One of the areas of my work which has been truly eye opening recently is the application of synthetic research (using AI generated data and personas to simulate real-world research scenarios) to explore ideas and open up new thinking.
Various pieces of research have shown how accurately AI engines can replicate the responses of human respondents to research. This particularly interesting study from Stanford, for example, replicated the personalities and behaviours of over 1,000 people using AI to conduct two-hour, voice-enabled interviews with the participants. Generative agents were able to replicate participants’ responses 85% as accurately as participants replicated their own answers.
But this isn’t a post about how synthetic research can replicate or replace the value of human research. It can’t. Quite apart from anything there’s huge value in the nuances, outliers, edge cases and under-represented viewpoints that can be found through human research. And as Mark Hadfield puts it, AI personas don’t live in the real ‘messy’ world. But it can be very complementary to traditional research and I‘ve found that it can be fantastically useful at exploring concepts, originating new ideas and opening up new possibilities (for more on how to use AI engines as a thought partner see last week’s post).
Synthetic personas may be customers, but they can also be employees, stakeholders, decision-makers, or any audience that may be useful in giving you an insightful perspective on your topic. I’m not a research expert so this is not a definitive guide, and it is focused on qualitative rather than quantitative (which is a whole other ball game). But this reflects how I (and teams I’ve worked with) have used these techniques to explore different ideas and scenarios. So to make this as useful as possible I’ve set out a typical workflow below.
Create a project space
You don’t have to use project spaces in synthetic research of course but the persistent context and memory is fantastically useful (more on using ChatGPT project spaces in strategy here, and you can also use Google Gemini Gems or Claude projects). It also allows you to set up multiple conversational threads which you can use to interview different personas or the same persona about different topics.
Remember to populate the source files with as much contextual information as you can (be careful about uploading sensitive information or data to open GPTs). For example, you can use the 4C’s: Customer (survey feedback, research data, review transcripts, social media reports, verbatim quotes etc); Company (client information, company reports, brand content); Category (sector insights, reports, trends, market maps); Culture (trends reports, cultural research or studies). Then take care to craft the project instructions (e.g. ‘Act as a world class research expert specialising in X, ensure your insights can inform outcome Y, if you don’t know the answer say that you are not sure and ask clarifying questions rather than make something up’).
Generate synthetic persona/s
Use as much relevant information as you have within your source material to give context. It can work well to do some human curation first (quotes, reviews, and survey nuggets) to bring some focus to shape how the GPT will construct the persona. You don’t need to necessarily do this but you can drop quotes, reviews, survey verbatims and trend snippets into a spreadsheet, tag these by source or topic, and use that – this helps give more context for the GPT to work from (as visualised below).
After the first pass at a persona you can get it to be more granular by asking about things like tensions, contradictions, decision-triggers, motivations or frustrations, which can help spark better ideas. Another good technique is to stress-test the persona using an out of scope question (like their favourite sport or how they would spend a free Saturday) to validate how accurate it has captured the audience.
Interview the persona/s
Use the GPT to generate simulated answers to your questions. You can get the GPT to suggest suitable questions that will help inform your objective (although be careful about confirmation bias). You can ask the GPT to do a ‘day-in-the-life’ walk through which can help surface hidden contexts, or ask questions like what they would show off about to reveal status drivers, or ask about frustrations with brands and then flip this as a way to reveal what good CX looks like, or ask it to imagine a future scenario where everything works in the way they want and describe what’s changed. You can even do things like ask it about weaknesses that competitors should exploit to steal customers as a way of exposing vulnerabilities.
Another good technique here is to use multi-angle interview loops. For example, use 3 or 4 different interview formats – in-depth interview , laddering (structured questioning technique that repeatedly asks the same question like ‘Why is that important to you?’ to climb from concrete product attributes to underlying personal values and beliefs), future back (where the persona imagines a point in the future and works backward to describe the steps, enablers, and barriers that would get them there). It can also be useful to do parallel interviewing – interviewing the persona tweaking the wording of each question each time, to add in variance, or creativity or different angles. You can then get the GPT to analyse and compare the different transcripts.
Thematic synthesis
You can simply ask the GPT to assimilate the key themes from your synthetic transcripts, and refer it back to the original persona inputs for thematic accuracy checking, but it can also be useful to ask it to list any unusual frustrations or unexpected ideas that showed up only once but which can inspire a fresh concept or feature. This surfaces the quirky, creative insights that often lead to different thinking.
Or you can put the draft theme list back into the GPT and ask it to act as a sceptical colleague to surface anything that might be missing or over-generalised. If you’ve interviewed the persona with multiple versions of similar questions you can ask it to highlight topics that at least three transcripts mention, and give one verbatim line for each. This can help identify themes that feel universal.
New perspectives, ideation and challenging your thinking
There are multiple ways in which synthetic personas can open up new thinking. You can ideate directly from the summarised themes, asking the GPT to use classic ideation techniques such as ‘What if?’ questions and scenarios, or something like SCAMPER (Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, and Reverse). I liked Ethan Mollick’s point about sequencing – coming up with your own ideas before prompting the AI helps avoid AI overly constraining your thinking in the same way that brainstorming individually before coming together as a group leads to a better quality and range of ideas. But you can use the AI to come up with a high quantity of different ideas against your brief, then use it to narrow down based on a set criteria.
A few other techniques which I’ve found useful:
Time-travel diaries
Similar to what I mentioned earlier it can work well to ask each synthetic persona to imagine a typical day five years in the future and narrate it in detail. This can surface hidden frictions, anxieties, or new possibilities.
Persona debates
Put two or three contrasting personas (for example one which is price-driven, one which is ethically-minded) in a group chat and have them argue which of your ideas or early-stage concepts best serves them. The friction can reveal trade-offs and areas which are compromises, sacrifices or must-haves.
Edge-case amplification
Deliberately create extreme or marginal personas (like the ultra-loyal superuser or the tech-skeptical laggard) and interview them as a way to challenge assumptions or generate new ideas. Or dial up a single attribute to the extreme and re-interview the persona to see where that leads.
Reverse-pitch workshops
Invite the persona to play the role of a strategist or innovator like you and ask it to complete your task (e.g. ‘design a product that my company should launch next year that would delight you’).
Cross-industry problem swaps
Clone your core persona, give each clone the mindset of a customer in a very different sector (for example, healthcare or gaming), and ask how they would solve your current challenge.
Use persona divergence to prototype early ideas. Present a rough concept to multiple personas (even if they’re all versions of the same type) and ask each for feedback. The divergence in reactions often helps you identify which idea elements are sticky, surprising, or misaligned—before you ever test with real users. Or present the existing problem statement to a different personas that may bring a different perspective to it, and then ask each persona to rewrite the problem ‘in their own words’ to help reframe it. Then you can generate ‘How might we…?’ questions from the most provocative reframed problems to flip them into solutions and ideas.
All of these approaches can act as a jumping off point for deeper human research, or prototyping and early stage testing. I’ll leave it there but suffice to say that I think synthetic personas are a hugely underrated way of using AI to open up new perspectives, ideas and thinking.
A version of this post appeared on my weekly Substack of AI and digital trends, and transformation insights. To join our community of over ten thousand subscribers you can sign up to that here.
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Photo by 3D illustrations on Unsplash
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