Tracking the progress of change means assessing how people are feeling. How can we use AI for tracking the process of change?
Impact assessments are an important part of the change management process. Ensuring process are running smoothly post change is as important as the change itself.
Measures for Tracking Change
When tracking change there are various elements to include;
Project Data Measures
When we track the progress of project outputs, we can measure objective data including financial benefits and efficiency gains. We have existing data to compare with the results post launch.
Because of the existing data and the quantitive nature of this data, the comparison is relatively straight forward which makes tracking progress of change simple.
Behavioural Change Measures
With behavioural change, the evidence that change has taken place is subjective. Each of us must decide we are going to try a new way of working. As we practice, we learn how to do things, and this knowledge builds our confidence. This motivates us to keep going.
The challenge is that we don’t have baseline information about our feelings before we started the change, so we have nothing to measure our progress against.
Measuring how we feel can be interpreted as intrusive. But…if we don’t track these increases in confidence and motivation for change, how can we evidence we are making progress?
We can ask for people to share their stories and gather subjective information about people’s experiences of change.
One of the problems is how we summarise this information so that it represents the experiences in a way that can be compared to other teams and departments.
How to use AI for Tracking Progress of Change
I am so excited about the advancements we are making with artificial intelligence, especially our ability to analyse vast amounts of data which will help in tracking the progress of change.
I can put the descriptions of how people are feeling, their personal benefits, improvements and advantages of the change into ChatGPT or one of the apps sitting on top of it (eg Microsoft’s copilot) and ask it to give me answers that demonstrate progress.
I can use tools to quickly and easily capture the personal stories of change. For example, I can video a team meeting where I ask people what difference the change is making to their day.
The video transcripts can be collected together and input into an AI tool. Prompts that help me sift this data include:
- Summarise this data
- Keywords from this data
- Most frequently cited benefit
- Most frequently cited feeling
Final Thoughts on using AI for Tracking Progress of Change
We are just at the start of advanced analytics of vast amounts of data but I believe these ideas have a big part to play in the effective adoption of change. As AI advances there will be more ways in which we use it, for now I’m excited about the way in which AI can be used for tracking progress of change – especially when it comes to the thoughts and feelings of individuals.