Inquiring Appreciatively in Albuquerque
By Kim Hansen

(A Two-Part Article on an Appreciative Inquiry conference attended in Albuquerque, written for the
Front Range Chapter of the International Society for Performance Improvement)

Part 1
What is AI?
Appreciative Inquiry is a methodology used by groups to cooperatively explore what is working well in an organization, so they may plan and implement further positive action. This process encourages change in other areas of the organization that may not be functioning as well. AI involves a pre-scripted approach to interviewing stakeholders, leading discussions, and creating shared goals, to focus an organization’s productive energy towards mutually positive outcomes. Planned exercises in visualizing “what could be”, as well as hands-on activities, and mind mapping techniques, are directed towards defining objectives. The goals are then implemented by creating concrete steps toward putting agreed upon changes into practice. The AI method inspires mutual imagination, innovation and creative thinking, to side-step habitual obstacles and ineffective ways of thinking about challenging issues, promoting positive action.

AI follows eight assumed observations of human systems.
1.In every society, organization, or group, something works.
2.What we focus on becomes our reality.
3.Reality is created in the moment, and there are multiple realities.
4.The act of asking questions of a group influences it in some way
5.People have more confidence and comfort to journey to the future (the unknown) when they carry forward parts of the known.
6.If we carry parts of the past forward, they should be the best about the past.
7.It is important to value differences.
8.The language we use creates our reality.

Most folks would agree that our perceptions affect the attitudes and outcomes of our endeavors. We have all experienced the challenge of working with negative, complaining folks who seem to suck the life out of every project, as well as the pleasure of those whose positive open-minded spirit enhances and improves every group process they touch. It is not too much of a stretch to contemplate that having a facilitator guide a system in positive directions would be more productive, or at least more pleasant. The simple homilies many of us grew up with lend belief that there is old wisdom to a positive focus. What is different is that AI has formed these concepts into organizational strategies, focusing whole systems toward positive change within competitive corporate cultures and high conflict geographic areas and situations.
Sounds nice, but what does this look like in action? AI presents four cyclical process phases of working with a system: Inquire, Imagine, Innovate, and Implement. During the workshop we toured these phases with the AI approach, reflecting on the value of each process as we experienced it.

Stage 1 Inquire – “Appreciating the best of what is.” Ms Webb began by having the groups focus on the issue of health and well being as a common concern and interest for most adults. We began by interviewing one another to recount past experiences of good health, reflecting on how we had felt, and the circumstances surrounding that sense of well-being. Our task as interviewers was to take notes, putting our full attention on the other’s story, while asking questions to prompt deeper reflection. This is a crucial stage in AI, as people experience uninterrupted time to tell their stories, emotionally connecting with the listener, who directs the tone to positive frames of reference. It is generally the area where people are most likely to want to vent their complaints, so focus is gently redirected towards positive reflection.
Stage 2 Imagine We were directed to share and discuss each other's stories in a group, asking, “What might be?” The task was to notice possibilities and clarify values, sharing visions of what health and well-being could look like in our present life. The usual goals of less weight and stress, more sleep, relaxation, and exercise topped most lists. We broke into small groups, indexing ideas on sticky notes. We then performed a large group activity of bringing our vision into more tangible design by creating collages of what process our group would work with towards achieving our goals. Creative energy was tapped, and it was a relief to take a break from the jargon and clichés that usually weigh down group discussion. We then listed our ideas under broader topic headings, further defining categories of health and well-being.
During Stage 3, we were asked to innovate, co creating our vision through brainstorming a series of poster boards, winnowing down the concepts into concrete steps such as eat five servings of fruits and vegetables and walk an hour a day, etc. This activity was meant to set the strategic directions and align our unique standards and systems with our vision of health.
The final Stage 4 is to Implement. During this phase, a group would navigate the change according to the planned steps, monitor progress and evaluate results. The forthcoming evaluation would return the focus to the initial phase of “Inquiry” for further refinement.
Examining the task of evaluation underscores the potential value of AI. It is common to see fear of criticism undermine evaluation, often to the point were the process is ignored, or meaningless. The consequence is that positive changes are not made, obstacles become fixed, and problems continue to fester. If the goal of evaluation is to enhance performance improvement, then it makes intuitive sense to examine and understand what is working well to copy and expand upon those successful strategies, in comparison to investing attention and scarce resources into fixing what is not working. This may feel contrary to traditional problem analysis and our own human natures. As any parent or student can report, shifting a pattern of focus to inquiring over successful practices in achieving an A, rather than interrogation and fretting over a D can be difficult, even though criticism, nagging and blame does little to bring out the best in children, or anyone else.
Given that our natural reactions are to shadow, rather than spotlight our shortcomings, is there a more effective way to honestly evaluate the systems in which we work? AI adherents believe that methods of encouraging communication and positive outcomes promote empathy, trust, and cooperation, enhancing creative energy, so that successful strategies may expand to less functional areas of a system.

Questioning the AI approach
How do we research and prove ROI in the AI model?
Where is the accountability in such a self-designed “feel good” approach?
What happens when there is little that is positive to inquire about, and the problems seem overwhelming?
What are helpful methods for giving negative evaluation feedback?
Is there a role for intelligent critical discrimination?
Are there situations where this approach may not be effective?
Finally, who is currently using AI in evaluation and performance improvement, and how has it worked?

Part 2
Evaluate: 1.To ascertain or fix the value or worth of. 2.To examine and judge carefully; appraise.
Evaluation is used to improve performance in organizations by identifying problems to develop solutions, or to answer critical organizational questions. Evaluation tools are created and chosen to bring awareness to the perceived obstacles, issues, and concerns, which limit the systems optimum performance. As instructional designers we are taught to objectively assess needs and problems to plan our interventions, filling the “gaps” between the skill or knowledge deficit, and the needed training. When we evaluate training we are determining our ability to have accurately identified the problem, and addressed it’s cause. How does this traditional process mesh with the Appreciative Inquiry (AI) approach?

Part 1 of this article briefly introduced the methodology and underlying rationale of the AI approach. Part 2 focuses on how evaluators may effectively use AI to identify performance issues in order to understand, duplicate and enhance high functioning areas and encourage this success in other parts of the system. AI adherents use methods of pre-phrasing questions when interviewing stakeholders, to promote positive language, creative thinking and imagery. They then facilitate the design and development of a mutually agreed upon plan of action, evaluating how the plan effects the whole system.

How does the evaluation process impact an organization?
We could view the past 9/11 hearings to see various examples of negative reactions to the inquiry of “what went wrong”. Is this process likely to bring about positive change, promote communication between departments, and create a broader awareness, or is it likely to create continued inter agency conflict, disfunctionality, and further weaken already broken organizations? AI proposes that a more functional method of examining and evaluating systems would be to phrase questions to promote a clearer understanding of how to create stronger interdepartmental information networks. AI would study examples of where these government agencies are working well together, recognizing good examples of coordination and sharing of information. In doing so they would ask questions to give departments an opportunity to articulate what they would envision as a high quality system, and what their role would be within it. AI assumes that most people in an organization are aware of its problems, and potential solutions. Workers want to believe that they add value through their work, and that their time spent is meaningful. AI‘s methods promote this belief through phrasing inquiry in ways that articulate what people value, appreciate and so may further develop within their organizations.

Examples of using AI in Evaluation
Dr. Hallie Preskill, from the University of New Mexico, and Prism Evaluation Consulting Services ( was a guest speaker on evaluation at the New Mexico AI conference. I talked with her recently about the ways she uses AI in evaluation with her clients:

She states that all these approaches emphasize what is already working in the organization being evaluated. To create the future people want, they should consider what has worked well, been effective, satisfying, and useful in the past. Sometimes it is important to give an example of how a typical problem solving approach to an issue has not succeeded in solving the problem, reflecting on how AI might shed new light on the issue, to create new approaches and possibilities. Thus evaluation acknowledges that we might have more to learn from exploring when, how, and why, things have worked well, if that is what we want more of.
Most existing evaluation tools are by definition problem focused; therefore I asked Dr. Preskill how she alters data collection instruments to correspond to the AI methodology. She answered that she designs her interview and survey questions for each evaluation she performs by looking at each unique situation, to see how it would respond to the AI methods. She first asks, “what is the purpose” of my evaluation. Depending on the organization’s needs, she collaboratively designs the evaluation to discover, and build upon the healthiest aspects of that system. She states in her articles that, “often the language of evaluation is deficit based,” given this traditional approach I wondered how one would rephrase the questions in evaluation. She responded that language is very much at the heart of the AI approach. AI questions are a good model for how one can focus a problem-based question to a more open-ended positive inquiry. For example, if we wanted to understand the ways and extent to which collaboration is working in an organization, typical questions might be:

1.What are the current barriers to collaborating across the five departments?
2.How could collaboration across the five departments be improved?

Using an AI approach, the questions might be rephrased like this:

1.Think of a time when you were collaborating with someone (or a group) from another department, and you felt excited, alive proud, and successful. Describe that time – what was happening? What made it successful? What was your role? What did others do to make it effective?
2.If you could have 3 wishes for ensuring more of these successful collaborations, what would those wishes be?

Lastly I asked Dr. Preskill about her experience using AI in evaluations with high conflict/stressed systems. She finds that AI evaluations work very well in these situations, because people are able to refocus their energies toward strengths, successes, and times they were effective, thus leading them to develop new understandings and appreciations for others. During the interview, process people pair up to talk and begin to question their assumptions about each other, begin to understand where the other is “coming from”, and very often discover something positive about the other. This conversation can lead to a foundation of mutual respect, from which they find common ground to work with one another more effectively. This communication must be maintained through building a culture change, modeling through leadership, using communication models, and a reward system, which recognizes performance improvement

The use of Language in AI

“ I had always thought we used language to describe the world – now I was seeing that is not the case.
To the contrary, it is through language that we create the world.”
—Joseph Jaworski

The use of language is crucial to facilitating the Appreciative Inquiry approach in organizational development and evaluation work. Rather than assuming the traditional position of objective observer, the evaluator uses the language of the inquiry to intentionally lead stakeholders toward mutually developed positive plans of action. Participants who’s habit is to air grievances, and attach blame could find this positive prompting to be irritating, stifling their need to complain, or air past grievances. My own experience in the conference was that this new language could feel forced, and awkward at times, though problems with clichés and jargon is a common hazard in any organizational development work. However, the discipline to frame communication within the boundaries of appreciating success, and opening up future possibilities also felt refreshing and hopeful. Intuitively I think it’s accurate that we create the reality we live in through how we perceive it, and transfer this to others through our conversations. If the communication holds a reminder to care for and inspire the systems around us, and we use these tools in an honest manner to grow, rather than stifle our conversations, then it seems incorporating these tools would benefit most interactions and interventions.

AI and ROI – A local case study at Hunter Douglas
There are a number of case studies on AI and ROI, found in the AI commons site
A case study at the local Hunter Douglas Company may be found at this link.
In summary, AI can be an effective evaluation tool for organizations to improve and enhance performance through examining success, rather than problems. While an aspect of evaluation is to objectively gather, analyze and interpret data, through the AI facilitation process it becomes a dynamic method of positively impacting a system to stimulate creative thinking and planning and so enhance performance. As an organizational development tool it complements much visionary thinking as a way to understand and work with our complex human systems. Perhaps in our professional toolbox we need a new broader definition of evaluation:

Evaluation redefined through AI
" Evaluation is a process for enhancing knowledge and decision-making within organizations and communities. It involves answering questions and/or addressing issues through the collection and analysis of information about programs, systems, processes, procedures, products, and services. Evaluation is best implemented as a systematic process that is planned and purposeful, and with a clear intention of using the evaluation findings. Evaluation is a means for understanding what we do and the effects of our actions in the context of the work environment and the society in which we live.” -Hallie Preskill

Below are links to find out more about AI and evaluation
Quarterly sourcebook book of the American Evaluation Association “Using Appreciative Inquiry in Evaluation” Editors Preskill and Coghlan. Winter 2003 N 100, Journal.

Please contact Kim Hansen at Transformative Designs for more information about AI