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Jonathan Price – CRE & Flex Expert
As AI transforms how we work, learn, and build, training programs can’t stay static.
Generative AI (Gen AI) is moving too fast for one-off courses or fixed curriculums. To keep pace, learning programs need to evolve in real time — using data, feedback, and real-world results to stay relevant, practical, and aligned with what organizations actually need next.
The Imperative of Continuous Improvement for Gen AI Initiatives
Static training programs risk irrelevance as business priorities change and technologies progress. This is particularly true for Gen AI, where quick advancements necessitate regular updates to training content and methodologies. Continuous improvement ensures that learning programs remain effective, engaging, and aligned with organizational goals.
At the heart of this process are two critical components: feedback from participants and data-driven insights.
Participant feedback provides invaluable qualitative insights into the effectiveness of a learning program. Employees can share their experiences, highlighting what worked well, what was challenging, and what could be improved.
This feedback can be collected through surveys, focus groups, interviews, or even informal discussions. When analyzed systematically, it provides a clear picture of the program’s strengths and areas for refinement.
For example, imagine a training module on advanced Gen AI concepts that multiple employees describe as overly complex. As a consultant who encounters such situations frequently, I would recommend breaking the module into smaller, more digestible sections or adding supplemental resources such as video tutorials or peer-led study groups.
These adjustments can make the content more accessible, ensuring that employees grasp critical concepts effectively.
Quantitative data complements qualitative feedback by providing measurable indicators of a program’s performance. Metrics such as engagement rates, assessment scores, and completion rates can identify trends and patterns that inform targeted improvements. For instance, if data reveals that interactive simulations consistently result in higher engagement and better learning outcomes, an organization can expand the use of this approach across its training modules.
In one case, a client I worked with, a mid-sized software development firm, was struggling with low engagement in its Gen AI training program. By analyzing data from the program’s learning management system, we discovered that employees were more engaged with interactive content than with traditional lectures.
Based on these insights, we redesigned the program to include more hands-on activities, such as simulated Gen AI problem-solving scenarios. This change not only boosted engagement but also improved the employees’ ability to apply their learning to real-world challenges.
Feedback and data-driven insights also ensure that Gen AI learning programs stay aligned with an organization’s strategic objectives. As business priorities alter, learning initiatives must adjust to reflect these changes.
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For instance, if a company begins prioritizing AI-driven decision-making, its training program should evolve to include advanced topics such as machine learning, data analytics, and ethical considerations in AI.
This alignment was critical for a global financial services firm I consulted for. The company wanted to integrate Gen AI tools into its decision-making processes but found that its workforce lacked the necessary skills. By developing a targeted training program informed by feedback and data, we equipped employees with competencies in areas like AI ethics, managing risks, and predictive analytics.
Regular updates to the curriculum ensured the training remained relevant as the firm’s AI capabilities expanded.
Client Case Study: Gen AI Initiative at a Mid-Sized Legal Firm
A mid-sized legal firm with just over 100 staff faced significant challenges with its Gen AI training program. The firm had invested heavily in upskilling its workforce but found that many employees were disengaged and struggled to apply their learning effectively. Recognizing the need for a comprehensive overhaul, the firm brought me on board as a consultant.
The first step was to gather participant feedback through surveys and focus groups. Employees reported that the training modules were too theoretical and failed to connect with their day-to-day responsibilities. Using this feedback, we redesigned the curriculum to include practical applications, such as legal case studies relevant to their roles and exercises on drafting contracts with the assistance of Gen AI tools.
Next, we analyzed data from the existing program to identify additional areas for improvement. Completion rates were particularly low for modules that relied heavily on generic training on Gen AI practices. By integrating case studies more relevant to law firms, such as prompts for drafting various legal documents, we made the content more engaging and accessible.
Finally, we aligned the program with the firm’s strategic goals. As the firm aimed to enhance efficiency and accuracy in legal document review, the revised training program included advanced topics such as using Gen AI for contract analysis, AI ethics in law, and integrating AI tools into client advisory workflows.
The results were transformative. Engagement rates soared, with completion growing by 56%, and employees reported 49% higher satisfaction with the training. Moreover, the firm saw tangible improvements in how AI tools were utilized in legal research and documentation, with a 36% productivity boost.
This experience underscores the importance of a data- and feedback-driven approach to continuous improvement in Gen AI training programs.
Creating a Culture of Continuous Learning
Beyond improving specific training programs, continuous improvement supports a culture of learning and innovation within an organization. When employees see that their feedback is valued and that the organization is committed to providing high-quality learning experiences, they are more likely to stay engaged and invest in their development.
This was evident in another client, a multinational manufacturing company. By embedding feedback mechanisms and data analysis into all their learning initiatives, the company not only improved its Gen AI training but also inspired employees to take ownership of their professional growth.
Over time, this culture of continuous learning became a key driver of the company’s innovation and competitiveness.
Practical Steps for Implementing Continuous AI Improvement
For organizations looking to adopt a continuous improvement model for their Gen AI learning programs, the following steps are essential:
- Establish Feedback Mechanisms: Develop structured channels for gathering participant feedback, such as post-training surveys or regular focus groups.
- Analyze Performance Data: Use quantitative metrics to assess the effectiveness of different program components and identify trends.
- Iterate and Adapt: Be prepared to make iterative changes based on insights from feedback and data.
- Engage Stakeholders: Involve employees, trainers, and leadership in discussions about program improvements to ensure alignment with organizational goals.
- Communicate Changes: Keep participants informed about how their input has influenced program updates, reinforcing the value of their feedback.
Conclusion
In an era of rapid technological advancement, static learning programs are no longer sufficient. Continuous improvement driven by feedback and data is essential for ensuring that Gen AI training programs remain relevant, effective, and aligned with organizational objectives.
The case studies demonstrate the transformative impact of this approach. By embracing continuous improvement, companies not only enhance their training outcomes but also build a culture of learning and innovation that prepares them for the challenges and opportunities of the future.
