Shoot for the moon. Even if you miss it you will land among the stars. – Les Brown

Appreciative Inquiry (AI) has been described as “an exciting way to embrace organizational change.” AI works by “identifying what is positive and connecting to it in ways that heighten energy and vision for change.” Through AI questioning, we discover what we value about what we already have. This allows us to dream of and envision realistic possibilities, and design and deliver positive future outcomes.

The more I learn about and work with AI, the more excited I get about the results. Many of my colleagues in training who stumble into Appreciative Inquiry (AI) for the first time have a similar reaction to my own. Once they get their head around the process and start working from an AI perspective, it just feels right—like “coming home.” Only, for most of us, it’s more like what we wish our home of origin could have been like. Imagine a group where every person is valued for their own unique strengths and has a voice that is always heard. Imagine a group where everyone is working together toward a common vision in order to make their collective dreams come true. That’s the goal of Appreciative Inquiry.

It’s precisely that sort of high-reaching, visionary talk that causes skeptics to dismiss AI as the Organizational Development “Flavor of the Day” or even some form of New Age wishful thinking. But the AI process has actually been in practice for more than 20 years and is based on solid, proven learning theory. The internet is dotted with research papers and case studies documenting how using Appreciative Inquiry has accelerated positive change in organizations around the world—including schools, governments, hospitals, religious institutions, not-for-profit agencies, and businesses. AI has literally transformed them—increasing productivity, renewing motivation, and focusing energy and action toward mutual goals for long-term, sustained performance improvement.

The Problem with Problem Solving

Appreciative Inquiry (AI) emerged in the 1980s as a groundbreaking, positively focused approach to organizational development through the work of David Cooperrider and colleagues at Case Western Reserve University. While performing a traditional problem solving analysis, Cooperrider noticed a negative change in the demeanor of his interview subjects. He observed that the questions he was asking to uncover possible problems focused on negative issues and actually appeared to be draining energy from those he interviewed. Even if a given problem didn’t actually exist in the organization, it seemed that just asking questions about it had power to change perception (at least momentarily) in the workplace.

It turns out this focus on the negative is precisely the problem with traditional problem solving. Looking for what is broken is a fine approach for troubleshooting a piece of equipment, but not so great for working with people. Performing a problem solving exercise automatically implies that something (or someone) within the organization is broken and needs to be ferreted out and fixed. Participants focus their thinking around the identified problem (usually a single issue), and brainstorm possible solutions. If the session is not carefully facilitated, it can quickly degrade into criticism and finger-pointing. A clear solution may not emerge, leaving participants feeling demoralized and drained, with no clear path—and little hope—for future positive change.

Enter Appreciative Inquiry

AI is sometimes referred to as a positively focused problem solving approach, but it is actually an alternative to traditional problem solving altogether. AI assumes that the members of each organization together already have within them the vision and power to raise organizational performance from “good” to “great.” In other words, smart people drawn together around a common purpose already have all the answers. An AI practitioner asks a series of carefully crafted envisioning questions to help participants articulate and embrace what the group already knows works, so they can purposefully do more of it. Then, on top of this confident foundation, realistic stretch goals are revealed.

It’s not that AI practitioners simply paint a rosy picture so that everyone can view the glass as half full. Nor do they believe that AI can make difficult organizational problems magically disappear. Rather, by performing a specific process of positive questioning and analysis and helping members of the organization understand and focus on what they truly value, an exciting new path emerges that energizes the organization to work together toward mutually desirable future outcomes. Team members move forward with a new focus, renewed energy, and a clear sense of direction.

Where do you want to go?

The power of AI comes from asking powerful, positive questions around an affirming topic. The entire AI process will build out from this topic, so it needs to be something relevant and somewhat urgent for the organization—such as growth, improved morale, process or quality improvement, customer service, quality of life… any important and highly desirable goal that you imagine could someday be possible. It should be a stretch, but must be feasible, although you may not yet understand how you will possibly get there. Remember this is not a problem solving exercise; instead, focus on the desired outcome—what it will look like when things go right.

For example, you might envision a department with high morale as:

…comprised of confident, happy team members who enjoy working together to develop award-winning work and exceed customer expectations. They take pride in solving difficult challenges—together and on their own. We want to increase the fun and satisfaction in our work by becoming a department of high morale.

Next, participants begin a four step process that AI practitioners call the “4-D Cycle:”

  • Discover
  • Dream
  • Design
  • Destiny (or Deliver)

The phases do sound a bit New Age, don’t they? In my next post, I’ll explain in plain language and for AI skeptics, I’ll provide a simplified description of how a typical AI initiative plays out in practice.