Extract from "Appreciative Inquiry in the Praxis of Reconciliation" by William A. Nordenbrock, C.PP.S.
(Pages 29-33)
Appreciative Inquiry and Responding to Perceived Negative Events
As people first learn about AI theory and practice, they often question if AI can
adequately respond to problems or negative events. The concern is that by choosing to
always focus on that which is positive or life-giving, problems will not be addressed
because the AI process “sugar coats reality” and fails to tell the truth of the adverse
situation. This is especially a concern when there is perceived injustice within the human
system or organization.
To such concerns, Watkins and Mohr respond:
AI can be used to solve problems; it just approaches problem solving with a different perspective. Traditional problem solving looks for what is wrong and “fixes” it, thereby returning the situation to the status quo. Appreciative Inquiry solves problems by seeking what is going right and building on it, thereby going beyond the original “normal” baseline.58
Problem-solving strategies arise out of the assumptions inherent within the paradigm that you use to understand organizations and human systems. In the old paradigm that views organizations as finite systems, problems “need to be tackled” and injustice has to be confronted with the truth of justice, usually through an accusation of wrongdoing. If you hold to the theory that organizations are socially constructed, then the problem solving strategy changes as you recognize “that both problems and resolutions are social constructions, created by our dialogue and generalized into social norms and beliefs. In this situation (using AI), resolution is generalized throughout the system and builds in the potential to move continuously towards our highest image of ourselves and our systems.”59
In their book, Watkins and Mohr provide a case study of the AI process that was led with Avon Mexico. Avon Mexico wanted to respond to concerns of gender inequality. Instead of using a problem solving approach which might have confronted the injustice of sexism that was inherent in the system, they began an AI process with the positive focus of: Valuing Gender Diversity. The process used the 4-D model and it transformed the organization, making it not only more profitable but also a national award-winning organization for having policies and practices that benefit women in the corporation. By recognizing that in every human system there are positive aspects which can be discovered and which can become the foundation on which the dream of a more desired organization can be built, problems are addressed.60
AI can be used to solve problems; it just approaches problem solving with a different perspective. Traditional problem solving looks for what is wrong and “fixes” it, thereby returning the situation to the status quo. Appreciative Inquiry solves problems by seeking what is going right and building on it, thereby going beyond the original “normal” baseline.58
Problem-solving strategies arise out of the assumptions inherent within the paradigm that you use to understand organizations and human systems. In the old paradigm that views organizations as finite systems, problems “need to be tackled” and injustice has to be confronted with the truth of justice, usually through an accusation of wrongdoing. If you hold to the theory that organizations are socially constructed, then the problem solving strategy changes as you recognize “that both problems and resolutions are social constructions, created by our dialogue and generalized into social norms and beliefs. In this situation (using AI), resolution is generalized throughout the system and builds in the potential to move continuously towards our highest image of ourselves and our systems.”59
In their book, Watkins and Mohr provide a case study of the AI process that was led with Avon Mexico. Avon Mexico wanted to respond to concerns of gender inequality. Instead of using a problem solving approach which might have confronted the injustice of sexism that was inherent in the system, they began an AI process with the positive focus of: Valuing Gender Diversity. The process used the 4-D model and it transformed the organization, making it not only more profitable but also a national award-winning organization for having policies and practices that benefit women in the corporation. By recognizing that in every human system there are positive aspects which can be discovered and which can become the foundation on which the dream of a more desired organization can be built, problems are addressed.60
I would liken the AI approach to problem-solving to the use of a lever to lift an object. When faced with the problem of lifting a 500 pound rock, you can try to get you arms around it and (unsuccessfully) try to raise it up; or you can place a fulcrum and use a lever and lift the rock by pushing down on the lever. Just as focusing our efforts on the lever will accomplish the desired effect on the rock, by focusing on the positive and the life-giving aspects that are present within the organization, AI addresses the negative situation or problem.
In summary, AI responds to problems by approaching the problem from the “side” of the solution; by transforming the organization into the organization that it dreams it can be (without the negative situation or problem). Even in the most egregious inequitable situations, the “solutions” are embedded within the organization and they can be discovered through an Appreciative Inquiry of the positive life-giving forces that are present.61
Within the AI framework, effective leaders must have the necessary appreciative competencies to assist the organization to be an appreciative learning organization. Appreciative Inquiry is not just a change management tool. It is a mind set; a way for people to understand their organization; an orientation that guides human interaction within human systems. A primary task of effective leadership is to assist the organization to function within that framework. This requires participative management and a spirit of collaboration where all in the organization can participate in the dialogue which constructs an effective organization.
In organizations that are experiencing a negative situation, effective leadership is critical. In negative situations, an important leadership task is to manage the dialogue within the organization. Inquiry into the negative aspects of an organization must be done in a way that solicits positive data which can assist in the transformation of the organization. Again, the case study of Avon Mexico is illustrative. When faced with concerns about gender inequality, the initial task was to shift the focus to the positive or desired alternative valuing gender diversity. Instead of leadership searching for examples of inequality and assigning blame and demanding accountability, the AI process began with the discovery of the opposite: tell a story of when you have seen women and men working together effectively here at Avon Mexico. Those positive images were the foundation of their successful transformation into a organization that valued gender diversity.
While the case of Avon Mexico is an AI intervention, it reflects the same AI pathway that effective AI leaders will use in responding to conflicts or negative situations within their organization. The task is not to deny or “white wash” problems as they are identified. It is not a Pollyanna approach that censors truth telling. Rather, rooted in a conviction that positive actions only flow out of positive images, a leader responds to a negative situation by inquiring: Yes, that negative situation exists; so what is the positive alternative that we desire? An effective leader responds by saying: What is our dream of being a better organization and what can we discover in our history to build that dream upon? How can we design and live that dream of an organization into reality? Effective leaders do not deny negative situations. Rather, with an AI orientation, leaders transform negatives into positives.
58 Ibid., 195.
59 Ibid., 197.
60 Ibid., 123-126.
61 Ibid., 198.
59 Ibid., 197.
60 Ibid., 123-126.
61 Ibid., 198.
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