We create a fictional customer. Give them a name, an age, a job, a family situation, a few interests and perhaps a favourite brand or two. Then we use that persona to guide our communication.
It has been useful. But it has also often become static.
The world changes. People change. Markets change. New competitors appear. And suddenly, the persona we created 18 months ago no longer tells us very much.
AI is opening up a new way of working with audiences, personas and customer insights.
Not by replacing real people or traditional research, but by making the process more continuous, exploratory and accessible.
Start with hypotheses, not assumptions
AI can help us get started quickly.
Before investing heavily in a large research project, we can use AI to explore a market, identify potential audience segments, map motivations and formulate hypotheses.
What might this audience care about?
What are their barriers?
What could make them choose one brand over another?
What language might resonate with them?
These are not answers. They are hypotheses.
And that distinction is important.
AI gives us a fast and cost-efficient way to create a starting point. We can develop early concepts, messages and creative territories, then see how they perform and use what we learn to move the work forward.
Instead of waiting for the perfect insight before creating, we can create, test and learn.
Invest in your own data
But there is an important next step.
Your own customer data is far more valuable than a generic AI-generated persona.
Interview people who represent your primary audience. Run a survey. Talk to customers, prospects or people who chose a competitor.
Ask them about their lives, needs, frustrations, motivations and the language they actually use.
Then feed that research into an AI tool designed to work with qualitative or quantitative data.
Suddenly, your AI persona is no longer based primarily on assumptions.
It is based on your audience, your market and your questions.
You can then use it as a strategic sparring partner.
Would this concept appeal to this audience?
What would make them sceptical?
Which of these two messages feels more relevant?
What would make this brand feel more trustworthy?
How would this audience describe this problem in their own words?
You can even A/B test early messaging or creative territories against different audience models.
It doesn’t replace testing with real people. But it can help you decide what is worth testing with real people in the first place.
From persona to living audience
This is where things get really interesting.
Instead of creating a persona once and putting it in a presentation, we can treat it as something that evolves.
Use AI to continuously monitor what is happening around your audience.
Are new competitors entering the market?
Are attitudes changing?
What cultural conversations are influencing your customers?
Are new technologies changing their expectations?
Has a new behaviour emerged that could affect your positioning?
AI can help us scan, summarise and connect these signals much faster than a traditional research process allows.
The persona becomes less like a static character on a PowerPoint slide and more like a living hypothesis about your audience.
So where do we come in?
This doesn’t make agencies less important.
In many ways, it makes our role more important.
AI can find patterns, generate hypotheses, simulate conversations and help us explore hundreds of directions.
But it doesn’t know which question is worth asking.
It doesn’t understand your business ambition, your brand history, your competitive reality or the cultural nuance behind a particular insight.
And it certainly shouldn’t decide what your brand should stand for.
That’s where human strategy and creativity come in.
We help define the right questions. We design the research. We challenge the AI’s assumptions. We connect data with culture, business and human behaviour. And we turn insights into positioning, concepts, experiences and communication that people actually want to engage with.
The future isn’t about choosing between AI and traditional customer research.
It’s about combining them.
Real people provide the foundation. AI helps us explore it. Humans turn what we learn into something meaningful.
And perhaps the biggest shift is this:
We no longer have to think of understanding our audience as a research project we do before creating.
We can make it a continuous part of how we create.
