Appreciative Inquiry—A Transformational Change Management Tool
Peter Furst | May 12, 2022
Improving business outcomes have been pursued by the use of a number of popular methods, including Total Quality Management, Continuous Quality Improvement, the Balanced Score Card, and Appreciative Inquiry. Traditionally, businesses commonly focused on what was not working in order to "fix it" and improve organizational and operational outcomes. This is one way to achieve improvement, but it creates a negative outlook, focus, and mindset.
This is especially true of safety improvement, where the primary focus is on accidents, unsafe conditions, or acts. The Appreciative Inquiry (AI) Model is a positive organizational approach for improvement change initiatives.
The development of the AI Model was influenced by numerous research studies from the fields of medicine, behavioral sciences, and sports that demonstrated the power of positive images on outcomes. A few key studies included the placebo effect, in which about half of patients believed they had received effective treatment and showed marked improvement in their symptoms. Another study of surgery outcomes showed that patients with more positive thoughts recovered at a much faster rate. An additional study involving the Pygmalion effect demonstrated the relationship between the images teachers have of their students and the students' levels of performance as well as long-term future outcomes.
The AI Model was developed in the mid-1980s from an earlier research study of physician leadership in one of the highly regarded medical centers in the United States. Physicians were asked to describe their successes and failures; their responses painted a picture of amazing cooperation, incredible innovation, superior involvement, and fair-minded execution when they were most effective. The resulting findings developed into the AI Model for a successful change initiative implementation and management.
Appreciative Inquiry Basics
The AI Model has grown into one of the more influential movements for positive organizational change and development in recent decades. Contrary to the traditional problem-solving approaches of organizations that started with management determining that some problem(s) have become intolerable and decided to change things. The next step is to identify and list the causes, followed by outlining possible solutions, after which the "best" solution is selected to replace the deficient process, practice, or procedure.
AI looks at organizational issues and concerns in a significantly different way. Rather than focusing on problems to fix, management looks for what is working well in their organization. Then, they try to envision what it might be like if the well-functioning means and methods worked in the very best possible way. This then puts the focus on identifying what is required in terms of both tasks and resources to bring about the desired improved future. Finally, organization members implement their desired changes.
The AI Method
David Cooperrider, the creator of the AI Model, proposed that the process of inquiry into the potential of a social system rested on four principles, starting with appreciation. Done collectively and provocatively, it should be applicable. These general philosophical underpinnings resulted in various ways of performing AI.
- Analysis to identify the best of what exists
- A vision of what is possible
- Consent through collaborative dialog about what should prevail
- Collective analysis of what improvement can be achieved
It wasn't until 1997 that the 4-D model of AI was created. The general outline of the four Ds includes discovery, dream, design, and delivery or destiny.
Discovery
The first step involves identifying the "best of what is occurring" in the organization. This could include systems, policies, protocols, processes, procedures, or practices that were driving good results primarily in their organization but could be best in class. The results of this inquiry are then carefully analyzed and/or summarized to identify "better" ways to achieve more desirable outcomes. Additionally, as part of AI, each member of the group and possibly others will be interviewed for their "best" experience in the related topic or area. This engages all participants as both interviewer and interviewees in the act of inquiry.
Dream
This phase involves imagining "what might be" or the future state of the area under consideration in the previous step. The aim is to try to envision these aspects, functions, or outcomes in their very best state as imagined by the group. The extent of preparation, the intensity of the analysis, and the degree to which clarity about that common dream is sought or prevails varies widely by application. This is then articulated as clearly as possible.
Design
The aim of the third phase is to get the participant to create or design an organizational or operational system, policy, protocol, process, procedure, or practice that supports the "dream" as defined in the previous step. In this phase, the emphasis shifts from dreaming to creating. This requires the development of concrete proposals reflecting the means and methods to create the desired future state.
Delivery/Destiny
In the initial 4‐D model, the fourth stage was called delivery, which Mr. Cooperrider thought conjured up images of traditional change management practices, so he changed it to destiny. Its aim is to sustain the developments and innovations of the inquiry process and to nurture a collective sense of destiny. In this phase, everyone makes a commitment to devise a practical means to enable the transition of the design to fruition.
Leadership's role involves enabling, encouraging, and supporting the people in the AI process, as well as ensuring all the people in the organization are actively engaged in the deployment and successful implementation of the desired changes.
AI's Effect on Participants
Research suggests a significant positive relationship evolves when people participate in the AI process to achieve improvement of the organization's outcomes. Participation fosters the liberation of personal as well as organizational power, which drives excellence. Participants gain confidence and feel safe to engage in any one or all of the six defined freedoms.
- Freedom to be positive
- Freedom to act
- Freedom to be heard
- Freedom to contribute
- Freedom to conceptualize
- Freedom to excel in a relationship
Any one of these six freedoms can significantly change people's perception of their power within the team as well as the organization at large. People learn, grow, and gain confidence as they experience the different ways and means of expressing themselves. The more confident they become, then the more effective they are in contributing to the betterment of the organization as a whole. Participants in one complete iteration of the 4-D cycle, in all likelihood, will experience many or even all of the six freedoms. Because of this, the AI process is capable of transforming personal and collective realities more than many other organizational change processes.
AI can also become transformational. As an example, after analyzing a participant's responses to questions, it is possible to identify predominant thoughts or feelings expressed by a large number. By feeding back this information to the group, it will be discussed and acknowledged, potentially causing "the ground to shift" (culture change). This is significant because what is in the process of change are the core assumptions the people held about the norms, beliefs, or values of the organization.
Another transformational element of AI is that people stop thinking in the traditional way of engaging in the change process of creating steering committees, assembling teams, devising plans, and following problem-solving steps. Instead, the first three elements of AI bring out a set of ideas that are powerful and compelling to the participants, driving them to find means and methods to transform their processes, practices, and/or procedures.
How To Make an AI Process Successful
Starting with a compelling objective is critical if management is going to take a dozen or even more people offsite for several days to participate in this important event. Management must ensure that the group's mission is clearly understood and innovative thinking is valued. Depending on the group's expertise in the AI process, management should assign a knowledgeable person to provide guidance, support, and encouragement if necessary so as to facilitate engagement, remove barriers, and ensure success.
A common concern is determining how broad the scope of the topic should be. Obviously, this depends on a number of factors, but generally, a narrow scope tends to be more effective in getting tangible results. Also, invariably during the interviews, scope creep occurs through the introduction of related functions or factors. Another reason is that, as the topic gets broader, the harder it is to be specific about the desired outcomes.
Train a Core Team
To run successful AI summits requires that some of the participants have prior experience so as to be able to provide guidance and support to the rest of the group. It will also require people working in the departments where the improvement (change) is required for their intimate knowledge of operations and challenges for their input. Situations necessitating the running of an AI summit with a large number of people requires a lot of preparation and planning. It will require some structure as well as some participants from the core team to facilitate smooth operation.
The core team will require formal training and some practical experience to facilitate expanding the AI process successfully throughout the organization. The responsibilities of the core team include the following.
- Acting as internal champions of the AI process
- Supporting the AI teams in their improvement summits
- Providing guidance in mini problem-solving situations
- Interfacing and communicating with the organization at large
Selecting the right people for the core team is important. They have to have some leadership qualities, enjoy working with people, relish learning and problem-solving, and be motivated to improve operational processes as well as organizational outcomes.
Design the AI Means and Methods
The AI inquiry process requires agreement on the topic to establish the focus of the summit. This is followed by devising the questionnaire to guide the fact-finding. This starts off with a set of generic questions regarding positive experiences at work. The next set of questions revolves around the summit's topic. This is then followed up with open-ended questions focused on deveining what the ideal state might look like or how superior outcomes might be achieved.
The number of questions, as well as the number of people interviewed, depends on several factors: the complexity of the system or function and the diversity of applications or usage, as well as its significance or importance to the organization's performance. This highlights the importance of the preliminary preparation for the success of the outcome.
AI and Safety
Research has shown that engaged employees tend to have fewer accidents due to their greater level of involvement, which is an indirect benefit of applying AI thinking to organizational systems along with operational practices and procedures. The application of AI thinking to safety management means and methods will identify best practices and engage the workforce in devising innovative ways for improving their utilization as well as outcomes. AI thinking will also foster greater acceptance and usage of preventive means and methods by the workforce in their daily activities. Areas where AI thinking may be pivotal in improving safety results include the following.
- Accident investigation
- Education more than training
- Task design to address risk
- Task demand to match capabilities
- Performance expectations
- Communication
Research has shown that AI thinking has the ability to foster exciting transformations in the way people work together and in the results they achieve. It has the potential to lead to greater innovation, higher productivity, safer work, better communication, and improved employee satisfaction and profitability; it also has the tendency to enhance relationships and empower people. All of this has a positive effect on people interacting, listening to one another, and looking out for each other, which directly impacts how safety is treated, accepted, and promoted in the organization.
Conclusion
The AI Model is a change philosophy and methodology that carries the best of the past forward and improves its outcomes to the best possible. This harnesses employees' power of unconstrained imagination and creates a process for a change that distills the best of the past with possibilities for potential future enhancements. The desire for an improved future creates tension that motivates action.
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