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Originally proposed by David Cooperrider and Suresh Srivastva in 1987, Appreciative Inquiry is a theory, methodology, and process of organizational and social change that has given rise over the past few decades to a global network of researchers, practitioners, trainers, and consultants. Appreciative Inquiryor AI as it is commonly known—grew out of the fields of organizational management, development, and action research, but it has since evolved into a process that is widely used and adapted by engagement professionals and facilitators. Appreciative Inquiry even has its own dedicated international journal called AI Practitioner.

“[A]ction-research has become increasingly rationalized and enculturated to the point where it risks becoming little more than a crude empiricism imprisoned in a deficiency mode of thought. In its conventional form action-research has largely failed as an instrument for advancing social knowledge of consequence and has not, therefore, achieved its potential as a vehicle for human development and social-organizational transformation. While the literature consistently signals the worth of action-research as a managerial tool for problem solving (‘first-order’ incremental change), it is conspicuously quiet concerning reports of discontinuous change of the ‘second order’ where organizational paradigms, norms, ideologies, or values are transformed in fundamental ways.”

David Cooperrider and Suresh Srivastva, “Appreciative Inquiry in Organizational Life,” Research in Organizational Change and Development (1987)

Appreciative Inquiry is commonly called an “asset-based” or “strengths-based” approach to systems change because it emphasizes positive idea generation over negative problem identification (the later is often framed as a “deficit-based” approach). The model utilizes questions and dialogue to help participants uncover existing assets, strengths, advantages, or opportunities in their communities, organizations, or teams, and then collectively work toward developing and implementing strategies for improvement.

Appreciative Inquiry is grounded in social-constructivist theory, which posits that human development is a fundamentally social process, and that both knowledge (how people come to understand, interpret, and experience the world and others) and organizations (how people organize themselves to achieve goals) are constructed through social and cultural interactions, relationships, and dialogue. In a 2012 overview of the history and foundations of Appreciative Inquiry, Gervase Bushe, a leading researcher in the field, provided the following useful description of social constructivism and its application in Appreciative Inquiry:

“Social constructionists argue that all research only makes sense within a community of discourse and that social science research, in particular, constructs the world it studies. As a result, social constructionists do not believe that any theory or method is about ‘the truth’ (including social constructionism) but, rather, that every theory and method is a human construction that allows for some things to be seen and done and for other things to be overlooked or unavailable. From this point of view, AI as a research method is not interested in discovering what is but in allowing a collective to uncover what could be. Similarly, it doesn’t make sense to ask whether AI (or any OD [Organizational Development] method) generates valid information. Instead, AI advocates would ask of AI (and any OD method) whose interests does it serve and is it generative in the service of those interests?” [emphasis added]

In other words, if humans socially construct their perception of the world and others, then certain problems, solutions, ideas, or opportunities will becomedepending one’s social or cultural contexteither more or less “visible” (and therefore more or less “changeable”). For example, those with social privilege, whether that privilege is due to wealth or membership in a racial majority, may be more likely to perceive social problems as being “caused” by the behaviors of poor people or non-dominant racial groups, rather than being caused by the systems and policies that advantage some (those with wealth and a certain skin color or ethnic background) and disadvantage others.

In this case, if the questions “Whose interests does it serve” and “Is it generative in the service of those interests?” are asked, it may become apparent that framing social problems in terms of negative group behaviorsrather than in terms of systemic structural biases in society that give rise to negative behaviorsserves the interests of those who benefit from that bias, which then perpetuates a worldview that sees inequitable policies as “solutions” to the very problems those inequitable policies created.

Appreciative Inquiry, therefore, could be seen as an attempt to use collective inquiry and dialogue to generate positive ideas that might otherwise be masked by unproductive, though hidden, cultural biases. In this way, positive socially constructed ideas that are revealed and developed through the Appreciative Inquiry process—ideas that might have otherwise remained invisible and unconsideredbecome an antidote to negative socially constructed “problems.”

Appreciative Inquiry Model

While Appreciative Inquiry takes many forms, and the approach has been widely adapted for different purposes across the globe, a standard model has emerged in the AI community over the past three decades. The original Appreciative Inquiry framework consisted of four steps—called the 4D Cycle—and five principles, but some practitioners later recognized a fifth step, leading to the creation of a 5D Cycle. For the purposes of comprehensiveness, the 5D Cycle is presented and described here.


The original Appreciative Inquiry framework consisted of four steps—called the 4D Cycle— but some practitioners later recognized a fifth step, leading to the creation of the 5D Cycle. The 5D Cycle references the “five Ds,” or the five terms beginning with the letter D, that describe each step in the Appreciative Inquiry process. Somewhat confusingly, due to different interpretations and presentations, there are actually six “Ds” associated with the model. Source: This image is a modified version of the 4D Cycle presented in “A Positive Revolution in Change: Appreciative Inquiry” (2005) by David Cooperrider and Diana Whitney

The 5D Cycle of Appreciative Inquiry:

1. Definition (Clarifying)

The first step in an Appreciative Inquiry process is defining the central question or topic of the inquiry, dialogue, or engagement process. The definition phase establishes the scope and goals of the inquiry. Importantly, AI emphasizes a positive, solutions-oriented approach to defining the process. While a more traditional “problem-solving” process might begin with collecting data and diagnosing weaknesses, AI begins with positive, asset-based framing questions to determine what’s already working in a community, organization, or team.

According to the Center for Appreciative Inquiry at Champlain College, “The difference is in the questions asked. ‘What can we do to minimize client anger and complaints?’ is an example of an old-style question. In an AI process, we would ask, ‘When have customers been most pleased with our service and what can we learn and apply from those moments of success?’” In the AI community, this step is also sometimes called the “Affirmative Topic.”

2. Discovery (Appreciating)

In the second step of an Appreciative Inquiry process, participants engage in a dialogue designed to surface the most positive features of a community, organization, or team. By beginning with positively framed questions, participants discuss and come to appreciate what’s already working. According to David Cooperrider, one of the co-founders of AI, “This task is accomplished by focusing on peak times of organizational excellence, when people have experienced the organization as most alive and effective. Seeking to understand the unique factors (e.g., leadership, relationships, technologies, core processes, structures, values, learning processes, external relationships, planning methods, and so on) that made the high points possible, people deliberately ‘let go’ of analyses of deficits and systematically seek to isolate and learn from even the smallest wins.”

In some presentations of the model, the Discovery step is divided into two phases: the first phase is to identify and discuss positive, effective, or exceptional moments, events, or periods of success, and the second phase is to look for themes or common elements among those positive moments, events, and successes.

Life Giving Forces: In the parlance of the AI community, these moments and themes are sometimes called “Life Giving Forces” (or LGFs), which the Center for Appreciative Inquiry defines as “elements or experiences within the organization’s past and/or present that represent the organization’s strengths when it is operating at its very best.”

Positive Core: Another common term in the AI community, the “Positive Core” refers to the central assets of community, organization, or team. According to Cooperrider, “AI has demonstrated that human systems grow in the direction of their persistent inquiries, and this propensity is strongest and most sustainable when the means and ends of inquiry are positively correlated. In the AI process, the future is consciously constructed upon the positive core strengths of the organization.”

3. Dream (Envisioning)

In the third step of an Appreciative Inquiry process, participants collaboratively envision a desired future for their community, organization, or team. According to Cooperrider, “One aspect that differentiates AI from other visioning or planning methodologies is that images of the future emerge out of grounded examples from its positive past.” Rather than imagining hypothetical strategies to address past problems, AI asks participants to consciously envision a preferred future that is grounded in past successes but imaginatively and creatively unrestrained.

Provocative Proposition: In the AI community, a “Provocative Proposition” refers to a collectively produced statement or, in some cases, a graphic or illustration that captures the outcome of the dreaming/envisioning process. According to the Center for Appreciative Inquiry, “The provocative proposition bridges the best of ‘what is’ with your/their own speculation or intuition of ‘what might be.’ It is provocative to the extent that it stretches the realm of the status quo, challenges common assumptions or routines, and helps suggest real possibilities that represent desired possibilities for the individual, group, or organization.” In some AI processes, Provocative Propositions are used (or also used) in the Design phase and are sometimes called Possibility Propositions because, as Cooperrider explains, “They bridge ‘the best of what is’ (identified in Discovery) with ‘what might be’ (imagined in Dream).”

4. Design (Co-Constructing)

In the fourth step of an Appreciative Inquiry process, participants begin to co-constructively design a new or refashioned community, organization, or team. While participants imagined possibilities in the Dream stage, they start to assemble the practical elements of a plan in the Design stage. 

5. Deliver/Destiny (Innovating)

The final step in an Appreciative Inquiry process is the implementation of the collective design. In his original formulations of the model, Cooperrider called this final step Deliver, but later changed it to Destiny because, according to Gervase Busche (2011), “Delivery evoked images of traditional change-management implementation.”

Importantly, the Center for Appreciative Inquiry notes that during this phase communities, organizations, or teams “innovate and improvise ways to create the preferred future by continuously improvising and building AI competencies into the culture,” which includes “noticing and celebrating successes that are moving the system toward the preferred future the organization or group co-created.” 

The Principles of Appreciative Inquiry 

According to AI Commons, a project of the Center for Appreciative Inquiry, the Principles of Appreciative Inquiry “describe the basic tenets of the underlying AI philosophy” and “serve as the building blocks for all AI work.” While the principles have undergone revision and adaptation over the years, they can be traced back to the original 1987 article on Appreciative Inquiry written by David Cooperrider and Suresh Srivastva.

In a later formulation, Cooperrider and his colleague Diana Whitney (2001) proposed and described the five principles that are now considered standard: Constructionist, Simultaneity, Anticipatory, Poetic, and Positive. The definitions below are taken directly from AI Commons.

The five principles of Appreciative Inquiry:

  1. Constructionist (Words Create Worlds): Reality, as we know it, is a subjective vs. objective state and is socially created through language and conversations.
  2. Simultaneity (Inquiry Creates Change): The moment we ask a question, we begin to create a change. The questions we ask are fateful.
  3. Anticipatory (We Can Choose What We Study): Teams and organizations, like open books, are endless sources of study and learning. What we choose to study makes a difference. It describes—even creates—the world as we know it.
  4. Poetic (Images Inspire Action): Human systems move in the direction of their images of the future. The more positive and hopeful the image of the future, the more positive the present-day action.
  5. Positive (Positive Questions Lead to Positive Change): Momentum for small- or large-scale change requires large amounts of positive affect and social bonding. This momentum is best generated through positive questions that amplify the positive core.

Discussion: Criticisms of Appreciative Inquiry

Several criticisms of the Appreciative Inquiry model have emerged over the years, but the most salient and widely discussed tend to focus on (1) the lack of strong evidence supporting the model’s efficacy and (2) the model’s emphasis on positivity. In addition, the evangelical manner and idolatry of some practitioners in the AI community, as well as the community’s sometimes quasi-mystical language, have made some observers skeptical of both the AI process and the objectivity of the AI community.

When theoretical, conceptual, or procedural models are applied in community, organization, or team processes, the efficacy of a given model will depend on the quality of implementation, which can encompass a wide range of complex factors that can positively or negatively impact outcomes (e.g.: Was the facilitation strong or weak? Did the facilitators understand the model and did they maintain fidelity to the model? Was a sufficient amount of time allocated for the process or was it rushed? Etc.). Consequently, it is often difficult to determine what may have gone right or wrong with the application of a given model or process.

In a 2005 article, Gervase Bushe and Aniq F. Kassam discuss the results of a “meta-case analysis” of AI applications that found only 35% of the 20 cases studied resulted in “transformational outcomes.” While in all 20 cases the practitioners followed the recommended Appreciative Inquiry principles, methods, and processes, Bushe and Kassam conclude that two qualities of appreciative inquiry are necessary to achieving AI’s transformative potential: “(a) a focus on changing how people think instead of what people do, and (b) a focus on supporting self-organizing change processes that flow from new ideas.” 

Perhaps the most conspicuous criticisms of Appreciative Inquiry center on the model’s insistence on positive framing. In “Appreciative Inquiry: Theory and Critique” (2011), and in his 2012 article on the history and foundations of Appreciative Inquiry, Gervase Bushe discusses and responds to the major criticisms that have emerged over the past three decades.

In some cases, critics of AI claim that positive transformational change is unlikely to take hold in a community, organization, or team if problems are ignored, overlooked, and left unaddressed, though proponents of AI would argue that “deficit-based” processes also have their own problems and downsides, including ample evidence that more traditional problem-oriented approaches also routinely fail to result in positive transformational change.

One compelling argument against AI’s emphasis on positivity, however, is that community, organization, or team leaders may use AI’s positive framing to shutting down discussion of problems. In this case, for example, an organization’s directors may have a vested interest in avoiding discussions of problems in the organization because leadership quality may be cited as one of the organization’s biggest problems. As Gervase and others have discussed, AI does not necessarily exclude all forms of negativity, and AI processes can be designed to frame discussions of problems in ways that are “generative” and productive.

Perhaps the most potentially problematic dimension of Appreciative Inquiry’s positive framing is that an AI process may be used in ways that reinforce and perpetuate racial or cultural bias, prejudice, and discrimination. By insisting that an inquiry, dialogue, or engagement process focus exclusively on positive questions, comments, and ideas, for example, the AI process can potentially be used—whether it’s intentionally or unintentionally—in ways that silence legitimate concerns and criticisms raised by the victims of bias, prejudice, and discrimination.

When applied to equity-based dialogues or engagement processes, AI’s perceived prohibition on negativity raises both serious and well-founded concerns, given that silencing legitimate anger, frustration, and complaints is a ubiquitous feature of inequitable, discriminatory, and oppressive systems. Consequently, engagement professionals and practitioners should be mindful of their cultural biases and motivations when facilitating AI-based processes, especially in diverse communities and workplaces, and they should consider adaptations that do not stifle necessary discussions about uncomfortable or troubling issues such as racial prejudice, gender discrimination, or workplace abuse.

For more detailed discussions of these issues, see “Embracing the Shadow through Appreciative Inquiry,” the November 2012 issue of AI Practitioner: The International Journal of Appreciative Inquiry.


Additional Resources

References

Bushe, G. R. (2012). Foundations of appreciative inquiry: History, criticism and potential. AI Practitioner, 14(1), 8–20.

Bushe, G. R. (2011). Appreciative inquiry: Theory and critique. In Boje, D., Burnes, B. and Hassard, J. (Eds.). The Routledge Companion To Organizational Change (pp. 87–103). Oxford, UK: Routledge.

Bushe, G. R. & Kassam, A. F. (2005). When is appreciative inquiry transformational? A meta-case analysis. The Journal of Applied Behavioral Science, 41(2), 161–181.

Center for Appreciative Inquiry. Generic process of appreciative inquiry. Retrieved from centerforappreciativeinquiry.net/more-on-ai/the-generic-processes-of-appreciative-inquiry.

Cooperrider, D. L. & Srivastva, S. (1987). Appreciative inquiry in organizational life. In Woodman, R. W. and Pasmore, W.A. (Eds.), Research in Organizational Change and Development, 129–169. Stamford, CT: JAI Press.

Coopperrider, D. L., & Whitney, D. (2001). A positive revolution in change. In D. L. Cooperrider, P. Sorenson, D. Whitney, & T. Yeager (Eds.), Appreciative Inquiry: An Emerging Direction for Organization Development (pp. 9–29). Champaign, IL: Stipes.

David Cooperrider and Associates. What is appreciative inquiry? Retrieved from davidcooperrider.com/ai-process.

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