And Start Designing for People Who Are Actually Under Pressure
Let’s start with a painfully simple exercise.
Male
Born in 1948
Raised in the UK
Married twice
Lives in a castle
Wealthy and famous
You’ve just described Prince Charles and Ozzy Osbourne.
Same demographics.
Same bullet points.
Wildly different humans.
If your immediate reaction is:
“Great, now let’s design a product experience for this person”
Please pause.
Close your persona template.
And take a long walk.
Because this is exactly how bad products are born.
Demographic Personas: The Corporate Comfort Blanket
Demographic personas exist because they are:
- Easy to create
- Easy to defend
- Easy to present
They make strategy feel scientific without being useful.
They usually sound like this:
“Ravi, 36, mid-level manager, urban, married, owns an iPhone, tech-friendly”
Wonderful.
Now try using that to answer even one real question:
- Why does Ravi delay decisions?
- What would make him override the system?
- When does he trust automation, and when does he fight it?
- What mistake would actually get him into trouble?
No answers.
But the slide looks great.
And that’s the real purpose, isn’t it?
People Don’t Behave Like Demographics
They Behave Like People Under Pressure
No one wakes up thinking:
“I am a 36-year-old professional in an urban setting.”
They wake up thinking:
- “Why is this still manual?”
- “Why do I have to cross-check everything myself?”
- “Why does this take so long every single time?”
- “Why am I accountable when the system is clearly broken?”
This is where most products fail — not because they lack features, but because they don’t understand stress.
And stress has nothing to do with age, gender, or location.
Why This Gets Worse with AI (Not Better)
AI didn’t magically fix bad thinking.
It just scaled it.
If your personas are shallow:
- Your recommendations will be generic
- Your automations will be unsafe
- Your insights will be ignored
- Your dashboards will be “interesting” and unused
AI trained on demographic assumptions doesn’t become intelligent.
It becomes confidently irrelevant.
A Different Way: AI Personas Built Around Reality
At Drylogics, when we design intelligent systems and automation layers, we deliberately avoid demographic personas.
Instead, we model people the way reality presents them:
as problem-holders with consequences.
Our AI personas are not polite.
They are practical.
The AI Persona Framework (Used in Real Systems)
1. Pain Persona
What recurring problem makes this person dread Mondays?
Examples:
- Rework
- Delays
- Broken handoffs
- Inconsistent data
- Endless follow-ups
If there’s no recurring pain, the system does nothing.
No fake alerts.
No noise pretending to be insight.
2. Fear Persona
What failure would damage this person’s credibility?
Examples:
- Missing targets
- Public mistakes
- Losing stakeholder trust
- Making the wrong tech bet
This determines how aggressively the system automates vs assists.
Because not everyone wants speed.
Some want protection.
3. Control Persona
How much autonomy are they willing to give up?
Examples:
- High control → visibility, approvals, checkpoints
- Low control → defaults, automation, AI-first actions
Same role.
Same team.
Completely different tolerance for risk.
4. Decision Persona
Who actually owns the outcome — and who absorbs blame?
Most tools treat everyone as equal users.
Reality doesn’t.
Good systems understand:
- Who decides
- Who executes
- Who gets blamed when things go wrong
And they adapt behavior accordingly.
5. Outcome Persona
What does “this worked” actually mean to them?
Not charts.
Not dashboards.
Not buzzwords.
Real outcomes:
- Time saved
- Fewer escalations
- Predictability
- Reduced dependency on memory and heroics
If the system can’t measure this, it doesn’t claim success.
Why This Feels Uncomfortable (And Should)
Because it forces teams to admit something inconvenient:
Most personas exist to make stakeholders comfortable —
not to make products effective.
They avoid:
- Fear
- Accountability
- Consequences
- Failure modes
But these are exactly the things that drive real behavior.
Ignoring them doesn’t make them go away.
It just guarantees your product won’t be trusted.
The Cost of Getting This Wrong
When personas are wrong:
- Features go unused
- Automation is overridden
- AI recommendations are ignored
- Adoption stalls
- Blame shifts to “change management”
But the root cause is simpler:
the system never understood the human in the first place.
Final Thought (This Is the Part That Matters)
Two people with identical demographics
can behave nothing alike.
Two people with nothing in common on paper
can be solving the exact same problem.
Designing for demographics creates average systems.
Designing for pressure creates useful ones.
At Drylogics, We Don’t Design for “Users”
We design for:
- People under pressure
- People with accountability
- People who don’t get a retry if the system fails
Good software doesn’t care who you are.
It cares:
- What’s broken
- What’s at stake
- And how quickly relief is needed
Everything else is demographic noise.
If This Resonates
If you’ve ever:
- Rolled your eyes at personas that felt pointless
- Built systems people “should” use but don’t
- Watched AI get ignored because it felt unsafe
- Felt the gap between strategy slides and real behavior
Then we should probably talk.
Reach out.
You know where to find us.