Appreciative Inquiry has acquired a reputation for avoiding deficit-based, problem-solving approaches to organisational development and change management, embracing a more positive, forward-looking perspective instead. It doesn’t ask what the problems are. It focuses instead on questions such as what works best? How can the best could be fortified and/or reimagined? What needs to be done to bring that about? There’s an inherent ambiguity, or perhaps ambivalence, about such inquiry. What if discussing what works best evokes sadness, anger, and frustration that that era has passed, or despair that something valuable has been deeply eroded? It seems that a focus on the positive, implicitly or explicitly, simultaneously evokes a shadow narrative of negativity.
Recently, practitioner scholars in AI have been thinking a lot about this. Gervase Bushe, Professor of Leadership and Organization Development at the Beedie School of Business in Canada, has added another vibrant string to the AI bow with his concept of “generativity” – a concept he argues equips AI with transformational possibilities.
What is generativity, and how does it work? Essentially, it emerges through group exercises aimed at facilitating the emergence of new images, physical representations and metaphors that possess two key properties: they alter the way people think, opening up new options for decisions and/or actions, and they’re compelling enough to make people want to act on them.
Generative images, metaphors and representations allow people to see the world anew, devise new strategies, and even reform their identities. As people practise different decisions and take different actions, a new, reformed normative order of shared assumptions emerges, changing the organisational culture in the process.
A danger in AI is that an exclusive emphasis on the positive can function to repress real and just grievances. Generativity doesn’t adopt an avoidant approach to negativity, it just uses it as grist to the mill for facilitating creative thinking and acting.
I’ll say a little more about this in Part 2.
