AI vs. Sensory Science: Finding the Balance in 2026

𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗔𝗜

There is no doubt that Large Language Models (LLMs) are 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 our industry. At DSI, we use them daily to automate client-related work and increase efficiency. The speed and potential of these tools are undeniable.

𝗧𝗵𝗲 𝗦𝗲𝗻𝘀𝗼𝗿𝘆 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻: 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗥𝗶𝗴𝗼𝗿

However, having studied under Michael O’Mahony and Daniel Ennis, my focus and passion have been on 𝘁𝗵𝗲 "𝗵𝘂𝗺𝗮𝗻" 𝘀𝗶𝗱𝗲: 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘪𝘯𝘨 𝘵𝘩𝘦 𝘴𝘦𝘯𝘴𝘦𝘴 𝘢𝘯𝘥 𝘩𝘰𝘸 𝘪𝘯𝘥𝘪𝘷𝘪𝘥𝘶𝘢𝘭𝘴 𝘮𝘢𝘬𝘦 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴. This human-centric focus from a great group of people is what led to the 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗺𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝗶𝗲𝘀 we use today. And much more will be achieved in the years to come.

𝗔 𝗚𝗿𝗼𝘄𝗶𝗻𝗴 𝗖𝗼𝗻𝗰𝗲𝗿𝗻

While recent conferences have showcased very beneficial AI tools that speed up our work and allow valuable harvesting and compiling of historical data, I’ve noticed a surprising 𝗿𝗲𝗹𝗮𝘁𝗶𝘃𝗲 𝗱𝗲𝗰𝗿𝗲𝗮𝘀𝗲 𝗶𝗻 𝗻𝗲𝘄 𝗺𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵. Our field was built on the work from scientific pioneers like Rosemarie Pangborn, Herbert Stone, Gail Vance Civille, Tom Carr, Michael O'Mahony , and Daniel Ennis and continued by modern leaders like Hye-Seong Lee , Chris Simons, Julien Delarue, and many others. To remain a true science, we cannot rely on automation alone.

𝗠𝘆 𝗛𝗼𝗽𝗲 𝗳𝗼𝗿 𝟮𝟬𝟮𝟲

I hope that as we embrace AI, 𝘄𝗲 𝗱𝗼𝗻’𝘁 𝗮𝗯𝗮𝗻𝗱𝗼𝗻 a significant building block of our field: High-quality, rigorous research 𝗴𝗿𝗼𝘂𝗻𝗱𝗲𝗱 𝗶𝗻 𝘁𝗵𝗲 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗠𝗲𝘁𝗵𝗼𝗱:

𝟭. 🔬 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻 & 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗶𝘀

𝟮. 🧪 𝗥𝗶𝗴𝗼𝗿𝗼𝘂𝘀 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻

𝟯. ⚙️ 𝗖𝗼𝗻𝘀𝘁𝗮𝗻𝘁 𝗥𝗲𝗳𝗶𝗻𝗲𝗺𝗲𝗻𝘁

𝟰. 📊 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗠𝗼𝗱𝗲𝗹𝘀

Let’s ensure Sensory and Consumer Science continues to evolve through robust methodological research based on the 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗼𝗳 𝗵𝘂𝗺𝗮𝗻 𝗽𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻, not just 𝘧𝘢𝘴𝘵𝘦𝘳 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨.

❓ What are your thoughts? Is the rise of AI 𝗼𝘃𝗲𝗿𝘀𝗵𝗮𝗱𝗼𝘄𝗶𝗻𝗴 the development of new sensory methodologies, or can the two 𝗴𝗿𝗼𝘄 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿?

❓ Could you identify potential contributions of AI to 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝗺𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 of Sensory Science?