In an article in the journal AI (Appreciative Inquiry) Practitioner in 2012, the author and AI practitioner Gervase Bushe quotes from some of his personal correspondence with one of the founders of AI, David Cooperrider. They had both been deliberating over the reflexive turn that AI scholarship has taken during the last few years, where it has begun to acknowledge what it refers to as the ‘shadow side’ of organisational life, which practitioners have begun to worry may have been covered over by an appreciative approach, or even may be provoked by it. Cooperrider is tempted to resist this critical development, concerned as he is at the possible reintroduction of what he considers ‘deficit modelling’ and draws on William James’ Varieties of Religious Experience to describe a kind of ‘hot and alive’ state that he is trying to engender: ‘I think we are still on this quest for a full-blown non-deficit theory of change…Whether someone would call the initiating experience ‘positive’ or ‘negative’, the transformational moment is a pro-fusion moment when something so deeply good and loving is touched in us that everything is changed…I don’t think we really understand the possibilities of that kind of change that kind of change yet and we aren’t going to until we take this to the extremes.’
Although he slightly mangles the quotation in Bushe’s article, Cooperrider is drawing from one of James’ chapters on religious conversion, where he describes the psychological changes which occur when someone experiences a profound religious conversion: ‘All we know is that there are dead feeling, dead ideas, and cold beliefs, and there are hot and live ones; and when one grows hot and alive within us, everything has to re-crytallize about it.’ James argues that such experiences can be transformative and create new and stable states of equilibrium. The new state of conversion is experienced by the individual as overcoming a divided and wavering self, which has previously comprised a lower and higher part of him or herself. To experience religious belief is to identify with the higher part:
He becomes conscious that this higher part is coterminous and continuous with a MORE of the same quality, which is operative in the universe outside of him, and which he can keep in touch with, and in a fashion get on board of and save himself when all his lower being has gone to pieces in the wreck.
Cooperrider, via James, is making a direct claim for what he clearly considers to be the spiritual and transcendental potential of AI, that by enquiring into the good we can transform people, and institutions, to the good.
Over the next two posts I am going to engage more fully with Appreciative Inquiry (AI), a method which has become very popular over the last 25 years amongst organisational development (OD) consultants following the seminal article by Cooperrider and Srivasta in 1987. I will attempt in this post briefly to explore AI in its own terms, and will consider some of the critiques of the method which have emerged from within the AI community, then will go on to look at some critiques from without. In the next post I intend to add some of my own critique from the perspective of complex responsive processes of relating, a perspective we take up on the Doctorate of Management at Hertfordshire Business School. I will be exploring AI’s appeal to religious experience in organisations, which the Canadian philosopher Charles Taylor has argued has a very strong attraction in what he terms the flattened world of modernity where technical rationality reigns. As with other systems-based organisational development interventions, AI is an invitation to employees to abandon their bad selves and lose their newly identified higher selves in a transformational and transcendent ‘whole’.
What is AI?
AI, in the terms of people who practice it, makes a variety of claims: it is a form of action research; it is a practice, a method, an approach, a philosophy and a worldview. However, in order to cut down the claims so that they cover less and illuminate more, it appears that AI sits broadly within the voluminous church of OD interventions taken up by managers and consultants to ‘bring about change’ in organisations. The kind of change that AI aspires to is understood to be wholesale, positive , transformational and enduring: it makes claims to transform whole organisations or ‘social systems’ for the good. Over 25 years AI has developed particular working methods and is underpinned by particular principles. The working methods are deemed to be helpful but not obligatory to taking an AI approach and the principles are said to be still evolving.
Starting with the principles, AI claims to be a positive, strength-based approach to change. That is to say AI literature argues against the predominance of problem-based or deficit models of organisational life. Instead staff in organisations should be encouraged to inquire into the best in others and in the world around them with a view to creating inspiring futures together. AI as method encourages staff to amplify the positive and to ‘unleash the positive core’ of their community.
There are five ‘original’ AI principles and some additional ones which have evolved in the community. Firstly AI literature cleaves to what it understands to be a social constructionist principles drawing on the work of Kenneth and Mary Gergen. These assumptions are that reality and identity are co-created; that truth is local and that there is no absolute truth; that humans are interconnected; and that language creates reality. Secondly, comes what is described as the poetic principle which involves learning to appreciate the richness of life experience. Third comes the simultaneity principle which claims that change begins the moment we question, and that the ‘unconditional positive question is transformational’. Fourth is the anticipatory principle which states that creating a vision precedes action, and that positive images create positive futures. And finally comes the positive principle which stresses the importance of positivity for expanding the ‘positive core’.
Additionally, and in brief, further principles draw attention to the importance of ‘wholeness thinking’, which is seen as an antidote to the reductiveness of dominant ways of thinking about organisational ‘problems’. There are also principles which privilege awareness, narratives, embodiment and ‘inner clarity’ in order to facilitate ‘freedom’ of living.
In terms of method, AI practitioners are encouraged to use a four or five ‘D’ cycle: define, discover, dream, design and destiny. This is provisionally offered with the more recent acknowledgement that the method can get in the way of the work, but nonetheless, the assumption is that AI consultants can help staff in an organisation to: ‘define/discover the best of what is’, which means appreciating what gives ‘energy and life’ to people. Thereafter, they dream about what might be, which involves processes of idealization about how the organisation might look in the future. The third phase involves designing organisational structures processes and even relationships which would support the dream articulated in the previous phase. And finally, the final phase is to ‘nurture a collective sense of destiny’ for the group.
‘Internal’ AI critiques
During the last few years and as I mentioned previously, the AI community has developed a reflexive turn where some of the taken-for-granted assumptions about the discourse have surfaced in the literature and have been more critically appraised. In general terms this is quite a gentle auto-critique, which to a certain extent is inevitable, if, following Ludwig Fleck, we accept that particular communities reflect certain thought styles. Expressing fundamental doubt could produce the kind of rupture that would inevitably mean AI practitioners leaving the thought community of which they are part. Scholars who identify with AI usually critique it extremely carefully, offering their thoughts as a means of making it ‘more holistic’ or more ‘honouring’ of the diversity of human experience.
So more reflexive ‘internal’ critiques of AI acknowledges that the method sometimes brings about the negativity which it seeks to avoid: by encouraging groups of staff to reflect upon their positive experiences at work, it may provoke them to realise that these have been far outweighed by the negative experiences. Equally and at the same time, the practice of AI can be experienced negatively, depending on who gets to define what is positive. It can be experienced as totalising and evangelical. AI practitioners have begun to realise and to detail the unintended consequences of believing that enquiring into the good can necessarily bring about the good. Critical AI scholars have started to explore the dualism that this way of thinking is based on (e.g. positive vs negative), and acknowledge the cultural specificity of what they recommend (it fits much more comfortably with idealised North American norms of cheerful, can-do individualism). Some AI scholars have even begun to explore what they might previously have considered AI’s evil twin Critical Theory (CT), to make connections with Habermas’ concept of the ideal speech situation. AI scholars point out that this is not an obvious link for them to make since CT is often accused, even from among its own adherents, of excessive negativity. Nonetheless, in exposing themselves to difference, some AI scholars claim that AI and CT may be aiming at something similar, but by different routes.
In a wide-ranging article in the Academy of Management Review, Stephen Fineman sets out a very detailed critique of the positive tendency in organisation studies and deals not just with AI, but with other manifestations of positive psychology and organisational scholarshiop, such as positive deviance and ‘spirals of flourishing’. It could be claimed that in this article Fineman has presented AI scholars with some of the critique with which they have subsequently felt impelled to deal. He notes the ‘revolutionary verve of positive scholars’ who ‘fuse positive assumptions about human nature with moral rectitude’, and in doing so he draws attention to the humanist assumptions of AI (which is also shared with CT, although in more radical form), which imagines in every individual a creative, positive essence, which, given the right conditions, is clamouring to be expressed. Fineman notes the particular attraction of a set of ideas which suggest an appealing vision of a recovered good self ‘which is directed towards morally well-defined ends that embrace others’ feelings and needs.’ Change can be brought about through releasing our positive potential that is already within us. For Fineman this represents a form of essentialism and determinism, with which I would concur.
Fineman goes on to question whether it is really possible to separate good and bad emotion, and whether the encouragement of one pole doesn’t automatically call out the other: for example, happiness may trigger anxiety (‘will it last’), love can be mixed with bitterness and jealousy. He also wonders from a psychoanalytic viewpoint whether personal robustness arises not from being optimistic and positive but rather from an ability to acknowledge and integrate both positive and negative experience. He challenges the notion, often and robustly expressed in the AI literature, that positivity and optimism are self-evidently axiomatic to a healthy workplace and prosperous life. From a CT perspective he develops the argument, more gently expressed in the ‘internal’ AI literature, that in contemporary organisations employees are encouraged to be positive, happy, creative and empowered as long as this fits with and fulfils senior managers’ ideas of organisational goals, a point forcefully made by Hugh Willmott in articles elsewhere. The ideology of creating a ‘culture’ of positivity at work is potentially stigmatising and excluding for those who do not comply with it. From a CT perspective Fineman asks the question ‘whose positiveness is really being served, and to what ends’ and claims that AI is relatively depoliticised: it rarely calls into question the managerial prerogative and the structural/economic conditions of the workplace are taken for granted.
In the next post I will continue with this discussion of AI.