A New Approach to Training for New Technology
When introducing Generative AI (genAI) to our teams, I quickly realized that traditional training methods weren’t going to cut it. AI is not just another tool; it’s a new way of thinking, working, and solving problems. As a result, the training process had to be approached differently from anything we’d done before. This case study outlines how I assessed and implemented different training modalities to support the adoption of AI, the outcomes of these efforts, and the lessons learned from this pilot program.
The Challenge: AI Isn’t Just Another Tool
Unlike traditional tools, where training focuses primarily on functionality, AI introduces a completely different paradigm. AI training involves reframing how employees approach their work—understanding that AI isn’t a replacement for human effort but an enabler of smarter, more efficient workflows. It’s also about building trust with a technology that operates autonomously, learning from data and evolving over time.
As we began the AI pilot, I recognized that training needed to focus on:
Shifting mindsets: AI is not “just another tool.” Employees had to think about AI as a partner, not a feature.
Practical usage: Training needed to focus on how AI could be applied in their day-to-day workflows.
Adoption and integration: AI doesn’t fit seamlessly into existing processes without careful guidance.
We needed to think about how people learn, not just what they learn. It wasn’t about making employees familiar with new software—it was about introducing them to a new way of working.
The Approach: A Multifaceted Training Strategy
Recognizing that AI training couldn’t be one-size-fits-all, I explored various training modalities to meet the diverse needs of employees at different levels of technical proficiency. The goal was to offer a blended training experience that would ensure both practical knowledge and conceptual understanding of AI’s potential.
1. Hands-on Training and Interactive Workshops
To shift the mindset around AI, I started with hands-on workshops where employees could directly engage with the technology. The focus was not on theoretical knowledge but on practical application. During these sessions, employees were shown how AI could be integrated into their existing workflows—whether for automating repetitive tasks, generating insights, or streamlining decision-making processes.
Outcomes:
Employees quickly gained familiarity with how AI operated in real-world contexts.
The sessions helped build confidence in using the technology and encouraged employees to think about how they could leverage AI for innovation in their specific roles.
Open discussion problem-solving and solution-sharing helped employees work through challenges together, expanding thought and approach.
2. Modular E-learning Programs for Self-Paced Learning
Recognizing that some employees may need more time to absorb complex concepts, I implemented self-paced e-learning modules. These covered foundational AI concepts, ethical considerations, and how to work collaboratively with AI. By allowing employees to work at their own pace, we ensured that everyone had access to training that suited their individual learning speed.
Outcomes:
E-learning provided employees with the flexibility to engage with content as needed, ensuring deeper learning for those who wanted to dive deeper into the technical aspects of AI.
We noticed a significant improvement in employee comfort levels, with many participants reporting that they now felt more confident in engaging with AI tools.
3. Real-Time Feedback and Continuous Learning
AI isn’t static, and neither is the learning process. I implemented real-time feedback mechanisms, including weekly check-ins and progress surveys. These allowed employees to ask questions, voice concerns, and share experiences. Additionally, these feedback loops helped us adapt the training in real-time based on user input, ensuring that our approach remained relevant and effective throughout the pilot.
Outcomes:
Real-time feedback provided immediate support, reducing frustration and increasing adoption rates.
Teams reported feeling more comfortable with the tool as their questions were addressed promptly, which increased engagement and participation.
4. Role-Specific Training and Peer Learning
AI adoption is deeply contextual—what works for one team may not work for another. Therefore, I tailored role-specific training that aligned with the different use cases and tasks of each department. This customization ensured that teams learned how AI could directly impact their daily tasks. To further encourage knowledge sharing, we also set up peer learning groups, where employees could learn from each other’s experiences and successes.
Outcomes:
Customizing training based on roles helped employees connect the training directly to their tasks. This made the AI tools more relevant and showed the immediate impact they could have.
Peer learning created a collaborative environment that encouraged the sharing of best practices and tips.
The Results: Building Trust and Confidence in AI
By combining different training modalities, we saw successful outcomes across the board:
Increased Confidence: Employees demonstrated a clear understanding of how to use AI tools effectively. Feedback surveys showed that confidence in using AI increased by 30% after the training.
High Engagement: 85% of participants engaged with the self-paced e-learning modules, with many opting for deeper dives into advanced topics.
Stronger Collaboration: Employees shared how the training created a sense of shared ownership of the AI process, particularly within peer learning groups. These discussions allowed them to exchange tips and collaborate more effectively with AI.
Lessons Learned: Training for the Future of Work
The experience of training teams for the AI pilot taught me several critical lessons about AI training:
AI Requires a Paradigm Shift
Unlike traditional tools, AI requires a mindset change. Employees need to see AI not as a tool to replace work but as a partner that enhances their capabilities. This shift takes time and thoughtful training, which is why a mixed modality approach is essential.
Blended Learning Drives Engagement
Offering a combination of hands-on workshops, e-learning, and peer learning allowed employees to engage with the material in different ways. The more diverse the learning methods, the higher the engagement levels.
Continuous Feedback is Critical
AI adoption is a continuous process, and training should mirror that. Real-time feedback loops allowed us to adjust the program based on how employees were interacting with the technology, making it a living, evolving process.
Role-Specific Training Enhances Relevance
Tailoring training to the specific needs of different teams ensured that each group could immediately see the value of AI in their day-to-day work. Personalized training increases adoption and makes the tool more relevant to each person’s role.
Conclusion: The Future of AI Training
The success of the AI pilot reinforced the importance of thoughtful, diverse training in the adoption of new technologies. AI is more than just a tool—it’s a new way of thinking and working. Training for AI requires flexibility, customization, and continuous learning to ensure successful integration.
Moving forward, we will continue to apply these insights as we scale AI across the organization. The lessons learned during the pilot will guide our future training initiatives, ensuring that we equip our teams not just with the skills to use AI but with the mindset to collaborate with it, unlocking even greater potential as we move toward the future of work.
About the Author
Jessica Zeba-Snow, DrPH
Head of Remote Operations and Culture | Skillable
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