The evolution of research: from human to AI

Research has always been a foundation of progress, driving innovation and expanding our understanding of the world and our customers. Traditionally, research has been labor-intensive and required countless hours of collecting, analyzing, and synthesizing data.

In the early days of research, human intellect was the only way to discover insights. Collecting data, performing calculations, and finding patterns were all done manually and based on human intuition and experience. This way of working limited the pace of progress and innovation.

With the rise of computers from major tech companies like Microsoft and Apple, researchers gained a much more efficient way of calculating. For the first time, they could perform complex calculations rapidly, analyze much larger datasets, and simulate models that extended the reach of human research.

What are AI research assistants?

AI research assistants are often described as tools that use machine learning, natural language processing, and other AI technologies to support researchers in different tasks. If we look in the dictionary we could define AI interviews as follows:

A person who assists someone or a device or product that provides assistance.

With that definition, we can define a research assistant as a person, device, or product that provides support during research. By adding AI, we move many of these tasks to AI tooling that helps perform work across the research process.

AI research assistants can process vast amounts of information at speeds not comparable to human researchers, identify patterns, and provide insights that might otherwise be missed.

The goal of using AI research assistants in your research

The main goal of AI research assistants is to make research easier, faster, and potentially less affected by human bias. Since much of the work is done by computers, AI assistants can handle large amounts of data very quickly and help researchers focus on decisions and interpretation.

We can see it as the middle ground between qualitative (detailed and descriptive) and quantitative research (numbers and statistics). The benefit of having AI research assistants is that they are highly efficient and provide major scale benefits.

A good example is using an AI research assistant to speak with respondents. Researchers, designers, and product managers can talk with many users at the same time, resulting in a larger sample, more insights, and higher confidence in findings.

How do AI research assistants work?

AI research assistants exist in many forms and can be used for different use-cases. How they work depends on the use-case and the type of work you want them to perform. To understand this better, it helps to understand how generative AI works.

We have written about AI research assistants and how artificial intelligence works on this page.

Benefits of AI research assistants

While humans remain essential in research and full automation is still far away, there are major benefits to using AI research assistants. We list a few below:

Speed & efficiency

With human interviews, the sample is often limited to a small number of personas, usually between 10 and 20 (depends on the number of personas). This is often due to limited project time and analysis effort. Since AI interviews can be conducted without human intervention, it becomes possible to scale to much larger numbers.

Accuracy

AI systems can reduce human error and bias in data analysis, leading to more reliable results.