AI in Change Management: Early Findings, Challenges, and Opportunities
It’s early 2024 and we’re just beginning to understand the role of Artificial Intelligence (AI) in the practice of organizational change management. Let’s dive into Prosci’s research to understand its impacts on our discipline, including the extent to which change leaders are using AI tools in change management, how they’re using them, and the early benefits they’re reaping.
Current Landscape of AI and Change Management
Based on Prosci’s snapshot of AI study findings in October 2023, 84% of change practitioners they surveyed are moderately familiar to very familiar with AI. However, only 48% say they currently use it in their change management work. Those who aren’t using AI tools in their work offered several primary reasons:
The top three responses relate most directly to an overall lack of understanding, such as being unsure about how to use AI effectively, inadequate experience with AI, and fear of risks that have yet to be identified. A lack of use cases for AI’s application in change management contributes to this problem that collectively impacted 53% of respondents.
“I am not sure how to do it. Or where to go. Or what’s acceptable for use with my company.”
Similarly, respondents cited limited access to tools and resources for applying AI in change management, and being unfamiliar with the tools and resources that are available, as well as how to apply them in their change management work. Access to reliable AI tools, constrained budgets, and inadequate organizational support also contributed to this limitation reported by 16% of those surveyed.
Respondents also have insufficient time and competing priorities in their daily work, which keeps 15% of respondents from prioritizing AI exploration and learning.
“It’s one more thing I don’t have time to learn.”
Unanswered questions about data privacy and security concerns impact 8% of those surveyed, including how data will be used and protected, as well as the security of any AI systems being adopted by the organization.
Finally, respondents identified inadequate organizational maturity as a reason for not using AI, including the right depth of change management expertise necessary to effectively implement AI technology in practice.
Demographic patterns in the data show that expert change management professionals with more than five years of experience use AI in their practice more than novices. Professionals with split responsibilities—perhaps as strategy consultants, business leaders, project managers or executive sponsors—also report higher AI usage in their work.
Early Benefits of AI in Change Management
What are the impacts of AI on your change management work? That’s a key question we asked respondents in our initial study.
Key themes in the research range from increased efficiency to improved workload management. Respondents told us the tools they’re using have helped them become more efficient and productive by automating processes, quickly analyzing data, brainstorming ideas and outlines, generating draft communications and change management plans, improving response times, and much more.
One study participant uses an AI as an “assistant” for workload management, with clear benefits:
“[AI] has complimented my current practice—it provides a depth of information that would often take hours to accumulate, let alone synthesize the information. [AI] helps me discover opportunities to build knowledge and understand of change management in scientific and technology fields faster.”
How to Use AI in Change Management Work
In addition to inquiring about impacts, we asked how change management professionals currently apply AI tools and technologies in their work. Here are the five primary ways they use it today, along with sample activities extracted from participants’ words:
1. Communications support (primarily for existing content)
- Rewrite or rephrase content
- Answer grammatical questions
- Filter presentations for improvements
- Refine communications
- Target the copy to different audiences
- Obtain a starting point
- Perform gut-checks on messaging tone
- Repurpose source content for different modes (e.g., slides, image, text) and audiences
Example: “Evaluate the tone of the CEO’s announcement for appropriateness with companywide staff during a rebranding phase.”
2. Content creation (primarily for generating new content)
- Write training guides
- Create fictional, industry-specific case studies
- Develop slide decks
- Draft communications quickly
- Break complex topics into manageable chunks
- Create user personas
- Summarize communications
- Brainstorm creative headlines
- Apply a unique voice or format (e.g., a Dr. Seuss poem)
Example: “Break down the complex topic of organizational restructuring into smaller, manageable segments for employees, focusing on individual roles and impacts.”
3. Strategy and planning
- Brainstorm different tactics to apply in a company
- Help build communications and training plans
- Suggest improvements to communications plans
- Conduct simulations and scenario planning
- Assemble specific change management plans (e.g., resistance management)
- Use as a sounding board
- Create outline of a change management plan
Example: “Provide feedback on the proposed employee engagement strategy related to the new organizational structure, focusing on its impact on team morale.”
4. Automation and efficiency
- Build an in-house chatbot for personalized training and answering questions
- Design bots for FAQs and links to resources
- Use chatbots for stakeholder feedback and Q&A
- Create user personas
- Repurpose written text for other channels
- Analyze and forecast individual behaviors
- Produce explainer videos
- Generate coherent text from key words
Example: “Create a chatbot to collect feedback on new HR policies and answer questions from department managers.”
5. Data analysis
- Conduct data analysis on survey results
- Aggregate data
- Check business cases
- Deliver real-time insights based on personal data
- Analyze and segment data to customize content
- Gather industry-specific information
- Test various hypotheses
- Analyze key themes
Example: “Perform a thematic analysis of customer feedback on recent product launches to identify key themes in customer satisfaction and areas for improvement.
Challenges and Opportunities in AI and Change Management
Although AI holds extraordinary promise in change management and beyond, many practitioners have valid concerns. Will AI replace my job? Are the tools available today trustworthy and secure? Am I at risk if I keep using pen and paper? Will I be replaced by younger workers?
In a recent webinar, an attendee chatted a poignant comment:
“You will be left behind…not learning about AI is like not learning about how to use the internet.”
Whether they want to use AI in their work or not, many change management professionals simply don’t know what AI tools are. That may explain why 32% of respondents say they don’t use AI tools at all.
The more likely reality is that you’re already using some AI in your work without realizing it. Popular tools for transcription, translation and sentiment analysis use AI. Predictive text on phones, chatbots in user support, and online ads that feed you customized content are AI-based. AI works quietly behind the scenes in analytical tools integrated with customer relationship management (CRM) systems and cyber security too.
Yet, the world is just starting to understand the power of AI and its many uses.
By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.”
—Eliezer Yudkowsky, American AI Researcher
Sixty-five percent of Prosci research participants agree or strongly agree that AI will help them be more successful in the future. When asked about potential opportunities with AI and change management practices in the next two years, 37% report deficient AI capabilities in Enterprise Change Management and the Change Management Office (CMO)—a clear area of opportunity.
The truth about AI application, at least today, is that it’s not perfect, but the more you work with it, the better the quality of outputs you will generate. After practice and many iterations (or what Tim Creasey refers to as “prompts engineering, not prompt engineering”), you may be able to see the full value for your use cases today. It’s critical to develop a mindset of continuing the conversation. AI is still emerging—there are still many unknowns and many innovations to come.
Although 30% of webinar attendees see organizational adoption of AI as urgent (i.e., right now) others are comfortable kicking the can down the road three, six or 12 months, perhaps to see how AI tools and technology evolve.
Rest assured, heavy users of AI today are very much early adopters. In fact, when we asked survey respondents a question based on Everett Rogers’ Diffusion of Innovation curve, 53% considered themselves to be early adopters of AI—they like to be the first to try technology. However, in practice we see that only 16% report using it quite a bit or a great deal in their work.
AI and Change Management Research
From Prosci
AI is an emerging discipline, not just in change management but everywhere. Although early adopters are using AI in their change management work today, we fully expect that our understanding of AI’s use will continue to grow in depth and width of application at a rapid pace.
Why should this matter to you? The data presented here is a snapshot, a moment in time. We’re just starting to understand the current and merging landscape. New Prosci studies are coming to extend and deepen our understanding. As AI grows and evolves, our “living study” of AI and change management will continue to grow and evolve along with it. This is just the tip of the iceberg.