Personas That Work

I’ve been spending a fair bit of time with personas recently, and it reminded me why they get such mixed reactions. Some people love them, some think they’re a waste of time. Honestly, both camps are right — it just depends on how they’re done.

When they’re helpful, personas cut through the noise and help teams design with real users in mind. When they’re not, they turn into glossy posters that never get mentioned again. Here’s where I think the difference lies.

• They humanise research into something memorable.
• They shift the lens from us to them, so decisions aren’t just personal opinion.
• They focus priorities — you can’t design for everyone at once.
• They provide teams with a shared reference point, thereby speeding up alignment.

• Speculative personas → built on assumptions, not research.
• Demographics overload → age or hobbies rarely drive behaviour.
• Too shallow → profiles without context or scenarios.
• Static artifacts → PDFs that never resurface in real work.

The fix is simple: keep them grounded in real goals and jobs to be done (more on this another time). That’s often the most valuable part of a persona, it cuts past fluff and gets to why someone actually uses your product.

I’ve always seen personas as more than a design thing. When you frame them around real jobs to be done, they give every part of the business a clearer line to the customer.

Marketing gets sharper because it’s working off real motivations, not just audience segments. Sales can pitch in a way that makes sense to different decision-makers. Engineering finally gets the “why” behind a feature, not just a ticket in Jira. Support teams can spot common frustrations before they escalate. And leadership stays grounded in customer reality instead of running on assumptions.

One thing I've been exploring is how AI can speed up the grunt work of personas. It's not a replacement for research, but it's surprisingly effective as a low-cost way to explore ideas early.

A few ways I’ve used it:

• Role-play testing: I'll tell the AI “You’re a small law firm partner. Your job is to manage the practice while also bringing in new clients.” Then I’ll walk it through an early product concept and see what questions or objections it raises. It’s not perfect, but it helps pressure-test assumptions.
• Jobs to be done framing: By feeding the AI a rough description of a customer job — e.g. "reconcile the firm’s trust account at month end so the books are accurate” — it can generate scenarios, pain points, and edge cases I might not have listed yet.
• Quick feedback loops: Before I have a polished prototype, I’ll describe a flow to the AI and ask it to respond in character as the persona. It’s a way to catch friction points or missing context before showing it to real users.
• Synthesis at speed: Drop in raw notes from interviews, chat logs, or support tickets, and ask AI to cluster patterns by goals, blockers, and motivations. It won’t replace deep analysis, but it creates a head start I can refine.

The key is treating this as an experiment. The output is never the final persona or research insight — it’s a starting point, a way to make early design thinking more tangible and testable. I'll always go back and validate with real users.

The important thing about personas is that they only have value if they’re used. They should be refreshed as your understanding of users evolves, and they should show up in the conversations where real decisions are made — product planning, marketing campaigns, sales strategies.

The real value for me is when personas stop being a design artifact and start being a tool everyone leans on and contributes towards. So with that said:

Designers, use it to build empathetic experiences.

Engineers, use it to understand the context behind what’s being built.

Marketers, use it to sharpen messaging and timing.

Sales, use it to tailor pitches.

Leadership, use it to stay grounded in customer reality.

Get this right, and your personas will stop being a theatre exercise; they will start doing real work.