AI is everyone’s favorite buzzword these days and there is beginning to be massive FOMO to launch some sort of AI Initiative. However, after 20+ years watching technology rollouts succeed and fail, I’ve learned that *any* AI solution is worthless if your team won’t use it.
Many of us have seen this pattern before with AR/XR implementations in manufacturing, digital factory initiatives, and IoT sensor deployments. The technology works beautifully in the demo. But six months later? Adoption rates hover around 20%, and teams have quietly reverted to their old processes.
The biggest barrier isn’t technical. it’s human.
People naturally resist leaving their comfort zone, especially when new technology feels threatening. With AI, that fear is amplified by concerns about job displacement and loss of control.
Here’s what I’ve learned drives successful adoption:
Address the fear head-on. Don’t pretend it doesn’t exist. Create open spaces for honest conversations about how AI will augment (not replace) roles. Create a culture where people recognize AI as their “Cyber Teammate” instead of a threat. When we implemented our latest digital factory solutions, the teams that discussed concerns openly had 3x higher adoption rates than what we saw the last time when the workforce was just told to “start using the new system”.
Be radically transparent. Share your decision-making process, not just decisions. Explain the “why” behind tool selection and acknowledge what you don’t know yet. Uncertainty breeds resistance.
Build solutions together. The fastest way to kill an AI project is to disappear into a room, emerge with a “perfect” workflow, and expect everyone to adopt it. People don’t embrace what they didn’t help create. Identify the team members that will be using the new tools and get their input. Many times their feedback will provide tremendous value.
Whether it’s computer vision for quality control, predictive maintenance algorithms, or AI-powered design optimization, the technical capabilities are rarely the limiting factor anymore.
The real competitive advantage goes to organizations that master the human side of technological change.
How are you addressing change management in your AI initiatives?
#ChangeManagement #DigitalTransformation #TechnologyAdoption
Change Management: The Make-or-Break Factor for AI Project Success
AI is everyone’s favorite buzzword these days and there is beginning to be massive FOMO to launch some sort of AI Initiative. However, after 20+ years watching technology rollouts succeed and fail, I’ve learned that *any* AI solution is worthless if your team won’t use it.
Many of us have seen this pattern before with AR/XR implementations in manufacturing, digital factory initiatives, and IoT sensor deployments. The technology works beautifully in the demo. But six months later? Adoption rates hover around 20%, and teams have quietly reverted to their old processes.
The biggest barrier isn’t technical. it’s human.
People naturally resist leaving their comfort zone, especially when new technology feels threatening. With AI, that fear is amplified by concerns about job displacement and loss of control.
Here’s what I’ve learned drives successful adoption:
Address the fear head-on. Don’t pretend it doesn’t exist. Create open spaces for honest conversations about how AI will augment (not replace) roles. Create a culture where people recognize AI as their “Cyber Teammate” instead of a threat. When we implemented our latest digital factory solutions, the teams that discussed concerns openly had 3x higher adoption rates than what we saw the last time when the workforce was just told to “start using the new system”.
Be radically transparent. Share your decision-making process, not just decisions. Explain the “why” behind tool selection and acknowledge what you don’t know yet. Uncertainty breeds resistance.
Build solutions together. The fastest way to kill an AI project is to disappear into a room, emerge with a “perfect” workflow, and expect everyone to adopt it. People don’t embrace what they didn’t help create. Identify the team members that will be using the new tools and get their input. Many times their feedback will provide tremendous value.
Whether it’s computer vision for quality control, predictive maintenance algorithms, or AI-powered design optimization, the technical capabilities are rarely the limiting factor anymore.
The real competitive advantage goes to organizations that master the human side of technological change.
#ChangeManagement #DigitalTransformation #TechnologyAdoption
