appreciative_inquiry

Appreciative Inquiry and Problem Solving: Contradiction or Complimentary?

by Loretta Rose, Coracle Consulting In their book Appreciative Inquiry: Collaborating for Change, David Cooperrider and Diana Whitney propose a "eulogy for problem solving," suggesting that "the problem-solving paradigm, while once perhaps quite effective, is out of sync with the realities of today's virtual worlds.1" The appreciative mode is powerful and filled with potential, and is infinitely more generative than problem solving. Anyone who has experienced appreciative inquiry can attest to this. But - is this all there is to the story? Some of the other story lines - or at least subplots are: •

What are the defining characteristics of each mode?

•

Are AI and problem solving mutually exclusive?

•

If they're not, how can problem solving be used well; and when is it appropriate to use each approach?

When the story of appreciative inquiry is told, AI is often contrasted with problem solving something like this: Problem Solving Appreciative Inquiry "Felt Need;" identification of the problem

Appreciating and valuing the best of "what is"

Analysis of causes

Envisioning "what might be"

Analysis of possible solutions

Dialoguing "what should be"

Action Planning

Innovating "what will be"

Basic Assumption: An organization is a problem to be solved?2

Basic Assumption: An organization is a mystery to be embraced

This "compare and contrast" approach has the benefit of simplifying the two processes enough to make them readily understandable; but it may be too simple: nuance is lost. And the nuances are important.

For example, when appreciating and valuing the best of "what is," problems are often discussed. If there are strong felt needs, they cannot help but be surfaced and expressed in an environment of open exchange. It's simply a natural human phenomenon.

And, while the "innovation" step of appreciative inquiry sounds more inspiring than "action planning" on the problem-solving side, in fact - at some point - it involves just that, so that dreams can be put into practice. Thus, while the contrast helps to distinguish the two approaches, the symmetry of the model obscures important meanings. So, back to the first of the three questions posed at the beginning of the article: What are the defining characteristics of appreciative inquiry and problem-solving?

Appreciative inquiry and problem-solving are not simply mirror images of each other, but are two very different approaches, suited for different situations and purposes.

Problem-solving is a technical response. It focuses on breaking down the situation into component parts, analyzing them, identifying trouble spots and fixing them, and then building up the system to its original state. Familiar examples are the use of problem-solving in situations such as repairing an airplane engine or conducting emergency heart surgery. In neither case do participants wish to imagine a new future; they just want to fix the problem and get things working as they were before.

It may even be possible that creative problem-solving can return the system to working order at a higher level than it functioned at previously; but it's still a repair mode: it does not essentially change or recreate the system. Moreover, while truth is important in problemsolving inasmuch as accurate data is essential, about the larger truths of human understanding and desires, problem-solving has nothing per se to comment.

Appreciative inquiry is very different. It puts all its energy into creating and looking at big

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issues. Far from being technical, it's participative, and sometimes even chaotic (open ended and not controllable). In fact, AI and problem-solving are so different that they're not easily compared.

Below is a summary of the characteristics of AI and problem-solving discussed (and by no means exhaustive).

Problem Solving..

Appreciative Inquiry..

•

Is a technical activity

•

Is focused on creation

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Responds to a specific "broken" situation

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Looks at big issues

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Is restorative - seeks to return the system to functioning, but doesn't seek to re-create the system or to create a new way of doing things.

•

Is participative

•

Is chaotic (open-ended and uncontrollable)

•

Truth (i.e. understanding human truth) is important

•

Explores large questions

•

Examines the human condition

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Can and should look at problems (peoples' real situations) that arise in the environment of open exchange

•

Is iterative and expansive (see below).

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May be able to restore the system at a higher level of functioning (but still doesn't change its essence)

•

Can be creative

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Can be exciting (see below)

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Truth (i.e. accurate data) is important

Thus: two very different modes - not the flip side of each other.

Are appreciative inquiry and problem-solving mutually exclusive - and by nature in contradiction?

Deciding to use AI or problem-solving is not like making a choice between using a Roberts or a Philips screw driver; it's more like the difference between using a screwdriver and understanding the implications of the human characteristic of being a tool-using creature.

Both may be appropriate in context; but asking which activity is better makes no sense.

They're completely different kinds of things.

The AI/problem-solving question doesn't need to be seen as a conflict. It's more an issue of recognizing and articulating that problem-solving is not appropriate to the purposes of AI - and visa versa.

Is the group interested simply in fixing a specific thing without reinventing their common understandings and practices? Then problem-solving is likely to be useful. But if the group wants to explore large questions, to dream, to create new ways of doing things, then appreciative inquiry is surely more appropriate.

And this doesn't trivialize problem-solving. Certainly, problem-solving can often be quite mundane; but there are also situations in which it can be thrilling. Talk to a surgeon. I think, also, of discussions with colleagues working in relief projects in Africa. We got caught in the rains, and the trucks with the milk powder are stuck in the mud on a terrible stretch of washed-out road along the border. We're trying to keep the powder dry and dig out, and meanwhile we're having trouble with customs…

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Ultimately, bigger picture visioning might change conditions so that such situations could be avoided; but for our relief workers in the moment, what's needed is top-notch problem-solving.

How I've admired the resourcefulness of those who can figure out how to deal with such circumstances and still get the milk powder to its final destination and into the bellies of hungry children!

How can problem-solving be used well, and when is it appropriate to use each approach? Is there complementarily?

But, while problem-solving can respond to specific situations, it cannot address the wider human condition; so again, we come back to the question of appropriateness. Both approaches are needed.

One possible way to describe this graphically is represented below. In this model, AI and problem solving are presented as two distinct approaches, but not as mirror images of each other3.

A Problem Solving Approach

An Appreciative Approach

Complementarity between the two approaches is more easily seen, I believe, if two important nuances are articulated.

Nuance 1: An important distinction between AI philosophy and AI methodology.

Because they've grown up together, sometimes the philosophical principles of AI are confused with the methodology of AI. While the first is foundational, the other is infinitely malleable. An appreciative stance - which flows from the philosophy - can always be present, even if the method of choice is problem-solving. Does this sound counter-intuitive? It shouldn't be, because even when solving problems, it's possible to remember to notice and give energy to the best practices and successes that emerge.

Given our competence at seeing deficits, this may be very difficult to do in practice. But there's no reason why skilled mindfulness cannot help us get better at maintaining an appreciative stance.

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Nuance 2: An important distinction between problems and problem solving.

But this does not answer a critical question: what do we do with those areas of unhappiness, discomfort, and - well - problems? AI has been accused of sweeping issues under the rug and looking at the world through rose-colored lenses. This can certainly be the case if a clear distinction is not made between problems and problem solving.

There often seems to be a failure to differentiate between "things that happen to people" or "situations that people experience" - problems - and problem solving, which is a practical, purposive activity.

Problems, it's true, are difficult by definition, and therefore elicit low rather than high energy states, tend to encourage "can't-do" rather than "can-do" talk, and so on. But there are many shades of meaning to that word problem. Just take a look at the entries in Merriam-Webster's dictionary: Noun 1a: a question raised for inquiry, consideration, or solution; 1b: a proposition in mathematics or physics stating something to be done 2a: an intricate unsettled question 2b: a source of perplexity, distress, or vexation 2c: difficulty in understanding or accepting ("I have a problem with your saying that.")

Adjective 1: dealing with a problem of conduct or social relationship (a problem play) 2: difficult to deal with (a problem child)

The thesaurus yields these further, intriguing words: Synonyms: example, ensample, illustration; issue, nut, question.

Something requiring thought and skill to arrive at a proper conclusion or decision (what to do now is a problem).

Related Words: enigma, mystery, puzzle, bugaboo, bugbear; count, point

Some of these are not words that AI practitioners have a problem with. And there's that word again; it's hard to avoid, because it has so many meanings. "Problem" is not a monolithic thing. Some aspects of what we call problems, we acknowledge as having a positive side. Are not the lacks we feel part of what drives us towards creation, renewal, and searching for better ways? What meaning can have, if not against the backdrop of something which seems to us to be insufficient?

More important, if appreciative inquiry cannot enter into peoples' real situations no matter how complicated or unpleasant they may be, is it really a breakthrough? When someone shares a problem, it's a terrible thing to refuse to hear it or minimize it because we want to focus on the positive instead. How then, can we respect peoples' truths, and at the same time, go forward to create new and good things out of problem situations?

Surely it's by tackling problems head on: not ignoring them, but acknowledging them respectfully; and then asking questions that lead people towards identifying their aspirations, recognizing their competencies, developing their potentials for empowerment, and building on their strengths. Because respecting peoples' truths means respecting all of it; and problems are never the whole story.

Creating new futures on the basis of "the best" is possible, while still appropriately recognizing difficult circumstances in which people sometimes find themselves. This is audacious; and it's what AI is all about - reaching beyond present realities to dreams, and thence to fashioning dreams into reality.

And though problems may be recognized, none of this is problem-solving.

So, to apply AI effectively in difficult circumstances - or, indeed, anywhere - it's critical to clearly distinguish between problem-solving and problems. Cooperrider and Whitney have noted how problem vocabulary can so easily confuse:

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"It is not problem-solving methodologies per se that are of concern, but that we have taken the tools a step further. Somewhere this shift has happened: it is not that organizations have problems, but that they are problems.4" With clarity, however, both problem-solving and AI can be applied effectively at the appropriate times, and a spirit of finding and building on the best in people can infuse all the work we do. And this is much more powerful than if we try to ignore the reality that problems do exist.

Appreciative inquiry and problem-solving are not in contradiction; they're two very different and very important approaches that can complement each other, if each is used well. Open discourse about how this can be will help us to become more constructive and empowered contributors to social change.

by Loretta Rose feedback is welcomed! coracle@bserv.com

1David

L. Cooperrider and Diana Whitney, Appreciative Inquiry: Collaborating for Change, Berrett-Koehler Communications,

Inc., San Francisco, 1999, p. 21.

2Sue

Annis Hammond and Cathy Royal, eds., Lessons from the Field, p. 12 (adapted from Cooperrider and Srivastva (1987)

"Appreciative Inquiry into Organizational Life," in Research in Organizational Change in Development. Passmore and Woodman (eds), Vol 1, JAI Press.

3These

models, created by Loretta Rose, were first published in Envision, Volume 1, Number 1, Spring 1999, World Vision

Canada. The words used in the AI spiral are adapted from the APA framework, as outlined in Malcolm Odell, Jr., Appreciative

Planning and Action: Experience from the Field in Evolving a New Strategy for Empowering Communities, The Mountain Institute, 1997. In this framework, Discovery is a guided process of telling stories about what works and what empowers in order to discover the root causes of success and how to build on them together. Dream is understanding the root causes of success and from them, creating community vision. Design is making an action plan based on what the community can do for itself. Delivery is taking action together. Eventually, this leads to Re-Discovery: rather than merely evaluating its actions, the community uses AI to continue to point towards the best and build upon that increased understanding. This is why the spiral is depicted as expanding.

4David

L. Cooperrider and Diana Whitney, Appreciative Inquiry: Collaborating for Change, Berrett-Koehler Communications,

Inc., San Francisco, 1999, p. 23.

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