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Grand Studio has a long history of working with various AI technologies and tools (including a chatbot for the underbanked and using AI to help scale the quick-service restaurant industry). We’ve created our own Human-Centered AI Framework to guide our work and our clients to design a future that is AI-powered and human-led and that builds on human knowledge and skills to make organizations run better and unlock greater capabilities for people. When ChatGPT hit the scene, we started experimenting right away with how it could improve our processes and make our work both more efficient and more robust.
Given our experience with what AI is good at doing (and what it’s not), we knew we could use ChatGPT to help us distill and synthesize a large amount of qualitative data in a recent large-scale discovery and ideation project for a global client.
Here are some takeaways for teams hoping to do something similar:
1. Don’t skip the clean-up. As they say: garbage in, garbage out. Generative AI (GenAI) tools can only make sense of what you give them – they can’t necessarily decipher acronyms, shorthand, typos, or other research input errors. Spend the time to clean up your data and your algorithmic synthesis buddy will thank you. This can also include standardized formats, so if you think you may want to go this route, consider how you can standardize note-taking in your upfront research prep.
2. Protect your – and your client’s – data. While ChatGPT doesn’t currently claim any ownership or copyright over the information you put in, it will train on your data unless you make a specific privacy request . If you’re working with sensitive or private company data, do your due diligence and make sure you’ve cleaned up important or easily identifiable data first. Data safety should always be your top priority.
3. Be specific with what you need to know. ChatGPT can only do so much. If you don’t know what your research goals are, ChatGPT isn’t going to be a silver bullet that uncovers the secrets of your data for you. In our experience, it works best with specific prompts that give it clear guidelines and output parameters. For example, you can ask something like:
“Please synthesize the following data and create three takeaways that surface what users thought of these ideas in plain language. Use only the data set provided to create your answers. Highlight the most important things users thought regarding what they liked and didn’t like, and why. Please return your response as a bulleted list, with one bullet for each key takeaway, with sub-bullets underneath those for what they liked and didn’t like, and why.”
Doing the upfront human-researcher work of creating high quality research plans will help you focus on the important questions at this stage.
4. It’s true, ChatGPT gets tired. As with any new technology, ChatGPT is always changing. That being said, the 4.0 version of ChatGP that we worked with demonstrated diminishing returns the longer we used it. Even though the prompts were exactly the same from question to question, with the input of fresh data sources each time, ChatGPT’s answers got shorter and less complete. Prompts asking for three synthesized takeaways would be answered with one or two, with fewer and fewer connections to the data sets. By the end, its answers were straight up wrong. Leading us to our final takeaway:
5. Always do an audit of the answers! Large language models like ChatGPT aren’t able to discern if the answers they provide are accurate or what you were hoping to receive. It’s also incredibly confident when providing its answers, even if they’re wrong. This means you can’t blindly rely on it to give you an accurate answer. You have to go back and sift through the original data and make sure that the answers it gives you line up with what you, the researcher, also see. Unfortunately this means the process will take longer than you were probably hoping for, but the alternative is incomplete, or incorrect answers – which defeat the purpose of synthesis in the first place and could cause the client to lose trust in you.
Outcome: Did using ChatGPT speed up our synthesis significantly? Absolutely. Could we fully rely on ChatGPT’s synthesis output without any sort of audit or gut check? Not at all. We’ll keep experimenting with ways to incorporate emerging technologies like Generative AI into our workstreams, but always with research integrity and humans at our center.
Interested in how GenAI might work for your organization? Drop us a line – we’d love to chat!