Professionals working in many types of organization face changes and challenges to their ways of working.  Karl Weick is associated with “organizational sensemaking” research, the processes involved in such sensemaking in organizations to construct meaning to achieve mutual understanding and to produce knowledge to guide actions. People working in emergency services, or health care, are aware that changes in practise can have unintended consequences. Collective sensemaking, thinking together as one mind (collective mindfulness) may avoid disasters. In other settings, the pace of change in practice may be slower and collective sensemaking then involves many interactions over a long time. Sometimes organizational senior managers may act as the “sense-givers” but often middle managers mediate collective sensemaking, helping policy implementation and changes in working practice.

Uncertainty, and anxiety predominated in reactions to the COVID-19 pandemic and the working life of many health professionals was turned upside down. Studying how nurses made sense of circumstances throughout the pandemic should help policymakers plan better for future crises. The Royal College of Nursing Northern Ireland commissioned a study that collected 676 stories from nurses for the period April 2020 to March 2021. Nurses answered some follow-up questions based on their experience. The project used the SenseMaker® tool to collect, describe, visualize and make sense of the lived experience of nurses through the main phase of the pandemic.

Over many years Brenda Dervin developed Sense-Making Methodology (SMM) to help researchers and practitioners to understand how people are making sense, and “un-making”  sense over time, in different situations and with varying expectations.  The website for the Sense-Making Methodology Institute  provides advice on theory and application of SMM.  The use of SMM to study information behavior is well accepted, but there are other aspects of SMM to explore.

For example, the rise of AI and Generative AI in the workplace context comes with intended and unintended consequences. When employees begin to experiment with AI to get work done, there will be changes to work processes. Nevertheless, not all the questions have been raised. Not all answers and risks are known. No best practices have been defined in terms of how human and AI should collaborate. How can employees be supported to navigate into the unknown and become more capable? Instead of “teaching” the employees what to do, applying Dervin’s SMM (in a large pharmaceutical company), the employees can be invited to make sense of their individual and collective experience when collaborating with AI. Through collective sense-making, the participants let go of their ego and status, and hear one another’s situations, questions, muddles, joy and pain, and explore various ways to relate with AI. Sense-making becomes a peer-to-peer action learning tool and also a leadership capability to lead employees through organisation change.

One of the aims of the JASIST/ARIST review on sense-making/sensemaking was to reflect on future uses of sense-making methodology, and sensemaking methods in information science. We found a wide variety of settings, problems and applications. Education examples included maker learning, and staff attitudes towards an external marketing campaign. In health, sense-making research included a study on virtual health communities. Research on general social and political issues including working with migrant communities, understanding attitudes to controlled environment agriculture, and social media analyses.  It is not surprising that SMM should be suitable for problems that may be tricky for researchers to negotiate, in settings that require empathy from the investigators. We were interested to find a wide variety of complementary methods being used. Sometimes attitudes and assumptions need to be challenged carefully, and the methods used need to be appropriate for the situation. It seems best to use methods and theory that work together well to understand the origins of a problem as well as helping the debate about ways to move forward. Prediction and recommendation on the basis of data collected that has an inbuilt social bias merely reinforces the status quo, alas. Our sense-making needs to be more creative to provide innovative solutions to problems.