Whether you lead an insights team, drive marketing strategy, or steer innovation, you are likely feeling the pressure to conduct research faster and cheaper. 

Synthetic respondents (aka, AI models programmed to answer survey questions as if they were target customers), are being offered up as the solution.  

Some vendors are claiming they can replace 15 to 85 percent of the real people taking your surveys with synthetic respondents, promising faster and cheaper insights. However, this framing is aggressive and ahead of what the evidence supports.     

At the same time, some serious researchers argue that there is no theoretical reason for LLM respondents to be valid as samples.    

So, what is the evidence for using synthetic respondents in research? 

We spent a year running pilots, reviewing the academic research, and putting synthetic respondents head-to-head with humans to see exactly where AI models help, and where they do not belong.     

Our entire playbook for using synthetic respondents is now available.    

In it you’ll find: 

• A brief history of using data modeling in research  
• The risks of using AI models as survey respondents 
• How LLMs create contamination and bias in survey data 
• Five strategic research tasks where the evidence supports using AI survey takers
• Four key areas where simulated data does not belong
• Seven critical questions to ask any vendor before deploying AI 
• Five core principles to apply to any study using synthetic respondents 

This playbook offers business leaders a practical way to evaluate synthetic respondents before putting their research (and their decisions) at risk. It will also help you ensure that study results are valid and useful for decision making.    

Download your free copy.