The Comfort of Coherent Stories — and What They’re Costing Our Products

The Moment That Made Me Pause

In a usability session a few weeks ago, I watched a participant breeze through a complex SaaS dashboard. She hit every task successfully. No errors. No visible friction. When I asked how it felt, she smiled politely and said, “It’s fine once you know where everything is.”

Then she added, almost as an aside, “I just don’t think I’d ever explain this to someone else.”

That sentence landed heavier than any metric we collected that day.

I’ve been sitting with it as I read the current wave of product conversations — about storytelling as UX strategy, about AI standing in for user research, about testing faster so we can ship with confidence. They’re thoughtful discussions. Well-intentioned. And yet, taken together, they reveal a quiet pattern: we are getting very good at making products feel coherent to us, while slowly losing touch with how coherence actually forms for the people using them.

The deeper question underneath all of this isn’t whether stories, AI tools, or testing frameworks are good or bad. It’s what kind of certainty they give us — and what kind they quietly take away.

When Storytelling Becomes Compression, Not Understanding

There’s a reason storytelling keeps showing up in UX conversations right now. Modern software is overwhelming. SaaS products, in particular, have become dense ecosystems of settings, permissions, edge cases, and workflows layered on top of one another.

A good story helps teams:

• Explain complexity without flattening it
• Align cross-functional decisions
• Create a sense of flow where there is otherwise sprawl

I’ve seen storytelling do powerful things inside teams. In one B2B platform I worked on, reframing the product around “from first signal to confident action” helped engineers and designers make better micro-decisions for months.

But here’s the risk I’m seeing more often lately:

Stories are turning into compression tools instead of understanding tools.

We compress the product into a clean narrative arc — onboarding, activation, value, retention — and that arc starts to feel like truth. Anything that doesn’t fit gets treated as noise.

When a story feels complete, curiosity often stops.

In research, this shows up subtly. We stop probing the moments that feel awkward or hard to narrate. We smooth over the parts users struggle to explain — because they don’t sound like a good story yet.

And yet, those are often the exact moments where the real cognitive and emotional work is happening.

Practical insight

If storytelling is part of your UX strategy, ask:

• What parts of the user experience are hardest to explain — for users, not for us?
• Where do people say “it’s fine” but hesitate to describe it?
• What moments consistently get edited out of internal narratives?

Those gaps aren’t flaws in the story. They’re signals that the story is incomplete.

Simulated Certainty: AI as Research, and the Risk of Believable Answers

I’ve been following the surge of posts about using large language models as stand-ins for user research — turning tools like Claude or ChatGPT into early feedback engines.

I understand the appeal. Early-stage teams are under pressure. Access to users is limited. AI can generate instant reactions, edge cases, and even articulate objections that sound uncannily human.

And to be clear: I use these tools myself. They’re useful for:

• Stress-testing assumptions
• Generating alternative mental models
• Surfacing blind spots in early concepts

But here’s the distinction that keeps bothering me:

AI gives us answers without vulnerability.

In real research, insight often arrives wrapped in discomfort:

• Long pauses
• Contradictory statements
• Participants changing their minds mid-sentence
• Emotional leakage that doesn’t map cleanly to logic

A 2024 Nielsen Norman Group study found that nearly 40% of meaningful usability insights came from moments participants initially described as “uncertain” or “hard to explain.” These are precisely the moments AI is least equipped to replicate — because they aren’t just about language. They’re about hesitation, social risk, and incomplete understanding.

AI feedback tends to be fluent. Coherent. Confident.

Believable answers are not the same thing as lived ones.

When teams rely too heavily on simulated research, I see a subtle shift: decisions get faster, but judgment gets thinner. We stop building tolerance for ambiguity — which is, ironically, one of the core muscles good research develops.

Practical insight

If you’re using AI in your research process:

• Treat outputs as hypotheses, not evidence
• Notice what isn’t showing up: hesitation, emotion, self-doubt
• Pair AI-generated insights with at least a few real conversations, even if informal

Speed is valuable. But unearned certainty is expensive.

UX Debt Isn’t About Messy Screens — It’s About Accumulated Explanations

The recent conversations around UX debt have been some of the most honest I’ve seen in a while. Framing experience issues as debt gives teams shared language — much like technical debt did for engineering.

But I want to offer a slightly different lens.

In my experience, UX debt isn’t just the accumulation of inconsistent patterns or outdated flows. It’s the accumulation of explanations users are forced to carry.

Every time a product requires someone to think:

• “Oh, this works differently here.”
• “I remember this because it broke once.”
• “You just have to know that this screen comes later.”

…we’re asking them to maintain a mental model that the system hasn’t earned.

Research backs this up. A Microsoft study on enterprise software found that users who relied on workarounds and remembered exceptions reported 23% lower trust in the system, even when task success rates were high.

This is why UX debt often doesn’t show up in usability metrics right away. People adapt. They compensate. They learn the story we didn’t fully design.

But over time, that quiet labor erodes confidence — and makes the product harder to recommend, teach, or defend.

If a product only works when someone can explain it, the explanation is part of the cost.

Practical insight

To surface UX debt earlier:

• Ask users how they would teach this product to a new colleague
• Listen for phrases like “you just have to remember…” or “once you get used to it…”
• Track not just errors, but explanations

Debt isn’t just what’s broken. It’s what users are silently carrying.

Testing, Flow, and the Myth of the Clean Journey

There’s renewed emphasis right now on beta testing, intuitive flow, and cognitive load — and rightly so. Testing with real users remains one of the most reliable ways to avoid building in the dark.

But here’s a pattern I’ve noticed across multiple teams:

We test for completion, not for confidence.

In one beta program I helped run for a workflow automation tool, 87% of participants completed the core setup successfully. On paper, that was a win.

But in follow-up interviews, nearly half said they wouldn’t feel comfortable making changes without checking with someone else first.

The flow worked. The trust didn’t.

Cognitive load isn’t just about how many steps exist. It’s about how much interpretation a user has to do at each step. Stories and clean journeys can mask this if we’re not careful.

A smooth path can still be fragile if people don’t know why it works.

Practical insight

When evaluating flows:

• Ask users what they think will happen before they click
• Ask what they’d be afraid to change
• Pay attention to where people slow down even if they don’t fail

Confidence is a UX outcome. We just don’t measure it often enough.

What All of This Is Pointing Toward

When I step back from these conversations — about storytelling, AI research, UX debt, testing — I don’t see fragmentation. I see a shared longing for control in an increasingly complex product landscape.

Stories give us coherence. AI gives us speed. Frameworks give us structure.

But none of them can replace the harder work: staying present with how understanding actually forms for another human being.

That participant who told me she’d never explain the product to someone else wasn’t criticizing the interface. She was revealing a truth about ownership. The product worked — but it didn’t belong to her.

As researchers and designers, our job isn’t just to reduce overwhelm. It’s to reduce the private labor people do to make our systems make sense.

When we do that well, users don’t just complete tasks. They carry the product with them — confidently, explainably, and without apology.

And that, more than any story we tell ourselves, is the signal that the work is holding.