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Appreciative Inquiry: Generating Questions of Change
Appreciative Inquiry (AI) is increasingly recognized as a powerful tool in organizational change but its popularity is creating a few problems. Its positive approach to handling change projects has been potentially over-emphasized, leading to a vulnerability to being labeled as a fad and irrelevant to the often harsh realities of organizational life, according to Gervase Bushe, an AI Consultant and Professor at Simon Fraser University*. He argues that change agents need to better understand AI by grasping its central contribution - the idea of generativity. But what does this mean?
Generative Inquiry: The Capacity to Question
At the heart of AI is a view that organizational life is socially constructed by those who work in it – there are no hard and fast objective rules that need to be found or adhered to – and because of this our approach to change should be tremendously liberating. We create the organizational worlds we live in. The result is that there are multiple ways of interpreting the past, the present and the future and organizational life should be seen as a mystery or an adventure to be experienced rather than a series of problems to be solved.
Collectively we have the opportunity to appreciate our current position and be inspired to create a better future and if you are thinking this approach to be airy-fairy, think again. AI has many examples of very demonstrable returns on investment (investigate the 2007 International AI Conference website for examples of transformations in organizations as diverse as a Fortune 500 telephone company to a medical centre).
How can we create this type of organizational environment? Research into AI case studies show the importance of focus. As change agents we would concentrate on changing how people think instead of what they do. This requires developing generative capacity – the ability to challenge the taken for granted assumptions which often cause organizations to get “stuck†– frustrated and unable to adapt– and through the process generate a set of alternative ways of thinking.
This is achieved through organizational dialogues where new ideas, images and theories are generated and make available new options not previously imagined. AI proponents argue that there are 4 key stages (see diagram) but ideas are not generated at stage one and then neatly project managed and implemented – the idea is to build generative capacity into every stage, seeing innovations as appearing throughout the change process. One company produced 10,000 innovations as part of its AI program and research** into the programs that delivered transformational change (often termed radical or revolutionary change in that it involves a system wide change rather than an incremental improvement) showed the continued importance of improvisation throughout the change process.
Generative Inquiry: Questions that Change
Gervase Bushe argues that generativity is borne out of skillful questioning and that the seeds of change are implicit in the very first questions asked by a change agent.
“One of the most impactful things a change agent does is to articulate questions…..Even the most innocent question evokes change.â€***
So the challenge is to find ways to engage organizational members in ways that facilitate new ideas. And here is where a cursory glance of the AI approach indicates the power of the positive. Many practitioners focus on affirmative topics that encourage expressions of positive feelings about life in the system, for example,
“I would like you to focus on a high point, a time in your work experience here where you felt most alive, most engaged or most successful. Can you tell me the story?â€***
However, the power of positive is only a means to an end – its purpose is to generate more ideas and build rapport and relationships among teams as people share their different interpretations on their world without getting caught in what is described as the “vocabulary of deficitâ€. AI scholars point to an increasing use of language to describe problems with organizations and their members which potentially blinds and distracts from generating better ways to work. In other words, it is fine to identify problems but exploring them generally leads you into a change cul-de-sac.
Of critical importance is recognizing the power of metaphor in generating change for example considering the potential of viewing an organization as an open book compared to the often cited view of an organization as a machine. Questions need to have a element of surprise, be provocative and engage the respondent at a personal level. They should also build relationships, for example, AI Consultants train staff to carry out interviews within the organization therefore increasing the potential for the greater sharing of ideas and often set up organization wide “Summits†– special events where topics are debated. Ultimately the goal is to encourage the development of a new and dynamic perspective on organizational life.
The Role of the Change Agent
“Instead of trying to solve the problem, AI generates a collective agreement about what people want to do together and enough structure and energy to mobilize action in the service of those agreements. When that happens, many “problems” go away.”
Interestingly, it seems that perhaps the most critical part of an appreciative process is the change that often needs to happen is in the change agent! If you are used to being critical or providing corrective feedback it could be a tough shift in attitude to believe in the potential of people and processes. Gervase Bushe offers two strategies – tracking and fanning. Tracking involves identifying where things are working – where you want more of what already exists. Fanning involves amplifying these successes – like adding oxygen to a small fire to create a blaze. Change agents should look at activities that move the organization in the desired direction and find ways to amplify those efforts. If the generative process is successful then leaders might view planned change projects differently. Having set the boundaries they should then get out of the way.
*Bushe, G. R (2007) “Appreciative Inquiry is not about the positive” PDF Version
Bushe, G.R. (2001) Five theories of change embedded in appreciative inquiry. In Cooperrider, D. Sorenson, P., Whitney, D. & Yeager, T. (eds.) Appreciative Inquiry: An Emerging Direction for Organization Development (117-127). Champaign, IL: Stipes
**Bushe, G.R. & Kassam, A. (2005) When is appreciative inquiry transformational? A meta-case analysis. Journal of Applied Behavioral Science, 41:2, 161-181.
***Cooperrider, D. L. & Whitney, D. (1999). Appreciative Inquiry: A positive revolution in change. Cooperrider, D. Sorenson, P., Yeager, T. & Whitney, D. (eds.) Appreciative Inquiry: Foundations in Positive Organization Development/. Champaign, IL: Stipes, 2001, 2005. The original article outlining the 5 core principles of appreciative inquiry. Available online at http://appreciativeinquiry.case.edu/uploads/whatisai.pdf
Cooperrider, D.L. & Srivastva, S. (1987) Appreciative inquiry in organizational life. In R. Woodman & W. Pasmore (eds.) Research in Organizational Change and Development: Volume 1 (129-169). Greenwich, CT: JAI Press. The original seminal article. Available online at http://www.stipes.com/aichap3.htm
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I consider AI to be dishonest and manipulative. Taking a positive approach is fine but we also need to identify and focus on shortcomings if we are to eliminate them. We need BOTH approaches. One severe drawback to AI is the negative effect it can have on staff. Bad managements that have a history of treating employees terribly now have convenient way of sweeping all their shit under the carpet. Never complain about management otherwise you are being negative!
AI can be used to shame and manipulate staff into cheerful compliance with every whim of management.
Another drawback that destroys AI’s credibility is the strange cultish language. I’ve noticed that AI zombies really do butcher the human language. I personally cannot stand to see english perverted and reduced to bizarre word combinations which lack clarity and precision. Speaking in AI jargon turns off a great many people, especially rational and scientific types. It is suggestive of certain cults, in fact the whole AI movement bears a disturbing resemblance to the Landmark Forum (formerly E.S.T.) an infamous motivational movement that many consider to be a cult.
Mandatory optimism is simply not possible. You cannot alter what is in a persons heart. Attitudes are driven by feelings. Until an organization EARNS the respect it thinks it deserves AI is not going to change anybodies thoughts and feelings about their place of employment.
Justice will always trump AI in the hearts and minds of fair minded people.
‘Finding’ shortcomings is looking from the negative.
Example:
Daily eggs are collected from the hen house by the children, but lately they are arriving in the kitchen with 4 or 5 of the 10 to 12 eggs - broken.
When asked through the positive frame
‘Why do you think the eggs are getting broken?’ thought and exchange is provoked. It is not a lie of ‘ignoring fact’ that there is lack of success, almost half the time. The question frames success of all eggs as ‘the focus’.
The children then have the opportunity to report what they know, study what is happening, and participate in success. This builds team members into responsibility with the success of their operation, without discouraging participation.
To start talking of failure, emphasizing what is not positive, participants may feel responsible for breakage w/o being first being given an opportunity to discuss what happened with an active listener. In discussion, the problem is moved away for clarification of what is known, what needs to be discovered, and what is desired.
identifying problem without inquiry diminishes the positive environment where work, interaction and reporting could sustain interaction w/o placing ‘blame’, which is negative, causing participants to identify with the problem rather than formulating new approach(es) to success.
‘Finding’ shortcomings is looking from the negative.
NO - it is identifying reality. Shortcomings are a FACT of reality. Ignoring them is evading reality and entirely dishonest. Reality is not dictated by your perceptions. It exists independent of your wishes, hopes and fears.
It is up to human perception to correctly identify reality. Do you have any idea of the unethical and exploitive behavior that managements impose within the work environment. AI is an applied psychology used by unscrupulous managements to con manipulate employees into passive acceptance of exploitative policies by shaming employees if they dare criticize. “That’s just being negative”, they will say while you are shamed into accepting some shit you know is unfair.
This whole drama exactly unfolded where I am employed. A.I. is quite transparent to me in terms of what i does and who it is designed to benefit.
DON’T DRINK THE KOOLAID of A.I!
AI is an intervention to change not the THE intervention. Secondly, an intervention using AI can be simply what reality do you skillfully explore first, keeping the whole in mind. Exploring what is working well first is generative, it does not mean omitting exploring the gaps where they still exists. Both are real.