The Bull And Bear Case For Digital Design In The Age Of AI

 

As AI reshapes product design, it could give designers greater autonomy or expose the gaps that autonomy makes harder to hide. Exploring both the bull and bear cases, e examines what happens when designers need less permission to act.

Designers have spent years saying they would do better work if the organisation got out of the way. Not always in those exact words, obviously. It usually comes out as something more reasonable: we didn’t get enough engineering time, product had already decided the solution, the roadmap was too packed, leadership only cared about this quarter’s numbers, research got cut, the experiment was never run properly, the design debt was known about, but nobody wanted to spend a sprint fixing it.

Much of this is true. Most designers have worked inside that awkward middle space between product and engineering. Product frames the problem, or at least thinks it does. Engineering decides what is feasible, or at least what is affordable. Design is expected to make the thing clearer, simpler, more coherent, more usable, and occasionally more desirable, while also being careful not to disrupt the plan too much.

That position has always been uncomfortable.

Designers are told to think strategically, but often lack the power to act strategically.

They can spot the broken onboarding flow, the confusing upgrade path, the empty state that makes users feel stupid, the feature that looks reasonable in a product review but makes no sense in real use. Seeing the problem is one thing. Getting it fixed is another.

So design often becomes an argument. You make the case. You annotate the flow. You bring the research clip. You point to the support tickets. You show the Figma prototype. You explain why the “small edge case” is actually the first-run experience for half your new users. Then everyone nods, agrees it matters, and moves on to whatever had already made it onto the roadmap.

This is one reason AI is more interesting for design than the usual “will it replace designers?” debate suggests. The real change is not that designers can make more screens. Nobody needs more screens. The interesting change is that designers may need less permission.

The Bull Case: Designers Need Less Permission 

A good designer can now move from “we should fix this” to “I fixed this, and pushed it live.” They can prototype the alternative onboarding flow, write and test clearer product copy, build a rough working version of the interaction, clean up small pieces of design debt without waiting three months for a roadmap slot, and make the better thing visible enough that it becomes harder to ignore.

That changes the politics of the work. Design has often relied on persuasion because designers lacked direct means of production. AI weakens that dependency. Not everywhere, and not for everything. Complex products still have architecture, infrastructure, data models, permissions, security, compliance, legacy systems, and all the other unglamorous reasons software is hard. But the boundary is moving.

More of the gap between having the idea and making the idea real can now be crossed by a motivated designer with the right tools.

In this version of the future, designers become less permission-dependent: less reliant on product to bless the problem, less reliant on engineering to make every small improvement real, less trapped in the role of internal critic, taste-provider, or Figma operator. More able to make, test, repair, and ship.

The best designers start to look less like traditional product designers and more like hybrid product leaders. They still care about interaction, hierarchy, language, flow, brand and craft, but they also understand the commercial shape of the problem. They can make trade-offs. They can prototype in code, or close enough to code. They can use AI to explore options quickly, then use judgment to throw most of them away. They can sit with a founder or PM and move from a vague product concern to something tangible by the end of the day.

There may be fewer of these people, but they will be harder to ignore. The current design-org model was partly built around scarcity: scarce engineering time, slow production, expensive prototypes, handoffs between specialists, heavy coordination across teams. If AI reduces some of that scarcity, it probably reduces the need for some of the roles that grew around it. The optimistic case is not that every designer keeps their job and gets a productivity boost. That feels like wishful thinking. The more believable version is that the total number of designers goes down, but the designers who remain have more direct influence over the product.

That is not a bad outcome for the strongest designers. It may even be the thing many of them have wanted for years.

The Bear Case: Autonomy Exposes The Gaps

Autonomy has teeth. If AI gives designers more room to act, it also removes some of the cover. The same constraints that held good designers back have also protected weaker ones from being tested too directly.

For years, it has been easy to say: I had a better idea, but we never got the engineering time. Sometimes that was exactly what happened. Sometimes the better idea was never really more than a critique. It had not been made concrete. It had not been tested. It had not dealt with the awkward trade-offs. It sounded strong because it lived safely in opposition to the shipped thing.

A lot of designers are good at noticing what is wrong. Fewer are good at deciding what should happen instead. Fewer still can make that alternative real enough for other people to judge. AI will expose this gap.

If you can prototype the recommendation, the recommendation has to get better. If you can make the alternative flow, the flow has to survive contact with details. If you can test the product copy, you have to care what happens when users read it. If you can fix the small piece of design debt, you have to decide whether it was really worth fixing.

Some designers are not as strategic as they think they are. They have learned the language of strategy without the discomfort of owning outcomes. They can talk about user needs, business goals, systems thinking, and product quality, but struggle when asked to make a call. They want influence, but not the exposure that comes with it.

The profession has spent a long time arguing that design deserves more power. Fine. But more power means fewer excuses. It means the work is judged less by the elegance of the argument and more by the quality of the thing you made, tested, or changed. That is a better standard, but it will not be kind to everyone.

There is a second bear case, and it is probably the one large design teams should worry about most. Product and engineering already have more institutional power than design in most companies. They own the roadmap, the technical architecture, the sprint machinery, the metrics, and usually the language leadership understands. Design often has to translate its concerns into someone else’s terms before they count.

AI may not rebalance that power. It may hand product and engineering enough design capability to make design easier to bypass. A PM who can generate a decent flow, decent copy, and a decent prototype may not feel the same need to involve design early. An engineer who can use AI to produce a reasonable interface may decide the design system covers enough of the decision-making. A founder who can get to a polished demo in an afternoon may confuse polish with product thinking.

The problem is not that these people will suddenly become great designers. The problem is that many companies do not know the difference between great design and plausible design. Plausible design is dangerous. It looks coherent in a product review. It uses the right components. The spacing is fine. The copy is not embarrassing. The flow mostly works. Nobody in the meeting feels strongly enough to object. So it ships.

A lot of bad product decisions already survive because they look plausible. AI will produce more of them. This is where design could lose ground quickly: not because taste, judgment, research, and interaction thinking stop mattering, but because the visible outputs of design become easier for other functions to imitate.

If a company already thinks design is mostly screens, prototypes, and polish, AI gives it a cheaper way to get those things.

In that world, design does not gain more agency. It gets narrowed. The remaining designers manage the design system, police component usage, review flows that have already been decided, tidy the interface, maintain brand consistency, and get pulled into high-stakes launches, executive demos, and the occasional messy cross-platform problem. Useful work, but a smaller surface area. Less shaping the product, more maintaining the furniture.

This is why the “AI will automate the boring 20%” argument feels too comforting. In some companies, perhaps that is what happens. But in large tech organisations, where design teams grew around coordination, production and process, the cut could be much deeper. Not 20%. Maybe 50%. Maybe more. Especially in places whe

re leadership never really understood why the design team had grown so large in the first place.

 

Where I Think We Might End Up

The painful part is that both futures can be true at the same time. AI can make the best designers more capable and many average designers less necessary. It can give design more agency while reducing design headcount. It can help a small number of designers move closer to product leadership while pushing others into governance and clean-up work. It can free designers from waiting for permission, then reveal that some were more comfortable waiting than acting.

The designers who do well will not be the ones who merely use AI to produce more options. Options are cheap now. They will be the ones who know which option is worth pursuing, why it matters, how to test it, what to cut, where the product is lying to itself, and when “good enough” is quietly damaging the business.

“

They will have taste, but taste will not be enough. They will need product judgment, technical curiosity, commercial awareness and the nerve to make decisions before every variable is settled. They will need to be comfortable moving between a customer conversation, a prototype, a pricing concern, a brand question, and a messy implementation detail without insisting that all of those belong to someone else.

I’m not completely sure where we end up. I hope it is closer to the bull case: fewer permission structures, more making, more agency, better designers finally able to show what they can do without being held back by the machinery around them.

I fear it may be closer to the bear case: product and engineering absorb much of the work, companies decide plausible design is good enough, and design loses status, headcount, and strategic ground.

In reality, it will probably be some uncomfortable mix of the two. Some designers will use AI to gain more agency. Some companies will use it to need fewer designers. Some teams will produce better work because the distance between judgment and execution gets shorter. Others will ship more plausible mediocrity because nobody in the room can tell the difference.

For years, designers have said they could create more value if they were less constrained by the organisation around them. AI is about to test that claim. Some will finally get to prove it. Some will find out the constraints were doing them a favour.

Further Resources

• “Good from Afar, But Far from Good: AI Prototyping in Real Design Contexts,” Huei-Hsin Wang and Megan Brown (NN/Group)
  The UX design field has been flooded with AI-powered prototyping tools that generate interfaces from natural-language prompts. Despite the huge marketing hype, an evaluation with real design scenarios revealed that while these tools can follow instructions to achieve a general goal, they often lack the sophistication to weigh design tradeoffs and to produce thoughtful, high-quality designs without extensive guidance from humans.
• “AI Design Tools Are Marginally Better: Status Update,” Megan Brown, Caleb Sponheim and Taylor Dykes (NN/Group)
  AI-powered design tools have improved, yet we’re still nowhere near the usefulness we’ve been promised. This article reviews several AI tools and features, including: Figma’s Rename Layers, Rewrite This, Find More Like; Khroma Color; and Midjourney. The authors also take a look at the wireframe and prototype generation capabilities of some AI tools.
• “Using AI for UX Work: Study Guide,” Tanner Kohler (NN/Group)
  Unsure where to start? This curated collection of links to articles and videos about the best ways to introduce artificial intelligence for UX design work should help you.
• “I used AI for every task for two weeks,” Joanna Otmianowska (DEV Community)
  The author (who is a front-end developer) tried to use Claude Code for every task at work. This turned into a full-on experiment. In the article, Joanna shares all the details about the experience.
• “How AI will Affect the Design Industry,” Andy Budd
  It is likely that AI is not going to “kill design” in the next few years, as some are claiming. However, these are definitely times of change, and change means that there will be big opportunities for those who embrace new technologies early.
• “Design has been too settled for too long,” Andy Budd
  For a discipline that talks so much about change, design has been running on a surprisingly settled operating model. AI is starting to break that model. In this article, Andy reviews in detail the current trends regarding adopting AI in the daily workflows of design teams.
• “What Designers Should Take From Benedict Evans’ Latest AI Deck,” Andy Budd
  Benedict Evans has a useful habit of standing slightly away from the noise. For years, his big strategy decks have acted as a kind of weather map for the technology industry: mobile, media, ecommerce, platforms, regulation, capital flows, and now AI. They are not predictions in the cheap sense — they are attempts to show the shape of the system: where the money is going, what assumptions people are making, which comparisons are lazy, and where the industry may be fooling itself.
• design + AI conference

Back to School Trends: What Sold in 2025

 Back-to-school 2025: school uniforms up 231% MoM and backpacks up 180%, per Shopify data. See top categories by region and what's trending down.

Starting each July, families across the US flood stores—online and offline—to gear up for a new school year. This year, Shopify sales data shows which back-to-school categories are most popular. Explore which trending items saw the biggest jumps, which categories lagged behind, and what the differences across regions might reveal about changing school culture.
Back-to-school sales trends: All US
Product category MoM sales* School uniforms +231% School backpacks +180% Lunch boxes & totes +174% Bulletin boards +81% Stickers & sticker machines +80% Calculators +56% Folders +34% Pencils +27% Water bottles +24% Planners +19% Hand sanitizers & wipes +19% Notebooks & notepads +17% Art & craft paper +15% Laptops +11% Sticky notes +11% Binders +11% Pens +8% Sports uniforms +7% Book covers -1% Markers & highlighters -8%

*Shopify sales data comparing July 2025 to June 2025.

The back-to-school categories that saw the greatest spend in July were backpacks, sports uniforms, water bottles, lunch boxes and totes, pens, and notebooks. Categories with the most growth compared to the previous month were school uniforms (231%), backpacks (180%), and lunch boxes and totes (174%).

School uniforms, while a smaller category in total sales, posted the most dramatic month-over-month growth at +230.6%. This aligns with broader market trends: National Center for Education Statistics data shows that 18.8% of public schools required uniforms in 2019–2020, with requirements more common at the elementary level (21%) than in high schools (12%). Recent projections anticipate the school uniform market to reach a 6.1% compound annual growth rate by 2031, signaling stable, long-term interest.

Especially in uniformed environments, backpacks and lunch boxes play a key role beyond function in student identity—often serving as one of the few forms of self-expression. Water bottles, similarly, have become key accessories where students can showcase personal style—a small but important outlet for identity.

While water bottles, notebooks, and pens were among the most popular spending categories, month-over-month increases were more moderate—24%, 17%, and 8%, respectively—signaling the popularity of these purchases beyond the back-to-school season.

The only back-to-school items that saw month-over-month sales decline were markers and highlighters (8%), and book covers (1%). This decline could indicate that they aren’t requirements on school supply lists, or it could reflect increasing reliance on digital tools, like e-readers and styluses, particularly in higher grade levels.


The top-selling back-to-school item in the Midwest was sports uniforms, followed by water bottles, backpacks, pens, lunch boxes and totes, and notebooks. Stickers and sticker machines saw a more significant month-over-month sales increase compared to other regions, increasing by 174%. Calculator sales declined compared to the previous month, with an 11% drop—the only region to see a MoM decline.
Northeast

The top-selling back-to-school item in the Northeast was sports uniforms, followed by school backpacks, water bottles, pens, and lunch boxes and totes. Sports uniforms saw the strongest MoM growth in the Northeast (25%), while bulletin boards had the smallest MoM growth compared to other regions—24% compared to 104% in the Midwest, 86% in the South, and 49% in the West Coast.
South

The top-selling back-to-school item in the South was backpacks, followed by lunch boxes and totes, water bottles, sports uniforms, pens, and notebooks. The South recorded the strongest MoM growth in water bottle sales (35%), as well as calculator sales (124%), compared to other regions.
West Coast

The top-selling back-to-school item in the West Coast was water bottles, followed by sports uniforms, backpacks, pens, lunch boxes and totes, and notebooks. Even though it accounted for the second-greatest overall sales, sports uniforms sales declined MoM by 14%, perhaps indicating that purchasing starts even earlier compared to other regions.
Reference: https://www.shopify.com/blog/back-to-school-trendsBack To School Trends FAQ
When do most back-to-school shoppers begin buying?

Most families now start well before the traditional August rush. 

The Top 25% Isn't a Ranking. It's a Definition.

 

The idea that anyone can be in the Top 25% is not some crazy AI-inspired new math.

Just ask any hiring manager what someone’s needs to do to be considered in their Top 25%, and you won't get a percentile. Nobody answers, "outperforms three-quarters of their peers."

Here’s one response.

A top 25% performer consistently delivers results that exceed expectations, not just meets them. They don't need constant direction. They solve problems before they become crises. They make everyone around them more effective, and if I lost them, replacing them would be difficult.

It turns out the Top 25% is a floor, not a ceiling. And when viewed that way, it's a floor most people can reach in the right job, with the right manager, and in the right situation.

Todd Rose, who wrote the End of Average, agrees. In his book he tells a story about how the Air Force had a problem in the late 1940’s: its planes kept crashing because the seats were designed to fit the "average pilot" based on measurements of thousands of airmen. But after measuring 4,063 pilots on ten body dimensions not one single pilot was average on all ten. The fix wasn't better pilots – it was adjustable seats.

Rose’s conclusion for hiring: there is no average person. People are jagged – exceptional on some dimensions, ordinary on others – and their performance is not a fixed property they carry from job to job. It is created, or destroyed, by context: the actual work, the manager, the team, the pace, the stretch.

So while what follows is a reasonable definition of the Top 25%, finding people who fit this “adjustable seat” requires a different way of thinking. “Moneyball for HR!” is the solution.

Ability in relationship to fit drives motivation to excel. Defining the fit factors is the key for better hiring decisions.

One Reasonable Definition of the Top 25%

1. Delivers exceptional results. Regularly exceeds the performance objectives of the role. Produces higher quality work with fewer mistakes. Can be trusted with the toughest assignments.
2. Learns faster and adapts quickly. Masters new tools, products, and processes quickly. Adapts when priorities change. Doesn't need to be told twice.
3. Takes ownership. Acts like the business is theirs. Solves problems instead of escalating every issue. Makes decisions with good judgment.
4. Makes the team stronger. Helps others succeed. Shares knowledge. Raises the performance of the entire group.
5. Improves the business. Finds better ways to do things. Eliminates waste. Creates measurable improvements in productivity, quality, customer satisfaction, or profitability.
6. Can handle bigger challenges. Performs well beyond today's job description. Is promotiable.

Define the Job and Situation Before Defining the Person

Finding people who meet this standard of excellence starts by defining the actual job, the actual situation, the actual resources, the actual team, the true corporate culture, the actual company politics, the intensity of the situation and the hiring manager’s leadership style.

And the process must be bi-directional. All of this must be revealed pre-hire if you want to achieve Top 25% performance post-hire. Given this, you need to source, screen, recruit, hire and manage people who fit this true description of the job. This used to be really hard, pre-AI. Now it’s not.

In fact, we’ve built “The Perfect Hiring System to Hire the Top 25%” and we’re giving it away as a Claude skill.

It all starts with this simple question when opening a new job requisition, “What does the person in this job need to do to be considered a Top 25% person?”

The Perfect Hiring System changes the entire hiring ecosystem at less cost with better results.

In turns out top 25% performers aren't defined by what they know. They're defined by what they accomplish and how they accomplish it.

---

Back to School Trends: What Sold in 2025

 Back-to-school 2025: school uniforms up 231% MoM and backpacks up 180%, per Shopify data. See top categories by region and what's trending down.
A backpack in a circle on a blue panel on a light blue background.Starting each July, families across the US flood stores—online and offline—to gear up for a new school year. This year, Shopify sales data shows which back-to-school categories are most popular. Explore which trending items saw the biggest jumps, which categories lagged behind, and what the differences across regions might reveal about changing school culture.
Back-to-school sales trends: All US
Product category MoM sales* School uniforms +231% School backpacks +180% Lunch boxes & totes +174% Bulletin boards +81% Stickers & sticker machines +80% Calculators +56% Folders +34% Pencils +27% Water bottles +24% Planners +19% Hand sanitizers & wipes +19% Notebooks & notepads +17% Art & craft paper +15% Laptops +11% Sticky notes +11% Binders +11% Pens +8% Sports uniforms +7% Book covers -1% Markers & highlighters -8%

*Shopify sales data comparing July 2025 to June 2025.

The back-to-school categories that saw the greatest spend in July were backpacks, sports uniforms, water bottles, lunch boxes and totes, pens, and notebooks. Categories with the most growth compared to the previous month were school uniforms (231%), backpacks (180%), and lunch boxes and totes (174%).

School uniforms, while a smaller category in total sales, posted the most dramatic month-over-month growth at +230.6%. This aligns with broader market trends: National Center for Education Statistics data shows that 18.8% of public schools required uniforms in 2019–2020, with requirements more common at the elementary level (21%) than in high schools (12%). Recent projections anticipate the school uniform market to reach a 6.1% compound annual growth rate by 2031, signaling stable, long-term interest.

Especially in uniformed environments, backpacks and lunch boxes play a key role beyond function in student identity—often serving as one of the few forms of self-expression. Water bottles, similarly, have become key accessories where students can showcase personal style—a small but important outlet for identity.

While water bottles, notebooks, and pens were among the most popular spending categories, month-over-month increases were more moderate—24%, 17%, and 8%, respectively—signaling the popularity of these purchases beyond the back-to-school season.

The only back-to-school items that saw month-over-month sales decline were markers and highlighters (8%), and book covers (1%). This decline could indicate that they aren’t requirements on school supply lists, or it could reflect increasing reliance on digital tools, like e-readers and styluses, particularly in higher grade levels.

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Back-to-school sales trends by region
Midwest

The top-selling back-to-school item in the Midwest was sports uniforms, followed by water bottles, backpacks, pens, lunch boxes and totes, and notebooks. Stickers and sticker machines saw a more significant month-over-month sales increase compared to other regions, increasing by 174%. Calculator sales declined compared to the previous month, with an 11% drop—the only region to see a MoM decline.
Northeast

The top-selling back-to-school item in the Northeast was sports uniforms, followed by school backpacks, water bottles, pens, and lunch boxes and totes. Sports uniforms saw the strongest MoM growth in the Northeast (25%), while bulletin boards had the smallest MoM growth compared to other regions—24% compared to 104% in the Midwest, 86% in the South, and 49% in the West Coast.
South

The top-selling back-to-school item in the South was backpacks, followed by lunch boxes and totes, water bottles, sports uniforms, pens, and notebooks. The South recorded the strongest MoM growth in water bottle sales (35%), as well as calculator sales (124%), compared to other regions.
West Coast

The top-selling back-to-school item in the West Coast was water bottles, followed by sports uniforms, backpacks, pens, lunch boxes and totes, and notebooks. Even though it accounted for the second-greatest overall sales, sports uniforms sales declined MoM by 14%, perhaps indicating that purchasing starts even earlier compared to other regions.

Designing With Uncertainty: How AI Supercharges Probabilistic Thinking

 

In a world where AI is informing more design choices, it’s easy to mistake predictions for certainties. This article introduces Probabilistic Design, a mindset that allows UX and product teams to accept uncertainty, decipher AI outputs with nuance, and make smart, adaptive decisions.

In 2024, an Air Canada customer asked a chatbot about bereavement fares. The bot confidently gave him a refund policy that didn’t exist. The airline refused to honor it. A tribunal ruled in the customer’s favor. The bot hadn’t decided anything; it had predicted an answer based on patterns in its training data. The company treated that prediction as policy.

This is the risk at the heart of designing with AI today: probabilistic systems wrapped in deterministic interfaces. The AI offers a guess, the interface presents it as truth, and the user, or the organization, acts on it.

Humans are wired for deterministic thinking. We prefer to believe that past actions determine future outcomes. Flip a coin 999 times and get heads every time, the deterministic mind assumes the coin is rigged. The probabilistic mind accepts that the 1000th flip could still go either way. That second mindset is harder to hold onto, but it is exactly what designers need right now.

Products operate in complex, nonlinear environments, and AI is accelerating that complexity. When designers and product teams treat AI outputs as the answer rather than one of many possible answers, they build fragile experiences, and in some cases, like medical diagnostics or financial forecasting, genuinely dangerous ones.

This article is a practical guide to designing probabilistically with AI as a partner. It is about using AI to sharpen your thinking rather than outsource it, accounting for model bias, human sentiment, and perceived risk along the way.

Probabilistic Thinking + AI

Most questions we ask AI do not produce binary answers. They produce probabilities based on patterns in data. If you ask, “Do aliens exist?” the answer will be somewhere between plausible and uncertain. Scientists consider life elsewhere in the universe likely, but without any concrete evidence, we cannot confirm it. The answer doesn’t resolve the question; it frames it as a probability.

Designers should read AI outputs the same way. They are signals, not conclusions, possible outcomes that have to be interpreted within the context of product goals, user behavior, and business constraints.

Many digital products already work this way. Netflix doesn’t know you’ll enjoy Superstore because you watched The Office; it estimates the probability and surfaces the title accordingly. The interface is responding to a prediction.

Design decisions can follow the same logic. AI models can combine behavioral analytics with research insights to estimate the likelihood of certain outcomes, and those probabilities can act as a yardstick for design strategy. Consider a scenario where analytics suggest a 60% versus 90% confidence that users will complete a purchase. At 60%, the design has to do more persuasive work, testimonials, explanations, comparisons, and reassurance signals may help the user move toward a decision. At 90%, the user is already motivated, and the design should start removing friction so the action can happen quickly. Same screen, very different design problem.

Note: This is an oversimplification of the idea. Please be mindful of the intricate details of your product.

AI can also simulate outcomes using historical data and behavioral models before you commit to a direction. The value of those simulations depends heavily on how prompts are structured, the context they define, the hypothesis being tested, user motivation, and the edge cases you want stressed.

I can think of one such practical use: evaluating early designs through structured prompts, especially when you don’t have direct access to the user group you’re designing for. The prompt below is a starting point for evaluating a design from the perspective of neurodivergent users as well. Treat it as a template, adapt the user group, criteria, and output format to your product, and use it as a conversation starter with your team rather than a verdict.

Evaluate the [design file or weblink] for usability, accessibility, and content relevance from the perspective of neurodivergent users such as those with autism spectrum disorder, ADHD, learning disabilities, etc.
Please consider the following criteria:

1. Is the layout and navigation intuitive for neurodivergent users?
2. Is the language and content appropriate and engaging for neurodivergent users?
3. Are there any barriers (technical, cognitive, or sensory) that this group might face when using the site?
4. How well does the site meet the specific needs or goals of neurodivergent users?

Provide a SWOT analysis, probability score for successful use by neurodivergent users, and any recommendations for improvement.

Note: This is an oversimplification of the idea. Please be mindful of the intricate details of your product and make any appropriate changes.

That said, simulations do not replace experimentation. Because models are trained on historical data, they reflect past behavior more strongly than they predict future change. Imagine designing a voice interface for elderly users who struggle with touchscreens. A model trained on mobile interaction data might predict low engagement, not because the idea lacks value, but because the dataset reflects different user behavior. Simulations should always surface assumptions, not prevent innovation.

Be Cautious of Skewed Probabilistic Thinking Using AI

AI systems are built on historical data, more specifically, on the datasets they are trained on. That foundation shapes the outputs we receive. During the AI Summit in France, India’s Prime Minister Narendra Modi shared an example that illustrates this well. If you ask an AI model to generate an image of a person writing with the left hand, the output may still show a person writing with their right hand. The reason is statistical: most people are right-handed, and the training data reflects that. This may have improved over time, but the point remains relevant. I still occasionally see this behavior when generating images with similar models.

What you receive is not truth. It is the most statistically likely outcome given the data available. Always ask whether past data meaningfully predicts future behavior. If additional context can improve the prediction, include it. Without context, the output is just one of many possible answers dressed up as the only one.

(Large preview)

Confidence scores deserve the same scrutiny. Overtrusting a high-confidence output leads to the Air Canada situation. Dismissing a low-confidence one can cause teams to miss a real signal buried in noisy data. A prediction with 90% confidence is not necessarily correct, and a 40% signal is not necessarily useless. Designers must still weigh the possibilities, consider the case in front of them, and bring judgment to what the AI recommends.

Transparency is how you make that possible. As AI systems increasingly shape decisions, people need visibility into how outputs are generated, the sources, the reasoning, and the summaries behind a recommendation. Black-box systems breed distrust. Systems that reveal their reasoning let users evaluate outputs for themselves. That transparency is good design and ethical practice. It respects the trust people place in these tools.

Thinking in probabilities often means resisting the temptation of quick answers. AI can accelerate research and surface patterns faster than ever before, but those outputs are starting points, not final decisions.

Practice Probabilistic Design with AI

Design shapes how a product is ultimately experienced — the decisions designers make determine whether the experience feels adequate, intuitive, or exceptional. And design is inherently full of assumptions and bets. Even the most rigorous research can yield multiple valid solutions to the same problem, each carrying a different probability of success.

Thinking probabilistically means recognizing that design decisions rarely produce binary outcomes. They lead to a range of possible results, and the role of the designer is to navigate those possibilities and identify the path most likely to create value. This mindset also builds adaptability: user needs evolve, strategies change, and sometimes ideas fail. Teams that lean on data signals, experimentation, and learning loops move faster toward the most effective solution.

Before the practical principles, one fundamental idea:

Design decisions should be optimized for likelihood, not certainty.

“

Design for Likelihood, Not Certainty

Every design decision is a bet, not a guarantee. Even when decisions are informed by research and data, they are still based on smaller samples and assumptions about how users will behave at scale. A well-researched idea can still fail in the real world.

The Air Canada chatbot from the introduction is a design lesson as much as a legal one. The bot was doing what language models do, predicting plausible text. The interface, however, communicated that prediction with complete confidence, no caveats, no “here’s what our policy usually says,” no obvious path to a human. The user read confidence as commitment, and legally, so did the tribunal.

This is what happens when probabilistic systems are wrapped in deterministic interfaces. The interface transforms likelihood into certainty, and that is where the risk emerges.

Designing for likelihood means letting the interface continue to have uncertainty, visible fallbacks to human support, and clear labeling when content is AI-produced, preventing unforeseen issues.

Designers should avoid binary thinking — a great idea does not mean guaranteed success, and a familiar idea is not guaranteed to fail. Examine variations, confidence levels, and edge cases instead. AI can certainly help here, acting as a portfolio-thinking engine that surfaces different interpretations, highlights risks, and generates structured recommendations. The goal is not to optimize for certainty, but for value: it should always be value-driven.

Think of the moment in Avengers: Infinity War when Doctor Strange tells Tony Stark that out of millions of possible futures, there is only one where they win. AI cannot tell you the future, but it can help you explore the possible paths. Instead of asking whether an idea will succeed, ask AI to estimate the likelihood and get a score, and use those signals to guide decisions.

Use Data as a Compass, Not a Map

Even an actual probability is not a final answer. Imagine an AI model predicts an 80% likelihood that users prefer a minimal checkout experience. That does not mean the solution is simply “build a minimal checkout.” Data should function as a compass, not a map.

• Why did the model produce that prediction?
• What data influenced it?
• What assumptions is it leaning on?
• What user behavior is it actually detecting?

These questions help designers validate predictions through usability testing and additional research. AI excels at identifying patterns, but it rarely explains why those patterns exist. Understanding motivation is still a human-centered research task.

The clearest cautionary tale here is Amazon’s experimental AI recruitment tool, which the company reportedly scrapped after discovering that the model had learned to downgrade resumes from women. The training data, roughly a decade of historical hiring decisions, was skewed toward male candidates, and the model inherited that skew. It began penalizing resumes that included the word “women’s,” as in “women’s chess club captain,” and favoring language more commonly found on men’s resumes. The system was not intentionally biased — the data was. Amazon reportedly tried to adjust it and eventually shut the project down because they could not guarantee it would not surface other discriminatory patterns.

Examples like this are why interpreting AI output critically matters. Designers need to understand the data behind a prediction and evaluate the reliability of the models they depend on. A recommendation is only as good as the data it was trained on, and the only way to know what that data is hiding is to ask.

Experiment as a Learning System

Experimentation is usually framed as a way to validate a design decision. Want to lift the click-through rate of a CTA? Run an A/B test. Probabilistic thinking reframes this. Experiments should not only confirm solutions but also reduce uncertainty.

• Traditional approach: Testing features to confirm success.
• Probabilistic approach: Testing assumptions to reduce uncertainty.

Traditional A/B testing is expensive. It costs engineering time, traffic allocation, and user exposure, especially when a losing variant runs against a significant chunk of your audience. AI simulations can help filter weaker ideas before they reach production by making experimentation more efficient. User needs shift constantly, and the most effective teams iterate fast.

AI can help evaluate assumptions early by modeling potential outcomes based on historical and behavioral data. These simulations act as a hypothesis filter, pointing to the directions worth investing engineering effort in. This also supports personalization — different users may respond better to different experiences. Version A may resonate with high-intent users while version B works better for exploratory ones. Multiple experiences living side by side are not a flaw; they can be an intentional strategy.

AI amplifies probabilistic thinking by surfacing scenarios, assigning likelihood scores, and enabling personalization at scale. Experimentation becomes a continuous feedback loop:

Predict → Test → Learn → Adjust → Repeat!

A few steps to make it work:

• Shift the framing

  • So instead of saying: Will this feature succeed?
  • Ask: What assumptions are we testing?

  • Use this template to define the hypothesis:

    We believe [behavioral assumption] will impact [metric] because [reason]. We’ll know we are right when [evidence].

    Example: We believe simplifying the onboarding flow from 5 steps to 3 will increase completion rate because users experience decision fatigue when too many choices are presented. We’ll know we’re right when we see at least a 15% increase in step-to-step conversion with no drop in activation rate.

• AI simulations

  • Use AI to predict some of the assumptions.
  • Later, use the learning to identify the top candidates to test the hypothesis.

• Embrace multi-versions

  • It is absolutely fine to have two live versions.

• Fail fast

  • Reward learning vs success.
  • Normalize smaller experimentations instead of a sweep of large changes. So instead of taking on a risky bet, pick up a few probabilities and test them.

• Visualize probability

  • Create a probability table with probabilities of each variant and its prediction of success to keep track of all the changes.

• 
  

One of the hardest things for designers is making uncertainty understandable and actionable. When uncertainty is hidden, users treat AI outputs as facts. When it’s communicated clearly, trust increases.

Ranges, estimates, and confidence indicators go a long way. A delivery window of “Friday to Monday” tells the truth about variability without misleading anyone, whereas a specific timestamp that slips erodes trust every time. A face recognition feature that says “this looks like Pratik, is that right?” sets more honest expectations than one that just labels the photo with a name.

(Large preview)

Communicating uncertainty does not weaken trust — it strengthens it. The goal is not to eliminate uncertainty but to design for it intelligently.

Different users respond to uncertainty differently, and your design should account for that:

User type	Risk	Design goal
Overtrusting users	They act too quickly and trust AI results easily./	Show uncertainty more prominently.
Distrustful users	They ignore AI entirely.	Show historical accuracy or confidence levels.
Skeptical/balanced users	Uses AI as a guide, not as a rule.	Reinforce AI assistance and let them decide the sort of framing.

Keep Humans In the Loop

AI should augment human judgment, and certainly not replace it. The most trustworthy systems are designed with clear moments where people can review, challenge, correct, or override machine suggestions. Human-in-the-loop (HITL) is not a safety net — it is a refinement engine. Every override, correction, or rejection becomes high-quality feedback that improves the model over time.

Control is a prerequisite for adoption. Users are more willing to rely on AI when they understand how a suggestion was generated, can evaluate its implications, and can easily intervene. Well-designed products make this explicit: who is acting, what happens if the suggestion is wrong, and where the user can step in.

These interactions are also critical for system improvement. Every accept, reject, or edit is a strong signal, and compared to passive analytics, this kind of feedback produces far more meaningful training data. It closes the loop between real-world usage and model performance.

What Does HITL Look Like in Practice?

GitHub Copilot is a good everyday example. It offers inline code suggestions that developers can accept with a tab, edit, or ignore entirely. The system never commits code on the user’s behalf. Authorship stays with the humans. Every data point becomes implicit feedback about which suggestions were useful. Gmail’s Smart Compose works similarly, presenting predicted text as optional, keeping tone and intent in the user’s hands.

In higher-stakes contexts, HITL becomes more explicit. Risk and fraud systems typically use probability scores to route decisions: low-risk: proceed automatically; medium-risk: trigger additional verification; and high-risk: escalate to a human reviewer. This balances speed with judgment without removing oversight.

In safety-critical domains like healthcare, human oversight is non-negotiable. AI may flag anomalies or suggest a diagnosis, but the clinician retains final authority. Tools that explain the details help the practitioner understand why a recommendation was made, reinforcing confidence without removing accountability.

Designing for Human Judgment

From a UX perspective, HITL is about matching the interaction pattern to the level of risk. Simple accept/reject affordances work well for low-risk suggestions that improve speed without real consequences. As the stakes climb, impacting data, money, or people, preview and approval steps become essential. Explanations help users calibrate trust rather than blindly accept outputs.

What happens behind the scenes matters just as much. The system should capture user decisions with context, feed them into learning workflows, and log overrides for auditability. Over time, teams can track signals like override rate, confidence accuracy, time-to-approval, and perceived trust. A high override rate is not a user failure. It is a signal that the design or the model needs attention.

The Risk of Getting It Wrong

Poorly implemented HITL systems can fail in subtle ways. Human review can devolve into a rubber stamp. Workflows can slow down so much that users route around the safeguards. Feedback can skew toward a narrow subset of users. These risks are real, but they are design problems, not reasons to remove HITL.

The goal is not to maximize human involvement. It is to focus it where uncertainty, impact, or ethics demand it. Keeping HITL is less about control and more about clarity: clarity about who decides, when uncertainty matters, and how responsibility is shared between people and machines.

Optimize for Resilience, Not Just Conversion

Good design adapts as the landscape shifts. Product design, especially in AI-powered systems, can no longer afford to optimize only for short-term conversion metrics. User intent is fluid as well as ever-changing, environments change rapidly, and probabilistic systems continuously evolve too. What works today can quietly break tomorrow. Designing for resilience means building products that stay reliable, trustworthy, and useful even as assumptions, data, and user behaviors change.

Resilient design shifts the question from:

How do we maximize this metric right now?! → How does this system behave over time, under stress, and in uncertainty?

A resilient system is one that:

• Adapts as new data and behaviors emerge.
• Fails safely rather than catastrophically.
• Remains transparent and explainable.
• Avoids brittle, over-optimized interaction patterns.
• Anticipates second-order and unintended effects.

Do not just consider last quarter’s numbers. Peek into the following quarters to identify the shift and make changes accordingly.

Build Systems That Adapt as Probabilities Change

Likelihoods shift constantly, AI models drift, contexts evolve, and user needs mature as well, so designing as if conditions are stable creates fragility in probabilistic environments. A resilient approach assumes volatility as the default.

Think about how recommendation systems tend to evolve. The early version of a content feed optimizes for engagement, and for a while, engagement goes up. Then users start to notice the feed feels narrow, repetitive, maybe even exhausting. Resilient systems rebalance, introducing novelty, diversifying signals, and pulling in long-term satisfaction measures alongside short-term clicks.

Designers should create interfaces that expect change, dynamic re-ranking, contextual explanations, and escape hatches from stale personalization loops, all of which help systems stay useful as probabilities shift.

Optimize for Long-term Outcomes, Not Just Short-term Wins

Short-term conversion gains often hide long-term costs. Speeding up onboarding can reduce comprehension. Maximizing notification CTR can erode trust. Optimizing engagement alone can produce unhealthy usage patterns. Fragile systems maximize numbers while ignoring second-order effects, the downstream consequences that show up weeks or months later.

Duolingo’s hearts system is a good example of designing against this. It introduces friction: if you make too many mistakes, you run out of hearts and have to wait or practice older material to earn more. On paper, that looks like a conversion killer: fewer lessons per session. In practice, the team has publicly discussed how it supports long-term motivation and retention, which is the metric that actually matters for a learning app. Short-term engagement dips, but long-term outcomes improve.

Meta has made a similar, if more reluctant, shift. The company publicly acknowledged that optimizing purely for “time spent” produced unintended emotional and societal effects, which led to a stated pivot toward “meaningful social interactions” as a guiding metric. Whether that shift fully landed is up for debate, but the acknowledgment itself is the point: optimizing for the wrong thing at scale has real downstream cost.

So, designers must routinely ask:

• What behaviors are we unintentionally reinforcing?
• Will this interaction still be healthy if repeated at scale?
• Are we optimizing for the ecosystem’s wellbeing or just the next click?

Plan For Uncertainty the Way You Plan For Scale

Teams routinely plan for traffic spikes, but rarely for uncertainty spikes. Yet AI systems degrade, adversarial behaviors evolve, and external shocks can reshape user behavior overnight. Resilient design assumes variability and prepares for it.

This means designing for degrading confidence. What does your interface do when the AI isn’t sure? Does it quietly fail, or does it gracefully hand off? Does the experience still make sense if AI assistance goes away entirely? A good fallback strategy is as important as the happy path.

Some practical actions:

• Design for degrading confidence.
  Show fallback states, allow manual overrides, and visualize uncertainty where it matters.
• Measure long-term user health.
  Track satisfaction, retention quality, and unintended behavior, not just conversion.
• Build adaptability in.
  Use adjustable ranking rules, dynamic states, and continual experimentation across segments.
• Model second-order effects early.
  Every optimization casts a shadow; surface it before shipping.
• Use a resilience checklist before launch.
  How does the system behave under low AI confidence? What’s the safe fallback? What drifts do we anticipate?

Conclusion

If you take one thing from this article into your next design review, make it this:

Stop asking “Will this work?” and start asking “How likely is this to work, and what happens when it doesn’t?”

That single reframe changes how you write hypotheses, interpret AI output, scope experiments, and design for the moments when the system is wrong. Starting this week, name the assumption behind every AI recommendation you accept, find one place in your product where a probabilistic output is presented as a certainty, fix the framing, and design the fallback before the happy path.

The shift from deterministic to probabilistic design is less about new tools and more about a new posture. AI has not introduced uncertainty into our world. It has simply made the uncertainty that was always there impossible to ignore. AI can estimate, simulate, and recommend, but it cannot decide what matters, which users are being overlooked, or which unconventional idea is worth defending against a model trained on yesterday’s data. Those remain human responsibilities. Think in ranges, not points. Test assumptions, not features. Build for adaptation, not perfection. In a world where prediction is cheap, and judgment is rare, the most valuable thing a designer can do is keep asking, What else might be true?

 

The 8 Most Brutal Business Quotes Ever Said And The Stories Behind Them

 

Anyone can find a motivational quote.

What you cannot find easily is the story behind it. The moment it was said. What was happening. Why it actually meant something.

This edition is different. Every quote below comes with the context that makes it hit harder than any poster on a wall ever could.

"I knew that if I failed I would not regret that, but I knew the one thing I might regret is not trying."

JEFF BEZOS, 1994

Jeff Bezos said this in 1994. He was 30 years old, earning a six figure salary at a Wall Street hedge fund, and had just told his boss he wanted to quit to sell books on the internet.

His boss asked him to take 48 hours before deciding. Bezos went for a long walk. He came back and quit anyway. He drove cross country to Seattle with his wife, writing the Amazon business plan on the way.

At the time, only 16 million people had ever used the internet. Nobody was buying anything online. Every single person who heard his plan told him it would not work.

He has since said the regret minimisation framework, asking yourself what you would regret more at 80, not trying or trying and failing, is the single most useful thinking tool he has ever used.

The rest, as they say, is a $2 trillion company.

QUOTES YOU HAVE NEVER SEEN ON A POSTER

"Everybody has a plan until they get punched in the mouth."

MIKE TYSON

This was not about boxing. Tyson said this before a fight when a reporter asked how he felt about his opponent having a detailed game plan. It became the most quoted business line nobody expected to come from a boxer. Every founder who has ever had a beautiful pitch deck destroyed in the first investor meeting knows exactly what this feels like. The plan is not the strategy. The ability to adapt after the punch is.

"The most dangerous poison is the feeling of achievement. The antidote is to every evening think what can be done better tomorrow."

INGVAR KAMPRAD, FOUNDER OF IKEA

Kamprad built IKEA from a small mail order business in rural Sweden into the largest furniture retailer on earth. He was also famously frugal to the point of obsession, flying economy class until his 70s and driving a decades old Volvo. The quote above is why. He genuinely believed that the moment you feel satisfied is the moment you start declining. He ran IKEA with that paranoia until the day he died at 91.

"Your most unhappy customers are your greatest source of learning."

BILL GATES

Gates said this at a time when Microsoft was being publicly destroyed in the press for buggy software and poor customer service. Instead of defending the product, he built entire internal systems around studying complaints. The feedback loops he created became one of Microsoft's core operating principles. Most founders hide from negative feedback. Gates built a company around chasing it.

"I never took a day off in my 20s. Not one."

BILL GATES

This one is uncomfortable because it contradicts every work life balance conversation happening right now. Gates has since said he does not recommend this. But he also built Microsoft in those years. The honest version of this quote is not a prescription. It is a description of what it actually took, at that specific moment in history, to build something from zero to dominant. Draw your own conclusions.

QUOTES THAT GOT LAUGHED AT, THEN PROVED EVERYONE WRONG

1. "There is no reason anyone would want a computer in their home."

KEN OLSEN, FOUNDER OF DIGITAL EQUIPMENT CORPORATION · 1977

This was not mocked at the time. Everyone agreed with him. He was the most respected computer executive in America.

Three years later, Apple launched the Apple II. The rest of the industry followed. Olsen's company went from $6 billion to bankruptcy. The quote lives on as the most expensive wrong prediction in tech history.

2. "Nobody is going to pay to watch other people play video games."

EVERY MEDIA EXECUTIVE · 2008

Twitch's founders were laughed out of pitch meetings. The concept of live streaming gaming was considered absurd. One investor called it "the dumbest idea I have heard this year."

Amazon acquired Twitch in 2014 for $970 million. Live streaming gaming is now a $4.2 billion industry. The investors who passed have not commented publicly.

3. "There is no chance the iPhone is going to get any significant market share."

STEVE BALLMER, MICROSOFT CEO · 2007

He said this on live television. He laughed while saying it. He pointed out that at $499 the iPhone was too expensive to get traction. He was certain.

The iPhone generated $200 billion in revenue in 2023 alone. It is the single most profitable consumer product in history. Ballmer was Microsoft CEO for another six years after this interview.

The pattern across every quote above is the same one.

The people who said the thing everyone else thought sounded wrong were either completely right or completely wrong. There is no middle ground in bold thinking.

The people who nodded along and said nothing built nothing worth quoting.

Self Employed Insider covers the stories, decisions, and thinking behind the people building things worth remembering, every week, free.

Matching AI Modality To User Intent: Designing The Right Interface

 

We’ve fallen into conversational tunnel vision, defaulting every AI capability into a chat-based interface simply because LLMs are trained on dialogue data. But great UX is about matching modality to users’ context, intent, and cognitive load, so the interface adapts to the user, not the other way around.

The design community has entered a period of conversational tunnel vision. Because Large Language Models (LLMs) are trained on dialogue, the industry has collectively decided that the chat bubble is the natural home for every AI capability. While the chat interface is a viable and powerful option for many tasks, it is one tool in an expansive toolkit. UX and Product teams must be intentional about the modalities we choose for how users provide their data and commands, and how the system presents its output.

Modality is the way a person uses their senses to interact with a system: seeing, hearing, touching, speaking, or typing.

To pick the best method, you need to think about what the user wants to do, where they are, and how much cognitive effort they are already expending. This guide offers a clear way to figure out the best approach for any product, using two tools to assist in the process: a Task Audit and an Input/Output Alignment Matrix.

Picture a traveler jogging through a loud airport terminal after a sudden gate change. They are dragging their roller bag and carrying a coffee in the other hand. They need to open their airline app to ask the AI assistant where to go. The tool immediately fails the input modality test. It forces the traveler to stop walking, balance their coffee, and type a long booking reference number into a tiny chat box. When they finally hit send, the system fails the output modality test. Instead of flashing a large, high-contrast gate number, the AI returns a dense paragraph explaining the atmospheric weather patterns causing the delay. The actual gate number sits buried at the very bottom.

While they might make the flight just fine, the user won’t forget the moment of anxiety they felt while using the AI tool — an experience that could have served as a way to reinforce a commitment to UX has instead validated the common conception that companies don’t care about or understand customers using their products. In this scenario, the airline built a smart tool, but the interface failed the user. The input required physical dexterity, which the traveler lacked at the time of need. The output demanded a level of reading focus they could not spare. This article will cover how we can avoid this scenario in our AI-powered tools. In order to be successful, we must evaluate the physical and cognitive load of our users to match both the input and output modality to their immediate intent.

Let’s first discuss the limitations of a chat-based interface.

Myth of the Do-It-All Chatbot

The allure of the chatbot is easy to understand from a product development standpoint. It is a blank slate. It suggests that the system can handle anything the user provides. However, a text-heavy interface often causes a high adaptation load. This load increases cognitive demands on users. Over time, this cognitive burden turns into a psychological tax a person pays when changing natural thought processes to accommodate a machine.

When an interface relies solely on conversation, it imposes a dual burden: a linguistic challenge for input and a cognitive challenge for output. We’ll examine both separately below.

Input: Why a Text Box is a Linguistic Barrier

A blank chat box creates a major problem for users who need to discover what a tool can actually do. In a standard graphical interface, menus and buttons provide clear visual cues that signal every available option. A chat box often leads to choice paralysis because users are forced to guess what the AI is capable of. They have to remember the exact phrasing or technical terms required to get the result they want.

Consider a data analyst who wants to find a specific trend in a spreadsheet. In a traditional tool, they might click a filter or sort button. In a chat interface, they must suddenly become a writer and describe that complex logic in a complete sentence. Another example: a manager trying to reorganize a team schedule. Dragging and dropping blocks on a calendar is intuitive. Describing those same scheduling shifts in a text prompt adds a layer of work that makes the task feel more difficult than it should be.

Designing for input means recognizing that composing a prompt is a creative act. It requires a person to translate a vague thought into a specific command. For many professionals, this creates a linguistic barrier. A designer might know exactly how they want an image to look but struggle to describe the lighting or texture in a text prompt. In that case, a slider or a color picker is a much better input method than a text box.

Having addressed the linguistic barrier of constructing input prompts, we must now consider the other half of the conversational burden. This is the cognitive cost the AI imposes when it responds in dense blocks of text.

Output: The Cognitive Cost of Reading Long Text

When an AI responds in long blocks of text, it transfers the interpretive work to you, the user. Text is a serial medium: your brain has to read one word after the next to extract meaning. That takes time. Sequential reading is necessary in many scenarios. Complex legal analysis or reviewing nuanced medical histories requires reading full paragraphs. Teams create friction when they default to text for data that visual formats communicate faster. Visual methods allow parallel processing. You can view a chart and spot a pattern in under a second.

Imagine asking an AI for a project status update. Instead of a color-coded dashboard, you receive three paragraphs listing every task completed that week. Now you must read the entire response and mentally summarize it to find the one piece of information you needed. The quick visual check has been replaced by a reading assignment.

The cognitive tax of this work compounds with professional stakes. A doctor asking for a patient’s vital signs needs a clear numerical display, not a narrative describing the readings. A stock trader looking for a price spike needs a line graph immediately, not a written description of price movement over the past hour. In both cases, a text response forces the professional through a slow, error-prone extraction process when speed and accuracy are most important.

Figure 1: Redesigning for psychological fatigue. The linear text loop (left) forces exhausting sequential verification, causing anxiety. The graphical selection grid (right) permits instant, low-effort visual confirmation (glance verification). 

A Taxonomy of Input and Output Modalities

Before selecting a modality, practitioners need a shared vocabulary for what the options actually are. The table below maps common input and output modalities to the contexts where each performs best. This is not a ranking. Each modality has a role; the question is always which role it is playing in a given workflow.

Designing for modality inherently requires a strong focus on accessibility. While visual dashboards provide rapid insight for many people, designers need to provide screen-reader-optimized audio alternatives for users with visual disabilities. Modality choices should multiply pathways to information.

Input Modalities

Modality	Best For	Example Contexts	Cognitive & Physical Rationale
Button / Tap	Single-step, binary actions	Launching a feature; confirming an alert	Eliminates recall overhead by utilizing recognition; maximizes execution speed during time-sensitive tasks.
Voice	Hands-busy or eyes-busy contexts	Field technician query; driving navigation	Offloads physical interaction to speech, though bounded by ambient noise and social privacy norms.
Natural Language Chat	Ambiguous or exploratory queries	Researching options; asking follow-up questions	Offers users freedom in what they can say; however, the user must figure out how to phrase their request clearly.
Form / Wizard	Structured, multi-field data entry	Filling out a contract; configuring a report	Keeps users from missing information by breaking down a complicated task into clear, step-by-step visual sections.
GUI (Filters, Sliders, Drag-and-drop)	Complex parameter setting or spatial tasks	Scheduling; data filtering; image editing	Prevents mistakes and ensures users don't miss information by dividing complicated tasks into clear, step-by-step visual parts.
Multi-modal (Image + Text)	Visual input paired with description	Uploading a design mockup with annotation	Reduces the effort of explaining things because users can reference an object instead of having to describe it only with words.
Gesture	Hands-free spatial interaction	Waving a hand to acknowledge an alert in a sterile operating room	Allows physical interaction without touching a surface. This keeps users safe and clean in contaminated environments and allows for quick input or acknowledgement.

Output Modalities

Modality	Best For	Example Contexts	Cognitive & Physical Rationale
Push Notification / Alert	Time-sensitive, ambient awareness	Price spike alert; task completion notice	Provides a quick update that the user can process at a glance. It delivers information without demanding a full break in concentration from their primary task.
Audio Summary	Hands-busy or eyes-busy contexts	Status updates while walking; conversational voice agents providing real-time navigation	Delivers information directly to the user’s ear. Removes the need to look at a screen, keeping the user safe and aware of their physical surroundings while moving or working.
Short Text Summary	Focused queries needing brief answers	Definition lookup; single-metric status	Gives a fast answer to a direct question. Users can read a short sentence quickly without experiencing the fatigue of scanning paragraphs of text.
Visual Dashboard	High-density, comparative analysis	Project status; resource allocation	Enables visual trend and outlier detection. Avoids the mental effort of reading data line-by-line and cross-referencing in real time.
Interactive Canvas	Generative or iterative creative tasks	Design iteration; layout adjustment	Allows users to manipulate the output instead of asking an AI to move it via text instructions. Reflects a natural way to interact with the output.
Inline Confirmation	Guided task flows needing feedback	Step-by-step configuration wizard with in-line validation	Provides visual proof that the system recorded a choice correctly. Reduces users’ anxiety about wondering if an error occurred.

Table 1: Input and Output Modality Taxonomy. Use this as a reference during the Task Audit to identify candidate modalities before narrowing to a recommendation.

The following FigureFigure 2 illustrates the cognitive spectrum, mapping how mental effort scales across various interaction methods. This spectrum is a critical tool for designers to visualize the shift from low-effort, ambient interactions to high-effort, focused experiences. By understanding where a specific task sits on this spectrum, teams can identify whether a user needs a “glanceable” output that minimizes mental processing or a high-density format that supports deep, analytical thinking.

Figure 2: The Cognitive Spectrum of Modality. Both input (top) and output (bottom) move from low-effort, ambient interactions to high-effort, focused, and multi-modal experiences, illustrating why the context of use must dictate the design choice. 

With this taxonomy established, the next step is to apply a rigorous method to select the optimal input and output combination. Practitioners must ground this selection process in the user’s real-world environment and context.

Task Audit: A Framework for Modality Selection

To choose the right interaction method, practitioners should complete a Task Audit before interface design begins. A formal Task Audit is the framework that moves teams from assumptions about user behavior to evidence. This process gathers data about the physical, social, and cognitive context in which the work actually happens, which then drives all input and output modality decisions.

Use these four areas of focus to anchor the audit:

• Input Constraints: This addresses whether the user can physically interact with the system using their hands, such as typing or tapping. It often dictates the necessity of hands-free interaction methods like voice input when the user’s hands are occupied by tools or gear.
  • Can the user use their hands to type or tap? A mechanic working under a vehicle might need to ask a question using only voice because their hands are occupied and covered in grease.
• Output Constraints: This defines whether a user can safely and practically view information on a screen. It concerns situations where a user’s eyes must remain focused on their environment, making audio or glanceable visual cues the appropriate display method.
  • Can the user safely look at a screen to read information? A delivery driver’s navigation system should provide audio directions because reading a detailed map while driving through an intersection is dangerous.
• Social Constraints: This considers the environment’s tolerance for audible interaction, either speaking or listening to audio output. It helps determine if a quiet space requires silent alerts or if a loud environment demands a non-audio output method.
  • Is the environment appropriate for speaking aloud or listening to audio? An office worker in a quiet, open-plan space would prefer a silent text notification over a spoken voice response.
• Cognitive Load: This measures the amount of mental effort the user must already dedicate to their primary task. Teams must design the interface output to either minimize mental processing, such as with a quick visual indicator, or support deep thinking with a detailed summary.
  • How much mental effort does the task already require? A surgeon needs a quick visual red indicator during a procedure, while a lawyer researching case strategy needs a detailed text summary to absorb at their own pace.

The audit answers two questions for every feature:

1. What modality can the user physically use to provide input here?
2. What modality can the user realistically process as output here?

Here is how to gather the evidence to inform your task audit. Use one or more of these common UX research-related methods:

1. Contextual Inquiry and Observation

This is the most direct way to capture how people work in their natural setting, and it provides the richest data for identifying physical constraints on both input and output. Observation is necessary because users often perform hidden work: small steps or workarounds they forget to mention in an interview, or environmental details they do not think to describe because they have adapted to them.

 

The Approach: Go to the user’s actual workspace, whether a field site, warehouse, or office floor. Ask them to perform the task you are studying and observe closely.

What to Look For: This method is most revealing for Input Constraints and Output Constraints.

• Input example
  A technician diagnosing equipment who cannot put down their tools rules out typing and points directly to voice input.
• Output example
  A supervisor in a meeting who looks up and down repeatedly from a screen signals a need for glanceable, low-density output rather than a scrolling text summary.

2. Focused Interviews

Interviews surface the mental models and decision points that observation cannot capture. They are most valuable for understanding Cognitive Load.

The Approach: Conduct one-on-one sessions with end-users and the stakeholders who manage the outcome. Use a structured protocol focused on a specific task. Ask for stories about past successes and failures rather than general opinions.

What to Look For:

• The “Why” Behind High Cognitive Load
  Ask users to describe the hardest part of a task. A lawyer may explain that the volume of detail is not the challenge; synthesis for ethical or strategic judgment is. This confirms a need for detailed text output the user can read and absorb at their own pace, not a summary dashboard.
• Process Ambiguity
  Uncover situations that are unclear or error-prone, which identifies where AI capabilities provide the most leverage and what output format will reduce rather than increase ambiguity.

3. Collaborative Workshops

Workshops are essential for defining task boundaries and establishing required fidelity levels. Product managers and stakeholders bring foundational knowledge of system requirements; researchers apply audit criteria.

The Approach: Use workshops to build a shared Task Inventory. Bring designers, engineers, product managers, and business analysts together to map every step of the process. Product managers and business analysts ensure factual accuracy; the research team applies audit criteria to each step.

What to Look For:

• Social Constraints
  Confirm where tasks are performed. A workflow that takes place on a loud manufacturing floor versus a shared quiet library demands very different output modalities.
• Ambiguity and Speed Tests
  For every task in the inventory, apply two tests. First: Does this step require human ethical judgment? If yes, the AI output must support that judgment, not replace it. Second: Does this step require instantaneous execution? If yes, the interface must support fast input with minimal cognitive overhead.

Once you gather field evidence through these research channels, map your findings directly against the Modality Taxonomy. Each concrete physical or social constraint you document systematically eliminates mismatched interfaces. This process strips away design guesswork, narrowing your architectural choices down to the specific input and output combinations that survive the reality of the user’s environment.

When you ground input and output modality decisions in field evidence rather than interface convention, the resulting design reduces adaptation load for the user and grounds your modality choices in evidence. When you base decisions on field data, you move past interface convention and build a powerful case for the resources needed to create the right experience for your users.

Once the audit is complete, the final step is utilizing the Input/Output Alignment Matrix to formalize the connection between user intent and the optimal modality combination.

Input/Output Alignment Matrix

With Task Audit findings in hand, you can use an Input/Output Alignment Matrix to map user intent to specific modality combinations. This matrix is organized by what the user is trying to accomplish in a given moment. This distinction versus focusing on what your AI is capable of doing matters. If intent changes across a single workday for the same user, the interface should respond to those shifts.

Choosing the wrong modality for the user’s context can lead to user frustration. Users might feel mentally drained if a lot of information is delivered through a format that is hard to process, like getting a massive status update only in text. They may also start worrying if an action was actually completed correctly when a precise command is buried within a long chat exchange. Finally, the system can force users into finding clumsy workarounds, making them adapt to the machine’s method instead of working in their natural, most effective way.

User Intent	Optimal Input Modality	Optimal Output Modality	Environmental Fit
Quick Status Check	Voice or Single-tap Button	Audio or Push Notification	Hands-busy, Eyes-busy (e.g., Technician on ladder)
Specific Detail Query	Natural Language Chat	Short Text Summary	Focused, low-density data need
Complex Analysis	GUI (Filters, Sliders)	Visual Dashboard (Charts, Tables)	Desk-based, high-resolution screen
Creative Generation	Multi-modal (Image + Text)	Interactive Canvas	Design or drafting environment
Monitoring / Alert	Passive (background system)	Push Notification or Audio Alert	Any environment; task is ambient awareness
Guided Task Completion	Structured Form or Step-by-step Wizard	Inline Confirmation + Progress Indicator	Focused workflow; user needs verification feedback

Table 2: Input/Output Alignment Matrix. Map user intent to modality combinations using Task Audit evidence. The two added rows (Monitoring/Alert and Guided Task Completion) cover common enterprise and mobile scenarios not captured in simpler frameworks.

When teams coordinate these factors, they can move past the automatic default of adding a chatbot. Visual layouts enable rapid scanning. Structured inputs remove the burden of constructing perfect sentences. Audio outputs serve users whose hands and eyes are otherwise occupied.

The right modality combination respects the user’s physical and cognitive state at the moment of interaction.

A real-world scenario where environmental constraints dictated a shift in design strategy best demonstrates the practical application of this matrix and the broader audit framework.

Case Study: Adaptive Modality for Field Technicians

The Problem: Cognitive Overload in High-Risk Environments

Field technicians servicing high-voltage electrical grids often face a dangerous misalignment of interface modality. Traditionally, these technicians had to rely on ruggedized tablets to access technical manuals and log status updates. However, the physical constraints of the job — wearing heavy protective gloves and working in bucket trucks at significant heights — made interacting with a standard touch interface nearly impossible while on a job site. Additionally, attempting to read complex, text-heavy diagnostic reports on a screen while maintaining situational awareness created a high cognitive load that increased the risk of safety errors.

Research Methods: Capturing the Reality of the Field

To address this, researchers conducted a Task Audit utilizing three specific methods from this article. First, Contextual Inquiry and Observation revealed that technicians often worked in “hands-busy, eyes-busy” states where any manual input was a significant barrier. Researchers observed technicians wearing mandatory thick protective gloves while in the bucket truck, which made precise screen taps nearly impossible and often triggered the wrong commands.

High-altitude environments also introduced severe screen glare from direct sunlight, washing out the display and making text difficult to read even at full brightness. Furthermore, technicians faced the physical safety risk of trying to manipulate and secure a heavy, ruggedized tablet while balanced in awkward positions, creating a distraction that could lead to dangerous slips or equipment contact. These factors, combined with the need to constantly monitor live wires and the surrounding environment, meant technicians could not safely dedicate their eyes or hands to a standard tablet interface, confirming the severity of the eyes-busy and hands-busy constraints.

Second, Focused Interviews with veteran technicians validated the findings from the field. They confirmed that the operational challenges, including the thick gloves, screen glare, and safety risks, were not unique to one location but were commonly experienced across multiple sites, including high-altitude transmission lines and sprawling power substations. This broad confirmation solidified the need for a non-touch, voice-first solution. The interviews also surfaced a critical cognitive constraint: the need for glance verification of vital signs, such as voltage readings and temperature trends, rather than being forced to read a long narrative description of system health. Technicians stressed that their primary need was immediate, unambiguous verification (is this safe? or where is the fault?), not a lengthy diagnostic report, indicating that a text-heavy response was dangerous to their workflow.

These methods confirmed that the environment required a departure from the traditional chat or form-based AI capability interface.

The Resolution: A Multi-Modal Handoff Solution

The resulting solution implemented an adaptive modality handoff designed to mitigate the physical and cognitive barriers researchers identified during the research. While active on a job site, technicians utilize voice input to query the system. This method allows them to remain productive while wearing thick protective gloves that would otherwise prevent precise interaction with a touchscreen.

The AI responds with a short audio summary of immediate diagnostic data. This audio feedback bypasses the challenge of screen glare in high-altitude environments and allows the technician to maintain situational awareness of the high-voltage grid without the safety risk of looking away from dangerous equipment. By providing immediate answers to fault locations through audio, the system meets the technician’s need for glance verification through a hands-free and eyes-free channel.

Once technicians return to a truck and secure safety gear, a system automatically hands off workflows to a 15-inch visual dashboard mounted inside a vehicle. A rugged 10-inch field tablet lacks adequate screen real estate for complex schematics. A larger vehicle display allows for parallel processing of historical trend data and wide electrical grid maps. This case study reflects an actual field audit conducted for a national utility provider. Implementing this adaptive approach reduced diagnostic time by twenty percent and increased daily tool adoption among field crews.

Figure 3: Cross-modality handoff diagram for field technicians. Our solution to deliver AI capability using multiple modalities depending on context was the result of auditing user tasks and led to greater adoption of the tool.

Designing for the Environment

An AI capability is only as usable as the interface that delivers it. Researchers and designers must resist the pull toward the path of least resistance. Building a chatbot is fast and familiar, something we’ve been doing for decades now. Building an interface that feels like a natural extension of how someone already works is harder, and it is the work that matters.

Start by leaving the screen. The Task Audit requires presence in the places where work actually happens: the field site, the warehouse floor, the operating room. The physical and social realities of those spaces are not edge cases. They are the design brief.

The future of AI interface design is a diverse ecosystem: visual, vocal, haptic, and ambient, calibrated to user intent and environmental context. The chat window is one tool in that ecosystem. It is the right tool for specific jobs, and often the wrong tool for the jobs we reflexively assign to it.

In order for us to create the greatest likelihood of acceptance and use of the AI capability we offer users, we must fit the modality to the person and the place.

Where to Start

To get started immediately, run a lightweight version of the Task Audit before your next design sprint. Spend two hours observing the workflow in its actual environment. Conduct three to five interviews with the people who perform the task. Bring a PM or analyst into a 90-minute workshop to build a task inventory and apply the four audit questions. You will not have complete data, but you will have enough to make a defensible modality recommendation backed by evidence rather than convention.

I created a Modality Task Audit Template to help guide teams with moving forward. You can download this worksheet and take it directly to your next field observation. It allows design and product teams to document specific physical barriers before writing a single line of code.

• Download: Modality Task Audit Field Template (PDF)

Inside this Template:

• Step 1: Physical Reality Check.
  An observation checklist to log hand availability, eye focus requirements, and ambient noise levels in a specific workspace.
• Step 2: Cognitive Baseline.
  A scoring grid to rate required reading density and verification anxiety for a given workflow.
• Step 3: The Handoff Map.
  A blank flow diagram to chart where a user starts a task (for example, using voice on a mobile phone in a warehouse) and where they finish it (for example, reviewing a visual dashboard on an office monitor).

We focus heavily on training smarter AI models. We owe equal attention to human interfaces. A brilliant underlying model packaged in a lazy text interface fails. When you observe actual work environments and align interaction modalities to them, you remove adaptation friction.

Modality Task Audit Field Template

Use this worksheet during field observations. It allows design teams to document specific physical barriers before writing code.

Part 1: Physical Reality Check

Observe users as they perform a primary task in actual workspaces. Check all applicable conditions.

State of Hands

• Hands are free for typing.
• User holds a clipboard or a smartphone.
• User holds tools, drives a vehicle, or wears thick gloves.

Visual Focus Requirements

• User focuses entirely on a screen.
• User alternates glances between a screen and physical surroundings.
• User watches live machinery or navigates crowded spaces.

Ambient Noise Level

• Quiet environment (office).
• Moderate noise (busy cafe).
• Loud environment (construction site, loud retail floor).

Part 2: Cognitive Baseline

Rate the mental effort required to complete a specific workflow.

Metric	Low	Medium	High
Required Reading Density	Single numbers, binary states	Short summaries, simple instructions	Legal contracts, complex diagnostic reports
Verification Anxiety	Reversible actions, low stakes	Standard business operations	Irreversible actions, safety risks, financial transactions

Part 3: Handoff Map #

Chart user journeys across different environments. Document required input and output at each stage.

Stage 1: Initial Action

• Location: ________________________
• Input Method: ________________________
• Output Method: ________________________

Stage 2: Context Transition

• Trigger for environment change (example: user returns to a desk): ________________________

Stage 3: Completion Action

• Location: ________________________
• Input Method: ________________________
• Output Method: ________________________