Feedback-driven training could save your GenAI initiatives

In an era of rapid technological advancement, static learning programs are no longer sufficient, writes Dr Gleb Tsipursky.

As AI transforms how we work, learn, and build, training programs can’t stay static.

Generative AI (GenAI) 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 organisations actually need next.

The imperative of continuous improvement for GenAI initiatives

Static training programs risk irrelevance as business priorities change and technologies progress. This is particularly true for GenAI, where quick advancements necessitate regular updates to training content and methodologies. Continuous improvement ensures that learning programs remain effective, engaging, and aligned with organisational 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 analysed systematically, it provides a clear picture of the program’s strengths and areas for refinement.

For example, imagine a training module on advanced GenAI 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 organisation 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 GenAI training program. By analysing 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 GenAI 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 GenAI learning programs stay aligned with an organisation’s strategic objectives. As business priorities alter, learning initiatives must adjust to reflect these changes.

For instance, if a company begins prioritising 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 GenAI 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: GenAI initiatives at a mid-sized legal firm

A mid-sized legal firm with just over 100 staff faced significant challenges with its GenAI 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. Recognising 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 GenAI tools.

Next, we analysed 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 GenAI 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 GenAI 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 per cent, and employees reported 49 per cent higher satisfaction with the training. Moreover, the firm saw tangible improvements in how AI tools were utilised in legal research and documentation, with a 36 per cent productivity boost.

This experience underscores the importance of a data- and feedback-driven approach to continuous improvement in GenAI training programs.

Creating a culture of continuous learning

Beyond improving specific training programs, continuous improvement supports a culture of learning and innovation within an organisation. When employees see that their feedback is valued and that the organisation 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 its learning initiatives, the company not only improved its GenAI 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 organisations looking to adopt a continuous improvement model for their GenAI learning programs, the following steps are essential:

1. Establish feedback mechanisms: Develop structured channels for gathering participant feedback, such as post-training surveys or regular focus groups.
2. Analyse performance data: Use quantitative metrics to assess the effectiveness of different program components and identify trends.
3. Iterate and adapt: Be prepared to make iterative changes based on insights from feedback and data.
4. Engage stakeholders: Involve employees, trainers, and leadership in discussions about program improvements to ensure alignment with organisational goals.
5. 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 GenAI training programs remain relevant, effective, and aligned with organisational 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.

Dr Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps leaders transform AI hype into real-world results. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts and wrote seven bestselling books, including The Psychology of Generative AI Adoption.