Menlo Venturesmenlovc.com · 16 September 2026

Key Findings Consumer AI adoption hardly grew even as spending tripled. The share of U.S. adults using AI rose just three points, from 61% to 64%, while global consumer spend reached an estimated $40 billion in 2026, more than 3x last year. The real growth came from existing users going deeper and paying more: 55% of AI users now pay for at least one AI product, and 46% of payers spend more than they did a year ago, versus 12% who spend less. Power users are driving consumer AI market growth: 55% of AI users now pay for AI, but spending is highly concentrated; the top 14% of payers account for 60% of spending. Payers are also nearly twice as likely to use AI daily (50% vs. 26%) and 5x as likely to use AI agents (65% vs. 13%). AI is becoming a source of income: Nearly half (48%) of AI users have earned money with AI, equivalent to roughly 30% of Americans. Consumers are handing AI more control: 41% of AI users have tried an AI agent, 24% use one regularly, and 32% have let AI act on their behalf without final approval. Among agent users, 92% pay for AI, and 63% use it daily. AI-generated conte

Sidebar.iomaggieappleton.com · 15 September 2026 · not counted as a party

This talk is about collaborative planning with agents: divided worlds, boundary objects, and thicker interfaces.The European navigator would begin every journey by making a plan. They would plot a course from maps and general, abstract principles, trying to do most of their thinking in advance.Once at sea, they would compare every move against the plan and work to stay on the course they had set. When something unexpected happened, they would have to revise the plan.The Chuukese navigators work differently: they do not make plans ahead of time.They instead begin with an objective, like reaching a certain island, and set off without a firm route.Instead, they respond to conditions as they arise, reading the wind, waves, currents, sun, and birds and steering accordingly. They’re continuously thinking about how to reach the objective and making ad hoc decisions, constantly improvising based on the context.Suchman is a famous HCI researcher and cultural anthropologist who worked at Xerox PARC https://en.wikipedia.org/wiki/Xerox_PARC in the 80s and 90s. Her book is all about planning and communicating with machines, and despite being written in 1987, it’s still very

Maggie Appletonmaggieappleton.com · 14 September 2026

This talk is about collaborative planning with agents: divided worlds, boundary objects, and thicker interfaces.The European navigator would begin every journey by making a plan. They would plot a course from maps and general, abstract principles, trying to do most of their thinking in advance.Once at sea, they would compare every move against the plan and work to stay on the course they had set. When something unexpected happened, they would have to revise the plan.The Chuukese navigators work differently: they do not make plans ahead of time.They instead begin with an objective, like reaching a certain island, and set off without a firm route.Instead, they respond to conditions as they arise, reading the wind, waves, currents, sun, and birds and steering accordingly. They’re continuously thinking about how to reach the objective and making ad hoc decisions, constantly improvising based on the context.Suchman is a famous HCI researcher and cultural anthropologist who worked at Xerox PARC https://en.wikipedia.org/wiki/Xerox_PARC in the 80s and 90s. Her book is all about planning and communicating with ma

OpenAI newsopenai.com · 14 September 2026

Fyxer uses OpenAI models, fine-tuning, memory, and real user feedback to organize inboxes and draft emails in each user’s voice.

GitHubgithub.blog · 10 September 2026 · 2 posts

Checking agent-generated code usually means hopping between tabs. Learn how to view diffs, run terminal commands, and preview web apps side by side in the GitHub Copilot app. September 10, 2026 | 3 minutes Share: When an agent makes a change to your code, you want to review it, run it, and see what changed. Great news: now you can do all three without having to leave the GitHub Copilot app. Before, doing those three jobs would mean having to bounce between your editor, terminal window, and web browser. But when these steps live side by side as built-in panels in the Copilot app, checking your agent’s work is simple. Let’s walk through each of the panels in the app and how we’ll use them to complete the AI coding loop. Using the diff panel to review changes The diff panel (a diff is a before and after comparison) shows exactly what lines were added, removed, or changed, with additions highlighted in green and deletions in red. This gives you complete clarity, so you can choose what happens. You can accept changes, lea

Dan Maccaroneuxdesign.cc · 9 September 2026 · 3 posts

From ink and quill to glowing code: Shakespeare’s exploration of prophecy and equivocation, the art of speaking truths that mislead, anticipates the central challenge of our own relationship with artificial intelligence. Every warning we need about AI was written four hundred years ago. What witches, a forged letter, and a haunted prince can teach you about talking to a machine. The sharpest words ever written about artificial intelligence came from a man who never had electricity, a telephone, or a single line of code. He’s been dead four hundred years, and he still wrote thirty-seven plays about it. The obvious place to look for AI is science fiction, and plenty of people have gone looking. Patrick Neeman has spent a good chunk of this year grading Star Wars and The Matrix and Minority Report against the machines we actually built. It’s a great series. Then there’s Shakespeare. He wrote about AI constantly, but he just called it magic. Whether he did it literally or not, look at what “magic” in his plays actually does. It’s a power that a few people command and most people can’t understand. It speaks with total confidence, it’s usually right in

hello@smashingmagazine.com (Carrie Webster)smashingmagazine.com · 9 September 2026

Ever since the commercialisation of the Graphical User Interface pioneered by systems like the Xerox Star and popularised by the original Apple Macintosh, software has relied heavily on point-and-click interactions. If you wanted to book a trip, buy a pair of shoes, or research a health symptom, you were expected to navigate a labyrinth of user interfaces. You click menus, adjust range sliders, fill out multi-step forms, deal with cookie pop-ups, and open dozens of browser tabs just to cross-reference basic information. We have become accustomed to spending more time managing the software rather than actually achieving our goals. That contract is officially changing. Driven by advances in artificial intelligence and large language models, a new generation of web tools is pioneering a radical philosophy: Intent-Driven Design. Rather than expecting our users to learn complex menus and click through elaborate sales funnels, these platforms operate by capturing high-level human goals and silently executing the grunt work in the background. This vision builds on long-standing HCI concepts like Golden Krishna’s “The Best Int

Intercom Blogintercom.com · 9 September 2026

Businesses are earning trust in AI by accident. It should be earned on purpose. People become more accepting of AI the more they use it, but acceptance that’s built on mere exposure is passive. Businesses may benefit, but they’ve often done nothing to earn it. Our latest research shows people’s sentiment toward AI is higher than it was last year, and they believe the technology’s become more capable. Trust is also rising, but it’s more conditional. Download the 2026 AI Sentiment Report → For most people, how much they’re willing to trust AI depends on what they’re asking it to do. For queries that are nuanced or emotionally charged, full trust still needs to be earned. But we also found that trust isn’t fixed. Businesses can design customer experiences that earn user trust. In The 2026 AI Sentiment Report, we explore how. Here’s a look at our findings. Sentiment is warming, but trust is still conditional End users are feeling more positive than last year about interacting with an AI Agent for customer service. Forty-nine percent of people described having positive overall experiences with AI – up nine per

Patrick Neemanuxdesign.cc · 7 September 2026 · 6 posts

The call for an agentic standard: We need to stop shipping the same form four different times

Vercel changelogvercel.com · 25 August 2026

Agents increasingly write TypeScript programs to coordinate tools and process their results. Once those programs touch real applications, some steps require authentication, while others need human approval.Executing that code with eval gives it the same access as the application around it, including its secrets and internal services, and leaves no durable way to pause at those boundaries.Today, we're releasing the Run SDK, a package for executing untrusted JavaScript and TypeScript without giving it direct access to your application or system. Applications expose narrow host functions and can interrupt execution for authentication or human-in-the-loop approval. The program resumes after a decision without repeating completed work.pnpm add runCopy link to headingA small interface to the hostThe Run SDK evaluates JavaScript or type-stripped TypeScript in a fresh QuickJS context inside a worker thread, with no direct route to Node.js or the network.The application exposes selected operations through hostFunctions. These are regular functions that become callable globals inside the sandbox:import { run } from 'run';const result = await r

blog.ronbronson.comblog.ronbronson.com · 19 August 2026

9 August 2026 A Model for Dangerous Agent Situations Reading this twitter thread (yeah, I know) about agents and escalation paths, left me thinking more about some earlier posts about the mostly dormant agent experience space and how there should be more content about these paths when they’re discovered. The short version about the thread suggests an alarm call for an agent to relay back that they’ve reached a plac they cannot go any further and need human intervention, rather than trying to brute force their way to a solution. (Presumably, to make you the human pleased with their work and also to churn as many tokens as possible. Both with agents and sales, it’s always be closing I guess.) One reply to the thread referenced algedonic signalling, which sent me back to Stafford Beer. In the Viable System Model, an algedonic signal is an exception channel: information that can move outside the normal reporting structure when actual conditions have departed badly enough from what the system expects. Beer wasn’t thinking about “agent welfare” and to be honest, neither am I. At least it relates to anthropamorizing an agent’s welfare, when in re

Nicole Alexandra Michaelisuxdesign.cc · 14 August 2026

The gap between what AI is reliable for and what it looks reliable for is where damage happens, and enablement fails How to lead the change with AI in a nutshell I’ve been building with AI for three years now. I started by trying to scale UX writing via plug-ins and now have agents, workflows, MCPs, and apps under my belt. I feel comfortable building with AI, and have adopted it into many of my day-to-day tasks. Yes, I do think AI has gotten better over those 3 years. But I think we’re telling ourselves a story that isn’t true. AI has gotten very good at one thing: Starting. The blank page problem. Replacing lorem ipsum. But while starting is faster than ever and error rates may have dropped, the verification burden hasn’t. I think errors have gotten harder to spot, and models have gotten better at defending them. For many, getting from zero to a rough first version is the hardest emotional hurdle in any task, and AI clears it in seconds. No more staring at a blank page if you don’t want to. But I think that’s the ceiling. Once you’re past “getting started” and into “getting it right,” the story changes. The hallucination problem isn’t solved

UX Collectiveuxdesign.cc · 10 August 2026 · not counted as a party

Bottle your judgment, Chat is the wrong UI, Design.md

PostHogposthog.com · 6 August 2026

The screens worth building now do different work. You're designing for moments like:Approval – the agent has the change ready and needs a yes. Can the human tell what they're saying yes to in two seconds?Review – not a summary of what changed. The change itself. A diff beats a paragraph.Undo – when the agent gets it wrong, there needs to be a way back.

uxdesign.ccuxdesign.cc · 4 August 2026

Why AI is changing who reads first.7 min readAug 2, 2026--Press enter or click to view image in full sizeOne meaningful move surrounded by infinite possibilities. As AI makes generation abundant, the differentiator is no longer producing options, it is knowing which one deserves to be made. AI generated image, Aurélie Radom ©The audience changedFor decades, designers had one audience: people.Every method, framework and principle we adopted revolved around understanding human needs and designing experiences people could navigate, trust and enjoy. Human-centered design was never simply a methodology. It became the foundation of our profession. That foundation hasn’t disappeared, but something has changed around it.Today, many digital experiences have another audience before they ever reach a human. An article written to answer someone’s question may first need to be understood and cited by a language model. A developer trying to understand an API asks an AI assistant before opening the official documentation. A user searching for information may receive an AI-generated answer before ever visiting the original source.The first interaction with our work now happ

Aurélie Radomuxdesign.cc · 2 August 2026

Why AI is changing who reads first. One meaningful move surrounded by infinite possibilities. As AI makes generation abundant, the differentiator is no longer producing options, it is knowing which one deserves to be made. The audience changed For decades, designers had one audience: people. Every method, framework and principle we adopted revolved around understanding human needs and designing experiences people could navigate, trust and enjoy. Human-centered design was never simply a methodology. It became the foundation of our profession. That foundation hasn’t disappeared, but something has changed around it. Today, many digital experiences have another audience before they ever reach a human. An article written to answer someone’s question may first need to be understood and cited by a language model. A developer trying to understand an API asks an AI assistant before opening the official documentation. A user searching for information may receive an AI-generated answer before ever visiting the original source. The first interaction with our work now happens through another machine. These systems are no longer only tools we use during creation. They hav

blog.jim-nielsen.comblog.jim-nielsen.com · 31 July 2026

Every zeitgeist comes with new design idioms unique to its challenges. Many of them disappear as fads change, but others bake themselves into deeper parts of existing software interaction paradigms. For example, there’s the hamburger menu (≡) which saw a proliferation during the rise of mobile due to the constraints around screen size. It has since spread to many other parts of software interaction design and will likely remain prevalent for a long time as a terse way of indicating “more menu-type content here”. As another example, before AI what were the connotations of the sparkle emoji ✨? Personally, I don’t know, but now it means AI. (AI = sparkles and rainbow colors — it’s funny when you think about it. They should’ve just thrown unicorns in there for the trifecta. AI = sparkles, rainbows, and unicorns ✨🌈🦄. Apt.) Some patterns are very specific to the interactions inherent to the nature of AI as a technology. For example: streaming text. This is a pattern made for and refined by chat interfaces, so it may not have tons of utility for reuse across other software interaction paradigms. Then there are other patterns that’ve been refined by AI interfaces and are

Darren Yeouxdesign.cc · 30 July 2026

Why the best UX often disappears. And when it shouldn’t When every interaction is frictionless, nothing sticks. Why the future of good UX depends on knowing when to slow users down. (image source: ABC Radio National) The paradox of “forgettable” design “Make the experience forgettable,” a senior business leader once told me. That might sound like a joke to a designer who prides himself on creating memorable moments. But as the head of airport operations, he was dead serious. Singapore Airlines handles thousands of travellers every day. For these passengers, joy comes from the in-flight experience and on-time departures. The last thing the operations team wants is for a customer to get stuck in a long queue or hit a snag like a failed boarding pass, only to face their wrath and complaints. At the airport, smooth is invisible. One reason Changi Airport remains among the best in the world is its relentless investment in removing friction. State-of-the-art passport-less immigration clearance was once thought impossible. Today, Singapore residents and eligible travellers simply walk through automated gantries without whipping out their passports. Withi

Figma blogfigma.com · 30 July 2026

Direct manipulation stages every edit in the prompt box so you can review each change before you commit to it, or discard anything that doesn’t look right. Once you’re happy, Make applies the changes and updates the underlying code, so what you build visually is what actually ships.

Figma release notesfigma.com · 30 July 2026 · not counted as a party

The new properties panel brings a more direct way to build in Figma Make. Click any element to adjust its properties by hand, or annotate a spot on the screen and let the agent make the change.

@uxdesignccuxdesign.cc · 29 July 2026

Information architecture is the orderly wing. Most content stores are the heap at the edge of the frame. AI assisted.UXInformation ArchitectureAIDesignEditor PicksEvery AI problem you chase — hallucinations, wrong answers, poor retrieval — traces back to the IA projects you never funded. Now’s the time to fund them.12 min readJul 26, 2026--For twenty years, information architecture was the discipline nobody funded. The job titles disappeared—I was one back in the day—and now it’s an art (and science) that needs to return. Teams didn’t align. Companies started showing their underwear.Doing it well is less about structure than about getting an organization to agree on one way to name and arrange things. Every team brings its own vocabulary, so aligning them is slow, political, and thankless, and it loses every quarter to whatever ships a feature. The work stayed invisible, and so did its failures.Then AI arrived, and the invisible foundation showed up on the balance sheet.The same gaps that once cost you a confused visitor now cost you a hallucination, a wrong answer, or an agent that confidently retrieves garbage and acts

bensbites.combensbites.com · 28 July 2026 · 3 posts

Hey folks, I’m back from holiday and straight back to messing around building. I’ve been kinda obsessed and enamoured with the demos from tldraw. It’s a free canvas/whiteboard app but their offline app (recently updated) is cool because agents can do all sorts with it; create visualisations to explain stuff, make games, interactive blog posts and a ton of other stuff. Like turning our logo into a little animating mascot. He’s called ‘bites’ btw. or little widgets like a timezone checker @tldraw offline ","username":"bentossell","name":"Ben Tossell","profile_image_url":"https://pbs.substack.com/profile_images/1878086921726943233/vOx1kjeP_normal.jpg","date":"2026-07-27T11:40:18.000Z","photos":[{"img_url":"https://substackcdn.com/image/fetch/$s_!7Ccf!,w_1028,c_limit,f_auto,q_auto:best,fl_progressive:steep/l_play_button_usfui2,w_88,e_colorize:0/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F__ss-rehost__tw-video-preview-13_2081706218259636225.jpg","link_url":"https://t.co/MMiGh3VMAS"}],"quoted_tweet":{},"reply_count":2,"retweet_count":1,"like_count":9,"impression_count":11165,"expanded_url":null,"video_url":"https://video.twimg.com/amplify_vide

Smashing Magazinesmashingmagazine.com · 28 July 2026 · not counted as a party

Many of the AI tools we interact with take the form of text boxes. But what if there was a different way to interact with AI? Oleksii Hrzhehorzhevskyi explores a different approach to creating a new AI assistant and how designers can navigate the field as AI continues to change it.

Latent Spacelatent.space · 28 July 2026 · not counted as a party

There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right. A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU n

Holger Maassenux4dotcom.blogspot.com · 27 July 2026

When people think about User Experience, they often think about apps, websites or software. They think about interfaces, buttons, navigation and polished screens.I have spent my career designing exactly those experiences.But one of the most important lessons I learned came very early—during my first years at Pixel-Factory in Germany. There, long before AI became part of our daily conversations, we shared a simple belief:User Experience never ends at the screen. In fact, it never began there.Many designers explain UX with the iceberg metaphor. The interface—the screen, the buttons, the visual design—is only the visible tip above the water.Beneath the surface lies everything that truly shapes an experience: expectations, emotions, trust, context, mental models, accessibility, culture and human behavior. The invisible part is what keeps the iceberg afloat.Long before I became a UX designer, I studied architecture and urban planning. There, I learned that the quality of a place is determined far less by what people see than by everything they don't. A building stands because of its

Hugo Palomaresmicrosoft.design · 27 July 2026

The future I expect looks familiar in terms of who produces good work. It just looks different in how much of it they produce, and how fast it is. Embodying the Ju/’hoansi I see a similar pattern in how we work with AI. It’s one I’ve been leaning on as I figure out how to turn AI into an effective productivity partner and collaborator in my own day-to-day. In this day-and-night analogy, daytime is the operational work we do with AI, the standard back and forth between human and agent that produces huge amounts of output. Then comes night. Gathered around the fire, we start to review and dig deeper into all of that output: reflecting on what has happened and solidifying our thinking. We seek to understand more deeply. These “nighttime” interactions – focused on driving understanding – are the vital balance to the high-output, more transactional “daytime” ones. As someone who works at the intersection of engineering and design, I spend a lot of time thinking about how we might design a UX that accounts for both types of interaction – day and night. What kind of affordances allow users to review output at scale

Saikiran Bavandlablog.gopenai.com · 27 July 2026

AIArtificial IntelligenceMachine LearningSoftware DevelopmentLLM4 min readJul 13, 2026--Your AI Remembers Nothing. That’s Why Users Quit After Week One.Your users open the app. They re-explain who they are. Again.Same context. Same preferences. Same frustrations. Every single session.They don’t file a bug report. They just stop coming back.The Core Problem Most Teams MisunderstandStateless AI isn’t a technical limitation anymore. It’s a product decision — and most teams are making it by default, not by choice.The assumption is that users tolerate re-prompting because they understand “how AI works.” They don’t. They compare it to every other software they use. Spotify remembers their taste. Gmail knows their contacts. Your AI asks them to start over.That gap kills retention before it ever shows up in a dashboard.Each Session Starting From Zero Is a UX TaxClaim: Making users rebuild context every session is invisible friction that compounds into churn.Why it happens: Most AI products are built around a single-turn or short-session mental model. The LLM call is stateless by design. Developers ship it that way

Vitaly Friedmansmashingmagazine.com · 27 July 2026

7 min readAI, UX, DesignMany companies assume everyone craves new AI features. But the reality is that most people don’t want more AI — at least not in the way most AI leaders envision it. Brought to you by Design Patterns For AI Interfaces, friendly video courses on UX and design patterns by Vitaly.Many companies silently assume that everybody wants more AI in their lives. That people are craving new AI features, new AI products, new AI workflows — that would all magically replace all existing outdated practices and broken ways of working.But in reality, it seems like people don’t want more AI at all — at least not in the way most AI leaders envision it. Unsurprisingly, many AI features have low adoption and retention — at a very high cost of delivery, and a high risk of reputation damage.The AI People Don’t NeedIt’s remarkably difficult to make a strong argument with senior leadership, but AI is not a value proposition. New AI features don’t magically make for happy or excited customers. Because AI features are often bolt-ons and separate tools for employees to use, they typically take people out of their regular way of working.AI is pr

Tony Aliceanngroup.com · 24 July 2026

Summary: As more interface work is AI-generated, the output of research and design shifts from documents written for humans to curated context that guides AI. Context Is the New UX Deliverable Everyone Is Designing AI-Ready Deliverables DESIGN.md A Hypothesis: UX.md Curation, Not Handoff Open Research Context Is the New UX Deliverable AI models produce output based on context. Context is everything the model can see when it does the work: your request, plus whatever instructions, standards, examples, and background information come along with it. Context enables you to avoid middle-of-the-road output. For example, an AI model has been trained on a huge number of search screens, so when you ask for one, it produces an average search screen. It knows what software generally looks like. It doesn't know your users, your domain, your design standards, nor anything your team has learned from research. Unless that knowledge is in the context, the model designs without it. Context leans a model’s output in a particular direction. Think of a skilled builder designing your house without ever meeting your family. They design the average house.

Nielsen Norman Groupnngroup.com · 24 July 2026 · not counted as a party

As more interface work is AI-generated, the output of research and design shifts from documents written for humans to curated context that guides AI.

Product Talk (Teresa Torres)producttalk.org · 23 July 2026 · 2 posts

Listen to this episode on: Spotify | Apple Podcasts How do you build trustworthy AI diagnostic tools in one of medicine's most historically under-researched areas? In this episode of Just Now Possible, Teresa Torres talks with Tulsi Patel (Director of Product and Technology), Lorna Brightmore (Head of Data and AI), and Jack Pickard (Head of Engineering) at Hertility, a UK and Ireland-based women's health tech company. Hertility combines an in-depth online health assessment with at-home hormone testing and clinician-reviewed reports to help diagnose conditions spanning menstruation to menopause. Built on seven years of data linking symptoms, blood results, and pelvic ultrasound scans for over a million women, the team walks through two AI products in development: Gyn.AI, a Bayesian network that gives clinicians probability-based diagnoses instead of binary calls, and a scan automation pipeline that classifies ultrasound images, measures follicle counts and ovarian volume, and drafts clinical letters using an agentic loop that checks its own output against patient data before a human ever re

vaughntan.orgvaughntan.org · 17 July 2026

9/7/2026 ☼ uncertainty ☼ innovation ☼ public sector ☼ risk ☼ experimentation tl;dr: Organisations say they want innovation but keep killing it. The problem is that the organisational decision-making machinery assumes you already know enough to justify a big bet, and genuinely new things never cross that bar. Big visible bets also trigger a predictable organisational immune response that neutralises, ejects, or quarantines anything new and unfamiliar. The way through is to make new things small, boring, and apparently routine, so they don’t set off the immune system in the first place. Adapted from my keynote at the Public Innovation Lab Congress, Santiago, June 2026. Japanese has a character, 青, that is used to refer to both “blue” and “green”: 青い空 is “blue sky,” 青草 is “green grass.” Now imagine a typical scenario where the protagonist in the action movie is defusing a bomb with the help of their handler in a faraway situation room: “The instructions say to cut the “青” wire to shut off the timer.” When one word covers two different things, dangerous mistakes can happen. We do this with the word “risk,” using it for almost every kind of not-knowing about the future.

Georgia Kenderova, Tanner Kohlernngroup.com · 10 July 2026

Summary: Handoff willingness, flexibility, proactivity, emotional responsiveness, and transparency help you build trustworthy AI chatbots that guide users well. Designing a site-specific AI chatbot requires making key decisions even before user testing begins. The five chatbot qualities described in this article — handoff willingness, flexibility, proactivity, emotional responsiveness, and transparency — can guide those early design decisions and provide a framework for evaluating existing chatbots. (But don’t neglect real testing. Real users will help you finetune the specific behaviors that will best serve their needs.) Getting these qualities wrong can be costly. People are still forming mental models of site-specific AI chatbots and figuring out whether to view them as human customer-support representatives or frontier-level LLMs. Many chatbots fall short of both types of expectations and quickly get abandoned by users. 1. Handoff Willingness: Respect Users' Desire to Speak with Real People 2. Flexibility: Go Where the User Wants to Go (Within the Chatbot’s Defined Guardrails) 3. Proactivity: Anticipate Needs and Suggest Next Steps 4

Katie Schmidt, Jessa Anderson, Hayley Mortinnngroup.com · 3 July 2026

Summary: Different enterprise roles need different types of explanations for AI outputs. Enterprise AI is a hot topic, but many organizations still struggle to achieve meaningful employee adoption and usage. This article focuses on what it takes to develop and deploy AI solutions that both organizations and employees can trust. In particular, it examines how AI explainability helps the people building enterprise AI (developers, system administrators, and domain experts) understand AI-system behavior, build trust in it, and support AI adoption. Because these technical roles bring different goals, expertise, and contexts, explainability cannot be one-size-fits-all. AI Explainability in the Enterprise Three Categories of Enterprise AI Users A Shared Scenario: Configuring an AI Help-Desk Agent The Role of Explainability in Fostering Trust Designing Explainability for Real Enterprise Impact AI Explainability in the Enterprise Helping users understand how AI systems work is a core best practice for building trustworthy tools and products. Common approaches include creating traceability, source attribution, and explanations of rea

hello@smashingmagazine.com (Victor Yocco)smashingmagazine.com · 2 July 2026

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

Taras Bakusevychuxdesign.cc · 30 June 2026

An applied framework for designing AI interfaces that support appropriate reliance, user control, transparency, and responsible autonomy. Traditional interfaces are built around predictable behavior. A control has a defined function. A workflow has known states. Errors can be anticipated and recovered from. AI systems are less deterministic. They introduce a property that most interface conventions weren’t designed for: the same input can produce different outputs. The same model can feel useful, confusing, or dangerous depending on the interface and instructions around it — product quality is not determined by model capability alone. AI introduces interaction problems that conventional UI patterns don’t resolve: When should the system suggest, ask, or act? How should uncertainty appear on screen? What evidence should accompany a generated answer? How much autonomy does a given action earn? Etc These are not cosmetic questions. They determine whether users can judge output, recover from mistakes, and remain responsible for consequential decisions. The central design question is How do we help users rely on AI appropriately? This arti

hello@smashingmagazine.com (Pratik Joglekar)smashingmagazine.com · 16 June 2026

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 t

Packy McCormicknotboring.co · 10 June 2026

Welcome to the 192 newly Not Boring people who have joined us since Monday! Join 269,285 smart, curious folks by subscribing here: Subscribe now Hi friends 👋, Happy Wednesday and welcome back! A couple of months ago, my friends Adam and Ben at Genius Ventures asked if they could introduce me to one of their favorite founders, Markie Wagner. The Markie Wagner? The Choose Good Quests Markie Wagner? The drop an all-timer then go quiet for years, cooking up something spoken of in hushed tones Markie Wagner? The grew up inside of a computer and dreamed as a young girl in Southern California, seriously, of making computers do the work that humans shouldn’t have to Markie Wagner? Of course I wanted to meet Markie Wagner. So we met a month ago at Soho Diner and I ordered a milkshake and she asked them to cut up a bowl of fruit. She asked for my lore, which was boring, and I asked for hers, which she weaved non-stop for the next hour, landing so naturally on why she’s building what she’s building that it seemed almost pre-destined. She also told me, before everyone else came to the same conclusion, that tokenmaxxing was bullshit, because behind closed doors, the Fort