Human-First AI Adoption Requires Change Management - Episode Hero Image

Human-First AI Adoption Requires Change Management

Original Title:

TL;DR

  • Organizations investing in AI without a human-first adoption strategy fail to see organizational productivity gains, as individual AI productivity improvements do not automatically translate to systemic impact.
  • AI adoption is fundamentally a human readiness challenge, not a technology problem, requiring a focus on change management to overcome psychological biases and resistance.
  • A human-first AI framework activates leaders to drive change top-down, empowers champions to inspire from within, and builds adoption bottom-up by fostering new work habits.
  • The "curious user" represents the largest segment of employees open to AI adoption but requires structured guidance, such as role-specific use cases and prompt examples, to integrate AI effectively.
  • Overcoming "reluctant users" involves demonstrating tangible personal benefits, such as automating tedious tasks, to outweigh their perceived losses and fears associated with change.
  • The four-mindset framework--assisted, explorer, editor, and coaching--provides a structured approach for individuals to identify AI applications for tasks, exploration, quality improvement, and personal growth.
  • Making AI usage visible through leader communication and champion-led initiatives is crucial for normalizing AI adoption and inspiring broader organizational buy-in beyond initial experimentation.

Deep Dive

AI adoption within organizations is fundamentally a human readiness challenge, not a technological one. While AI tools offer significant individual productivity gains, translating these into organizational transformation requires a deliberate focus on people, addressing psychological biases, and implementing a multi-layered change management strategy. Without this human-first approach, AI investments risk failing to yield expected returns, creating a gap between individual potential and collective impact.

The core of successful AI adoption lies in understanding and navigating human resistance to change, which stems from the status quo bias and loss aversion. Organizations must recognize that individuals fall into distinct user categories: champions who enthusiastically adopt AI, reluctant users who are skeptical or uninterested, and curious users who are open but require guidance. Acknowledging these differences is crucial for tailoring communication and support. To move reluctant users toward curiosity, organizations must clearly demonstrate the personal benefits, such as automating tedious tasks and freeing up cognitive capacity for more strategic work. This involves identifying specific use cases that directly address individual pain points and showcasing how AI can enhance, rather than replace, human capabilities. The perception of "cheating" by using AI is a significant hurdle, particularly for women, and must be countered by framing AI as a tool that augments human effort, requiring users to verify, edit, and refine AI-generated output, thus still owning the final work.

To facilitate widespread adoption, a three-layered framework is essential: leadership must champion the change from the top down, champions and enablement teams must inspire from within by sharing use cases and wins, and users must be supported in building new AI habits from the bottom up. Leaders play a critical role by making AI visible through their own usage and by consistently communicating its importance. Champions, often early adopters, can motivate others by sharing practical prompts and success stories, creating accessible resources like prompt libraries. Building AI habits requires foundational training in prompt engineering, followed by consistent practice and reinforcement. Visualizing progress through methods like journaling--tracking AI use cases and perceived output quality--helps users recognize their growth and maintain motivation. This approach transforms AI from a daunting technology into a practical tool that empowers individuals and drives organizational transformation.

Action Items

  • Create a framework for identifying AI use cases: Categorize tasks into four mindsets (assisted, explorer, editor, coaching) to guide users in applying AI effectively.
  • Develop a tiered AI adoption strategy: Activate leaders to set direction, empower champions to inspire from within, and build user habits from the bottom up.
  • Design a prompt library: Collect and share effective prompts from champion users to provide concrete examples and reduce the learning curve for reluctant and curious users.
  • Implement a habit-building program: Guide users through initial AI usage with foundational prompting training and encourage daily practice with journaling to track progress and build confidence.
  • Conduct leadership workshops: Educate leaders on AI adoption as a change management challenge, emphasizing the human element and their role in making AI visible and a priority.

Key Quotes

"I quickly pivoted because I was so amazed with the conversations you could have with a machine that I tried to see how I can use it in more useful ways both at work and in my personal life and I realized just how powerful the technology would be around the same time you also started to see research that people who are using generative ai at work were being more productive the quality of their work was improving etc and so in my mind I thought wow this technology is really going to change how we work and how we live as well but then fast forward for maybe a year year and a half so like early 2024 that's when a slightly different narrative also started popping up and that's all of these organizations who started investing in ai were saying that you know we're not really seeing impact from this ai investment we're not sure if this is actually worth it for us and that really struck me because I saw how powerful it can be at the individual level and research backed that up too so I had a fairly hard time understanding how these individual productivity gains didn't really translate to productivity gains at the organizational level and so I started digging into that."

Kristin Ginn explains her journey into AI adoption, noting the initial excitement around individual productivity gains from generative AI. Ginn highlights a crucial observation: these individual successes did not consistently translate into organizational-level impact, prompting her deeper investigation into this discrepancy.


"And so that's when it clicked for me that this is not really a technology challenge because the technology itself is relatively easy to use right if you can have a conversation with a human being you can pretty much have a conversation with ai but what it really came down to or comes down to is it's a human readiness challenge and that's why I founded my company transform ai because it's really about getting your humans ready to reimagine how work is done and that's one of the biggest challenges and so I truly believe that ai is a really powerful tool but it's only as powerful as the people using it so the work that I do is really focusing on driving change with ai for both individuals and small businesses medium businesses and even larger corporations but focusing that change on the success of the humans behind it so that's really how I got into the work that I do through transform ai."

Ginn identifies the core issue in AI adoption not as a technological hurdle, but as a "human readiness challenge." She emphasizes that the effectiveness of AI tools is directly tied to the people using them, which led her to found Transform AI to focus on preparing individuals and organizations for AI-driven work transformation.


"I think the biggest misconception is that anyone can just purchase generative ai tools roll it out and the magic is going to happen on its own that's not really how it's happening because when you look at generative ai and the type of technology that you're providing to your people whether you're a small business medium business or a large corporation it's very different from a regular tech rollout let me give you an example if you are a company that uses say gmail for your email but you're about to move over to microsoft and using outlook what that process usually looks like is everybody's using gmail you start rolling out outlook there's a transition period where your people can use both but then one day you're turning off gmail and everybody has to use outlook right so everybody is moving over to this new technology and your people don't really have a choice to work the old way it's very different with generative ai because a lot of times it doesn't replace old technology it's just giving your people a new way to work but because they have worked a certain way without ai their entire career what you're really asking your people is you're asking them to change and that change is actually really hard for a lot of people and I think that's where the misconception of oh we can just buy it and roll it out and the magic is going to happen is really holding a lot of companies back from really achieving what's possible with ai."

Ginn debunks the common misconception that simply acquiring generative AI tools will automatically lead to success. She contrasts this with traditional tech rollouts, explaining that generative AI introduces a new way of working rather than replacing an old system, thus requiring a fundamental change in behavior that many find difficult.


"I think there are a lot of benefits at different levels too if you look at the organizational level there's actually a lot of research that shows that those companies who are ready to embrace ai and who are doing a good job rolling it out and getting their people to actually use it they have a competitive advantage I think it was a center they did a recent study that showed that those organizations who are ready for ai they see a 2 4 times higher productivity gain than organizations who don't embrace ai and anyone who participated in a study with I believe that was McKinsey the participants said that if they have generative ai at work their productivity goes up by 80 because they can now focus on more strategic work they can get things done faster and so at the organizational level it's really important to start embracing ai to get these competitive advantages really at the individual level it's more about like reimagining how your work gets done like imagine all of these tasks that are just draining your energy because they're tedious they just take a long time but they don't really require you to think a lot I don't think ai is at the point yet where it can actually do a lot of like very critical thinking but those tasks like if you have a report that you have to write say at the end of the quarter it'll take you maybe a day to sometimes three just to get all the information you need for it start writing it because you start with a blank page right but imagine if you can use ai to do the first draft think of it almost like you have an assistant or an intern doing this for you it gets you 70 80 there it won't be perfect it won't be ready for you to just ship it off to your leadership but it gets you 70 there and then all you have to do is go in verify the information and edit it a little bit to make it your own but that can actually help you not only save time but it also allows you to focus on the more meaningful tasks that can actually help your team and your organization move forward and get more done so I think it's a combination of time savings but then also like improving the type of work that you do and the quality of work that you deliver as well I think those are probably the biggest benefits that you can get at the individual level."

Ginn outlines the dual benefits of AI adoption, citing research that indicates significant productivity gains and competitive advantages for organizations that embrace AI. At the individual level, Ginn explains that AI can automate tedious tasks, freeing up employees to focus on more strategic and meaningful work, thereby improving both efficiency and the quality of output.


"I think in addition to that you can also look at generative ai like a thought partner like yes it can get you started and give you you know a rough framework for example that you then react to you flesh it out you build it out a little bit

Resources

External Resources

Books

Videos & Documentaries

Research & Studies

  • Study by The Center - Showed that organizations ready for AI see a 2.4 times higher productivity gain than organizations that do not embrace AI.
  • Study by McKinsey - Participants stated that generative AI at work increases their productivity by 80%.

Tools & Software

  • ChatGPT - Mentioned as an example of generative AI technology released to the world.
  • Google Gemini - Mentioned as a generative AI tool.
  • Microsoft Copilot - Mentioned as a generative AI tool.

Articles & Papers

People

  • Kristen Jin - AI strategist, product marketing lead for AI adoption and usage at Microsoft, host of the "AI but Human" podcast.
  • Erica Stanley - AI educator.
  • Michael Stelzner - Host of the "AI Explored" podcast and founder of Social Media Examiner.

Organizations & Institutions

  • Microsoft - Employer of Kristen Jin, product marketing lead for AI adoption and usage.
  • Transform AI - Consultancy founded by Kristen Jin focused on human readiness for AI adoption.
  • Social Media Examiner - Producer of the "AI Explored" podcast.
  • AI Business Society - Offers live training with experts on AI marketing.
  • AI Business World - Hosting an event with early bird pricing ending January 16th.

Courses & Educational Resources

  • AI for Humans workshops - Offered by Transform AI, focusing on approachable and relatable AI training.

Websites & Online Resources

  • transformai.com - Website for Kristen Jin's consultancy, offering resources and information on workshops.
  • socialmediaexaminer.com/ai - URL to join the AI Business Society and access AI transformation resources.

Podcasts & Audio

  • AI but Human podcast - Hosted by Kristen Jin.
  • AI Explored podcast - Hosted by Michael Stelzner, brought to you by Social Media Examiner.
  • The Social Media Marketing Podcast - Another show produced by Social Media Examiner.
  • The Social Media Marketing Talk Show - Another show produced by Social Media Examiner.

Other Resources

  • Generative AI - Technology discussed as a tool for individual and organizational transformation.
  • Human Readiness Challenge - The core concept that human preparedness is key to AI adoption success.
  • Change Management - Identified as a critical aspect of AI rollout due to human nature to avoid change.
  • Status Quo Bias - A human tendency to prefer the way things have always been done.
  • Loss Aversion - The human tendency to perceive potential losses more strongly than potential gains.
  • Champion User - A type of user who is highly enthusiastic and actively explores AI.
  • Reluctant User - A type of user who is uninterested or skeptical of AI.
  • Curious User - A type of user who is open to AI but needs more guidance.
  • Assisted Mindset - Using AI to help get tasks done faster.
  • Explorer Mindset - Using AI to think in different directions and gain new perspectives.
  • Editor Mindset - Using AI to improve existing work or data.
  • Coaching Mindset - Using AI to learn new things in a personalized and relatable way.
  • Prompting 101 Training - Essential training for users to effectively structure prompts for AI.
  • AI Habits - Routines and practices developed around using AI daily.
  • Journaling AI Progress - A method to track AI use cases and perceived quality of output to visualize improvement.

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This content is a personally curated review and synopsis derived from the original podcast episode.