Core Concepts
Organizational resistance to adopting new technologies is often rooted in underlying data challenges and concerns, which can manifest in six common forms of resistance. Understanding and addressing these forms of resistance empathetically is key to driving technology adoption.
Abstract
The article discusses the common challenges organizations face when trying to adopt new technologies, and provides a framework for understanding and addressing the root causes of this resistance.
The author suggests that the key to overcoming organizational resistance lies in understanding that the blockers are often related to data challenges, such as concerns about data security, privacy, quality, and integration. These data-related concerns can manifest in six common forms of resistance:
- "Stop" - a deep-seated fear of new technologies or changes
- "No" - a fear of change or loss of control
- "Don't" - resistance without a clear rationale
- "I Don't Get It" - a communication gap and lack of understanding the value
- "Not Me" - a feeling of being overwhelmed or unsuitable for the task
- "We're Already Doing That" - a lack of interest in working together or a perception of competition
The author recommends addressing each form of resistance with a corresponding empathetic response, such as:
- Asking clarifying questions to understand the root cause
- Encouraging open discussion and consideration of facts and assumptions
- Prompting for further explanation and identifying communication gaps
- Reassuring the team and emphasizing the shared responsibility
The key is to approach resistance with empathy, identify the underlying concerns, and work collaboratively to find solutions that address the organization's needs and fears.
Quotes
"It's no secret that most organizations' data holdings are not AI-ready, unusable at scale, and consume the majority of IT budgets for professional services to maintain code between applications using duplicate data that no one can query."
"It's no secret that organizations overemphasize investments in AI/ML because everyone likes the model work but nobody wants to do the data work."
"It's no secret that upwards of 96% of organizations do not have AI-ready data."