Execution as Learning

Definition of Execution as Learning

The execution as learning model, proposed by Amy C. Edmondson, argues today’s central managerial challenge is to "Inspire and enable knowledge workers to solve, day in and day out, problems that cannot be anticipated". The Execution as Learning approach aims to radically change the traditional vision of Execution as Efficiency. Execution as Learning shows a totally different organizational mindset, focused not so much on making sure a work process is carried out, but on helping it evolve into day-to-day work in 4 phases (elaborated below).

Execution as Learning Perspective

While teamwork is mostly thought of as a noun, Amy Edmondson changes this frame of reference by viewing it as a verb; it’s a dynamic activity determined by the mindset and practices of the group – not the design and structure of effective teams. It involves coordinating and collaborating, regardless of structure or industry. Indeed, she views it as constantly changing based on the shifting nature of the work – so teams can disband and reorganize appropriately to accomplish their jobs. She is pointing to the process NASA used when it first developed its Matrix management system of using temporary project teams.

Within this frame of reference, one can ask then what is the team focused on. Every team is organized to execute; the question is for what purpose? Traditional approaches are for efficiency or effectiveness. The right answer, from a teaming perspective, should be "execution as learning": 'learning to team', and 'teaming to learn'. This means the focus is on the team’s activities and how well it uses them to learn. These activities include; Asking questions, Sharing Information, Seeking Help, Experimenting with unproven actions, Talking about mistakes, and Seeking Feedback.

The Execution as Learning perspective addresses all three of the contexts teams usually engage in:

• Routine operations (e.g., how are we performing against target measures),
• Complex operations in which new risks must be considered, and
• Innovation operations in which they explore new opportunities.

In each case, the team must diagnose the situation, design a system for performing, act, and then reflect on what happened, why and what lessons can be obtained.

Execution-as-Learning: Four Steps

Organizations that adopt an execution-as-learning model don’t focus on getting things done more efficiently than competitors do. Instead, they focus on learning faster. The goal is to find out what works and what doesn’t; employees must absorb new knowledge while executing, often sacrificing short-term efficiency to gain insight into and respond to novel problems. My research has revealed four steps for making this happen.

• Step 1: Provide process guidelines: Figuring out the best ways to accomplish different kinds of work in a rapidly changing environment starts with seeking out best practices gathered from experts, publications, and even competitors. The path to execution-as-learning is thus similar to the path to efficiency—it starts with establishing standard processes. But the goal of these processes is not so much to produce efficiency as to facilitate learning, because effective knowledge organizations recognize that today’s best practices won’t be tomorrow’s and won’t work in every situation. For example, the renowned design firm IDEO adheres faithfully to a standard process for developing its many innovative products. Similarly, in a hospital, even though each patient is unique, standard protocols make it easier for medical specialists to think in real time about the individual features of the case because the steps common to all patients with a particular condition are prescribed in advance. Standard processes both simplify routine action and highlight discrepancies in specific cases that suggest the need for process innovation or refinement. To understand how this works, let’s look at an extraordinary health care organization called Intermountain Healthcare (IHC), an integrated system of over 100 facilities—including 21 hospitals, and numerous health centers, outpatient clinics, counseling centers, and group practices—located across Utah and southeastern Idaho. To increase employees’ chances of making good decisions under pressure and reduce unwanted variability in patient care, senior management put together 60 teams of experts on different diseases to develop detailed process guidelines for treating patients with those conditions. The high quality of these guidelines—designed to reflect the current best practices in the medical literature—was the result of analysis and debate by professionals in nursing and medicine who held diverse points of view. Each team worked hard to develop a set of clinical-care processes outlining the way patient care should unfold on the front lines. Similarly, Children’s Hospitals and Clinics of Minnesota convenes teams to review and standardize different types of care, using principles of lean manufacturing.
• Step 2: Provide tools that enable employees to collaborate in real-time: No matter how much thought goes into advance planning, knowledge work often requires people to make concurrent collaborative decisions in response to unforeseen, novel, or complex problems. That is why another leading medical center, the Cleveland Clinic, developed its own state-of-the-art information technology systems that enable dispersed individuals participating in a particular patient’s care to work together virtually. Dr. Martin Harris, the clinic’s chief information officer, explains that the IT infrastructure “connects every caregiver in all of our facilities throughout Ohio and Florida into what is essentially a single medical practice. That means that all the vital medical information related to each patient is available to any caregiver in our health system whenever and wherever it is needed.” When a patient sees several physicians, as is often the case, caregivers working in different locations at different times can coordinate effectively. For example, through an automated alert function, physicians learn of drugs others have prescribed, thereby ensuring that medication decisions with interdependent consequences are made safely. Fostering face-to-face collaboration is also critical in the knowledge economy. The most effective organizations provide forums to build networks and training in team skills, both of which bring critical areas of expertise and responsibility together. For example, Groupe Danone, the global food company, uses knowledge “marketplaces”—lively events that occur during company conferences—to encourage frontline managers to share best practices and to innovate by suggesting new processes and products. Simmons Bedding developed an extensive training system to develop employees’ team skills, which helps them build relationships that foster collaboration within and across all of its plants.
• Step 3: Collect process data: Execution-as-efficiency focuses on performance data, which captures what happened. Execution-as-learning pays just as much attention to process data, which describes how work unfolds. IHC, for example, recognized that physicians, as highly educated experts, might resist process guidelines developed by a committee. For that reason and others, IHC does not discourage doctors from deviating from the guidelines. In fact, the organization invites them to, anytime they judge that good patient care requires it. The only condition: They have to help IHC learn by entering into the computer what they did differently—and why. This valuable feedback is captured in the system and periodically used by the expert teams to make updates or refinements. Most of the time, the deviations help identify ways the guidelines could be made more precise by taking relevant patient differences into account. The fact that protocols are not hard-and-fast rules but are instead flexible made them acceptable to physicians. Likewise, the Cleveland Clinic created a formal Quality Institute to standardize measures and supervise the collection and analysis of both process and outcome data to help identify and then spread best practices. At Minnesota Children’s Hospitals, data on both adverse events and close calls are captured as inputs to the next stage of the learning process.
• Step 4: Institutionalize disciplined reflection: The goal of collecting process data is to understand what goes right and what goes wrong, and to prevent failures from recurring. At IHC, teams of experts periodically analyze data collected during clinical activities. Often, these analyses suggest improvements to the guidelines, which are then integrated into the design of future processes. At the Cleveland Clinic, teams of physicians drawn from hospitals all over the system study process data and identify areas for improvement throughout the organization’s many sites. By 2006, the Clinic had seven such teams, including heart failure, stroke, diabetes, and orthopedic surgery. Process data showed, for instance, that stroke patients treated at various sites at the Clinic had not always received a blood thinner within the three-hour window that research identified as the standard of care. An analysis of patient outcomes helped to make the blood-thinner treatment the standard of stroke care for all Cleveland Clinic hospitals. As a result of this disciplined reflection, the hospitals doubled their use of the blood thinner and reduced complications from stroke by 50%. Similarly, at Children’s, unit-based safety action teams meet regularly to reflect on what they are learning about identifying hazards that can pose risks to their vulnerable young patients. It’s not easy for a hospital, or any other organization facing cost constraints, to do this. Disciplined reflection takes productive resources offline, and conventional management wisdom can’t help but see this as lost productivity. Nonetheless, the only way to achieve and sustain excellence is for leaders to insist that their organizations invest in the slack time and resources that support this step. This does not imply that old-style execution-as-efficiency must always go by the wayside. Obviously, there are workplaces—call centers, fast-food restaurants, and manufacturing plants, where doing things better and faster than the competition is critical. But even in such organizations, employees must learn if they are to improve. In work environments characterized by fear, the four steps described above become difficult, if not impossible, to follow. Fostering an atmosphere in which trust and respect thrive, and flexibility and innovation flourish pays off in most settings, even the most deadline driven. When managers empower, rather than control; when they ask the right questions, rather than provide the right answers; and when they focus on flexibility, rather than insist on adherence, they move to a higher form of execution. And when people know their ideas are welcome, they will offer innovative ways to lower costs and improve quality—thus laying a more solid foundation for their organization’s success.

Education as Learning Vs. Education as Efficiency

Below is a summary of the differences between execution-as-learning and execution-as-efficiency, the latter being the approach to performance that dominated management practice in the heyday of mass production. Edmondson cites seven salient contrasts, listed below (slightly edited), with each execution-as-learning practice in bold, and the corresponding execution-as-efficiency practice in normal type.

As suggested by the final item in the above list, an essential prerequisite for having employees speak up with their ideas, questions, and concerns is psychological safety — "ensuring that no one is penalized if they ask for help or admit a mistake." Managers signal that it is safe to speak up by acknowledging that they don't have all the answers, and by asking questions that clearly aim to elicit employee contributions.

See Also

Kaizen Philosophy
Learning Organization