3 Things I Learned Using Gen AI for ESG Problem Solving

Let me start with a confession: I’m no Gen AI or ESG expert. What I do bring to the table, however, is expertise in Design Thinking (DT) - a creative problem-solving methodology for tackling complex, human-centered challenges.

Over the years, I’ve had the opportunity to apply this approach in diverse contexts, and recently, I was involved in a unique project that combined employee upskilling with solving real ESG challenges.

The project had two primary objectives:

1. Upskilled employees with modern problem-solving capabilities using DT and Gen AI.

2. Exploring solutions for internal ESG-related challenges, such as employee retention, waste management, and energy efficiency.

   
   

While I may not be an expert in ESG or artificial intelligence, this experience taught me how powerful a structured problem-solving approach combined with AI tools can be in generating impactful ideas.

Here are 3 things I learned Using Gen AI for problem-solving during this journey:

1. You Don’t Need to Be an AI Expert to Get Started

At the outset, many of the participants (myself included) were unfamiliar with the intricacies of Generative AI. However, we quickly realized that you don’t need to be an AI specialist to benefit from it. What mattered more was having a clear problem and knowing how to frame it well.

In this case, we applied DT principles, starting with empathy to deeply understand the user and their needs. From there, we framed specific challenges, and that’s where Gen AI tools like ChatGPT came in handy. For example, we used ChatGPT to generate user personas, brainstorm ideas, and create visual storyboards.

Here’s an example of one of the personas we developed early on:

This persona helped us better understand the challenges faced by employees who wanted simple, non-intrusive solutions for saving energy at the workplace.

Lesson: You don’t need deep AI knowledge; you need clarity about the problem. AI tools are most effective when used to amplify a well-defined design process.

Takeaway: If you can define the right questions, Gen AI can be a powerful co-creator, helping you generate ideas faster and more effectively.

2. Visualizing Ideas Quickly Helps Build Momentum

One of the best ways to get buy-in for a new concept is to make it tangible. In this project, we used Gen AI to create a storyboard for an early concept called “AutoSwitch,” designed to automatically power down workstations when employees leave their desks.

Within 10 mins, we had a low-fidelity storyboard illustrating how AutoSwitch could work in practice. While this storyboard wasn’t presented to stakeholders, it served a critical purpose: it helped us align on a shared vision as a team. Until then, we all had slightly different ideas of how the final concept might work. Seeing the storyboard brought clarity and sparked meaningful discussions that helped us refine the idea further.

Here’s the initial storyboard that facilitated those internal conversations:

We visualized the idea quickly and created a shared understanding among team members and stakeholders. This clarity helped us confidently move forward and focus on refining more viable solutions.

Lesson: Storyboards aren’t just tools for presenting ideas. They’re also powerful for aligning teams early and uncovering blind spots.

Takeaway: Before you aim to present your idea externally, visualize it internally to foster alignment and create those crucial “aha!” moments within your team.

3. Iteration is the Secret Sauce

The initial storyboard was just a starting point. Once the team was aligned on a shared concept vision, we shifted our focus to iteration - to improve the idea for ourselves and make it more understandable for others outside the team.

Our goal wasn’t to refine it until we were satisfied but to develop a higher-fidelity version that could stand independently. We wanted a prototype that stakeholders could review without requiring us to explain it repeatedly.

This process of continuous iteration taught the team a valuable lesson: innovation isn’t about getting it right the first time. It’s about listening to each other, learning, and improving with each step. Even though AutoSwitch wasn’t the final concept, the experience of iterating on it helped us develop a solution that ultimately better aligned with business priorities and earned management’s approval.

Here is the final version of the AutoSwitch storyboard, which took the team 45 minutes to complete and was presented for feedback.

Lesson: Iteration isn’t just about fixing problems—it’s about making your idea clearer, stronger, and easier for others to understand.

Takeaway: Encourage a feedback-driven culture where continuous improvement is a core process. Aim to create outputs that can be understood without constant explanation to be shared and scaled more effectively.

Final Thoughts: Structured Problem-Solving + AI = Faster, Better Solutions

This project showed me that with the right approach and tools, it’s possible to make real progress on complex challenges - no matter how unfamiliar the problem might initially seem. By combining a structured problem-solving methodology like Design Thinking with powerful tools like Gen AI, teams can quickly generate, test, and refine ideas.

Even though I’ve been using Gen AI - specifically 80% ChatGPT and Fireflies for over a year, I often felt hesitant to share my approach more broadly. I thought... what I know is probably what everyone else already knows.  However, after this project, my client encouraged me to offer the same workshop to more people.

So, I’ll ask you:

👉 Let me know in the comments by typing YES, and I’ll share the training outline for your reference!

[About the Author]

Jentz has a deep background in innovation and design across Europe and Malaysia. Committed to enhancing design practices, Jentz aims to help position Malaysia as a hub for creative and impactful solutions. Join him in exploring the potential of design thinking to tackle immediate challenges and inspire transformative change and innovation.