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“Beyond the Code: The Critical Role of Behaviour in Generative AI Adoption”


Remy Middelhoff




min read

Recently the Harvard Business Review highlighted in their article “Why Adopting Gen AI is so difficult” a few technical reasons that underline challenges with implementing Generative AI (GenAI) in organization. In our experience, the behavioural aspect of successfully adopting AI gets less attention and is, arguably, an even harder and more important factor to influence.

You don’t need any coding skills, but only your native language to interact with GenAI. This means that whilst use cases for traditional AI were often centered around Data Science teams, GenAI solutions such as ChatGPT and Copilot involve everyone. Therefore,  we should be looking at this more as a workforce transformation than a technical challenge. We need to understand what drives or block people to use GenAI to implement the best adoption interventions.

From our experience with clients and our behavioural change research, we see many different behavioural challenges that hinder adoption. Next to the important issues around security, we highlight 4 common challenges from a behavioural perspective:

  • A lack of knowledge/competence. GenAI is new, both in ‘what’ it is, and ‘how’ you can work with it.
    • One training session is not enough. As many of us know, most learning occurs on the job. Therefore, giving people the opportunity to experiment with GenAI in their day to day tasks and experience small wins is crucial. For example: have colleagues with GenAI experience walk around the office and help people in a 1-1 setting on specific tasks their working on
  • No social norm. People follow the behaviour of other people, especially their leaders.
    • Leaders need to set an example, role modelling the new behaviour of using AI as a personal efficiency booster in your work and setting up small experiments. This creates a safe learning environment and changes the social norm. For example: Set a norm to use transcriptions to summarize important meetings quickly and share this information easily with your team.
  • Resistance to change. People simple prefer to keep things as they are and change often evokes negative emotions.
    • When dealing with resistance, we often focus on increasing the ‘pulling’ forces (e.g. we try to make the change more appealing). Sometimes, the solution is more in decreasing the ‘pushing’ forces (e.g. decreasing the resistance itself). Simply acknowledging that GenAI may be scary and uncertain, can already take out the elephant in the room. For example: organise a low key lunch session where people can openly share their struggles and concerns about using GenAI.
    • Want to know more about the various ways to deal with resistance? Keep an eye out, we’re writing our next article about this!
  • Losing autonomy. GenAI brings uncertainty about one’s own capacities and relevance, resulting in a lowered sense of control and self-efficacy.
    • According to Self Determination Theory, autonomy has a direct impact on motivation. Organisations need to help people understand which parts of their work can benefit from GenAI and how they still remain relevant in this new way of working. For example: organise group discussions about the consequences of GenAI on specific functions. Provide people with information about how GenAI can make their work easier, combined with appreciation for the work they remain to add value to.

These 4 factors are very common, but there will always be context specific behavioural challenges at play. Therefore, to successfully adopt AI you have to combine a holistic change approach with a thorough understanding of the root causes of behaviour.

By Paulien Goudsmit and Remy Middelhoff