of both outcomes, allowing teams across the company to learn from each other’s experiences without repeating costly mistakes. this systematic approach to failure analysis – treating each setback as a case study – helps prevent institutional knowledge from remaining trapped within individual departments.

the key lies in distinguishing between calculated risks and reckless gambles. a marketing team might launch an experimental campaign knowing it could fail but having clear metrics for success and learning. the insights gained, even from failure, inform future strategies. on the other hand, launching without any measurement framework or learning mechanism in place represents a missed opportunity at best and negligence at worst.

to draw lessons from failures, stories are a powerful tool – often more powerful than mere data when it comes to driving organizational learning. for example, a product team shares how their failed launch revealed crucial insights about customer needs. or a research division explains how a series of unsuccessful experiments ultimately led to a breakthrough. these narratives become powerful teaching tools that guide future decision-making.

in today’s world, this ability to learn from failure is more than just useful – it’s an essential differentiator between organizations that win or lose.

the most dangerous words in business might be “we've figured it out.’” this mindset, which equates success with stability, fails to grasp how modern organizations thrive. the traditional model of efficiency – where leaders provide answers, processes remain static, and change is viewed as disruption – has given way to something more dynamic and powerful: execution-as-learning.

consider intermountain healthcare, a health-care company which takes a unique approach to medical protocols. rather than treating best practices as immutable commandments, they're viewed as informed starting points. physicians are encouraged to deviate from protocols when their clinical judgment demands it, with one important caveat: they must document their decisions and the commensurate patient outcomes. this creates a continuous feedback loop – one where frontline experiences reshape and refine organizational knowledge.

for instance, what began as a single, standardized diabetes protocol evolved into a more sophisticated system of treatments tailored to specific patient subgroups based on age, gender, weight, and other factors – each refinement driven by documented clinical experiences and outcomes.

approaches like these can challenge the natural desire for certainty. people, especially in professional settings, crave clear instructions and guaranteed outcomes. yet the most successful organizations have learned to embrace what might be called “systematic uncertainty” – the understanding that today’s solution is tomorrow’s starting point. 

toyota’s production facilities exemplify this philosophy. there, assembly lines operate as teams, not because assembly itself requires this collaboration, but because process improvement does. their problem-solving cycles occur in near real-time, sometimes resolving issues within minutes of identifying them.

shifting from execution-as-efficiency to execution-as-learning demands a fundamental reframing of leadership. instead of providing answers, leaders set direction. rather than enforcing compliance, they cultivate judgment. it’s a complete reversal of traditional management psychology. fear, once used as a tool to ensure adherence to established processes, is poison here in the learning organization. when every process is viewed as inherently imperfect and improvable, fear of failure turns into curiosity about what’s possible. 

perhaps counterintuitively, success itself often can become the greatest obstacle to this approach. organizations that have “figured it out” tend to calcify around their solutions, missing the subtle signals that their competitive advantage is eroding. this phenomenon – where nothing fails quite like success – explains why industry leaders often find themselves blindsided by changing market conditions or disruptive innovations.

the execution-as-learning model addresses this danger through deliberate iteration – a constant cycle of diagnosis, design, action, and reflection – integrated into the fabric of execution. at toyota and intermountain healthcare, it’s an approach which has generated substantial competitive advantages, allowing the companies to simultaneously deliver excellent current performance while building capabilities for future challenges.

when a ten-year-old patient nearly dies from a morphine overdose, who do you blame? the author, amy edmondson, explored this and other questions in a case study at children's hospital and clinics in minneapolis, minnesota. the hospital’s culture, like many others, had long operated on what edmonson calls the “abc's of medicine” – accuse, blame, criticize – a mindset that treated errors as individual failures rather than opportunities for systemic learning.

under coo julie morath’s leadership, the hospital embarked on a different path. research shows that 98,000 americans die annually from medical errors; more than from car accidents, breast cancer, or aids. when confronted with these statistics, staff initially resisted, insisting they simply couldn’t apply to their hospital. rather than arguing, morath responded by encouraging staff to reflect on their past week with patience and ask themselves the simple question: “was everything as safe as you would like it to be?” this inquiry-based approach opened up honest conversations about safety that would have been impossible in a blame-oriented culture.

let’s look at a case of morphine overdose. this incident began when a ten-year-old patient was moved from intensive care to a regular surgical floor due to capacity constraints. an experienced nurse transferred the patient’s care to a recent nursing graduate, providing instructions for programming the morphine infusion pump.

recognizing his inexperience, the new nurse sought help from a senior colleague. but a crucial design flaw complicated matters – the morphine concentration information was printed on an oversized label that folded inside the medication cassette, hiding vital information. using only the visible information, the nurses programmed what they believed to be the correct dosage.

within minutes of starting the infusion, the patient developed severe breathing difficulties. the new nurse quickly recognized the crisis, shut off the infusion, called for help, and began emergency ventilation, saving the patient’s life.

this near-tragedy resulted from a cascade of system failures. under the old blame-focused system, attention might have centered solely on, for example, the nurses who programmed the pump incorrectly. instead, the incident became a catalyst for examining and improving multiple aspects of the hospital’s systems and procedures. these improvements included a system of blameless reporting, and a “good catch log,” where nurses could anonymously record near-misses and potential hazards.

the transformation at children's hospital demonstrates three essential ingredients of teaming and leadership in complex environments. first is psychological safety, exemplified by the shift from blame to learning in safety reporting. second is integration learning into daily operations – making it part of the workflow rather than a separate initiative. third is that cultural transformation follows changes in process. rather than launching educational programs to shift the culture, morath focused on making concrete changes to how people worked together, trusting the shift in mindset would naturally follow.

as the children's hospital study demonstrates, even in high-stakes environments where errors can have devastating consequences, it’s possible to move beyond a culture of fear and inertia to one of collective learning and improvement.

the main takeaway of this chapter to teaming by amy edmondson is that successful modern organizations must move beyond traditional static teams to a dynamic process of active collaboration and continuous learning. 

in today’s fast-changing industries, no single expert can have all the answers. instead, success depends on creating environments where people feel safe to take risks and share the results.

remember: what worked in the age of ford’s assembly line won’t cut it in an era where knowledge doubles every few months. build psychological safety, encourage experimentation, and treat failures as learning opportunities. the result will be systems that turn individual expertise into collective wisdom.

that's it for this chapter. we hope you enjoyed it. if you can, please take the time to leave us a rating – we always appreciate your feedback. see you in the next chapter.