Analysis of Resources and Strengths

For every organisation it is a hard and tedious task to achieve changes. Therefore, it is essential for the success of any attempted change to ask how it is possible to mobilise and increase the energy of change, and to make it flow.

Appreciative Inquiry (AI) is at the same time philosophy and instrument of change, which uncompromisingly addresses the strengths, capabilities, resources and potentials that exist in an organisation. The central idea of AI is the notion that any change is most likely to be successful if the largest possible number of resources from within the system are identified and used. AI is one of the methods from the field of "positive change" which is based on scientific principles and has proven itself for decades.

The AI Model of Change

1. A problem is a cause to learn, evolve and change. Therefore, every AI process starts with describing the situation that is called the "problem", the current situation.

2. Behind every problem there is a desired reality, a concept how it should be. We identify this desired situation, which then becomes the focus and the intended outcome of the change process. This conceptualised target will be the benchmark for the entire change process.

3. Starting from the target concept the organisation will then be scrutinised:

  • Identify: What works well in our organisation? Where is the target concept a reality already in today's practice? How did we solve similar problems in the past? What powers, strengths, resources can we tap to reach the goal?
  • Invent: Develop a shared vision about the desired future of the organisation; how do we want our organisation to be when we have reached the goal?
  • Plan: What is our plan to implement our vision?
  • Evolve: What are our first steps, how do we use all our powers, to reach our goal and to make our vision come true?

The main instrument of this method are "appreciative inquiries", i.e. interviews which can be conducted individually or in a group.

The analysis can be done internally - i.e. by members of the organisation (usually based on a training received from an AI specialist) - or by external consultants.

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