Spirit of appreciation

Dr Frank Barrett, one of the originators of Appreciative Inquiry (AI), has been quoted as saying: “Everything starts out as metaphor, and ends up as geometry.” He is referring to the desire we have in modern life to turn potentially new ways of thinking into mechanistic toolkits of methodologies and competencies.

Sometimes we do this for valid reasons – indeed, some of the methods and approaches developed in the past 20 years to support Barrett and David Cooperrider’s original Appreciative Inquiry proposition have been incredibly helpful.

In the second article in this series in July, we explored one such methodological approach – the use of the Appreciative Inquiry 4D process in a summit for a global values project at Nokia.

Sometimes we need the backbone of a structured method such as this to build confidence and gain buy-in.
However, occasionally we get hooked into methodologies because they are the easiest part of the overall conceptual approach to get our heads round, or because they are easier to sell (and can be distorted to ‘fit’ with dominant mechanistic change management). Sometimes a methodology can be brandished as a form of orthodoxy, and soon becomes a fad.

In this third and final article in the AI series, we will explore how Tim Haynes, an internal consultant at BP is moving beyond methods and orthodoxies by using the ‘spirit’ of AI in his work. It will illuminate some of the key theoretical propositions that separate AI from other approaches to human change.

Haynes, an organisational learning adviser for BP Europe, takes up the story of his first application of AI.
“One of the most significant personal learning experiences I’ve had in my recent career as an organisational development specialist at BP was discovering Appreciative Inquiry in 2001,” he says. “At that time, I was the learning and development manager for one of BP’s global fuels and lubricants business units, and I had been asked to come up with a process to ‘solve the problem’ of integrating two distinct organisational cultures.

“In 1999, BP acquired a leading global lubricants organisation, and found itself needing to marry the two company cultures. Employee morale was decreasing, conversations in meetings were rife with confrontation and cynicism, and, most critically, the business’ financial results were deteriorating.

“I felt the leadership of the business needed to move away from a combative conversation about which of the two organisations’ cultures and practices should prevail, to a participative inquiry into those aspects of both cultures that bring inclusion, engagement, collaboration and performance.

“AI offered a way to do this – an alternative way of looking at change, and a way to build a new organisational culture through the change process itself,” he says.

“The first step was to hold a two-day workshop based on an AI Discovery design with the extended leadership team. The leaders inquired into the factors and values they felt were present at times when the businesses were high-performing, energised and customer-focused. Over the next eight months, the same inquiry was repeated with groups throughout the business and around the world.

“The final session back with the leadership team then allowed them to identify four core values that were shared across all employees in the new organisation. They were universal values that everyone could identify with. But the conversational process was more important. It was through conversation that things started to change. In the end, articulating the four values was the icing on the cake. Today, BP is seeing employee morale on the increase and financial performance has stabilised,” he says.

Although Haynes was finding a way forward that didn’t follow the orthodoxy of the AI 4D methodology, it demonstrates the core principles and values of AI in some important ways.

AI is fundamentally based on a way of thinking known as Social Constructionism. Social Constructionists believe all observation and perception of reality is filtered through our own stories, belief and value systems and theoretical ‘lenses’. This means that there is no objective stance that a human being can take where they can escape their own, deeply embedded and socially reinforced subjectivities and categorisations.

Through our interactions with others close to us, we hold these patterns of thinking evermore strongly. We have socially ‘standard’ expectations of what it means to be beautiful or plain, good or evil, right or wrong, doing well or doing poorly. We are often surprised by someone from a different familial, societal, religious, racial or organisational culture, who simply doesn’t understand or agree with our way of categorising our experiences.

This explains some of what was happening in the early days of the merger at BP. The acquired organisation had developed a cultural norm that privileged certain ways of looking, thinking and acting. To them, it was ‘obvious’ that this was the ‘right’ way of thinking about and doing things. In BP, a different cultural norm and set of categorisations and expectations had developed -equally ‘right’ to them.

In a situation like this, there is little point in imposing new values or trying to sell, tell, bully or reward our way into a new way of acting. The way to shift the increasingly destructive behaviour between the two ‘sides’ was to work through the same phenomenon that created the cultural positions (which the destructive behaviour was defending) in the first place – social interaction and conversation.

It was Haynes’ instinct to use an approach that put people together in a co-inquiry. This would not have been so effective if outsiders had undertaken the inquiry (appreciative or otherwise) and then fed back the results to the workforce (a common but dysfunctional application of AI). It is the act of participative co-inquiry located within the population itself that creates the change.

Since this time, the spirit of AI has increasingly extended across Haynes’ practice, as he explains.

“I was recently asked to ‘diagnose’ (or, in other words, ‘fix’) a ‘problem group’ of employees who were de-motivated and felt undervalued, and the situation was deteriorating into a potentially high-profile employee relations issue.

“The basis of my recommendation was to study the ‘positive deviants’ in the group – those who felt valued and were motivated to build on the circumstances that made their ‘positive deviance’ possible,” he says.

Nature of labels

This highlights another powerful factor that a practitioner working with AI needs to understand – the self-fulfilling nature of labels. At an individual and organisational level, we are most likely to act into the label that we create for ourselves (or others impose upon us.

An individual or group that is labelled as a ‘problem’ is more likely to start to act up to this label than overcome it, partly because everyone elses behaviour will subtly reinforce their ‘problem’ status.

This negative impact of labelling has been demonstrated in many organisational and scientific experiments, and yet we persist with performance evaluation schemes and labelling approaches based on a belief that being labelled a ‘poor performer’ or a ‘problem’ will help us improve. We spend 80 per cent of our intellectual and emotional energy on the 20 per cent of people we have labelled as a ‘problem’, and at the same time in most organisations there is an enduring perception that performance is misled and mismanaged.

With this group in BP, Haynes decided to reverse this by focusing attention onto those people demonstrating positive traits, and letting the focus on the ‘problem people’ simply drift away. As the positive deviancy received more and more attention, it spread and started to become a new, generative self-definition and standard for the group.

So we can apply the spirit of Appreciative Inquiry in many development interventions – culture change, refreshment of organisational values, getting the best from a merger, team and individual performance coaching, and in the design of formal learning and teambuilding events.

Haynes says: “I often design, into group sessions, time for people to pair up and discover something about each other without necessarily calling it ‘AI’. I have developed an appreciative approach to teambuilding sessions, which helps individuals in newly-forming teams honour the best of their past histories, and then dream and build a future with this new group (this can be very effectively achieved in just a half-day session).

“Subtle inquiry-based processes can create very different outcomes in my experience. I recently influenced the design of a new learning programme for financial controllers to focus on learning from best experiences of financial control, rather than learning from past control breakdowns, which I believe will create a different (and I would argue, more effective) learning experience for individuals on this programme.”

Difference of appreciative coaching

BASIC ASSUMPTION: Organisation or person is a ‘problem to be solved’

Problem solving coaching – leads to – Noticing that there is a ‘deficit/problem’ – identifying the problem and how often it occurs – leads to –Analysis of root cause(s) of problems – leads to – Analysis of solutions that eliminate the problem and its causes – finding ways of building a future based on ‘not getting it wrong’ – leads to – Putting in place the solution and measuring to see if the ‘getting it  wrong’ incidents decrease


BASIC ASSUMPTION: Organisation or person is a ‘possibility to be realised’

Appreciative coaching – leads to – Discovering and appreciating times when people ‘get it right’ (no matter how rarely this happens) – leads to – Finding out what happens when they ‘get it right’, what enables this, and finding ways to make this happen more often – leads to – Dialoguing and working together to find ways of building a future based on ‘getting it right’– leads to – Acting together to support the person to ‘get it right’ more and more often, and measuring to see if ‘getting it right’ incidents increase



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