AI's Role In Research: Linear Connections Not Lateral
medintel continues to test the possibilities of the latest AI platforms, and utilise how AI with phenomenal analytical results while safeguarding data privacy and maintaining standards in accuracy and validity.
The massive boost to productivity, and speed of turnaround of immediate topline findings is a significant benefit as well as the ability to interrogate data to pursue lines of enquiry.
However, the one-sided nature of the enquiry means it needs to be treated carefully and can easily result in false positives.
The lack of the ability for the researcher to see the whole picture in the data and see fresh perspectives can be a challenge. Researchers need to know the data to see the wider picture. In addition detailed storytelling still needs to be very much guided. AI serves as a powerful collaborative tool rather than a replacement for human expertise: for the time being anyway!
AI as Research Assistant, Not Replacement
‘The most successful researchers view AI as an extension of their capabilities, not a substitute for their expertise,’ was noted on a recent forum of qualitative researchers.
AI Does Not Think Laterally: It Is Built On Linear Connections
AI is built on a series of yes, no, yes, no linear connections. It is not intuitively designed to think laterally. When humans think laterally, they make creative leaps between concepts, draw unexpected connections, and approach problems from unconventional angles. This often involves intuition, emotional context, and life experiences.
AI systems can simulate aspects of lateral thinking by making connections between seemingly unrelated concepts in training data; generate multiple approaches to solving problems and identify patterns across different domains.
However, AI lateral thinking doesn’t have genuine intuition or emotional context that often drives human creativity. AI connections are based on statistical patterns in training data rather than lived experience. As Claude AI describes:
‘I don't have the embodied understanding that humans develop through physical interaction with the world.’
Freeing Researchers for Higher-Value Work
This limitation aside, AI technology excels at handling repetitive tasks including transcript processing, pattern recognition, and response clustering. This efficiency creates more space for researchers to focus on deeper analysis and meaningful connections.
Recognising Critical Limitations
There are significant limitations in current AI capabilities, particularly regarding emotional intelligence and cultural context and the one directional nature of the enquiry. AI outputs accepted without critical evaluation, especially under deadline pressure are a concern.
Strategic Implementation Critical
The most effective AI integration occurs when technology is used as a reflective partner that helps identify bias and validate conclusions rather than generating independent insights. This approach creates what one participant called "an inexhaustible thought partner" that enhances rather than replaces human expertise.
A Collaborative Future
Like with most professions, AI is firmly part of the future of research, and as it evolves it may well lead to a shrinking workforce. AI certainly has the potential to mean greater volume of research done by fewer humans, but the human touch to guide the interrogation and pitch the narrative appropriate to the audience will likely remain.
ENDS