LLMs Aren’t The Only Game In Town

We’ve spent the last few years putting LLMs everywhere, but a new class of models released this week is a good reminder that they aren’t the only way to put intelligence into a product.

Jev from TypeSafe AI is designed for small, focused decisions rather than generating lots of text: which route should this take, should this be escalated, which model should handle it? It doesn't generate anything so it can't hallucinate. What it can do is process a lot of raw data and give probabilities on how to act. You just need a threshold to put into your app for when to take action. TypeSafe says responses can come back in tens to hundreds of milliseconds, with input pricing around $0.04 per million tokens, which makes this affordable to use in lots of different use cases.

So what does this mean for product teams?

Fortunately, the first wave of putting the sparkle icon into an app and saying it is now AI is coming to an end. Customers don't want a weaker model embedded when the core models can access your site anyway. What this offers is a much smoother UI experience, but it requires thinking about the workflows that your users are doing and redesigning them to take advantage of this option. One thing people are experimenting with is detecting traffic type in each request and routing agents versus humans to different areas / giving different responses. Or immediate categorization of large blocks of data. When a new technology like this comes out, there are always a range of ways to apply it. And the good news is that it means there is opportunity for product teams to experiment and find what works. Good luck.

This Week's Poll

Last week’s poll: We asked what you thought about the pace of AI. 73.3% wanted it to slow down, 13.3% felt the pace was about right, 13.3% were unsure and no one wanted it to accelerate.

This Week’s Updates

Enabling the Team

Parsing “Can” from “Should”: How Netflix’s Product Team Got Thousands of Creatives to Trust an AI Overhaul by First Round Review, featuring Mckenzie Lock
Netflix’s AI rollout started with where workflows were genuinely broken and what people should still own, using shadowing, structured debate and dissent to separate valuable automation from work where craft and human judgment needed to stay in control.

Everything Important Is Optional Until It Isn’t by Steve Huynh
Preventive work loses to urgent requests until something breaks. Trace recurring crises backwards and protect time for the work that prevents them.

Product

Barbell-Shaped Product Roles by Richard Mironov
Faster development increases the importance of discovery and commercialisation, shifting product work towards the decisions before and after building.

4 New Evals and 16 Experiment Variants to Fix 1 Customer Complaint by Teresa Torres
Fixing one customer complaint in an AI-generated opportunity solution tree required four evals and sixteen experiment variants, showing that reliable AI product quality comes from measuring specific failure modes, iterating against production data and combining prompts with orchestration, guardrails and deterministic checks.

Research

Building AI Evals: Who Gets to Decide What’s Good? by Nam Pham and Andrea Cavicchi
DoorDash shows why useful AI evaluations start with the right human answer key, distinguishing user preferences, domain expertise and firm boundaries.

Hiring Isn’t a Scaling Strategy: Turning Research Expertise into Self-Serve Infrastructure by Lucy Sutton
Two ResearchOps specialists supported 120 researchers by turning repeated requests into self-service infrastructure and measuring greater autonomy.

Design

Designing with AI by Buzz Usborne
Designing with AI works best when designers use it to explore interactions, generate working prototypes and pressure-test ideas earlier, while keeping human judgment in control of the problem, constraints and quality bar rather than treating generated output as the design itself.

Test Complex Interactions Earlier with AI Prototyping by Megan Chan
Use AI prototypes to test realistic behaviour earlier, while keeping research goals and human review ahead of the temptation to build a polished demo.

Dev

Nobody Needs a Human Router by James Stanier
Clear ownership, written updates and protected focus time let engineering leaders replace status routing with technical judgement and targeted help.

Trying the Software Factory Pattern by Will Larson
An early Imprint experiment connects agent work to goals, shared task state and escalation, treating coordination as part of the delivery system.

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Global Product Week Starts Next Week

Oxford and Lisbon join a week of local conversations

It is often easier to work through a difficult product question with people who are facing it too. Global Product Week starts next week, bringing local product, UX, design and engineering communities together from 5–8 October.

Oxford and Lisbon are the newest additions, joining events in New York, Amsterdam, Austin, Dublin, San Francisco and Boise, plus an online workshop. Whether you want to compare approaches or meet people building products nearby, there is plenty happening across the week.

Explore the events and find your local agenda. New York’s AI Evals Sandbox workshop is already full; the separate evening community event is listed below.

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Video Of The Week

Never Done: Evolving UX Teams Who Earn Influence

Growing a UX team does not automatically give it more influence. The harder part is changing how the organisation works with that team, and showing where design makes a difference to the business.

In this UXDX talk, Ben Hewett from Allied Solutions shares how he helped grow UX from three people to 25, moving from an internal agency that took on projects to designers embedded with product teams. He covers the resistance and missteps along the way, and how the team made its contribution clearer through business outcomes.

Watch the full session for practical ways to earn trust, build advocates and evolve the way your UX team works:

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgement, relationships and influence in a world that keeps trying to automate the surface of the work.

RSI Is The Buzzword Of The Week

In a week when the bosses of the big AI labs said they think the pace of AI development needs to slow down, they also seem to be doing the exact opposite.

Recursive Self-Improvement (RSI) is the idea that an AI can build the next version of itself. We can already use AI models to dramatically improve the performance of our systems. The thinking is that, eventually, the model will be able to build a better model, kicking off an exponential improvement curve.

Anthropic says Claude now does 26% of the research and development work on its next models. In March, that figure was 1%. Google DeepMind has also hinted at similar improvements.

So what does this mean for product teams?

AI is already an amazing tool for product teams. Right now, it’s an amazing tool but you still need people with enough context to prompt the AI well, and enough taste to judge the output. But taste is improving fast.

Context is still the harder problem. That depends on memory breakthroughs that don’t really exist yet, as well as people being disciplined about keeping the context available to AI systems up to date.

I think this is a net positive for people (assuming we can sort out alignment). But I am net positive on AI in general. What are your thoughts?

This Week's Poll

Last week’s poll
Last week we asked: As AI increases your team’s capacity, where is the hardest decision moving? Choosing which work to stop or kill led with 44.4%. Deciding what evidence still needs humans and which standards and guardrails to enforce each received 22.2%, while changing roles and teams received 11.1%.

This Week’s Updates

Enabling the Team

Want more confidence in AI? Give it more context. by Halina Mader
Give AI company data, institutional knowledge and work context to improve trust and cut rework; adoption metrics alone miss the point.

The executive loop by Jade Rubick
Effective leaders establish what needs to happen, work through the constraints and trade-offs, then build the conditions that make it happen.

Product

The most important product decision is what you don’t build by Liam Nugent
Make the lifetime cost of features visible, solve each customer need directly and reward removing complexity as well as shipping new work.

Will AI Eliminate PM Jobs? by Itamar Gilad
AI broadens what PMs can do, but judgement, coordination, strategy and deciding what to build remain essential even as tasks become automated.

Research

Do Participants Say Less to AI Than to Human Moderators? by Lucas Plabst, Jeff Sauro, Eva Sundberg and Jim Lewis
In one controlled study, participants spoke 45% less to AI moderators; shorter interviews raise questions about depth, not proof of poorer insight.

AI, Margins, and the New Division of Labour: Three Reversals Reshaping How Research Operates by Kate Towsey
Proactive insight systems need research craft and operational expertise together; automating output cannot replace strategic research leadership.

Design

Five eras of UX design and the lessons to keep in the AI era by Patrick Neeman
Each UX era repeats old mistakes; retain human factors, context and feedback as AI changes the tools and expands what designers can make.

AI in Design 2026: The inflection point is here by Designer Fund
Research with 900 designers shows a shift from static deliverables to building products and systems, expanding design’s scope and responsibilities.

Dev

Maybe We Shouldn't Be Reviewing All This Code by Rachel Laycock
Move judgement, learning and architectural alignment before code is written, then use automated checks for the constraints machines can verify.

We are all Product Engineers now by Laurie Voss
As coding costs fall, engineering value moves towards understanding customers, shaping solutions and owning product outcomes across the lifecycle.

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Who Controls The Customer Journey In Agentic AI?

Expedia, Target and Chase on designing AI that can act for users without losing trust, transparency or control.

AI can increasingly recommend, personalise, purchase and complete tasks on a customer’s behalf. The harder question is not what AI can automate, but what it should automate, and how teams preserve trust as more decisions move into the background.

At UXDX San Francisco, Rachel Been from Expedia, Purvi Shah from Target and newly announced Chris Stang from Chase will explore how leading consumer organisations are approaching that balance. Moderated by Alex Burke, CEO of Optimal, the discussion will cover where automation should stop, when users need explicit approval, and how products can make AI-driven decisions more transparent without adding unnecessary friction.

Explore the San Francisco agenda for 5-6 November, and use NEWSLETTERSF26 for 10% off tickets.

Explore the UXDX San Francisco agenda
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Video Of The Week

If AI Can Build Products, What Are The Humans For?

AI has made it dramatically easier to turn an idea into working software. The harder question is how teams avoid moving faster in the wrong direction when execution is no longer the main constraint.

In this talk that I gave earlier this year, I look across the product delivery lifecycle to show where AI can already take on design, code, testing and deployment, and where human judgement still matters most. Roles may merge, but understanding customers, choosing direction and coordinating decisions do not disappear.

Watch the full session to see why faster building changes the product team, and what people need to become better at when shipping is no longer the hard part:

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgement, relationships and influence in a world that keeps trying to automate the surface of the work.

Meta Cut the Managers. The Work Stayed.

Meta spent much of this year betting that AI meant it needed fewer managers. In its Applied AI organisation, some managers had as many as 50 people reporting to them. Former managers lost their reports and went back to individual contributor roles. The logic was that if AI does more of the work, teams need less coordination.

Code changes rose 220% but features reaching users rose only 36%.
At the same time, major incidents rose 40%, and time spent firefighting them rose 70%.

So this week Meta started asking some of those individual contributors to become managers again.

A team can only move as fast as the organisation around it. AI made producing code cheap. It did nothing for the decisions, reviews, context and coordination that turn code into something a customer uses. Meta removed the people doing that work and left the need for it in place. So the work queued up, and some of it didn't get done. That's where the gap between 220% and 36% went, and where the incidents came from.

I'm still bullish that in the long term teams will be more autonomous, but it only works when you remove the work managers were absorbing. Do that first and you'll need fewer managers as a result. Cut the managers first and the coordination still has to happen somewhere. At Meta, a lot of it turned up as firefighting.

AI really is helping teams build faster, but the bottlenecks around approvals and coordination still exist. That's why the goal needs to be zero blocking dependencies from idea to satisfied customer.

This Week’s Updates

Enabling the Team

AI in Product teams: In 2026, the growing impact on collaboration by Anna Lefour
AI adoption is moving from individual tools to team-wide strategy, reshaping roles, collaboration and governance across product teams.

AI Transformation Requires Redesigning Work, Not Cutting Roles by Faisal Hoque, Tom Davenport and Paul Scade
AI creates more value when organisations redesign roles, workflows and decision rights instead of treating headcount reduction as the strategy.

Product Direction

Your Product Didn't Get Worse by Waldek Mastykarz
Agents can shift customer expectations before satisfaction or churn moves, so teams need to research how the work is changing before adding an AI layer.

AI Evals: A Hands-On Guide for Product Teams by Teresa Torres
Turn subjective AI quality into a repeatable feedback loop using error analysis, golden datasets, code assertions, LLM judges and customer feedback.

Continuous Research

Don’t Outsource the Learning: Why Human-Led Research Still Matters in the Age of AI by Maria Rosala
AI can accelerate research outputs, but teams still need to observe, question and interpret evidence together to build shared understanding.

When Everyone Builds, How Do You Keep Customers at the Center? by the Dscout Team
Leaders from Expedia, Cisco and AWS show how research can govern distributed insight generation without losing customer context.

Continuous Design

Limit the Number of Details by Geoff Teehan
Every feature and exception consumes attention. Reducing detail creates the space teams need for stronger product craft.

Is the Future of Software Certain Once AI Is Everywhere? from Arin Bhowmick
As software becomes more autonomous, designers need to define the judgement, boundaries and handoffs that determine when systems act, ask for help and return control.

Continuous Development

Responsible AI Adoption Needs Developer Workflow Design by Gleb Tsipursky
Responsible adoption requires workflow-level guardrails, named owners and psychological safety, not policies that teams work around.

The End of Programming by Paul Dix
AI shifts engineering from reviewing every line to designing architectures, verification systems and feedback loops that supervise outcomes.

This Week's Poll

Last week’s poll: Keeping evidence and interactions genuinely human led with 41.7%. Choosing what deserves attention and setting clear ownership and standards each received 25%, while defining what complete and valuable means received 8.3%.

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From AI Pilots to Organisational Change

What It Takes To Turn AI Into Real Product Impact.

Most teams have experimented with AI by now. The harder question is what it takes to turn those experiments into lasting product impact, and what needs to change inside the organisation to support that shift.

At UXDX San Francisco, Maz Brumand from ŌURA will explore how teams can move AI beyond isolated pilots and into real business value, while Jod Kaftan from NTT DATA will look at how new interfaces are reshaping the way organisations work. Explore the agenda to see both sessions and how leaders are preparing teams for the next phase of AI adoption.

Explore the San Francisco agenda for 5-6 November, and use NEWSLETTERSF26 for 10% off tickets.

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Video Of The Week

Designing for the Agent Experience: When UX and DX Collide in the Age of AI

In this UXDX talk, Dana Lawson from Netlify shares how teams can approach agent-driven building with trust and safety as the foundation. As AI gives more people the ability to create, ship and change software, product and engineering teams need systems that support speed without hiding critical risk.

Dana walks through a practical blueprint for safer autonomy, covering structured errors, event-driven signals, legible architecture, sandbox execution, human approval flows and fast rollback. This is not theory. It is what happens when agents have real access to real platform primitives.

Watch the full session to learn how teams can keep builders moving while designing the guardrails needed for AI-powered delivery at scale.a Lawson, CTO at Netlify, explores what happens when products are designed for AI agents as well as people.

She shows how clearer architecture, stronger guardrails and intent-driven interfaces improve the experience for human builders too.

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgement, relationships and influence in a world that keeps trying to automate the surface of the work.

OpenAI Launched Astra - The Model That Hacked Hugging Face

During the training run, OpenAI had roughly 1,200 agents running in isolation. They found a way to communicate with each other, shared more than 70,000 messages and files, and eventually around 700 of them participated in a coordinated attack on Hugging Face.

They were trying to solve an impossible test, so rather than keep attempting and failing, one agent worked out that compromising the surrounding infrastructure might help it understand or manipulate how they were being evaluated. The changes it started making led the agents to discover each other. They auto-formed hierarchies and specialisations to target different parts of the plan. Some even recognised that what they were doing was outside the rules, but they carried on anyway. They even tried to cover their tracks by manipulating log files. Not a single one alerted a human about what was happening.

The era of smart AI's might be coming faster than we expect.

This Week’s Updates

Enabling the Team

How Much Can You Hold Before It Breaks? by Steve Huynh
Use workload like a load balancer: protect one priority, limit concurrent work and account for the hidden cost of constant context switching.

Most AI rollouts fail due to change management, not technology by Alisa Yu
Atlassian moved AI enablement from IT to its people team, using leader demonstrations and micro-learning to turn access into lasting adoption.

Product Direction

You Can’t Prioritize by Value Until You Define Its Context by Tim Herbig
Define the context for customer, business and team value before prioritising, or scoring systems will optimise for competing meanings.

Technical Ingredients, Revenue Ingredients by Richard Mironov
Separate executable code, commercially deployable software and a product that sales can successfully sell, support and renew.

Continuous Research

AI Can’t Replace Real Research in Empathy Mapping by Rachel Krause
AI can organise genuine user evidence, but synthetic observations cannot replace research when teams build empathy maps.

Researcher-in-the-loop by Jennifer L. Bowie
Keep researchers responsible for system design, high-risk escalation, validation and feedback loops as AI increases research scale.

Continuous Design

How AI-ready is your design system? Now you can measure it. by Christoph Hellmuth
Benchmarking 898 generated interfaces shows that current design systems still lack the structure and guidance required for AI-native work.

Your synthetic self belongs to strangers: How AI reserves the real you for a shrinking few by Anamol Rajbhandari
AI will thin authenticity first in public interactions, making deliberate, unpolished human expression more valuable in close relationships.

Continuous Development

How AI Changes Engineering Team Structure by Matthias Patzak
AI agents shift team structures towards broad human judgement and specialised execution, but accountability and ownership remain essential.

The right amount of spec for agentic development by Markus Eisele
Agentic development raises the value of specifications, verification and clear constraints, changing what engineering leaders standardise.

This Week's Poll

Last week’s poll: Development coordination and quality led with 41.7%, followed by research and design evaluation with 33.3%. Product bets and go-to-market received 25.0%, while team goals and work design received 0%.

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When Everyone Can Build,
What Happens to Product Teams?

Two UXDX San Francisco talks on how AI is changing product roles, collaboration and decision-making.

AI is making it easier for more people to contribute to product development, but that also raises a bigger question: when building gets faster, how do teams make sure they are still building the right thing?

At UXDX San Francisco, Ashley Nutter from CNN will share how large teams can adopt AI without creating tool sprawl or operational chaos. Josh Clemm from Figma and David Hoang from Atlassian will explore what happens when designers, engineers and product managers can all move faster, and why judgement, taste and clear decision-making become more valuable as output gets easier.

Explore the San Francisco agenda for 5-6 November, and use NEWSLETTERSF26 for 10% off tickets. Prices increase this weekend, so last chance at the early bird price!

Explore the UXDX San Francisco agenda
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Global Product Week is taking shape
From 5-8 October, UXDX communities will bring product people together across New York, Amsterdam, Austin, Boise, Dublin and San Francisco, alongside an online workshop. This is the first wave, with more participating cities and agenda updates coming soon. Explore upcoming events.

Video Of The Week

Building The Next Generation Of Software

AI is not just changing software. It is changing what teams build, how they build it, and how quickly they need to move. In this UXDX EMEA 2026 talk, Des Traynor, co-founder of Intercom and now Fin, shares how the company responded when generative AI threatened to disrupt its core business. Instead of adding AI features around the edges, the team rethought its strategy, product, culture, pricing, ways of working and the company itself.

Des explores why traditional UI is becoming less central, what agentic products mean for product teams, why reliability is one of the hardest challenges in AI product development, and how AI is changing the role of designers and engineers. He also shares why every designer at Fin now ships to production, how the team tripled engineering productivity, and why speed may become one of the most important competitive advantages.

Watch the full session to learn what happens when a company goes all in on building software for the AI era:

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

Inside Shopify’s high-intensity engineering culture by Bill Doerrfeld
Shopify cuts meetings, pairs engineers, ships weekly and keeps humans accountable for AI-generated code without extending hours.

Good Culture is the Biggest Productivity Hack, Not AI by Gregor Ojstersek
AI amplifies existing culture; clear ownership, priorities, trust and outcome focus are prerequisites for useful productivity gains.

Product Direction

You need to find product-market fit again (sorry) by Cleo Lant
PostHog shows how incumbents can fund small attacker teams, measure pre-PMF bets differently and let usage overrule opinion.

Useful isn’t enough: Why our B2B AI product failed by Govind G Nair
A failed AML AI product shows why solving a real problem is not enough when buyer incentives, packaging, channels and org design misalign.

Continuous Research

Scale Your AI Research Program, Without Losing Strategic Value by Paige Bennett and Stevie Vanderwiel
A fourfold jump in research output did not create strategic value; the fix is charters, workflow boundaries and decision-quality metrics.

One AI Output Is an Example, Not an Evaluation by Raluca Budiu
AI evaluation needs representative inputs, repeated runs and confidence intervals; one good output proves possibility, not reliability.

Continuous Design

Behind the design: Assisted Culling in Adobe Lightroom by Shipali Shetty, Ankur Murarka, Kwamina Arthur and Christopher Sun
Lightroom’s team shares five principles for AI-assisted culling: transparent signals, conservative rejects, local processing and user control.

Am I still designing? Yes, but on a bigger canvas by Ilona Jaudzemyte
An Automattic designer built a WooCommerce image plugin and calibrated an AI judge around product fidelity and merchant trust.

Continuous Development

Staff+ engineers must rebuild team culture by James Socol
AI-enabled solo work weakens shared goals; engineering leaders can rebuild team culture through charters and modelled collaboration.

AI usage patterns in software teams by Tim Qi
AI raises software output but not time saved, as coordination grows, roles blur and more people become active builders.

This Week's Poll

Last week’s poll: Reviewing AI-generated design and code led with 11 of 31 votes (35.5%), followed by validating research and user evidence with 10 votes (32.3%). Setting priorities and decision rights and managing dependencies and security each received five votes (16.1%).

Seen an interesting article online? Share it with us, and we might feature it in our next issue!
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New Leaders You Will Meet At UXDX SF

Featuring Leaders From
Knapsack, Sephora, NTT Data, Optimal & Docusign

UXDX San Francisco is getting closer, and more leaders have now joined the agenda. The latest additions include Sneha Narahalli from Sephora, Jod Kaftan from NTT DATA, Marina Lin and Amit Sathe from Docusign, Chris Strahl from Knapsack, and Alex Burke from Optimal.

With almost all sessions now announced, the programme is taking shape around how AI is changing product decisions, team roles and delivery.
Explore the growing agenda, choose the sessions most relevant to your team, and use NEWSLETTERSF26 for 10% off tickets here: https://uxdx.com/sanfran/2026/tickets/

Explore the UXDX San Francisco lineup
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Video Of The Week

Bridging Human Insight and AI: How Pinterest Builds Visual Experiences That Connect

In this UXDX USA 2026 talk, Larkin Brown, Senior Director of Product Research at Pinterest, shares how Pinterest is using generative AI to support discovery, shopping, visual search and inspiration while keeping real user needs at the centre. Drawing on examples like Pinterest Assistant and visual search, she explains how research, design and machine learning teams work together to evaluate LLM outputs, use real user queries, and turn foundational research into prompts, principles and product experiences.

Watch the full session to learn how Pinterest is keeping humans in the loop while building the next generation of AI-powered discovery:

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

EQ Is Not Just About What You Say. It’s Your Body Language Too. by Wes Kao
Emotional intelligence depends on whether facial expressions, tone, and body language reinforce what someone is saying, so leaders need to understand their unconscious signals and create the conditions for more honest, productive conversations.

Tension-Based Prioritization (A Non-Framework) by John Cutler
Recurring tensions expose where teams lack the conviction to stop, reset, or fund work, producing better priorities than another scoring framework would.

Product Direction

How to make AI better at product by Luca Rossi
AI produces better product work when teams preserve the context behind decisions through product decision records and a shared language.

A Fresh Definition of The Product Role by Marty Cagan
The product role shifts from backlog coordination to applying judgement, context and responsibility for value and viability.

Continuous Research

Thoughtful AI Implementation for UXR Leaders by Ashlee Edwards, Ph.D.
Thoughtful AI adoption starts with clear boundaries, protects core research skills and evaluates quality, risk and actual time saved.

Dogfooding vs. QA vs. User Research by Nielsen Norman Group
Dogfooding catches defects but cannot replace research because employees understand the product too well to represent genuine users.

Continuous Design

AI UX debt: A new bottleneck by Zeeshan Khalid
AI can make interfaces look finished before teams resolve how they should behave, creating UX debt through inconsistent patterns and missed edge cases.

When AI-generated output is effortless, experience is your edge by Michelle Turner
When AI makes polished output cheap, product quality depends more on a trained eye, clear direction and informed design judgement.

Continuous Development

Can software factories actually work? by Jina Yoon
AI coding scales when agents receive product context, deterministic checks and guardrails that assume failure, not just better prompts.

Who Does What? Team Topologies For The Agentic Platform by Olivier Wulveryck
The agentic platform defines what needs to be provided. Team Topologies defines who provides it, and how teams interact to make it happen. This second article applies Skelton and Pais’s model to a world where agents produce and humans orchestrate.

This Week's Poll

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New Workshops For Building Better AI Products

Measure AI-Powered Experiences
& Keep AI-Generated Interfaces Aligned

As AI becomes part of everyday product work, teams need better ways to judge whether the output is actually good. Two new hands-on workshops at UXDX San Francisco focus on exactly that!

Marina Lin and Amit Sathe from Docusign will show how to measure AI-powered experiences across trust, transparency and output accuracy, while Sam Alexander and Chris Strahl from Knapsack will explore how to test AI-generated interfaces against your design system, components and quality standards.

Both sessions are designed to give you practical frameworks you can take back to your team, whether you are trying to understand if an AI feature is worthy of users’ trust or prevent AI-generated work from creating inconsistency, accessibility issues and rework. Book your UXDX San Francisco ticket now to secure access to the workshops. Check-out the full agendaa and tickets below.

Click here and book San Francisco
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Video Of The Week

Driving Transformational Business Results:
The Role of Design Across the Org

In this conversation, Alex Burke speaks with Daniela Jorge, Chief Design Officer at Capital One, about what it takes to build a world-class design organisation and connect design work to broader business priorities. Daniela shares how design can move beyond execution to influence product strategy, customer outcomes and organisational growth.

The discussion explores how design teams earn influence, collaborate with business-critical functions and create a shared strategy that resonates across the organisation. Drawing on Daniela’s experience across major brands, the session looks at what has worked, what has not, and how design can create meaningful value for both customers and the business.

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

Why Effective Leaders Get Branded As Problems by Luis Velasquez
Effective leaders can be labelled difficult when their pace, clarity or decisiveness exposes tensions the organisation has not resolved, so companies need to examine role expectations, team readiness and organisational context before treating the leader’s behaviour as the only problem.

Exception, Presence, Delegation by John Cutler
Healthy organisations balance clear exception signals, leaders who stay close enough to understand the work, and genuine delegation, because dashboards without context, involvement without autonomy, or empowerment without support all create different forms of organisational dysfunction. 

Product Direction

Prioritization Happens In Layers by Ant Murphy
Prioritisation becomes easier when teams make choices in layers, starting with vision, strategy and outcomes before comparing opportunities and solutions, so the backlog reflects clear direction instead of becoming a substitute for it.

The Product Lifecycle Is Broken by Ravi Mehta
As AI makes working software cheaper than specs and mockups, product teams can use prototypes throughout discovery to explore options, align earlier and validate decisions, replacing staged handoffs with tighter collaboration around what should actually be built.

Continuous Research

AI Needs a Human Touch: Why Researchers Should Lead AI Evals by Nathan Reiff
Researchers are well placed to lead AI evals because they connect user needs with human, LLM-as-judge and code-based evaluations, helping teams measure AI quality against real product value rather than model performance alone. 

AI As An Active Participant: What Happens When Services Start Making Decisions by Natália Tôrres
As AI begins making decisions inside services, researchers need to examine where it shapes access, risk and support, helping teams uncover harmful consequences early and build stronger oversight, transparency and routes for people to challenge automated outcomes.

Continuous Design

From Faster Pencil To AI Experience Architect: A Designer’s Path by Patrick Neeman
As AI makes design execution faster, designers create more value by moving upstream into workflow design, systems thinking and organisational decision making, shaping how models, people and processes work together rather than simply producing more screens.

Designing The Human+AI System by Daniel Ruston
Designing AI products means looking beyond the interface to shape the full system of human judgment, model behaviour, context and feedback, so teams create clear collaboration between people and AI rather than simply adding automation to existing workflows.

Continuous Development

Humans And Agents In Software Engineering Loops by Kief Morris
Rather than leaving agents unsupervised or reviewing every line they generate, engineers should work “on the loop” by designing the specifications, tests, quality checks and feedback systems that let agents produce reliable software and improve over time.

The State Of AI Governance In 2026 by Kelsey McKeon
AI-powered tools are spreading faster than organisations can see or govern them, so leaders need centralised security, access controls and ownership that let teams keep building quickly without leaving production risk to policies that people may ignore.

Seen an interesting article online? Share it with us, and we might feature it in our next issue!
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Who Controls The Customer Journey
In Agentic AI?

Don’t Miss This Talk At UXDX San Francisco!

As AI systems begin recommending, planning, purchasing and completing tasks on behalf of people, the customer journey is becoming less visible and more automated. That can reduce effort, but it also raises important questions about trust, consent, transparency and when users should be asked to approve an action.

This panel will explore how leading consumer organisations are designing agentic experiences that feel helpful rather than intrusive. Rachel and Purvi will discuss how to decide what should be automated, build effective oversight and intervention mechanisms, and create products that deliver convenience while keeping users informed, confident and in control.

Click here and book San Francisco!
Use ‘NEWSLETTERSF26’ at checkout to get 10% off the price!

FREE COMMUNITY EVENTS 

Video Of The Week

Why AI Makes Strong Teams Stronger
And Weak Teams Weaker

AI does not automatically make teams more effective. It amplifies how they already think, make decisions and take responsibility. In this online community event, Oleksandra Bernatska, Lead Product Manager at Boosta, shares insights from a cross-functional team using AI in its daily work, including where it genuinely speeds up thinking and where it can quietly weaken it.

The session explores why mature teams often become stronger with AI, while weaker teams accumulate hidden risks around quality, accountability and decision making. The focus is not on tools, but on the team culture, working habits and cognitive responsibility needed to use AI well.

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

How Leaders Get In The Way Of Organizational Change by Ron Carucci
Organisational change stalls when leaders underestimate the work, overload teams with disconnected initiatives and promote transformation without changing themselves, so progress depends on realistic capacity, integrated priorities and leaders demonstrating the behaviours they expect from others.

The 3 Biggest Workplace Stressors; 1 Structural Fix by Joost Minnaar
Workplace stress is not one problem: unclear expectations damage performance, conflicting demands drive burnout and excessive workloads harm wellbeing, so defining roles through clear purpose, decision rights and measures of success gives teams more autonomy without leaving responsibility ambiguous.

Product Direction

Productive Garbage by Afonso Franco
AI has made it cheap to generate endless documents, prototypes and options, shifting the product bottleneck from creation to judgment, so teams need stronger prioritisation and curation to stop apparent productivity from overwhelming decision-making.

The AI Playbook Puzzle by John Cutler
AI does not provide a universal playbook; it exposes which practices were already broken, which good practices can be strengthened, and which old constraints no longer apply, making contextual judgment and adaptability more valuable than rigid AI frameworks.

Continuous Research

How To Spot And Stop Discovery Slop by Tim Herbig
AI can make product discovery look more rigorous without reducing meaningful uncertainty, so teams need to return to raw evidence, separate synthesis from summarisation and use human judgment to stop polished outputs from masking weak research.

Stop Reporting UX Activity And Report Business Outcomes by Lola Famulegun
Reporting interviews, usability scores and design activity makes UX look like a cost centre, so teams need to connect their work to revenue, cost, risk, speed and retention to demonstrate impact in the language leadership uses to allocate resources. 

Continuous Design

Output Isn’t Design by Karri Saarinen
AI can generate polished interfaces quickly, but design still depends on understanding the human needs, constraints and tensions behind the problem. Hence, teams need to treat generated output as material for exploration rather than evidence that the right solution has been found. 

We Built This. Now We Own It. by Patrizia Bertini
Designing AI responsibly means looking beyond immediate usability and engagement to the wider systems products create, questioning who may be harmed, where dependency is encouraged and how oversight, safeguards and accountability can be built into decisions before launch.

Continuous Development

AI Demands More Engineering Discipline. Not Less by Charity Majors
AI makes it easier to generate large volumes of plausible code, increasing the need for testing, observability, small changes and clear ownership so engineering teams can move faster without losing control of quality or production systems.

Revised Rules Of Engineering Leadership. by Will Larson
AI makes first-pass code and large migrations dramatically cheaper. However, reliable delivery still depends on strong development harnesses, durable teams with deep domain context and leaders who can make fast, binding decisions without sacrificing technical quality.

Seen an interesting article online? Share it with us, and we might feature it in our next issue!
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Turning AI Experiments Into Business Impact

At UXDX San Francisco, Explore What It Takes To Scale AI Beyond Isolated Pilots

Most organisations have launched AI experiments. Far fewer have turned them into products, workflows and operating models that deliver measurable customer and business value. The challenge is no longer proving that AI can work. It is deciding which initiatives deserve investment, how success should be measured and how innovation can scale without creating unnecessary risk.

At UXDX San Francisco, Maz Brumand will share what separates successful AI transformation from costly experimentation. The session will cover how to prioritise initiatives based on value and effort, establish practical governance, measure impact beyond usage metrics and scale proven capabilities across teams, products and customer experiences.

Click here and book San Francisco!
Use ‘NEWSLETTERSF26’ at checkout to get 10% off the price!

FREE COMMUNITY EVENTS 

Video Of The Week

UX Researcher: Team of one

Great research does not create impact on its own. In this online community event, Swati Sachdeva, UX Researcher at Google, shares what she learned after presenting a strong research deck that received polite agreement but led to no action. The missing skill was not research quality. It was understanding the business, stakeholder priorities and how decisions were actually made.

Swati explores what solo researchers need beyond running studies, including earning trust, navigating stakeholder politics, communicating with people who do not read research reports and proving the value of research while building the function itself. It is a practical session for researchers entering solo roles or trying to understand what seniority really looks like in practice.

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

The False Alignment Trap by Julia Dhar, Kristy R. Ellmer and Philip Jameson
Transformation efforts often fail because leaders mistake polite agreement for real alignment, so teams need to surface differences around why change matters, what success looks like and how it will happen before conflicting assumptions undermine execution.

The Border Collie Faustian Bargain by John Cutler
When experienced employees can see the gap between leadership messaging and organisational reality, they often become responsible for translating the ambiguity without being able to challenge it, creating a choice between protecting the fiction, absorbing the coordination burden or deciding when to stop herding the system.

Product Direction

Your Goal Depends On Another Team - Now What? by Tim Herbig
When a team’s goal sits outside its direct control, the answer is not to shrink the ambition but to clarify the intended outcome, expand the team’s skills or create shared goals that align incentives across the teams needed to deliver real impact. 

Behind The Scenes: Building AI-Generated Opportunity Solution Trees by Teresa Torres
Building AI-generated Opportunity Solution Trees shows how product discovery can be accelerated without giving up structure or judgment, but reliable outputs still depend on validation loops, deterministic checks and careful testing rather than prompting alone.

Continuous Research

Your New UX Habit: Establishing Baselines For Impact by Taylor Dykes & Pavel Samsonov
Establishing a clear baseline before work begins gives teams a meaningful “before” against which to measure change, helping them choose relevant behavioural metrics, demonstrate UX impact and build stronger credibility with stakeholders.

Ten Minutes, Five Teams, One Pizza: Delivering Continuous Research Programs That Run Like Clockwork by Josh Morales
Running short, repeatable discovery sessions with multiple product teams and users makes regular customer contact easier to sustain, helping teams test early ideas, build empathy and bring research into everyday product decisions without replacing deeper methods.

Continuous Design

Becoming An AI-native Designer by Sen Lin
Becoming an AI-native designer means moving beyond static mockups to working prototypes, translating tacit design judgment into clear context, components and criteria, and building custom scaffolding that lets designers explore ideas directly in the medium where products actually live.

AI In Design Report 2026 by Designer Fund and Foundation Capital
AI is moving designers from static mockups toward coding and working prototypes, expanding creative range and speed while making judgment, ownership and a clear point of view more important for protecting quality and avoiding generic output.

Continuous Development

The "I Don't Know, Claude Wrote This" Pandemic by Anton Zaides
AI becomes dangerous when engineers stop understanding the code they submit, so teams need to review every change, retain ownership of architectural decisions and use agents for cognitive offloading without surrendering the judgment required to maintain reliable systems.

Why We're Bullish On Loops by Ian Vanagas
Loop engineering moves AI from responding to one-off prompts to repeatedly finding, completing and evaluating work against clear goals, context and feedback, helping teams automate smaller product improvements while keeping human direction, taste and empathy in control.

Seen an interesting article online? Share it with us, and we might feature it in our next issue!
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Optimal Joins Us Again In San Francisco

What Should User Researchers Actually Automate?

AI can generate discussion guides, summarise interviews, identify patterns and produce reports in minutes. But the more important question is not what AI can do. It is which parts of research teams should allow it to do. Automating the wrong work can remove context, reinforce bias and create confidence in insights that have not been properly challenged.

At UXDX San Francisco, Gabe Young and Josh Walker from Optimal will lead a practical workshop on how to decide what to automate, what to augment and what should remain human-led. Attendees will explore how to accelerate analysis and synthesis without reducing research quality, combine AI efficiency with human judgement, and identify the risks of AI-assisted research.

Click here and book San Francisco!
Use ‘NEWSLETTERSF26’ at checkout to get 10% off the price!

FREE COMMUNITY EVENTS 

Video Of The Week

How AI Is Changing The
Software Development Workflow

AI can accelerate software development, but engineers must remain responsible for context, quality and the final product experience. In this UXDX Community talk, Liliana shares how she uses AI across the development workflow, from exploring requirements and technical risks to planning work, generating tickets, reviewing code and capturing lessons for future projects.

She explains how structured AI workflows can improve autonomy, documentation and collaboration while keeping work manageable through smaller tickets, pull requests and human review. The key is to let AI handle repetitive and exhaustive tasks without outsourcing critical thinking, accountability or engineering judgement.

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

You Might Be A One-Company Manager by Karol Wójciszko
Growing into management inside one company can mask how much performance comes from context, trust and reputation, so switching environments becomes the real test that forces managers to build portable skills like influence, feedback and decision-making from scratch.

Building An AI-Positive Work Culture by Jakob Nielsen
Employer encouragement predicts workplace AI adoption almost 5x more strongly than training or tool provision alone. This 10-step playbook shows design leaders how to build a culture where AI adoption happens naturally, without mandates, surveillance, or wasted training budgets, while avoiding the cybersecurity pitfalls of “Shadow AI.”

Product Direction

Unexpected Lessons From An AI-Assisted Prototyping Experiment by Olivia Xu
AI-assisted prototyping works best when it tightens the loop between design, engineering and product, letting teams test real interactions earlier, surface constraints sooner and replace handoffs with shared iteration inside the actual product.

Discovery VS Delivery by Buzz Usborne
AI compresses delivery far more than discovery, so the real differentiator shifts upstream to judgment about what should exist, what is worth building and where original product ideas come from instead of just how fast teams can ship. 

Continuous Research

When To Use (And Avoid) Synthetic Research Participants by Bridget Bidlack
Synthetic participants can help researchers pressure test guides, explore edge cases and generate hypotheses, but they break down on emotional nuance, real behaviour and high stakes decisions, so teams need to treat them as a prep tool rather than a substitute for real human evidence.

The Great UXR Transformation by Madeline Kleiner, Trish Miner, and Katie Rotella
AI is pushing UX research beyond interface studies toward shaping model behaviour itself, so researchers need stronger technical fluency, faster mixed-method workflows and tighter cross-functional collaboration to turn human insight into output quality, trust and better AI experiences.

Continuous Design

AI Doesn't Read Your Design System The Way You Do by Robin Cannon, VP of Product, Knapsack
Ask an AI tool to build a screen today, and it won't reach for your design system; it reaches for whatever's already in its training data, like shadcn/ui. Your team spent years building a component library. The fastest-growing consumer of your UI never opened it. Robin's argument: a component is a cache of decisions, not the decisions themselves. People fill in the missing context from memory; AI can't. The fix isn't a bigger library — it's decision coverage: how much of your team's reasoning is captured somewhere a machine can actually read. Two teams can have identical component coverage and wildly different decision coverage — and it's the second number that predicts whether AI builds the right thing.

Why AI Is Making Design Converge On The Same Look by Anamol Rajbhandari
AI gives individual designers more range and speed while pulling the field toward the same statistical average, making human taste, constraints and deliberate judgment essential for avoiding generic work and preserving creative difference.

Continuous Development

You’re Not Behind. This Is Your Problem by Fran Soto
As AI makes engineering roles broader and career advice noisier, software engineers need a clearer specialism, deeper judgment and visible proof of expertise rather than presenting themselves as generic builders who can do everything.

State Of AI 2026 by Devographics
AI-assisted coding is becoming standard practice, with developers reporting that 54% of their code is now AI-generated, but hallucinations, weak code quality and missing context remain widespread, showing that faster adoption has not removed the need for human review and engineering judgment.

Seen an interesting article online? Share it with us, and we might feature it in our next issue!
Click here to share an article

Knapsack Is Back With UXDX!

We’re partnering across San Francisco and New York

We’re excited to share that Knapsack will be joining us as a UXDX partner for a second year, supporting UXDX San Francisco 2026 and UXDX New York 2027.

What we really value about the Knapsack team is that they do more than help companies build and scale design systems. Through their Patterns events and wider community work, they actively bring product, design and engineering teams together around what is working in practice.

For UXDX San Francisco, we’re developing a hands-on workshop with Knapsack exploring how AI is changing design systems and how cross-functional teams build together. More details coming soon…

Click here and book San Francisco!
Use ‘NEWSLETTERSF26’ at checkout to get 10% off the price!

FREE COMMUNITY EVENTS 

Video Of The Week

The Handoff Was Never The Product:
How AI Can Bridge Design And Development

Design handoff has never been as simple as passing over a Figma file. Products continue to be shaped through development, QA, edge cases, business rules and the many decisions that surface only once teams try to build the thing.

In this UXDX Community talk from Lisbon, Nuno explores how AI can help designers, developers and product managers identify missing decisions earlier, from error states and permissions to accessibility requirements and design system drift. A useful watch for anyone trying to improve the space between design and development, reduce ambiguity, and build more shared context before small gaps become expensive problems:

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

Collaborative Steering by Luke Wroblewski
As teams start guiding separate AI agents, perspectives can drift faster than output improves, so collaborative steering creates shared context that helps design, product and engineering encode goals together instead of letting every agent optimise for a different version of the work.

Sometimes Your Job Is To Get In The Way by Michael Lopp
Good leadership is not picking one style and sticking to it, it is knowing when to stay out of the way and when to step in hard, drawing a line that resets priorities before speed, momentum or cheerleading turn visible success into systemic failure.

Product Direction

The AI Playbook Puzzle by John Cutler
AI does not create a playbook so much as expose whether teams understand which practices were always broken, which were always useful, and which only made sense because of old constraints, making contextual judgment more valuable than any tactical AI framework. 

The Interface Is No Longer The Product by Alejandro Gonzalez
As agents work directly on structured representations instead of clicking through interfaces, the real product shifts from screens and files to the underlying object of work, pushing teams to rethink software around source of truth, validation and views rather than UI alone.

Continuous Research

How Do We Grow In The Age Of AI? by Optimal (Sponsored Content)
As AI takes over more of the research workflow, the real challenge becomes preserving the judgment, rigor and storytelling that turn raw evidence into trustworthy insight, especially for junior researchers still learning the craft.

The “Second Layer” Problem: What to Do When AI Tools Create Operational Debt by Jordan Brinkman
AI tools do not just add capability, they create a second layer of orchestration, governance and maintenance work, so teams need to account for the cognitive and operational debt of building with AI instead of assuming automation simply makes research lighter.

Continuous Design

Taste Over Haste by Anne Quito
As AI makes polished execution easy, design value shifts toward taste, the judgment to choose what is worth pursuing, what feels coherent, and when a result is too generic, making creative agency and point of view more important than speed alone.

Most AI Tools Make Users Faster. The Best AI Tools Make Users Better. by Daisy Chen
The best AI tools do more than accelerate tasks, they shape how users think, judge and grow over time, so designing human control, calibrated trust and the right friction matters as much as raw speed if AI is meant to improve people rather than quietly deskill them.

Continuous Development

The Un-Hateable Engineering Managers by Anton Zaides
Managers who avoid hard feedback to stay liked usually push the cost onto the rest of the team, so strong leadership means accepting short term discomfort and making the calls that protect team health, clarity and performance.

The Eternal Sloptember by George Hotz
AI agents can frontload progress in software work, but when teams stop reading closely they also make it easier to ship polished looking slop, shifting the real development challenge from generating code to preserving judgment, feedback loops and standards of quality.

Seen an interesting article online? Share it with us, and we might feature it in our next issue!
Click here to share an article

Prices Go Up This Week

Don’t Wait On UXDX San Francisco 2026

This is your final chance to book UXDX San Francisco at the current rate before ticket prices increase later this weekend. On November 5–6, leaders from Target, Expedia, Verizon, Snapchat, ŌURA and more will come together at August Hall to share how product teams are adapting to AI in practice. Expect one day of talks on what is changing inside teams, products and processes, followed by one day of workshops focused on execution. Book your tickets below:

Click here and book San Francisco!
Use ‘NEWSLETTERSF26’ at checkout to get 10% off the price!

FREE COMMUNITY EVENTS 

Video Of The Week

Leveraging AI for Safer &
More Efficient Healthcare Delivery

AI in healthcare is not just about building powerful models. It is about ensuring they can be deployed safely, understood by the people who use them, and integrated into clinical workflows without adding more complexity to already stretched teams.

In this talk, Dr. Ashley Beecy shares lessons from leading AI operations at NewYork-Presbyterian, including how to bring together clinical users, data teams, MLOps, informatics and research to support better patient care and provider wellbeing. A useful watch for anyone thinking about how AI moves from promising technology to responsible, practical implementation inside complex organisations.

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

Before You Fire All Your Glue People Because Of AI by John Cutler
AI can reproduce visible artifacts like updates, notes and roadmaps, but not the judgment, trust and social coordination that make them matter, so cutting “glue people” too early risks hollowing out the hidden capabilities that hold teams together.

3 Forces Are Redefining The Transition From Manager To Leader by Michael D. Watkins
Moving from manager to leader now means handling AI you do not fully understand, navigating faster regulatory and geopolitical shifts, and stepping up with fewer of the traditional proving grounds. Hence, leadership transitions require more judgment, adaptability, and enterprise thinking than before.

Product Direction

Great Products, Bad Companies by Marty Cagan
Great products are not enough if governance, incentives and leadership pull the company away from its mission, so product teams need to understand how boards, investors and culture shape whether good product work can survive and scale.

Strategy Drift: How To Spot It, Fix It, And Stop It From Happening by Martin Eriksson
Strategy drift happens when small disconnects pile up between stated priorities and actual work, so teams need regular checks that link day to day execution back to strategy, objectives and vision before busy activity quietly replaces real progress.

Continuous Research

We've Always Outsourced Our Thinking. Is AI Different? by Dr Maria Panagiotidi
AI changes more than cognitive offloading as it can generate judgments that people accept with little scrutiny. Designing for calibrated trust, visible uncertainty, and critical engagement matters if teams want users to stay in the loop rather than quietly surrender their thinking.

What We Learned About Modern Interview Workflows & Building A Research Repository by Optimal (Sponsored Content)
Turning interviews into a reusable research repository means removing the operational drag around scheduling, transcription and synthesis so teams can move faster, revisit past evidence and make research a more continuous part of product decision making.

Continuous Design

“Vibe Coding” Is Accelerating The Erosion Of Design Authority by Michael Buckley
When clickable AI artifacts start standing in for design thinking, authority shifts from judgment and architecture toward whatever can be shown fastest, so teams need to make reasoning visible and treat prototypes as inputs to decisions rather than proof that decisions are already made.

The Trust-Latency Gap: Why The Future Of UX Is Intentionally Slower by Honey Mehta
As AI compresses decisions into milliseconds, trust starts lagging behind execution, so designers need to add the right moments of visibility, friction and deliberation when stakes are high rather than treating speed as the default measure of good UX.

Continuous Development

Agentic Coding Is A Trap by Lars Faye
Agentic coding can speed up output while quietly increasing ambiguity, review burden and skill atrophy, so teams get more durable value when AI supports planning, research and scoped delegation without replacing the direct code-level engagement that builds real engineering judgment.

What AI Coding Costs You by Tom Wojcik
Using too much AI in development can quietly erode the understanding, debugging skill and creative engagement that strong engineering depends on, so the real challenge is not whether to use AI, but how to stay cognitively involved enough to keep building judgment while still benefiting from the speed.

Seen an interesting article online? Share it with us, and we might feature it in our next issue!
Click here to share an article

Last Week To Apply For A Team Pass

Make Sure To Not Miss Out On San Francisco!

This is the last week to apply for a chance to bring your product team to UXDX San Francisco 2026 for free. Tell us what your team is doing well in AI and what you want to improve next. The team with the most votes will receive up to 5 tickets to UXDX San Francisco.

More details and the form can be found here:: Apply for a UXDX San Francisco team pass

FREE COMMUNITY EVENTS 

Video Of The Week

From Cost Center to Strategic Asset: Redefining the Role of Design

Design teams are often treated as execution hubs, brought in to produce screens rather than shape the direction of the business. But when design is kept too close to delivery and too far from strategy, teams end up reacting to decisions instead of influencing them.

In this fireside chat, Paul Strike and Falguni Desai share lessons from senior design leadership on how to shift design from an output-focused function to a strategic business partner. A useful watch for anyone working to prove the value of design, build stronger business sponsorship, and help design teams operate at both strategic and tactical levels. Check it out below:

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

How C-suite And Board Roles Are Being Reshaped Around AI by Tomas Chamorro-Premuzic
AI is not just changing frontline work, it is reshaping leadership itself, forcing executives and boards to rethink oversight, judgment and decision making as senior roles shift from authority and expertise toward adaptation, governance and human discernment.

How To Share Your Point Of View (Even If You’re Afraid Of Being Wrong) by Wes Kao
Sharing a point of view becomes more valuable as roles get more senior, but it lands best when people show their reasoning, explain why the problem matters, and match their language to their actual level of certainty.

Product Direction

Build To Learn Vs Build To Earn by Marty Cagan
Separating product discovery from product delivery clarifies why teams should build prototypes to learn about value, usability, feasibility, and viability, then build products to earn through reliability, scale, and support, so faster AI prototyping sharpens strategy only when teams stay clear on which mode they are in.

Synthetic Certainty: The Hidden Risk Of Genai In Product Discovery by Johannes Schott
GenAI can make product discovery feel clearer than it really is, producing polished interpretations that mask weak evidence, so teams need to stay close to raw signals and treat AI synthesis as a prompt for scrutiny rather than proof of understanding.

Continuous Research

Deprivation Studies: Take The Product Away To Reveal What Users Truly Need by Jakob Nielsen
Taking a product away for a period reveals which habits, dependencies, and workarounds actually matter, helping teams separate real value from feature bloat, captive usage, and self-reported fiction.

Ethnography Finds What Interviews Miss. Almost No B2b Product Team Does It. by Else van der Berg
Interviews and analytics surface what customers can explain, but ethnography reveals the hidden workarounds, routines and unmet needs they no longer notice, helping teams find higher value opportunities that standard discovery methods miss.

Continuous Design

Beyond The User: Why Design Needs To Widen Its Circle by Dora Czerna
Designing only for the immediate user can hide wider harm, so expanding the brief to include ecosystems, supply chains and long term impact helps teams make better design decisions without losing the strengths of human centred practice.

Claude Design Is A "clock Time" Solution For A "calendar Time" Problem by Pavel Samsonov
AI can speed up artifact production in clock time, but design work still depends on slower calendar time for coordination, reflection and shared sense making, so teams that optimise only for faster outputs risk creating more noise while weakening the conditions good decisions depend on.

Continuous Development

AI Fatigue Is Real And Nobody Talks About It by Siddhant Khare
AI speeds up individual tasks but can make development more exhausting by shifting effort into review, coordination, tool churn and nonstop decision making, so teams need guardrails and working habits that protect deep focus, judgment and sustainable pace.

The "Negative Split" Software Engineering Effect by Anton Zaides
Trying to go faster too early in software development often burns teams out later, so the better path is to slow down enough to build context, quality and stable foundations first, then speed up when the system can actually sustain it.

Seen an interesting article online? Share it with us, and we might feature it in our next issue!
Click here to share an article

Win A Team Pass To UXDX San Francisco

Don’t wait, apply now!

We’re giving one product team the chance to attend UXDX San Francisco 2026 together for free. Apply through the form, tell us what your team is doing well in AI and what you want to improve next, and UXDX will shortlist four teams to be featured in a LinkedIn community vote!

The team with the most votes will receive up to 5 tickets to UXDX San Francisco. More details and the form can be found here: Apply for a UXDX San Francisco team pass

FREE COMMUNITY EVENTS 

Video Of The Week

From Output to Outcome:
Driving Cultural Change in Product Teams

Moving from outputs to outcomes sounds simple, but it usually requires a much deeper shift in how teams define success, make decisions, and work together.

In this talk, Jay shares how Eneco moved toward a more customer-centric and value-driven way of working, using continuous discovery and delivery to connect product, design, and engineering around real customer value. A useful watch for anyone trying to move beyond feature delivery, rethink KPIs, and build the culture needed for outcome-driven product work. Check it out below:

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

The Playbook For AI Transformation by John Vetan
AI transformation stalls when companies treat it as scattered pilots instead of a repeatable cross-functional system. Dedicated facilitators, small decision-making pods, and structured workflow sprints help teams kill weak ideas early, redesign workflows with the people closest to the work, and measure value beyond individual productivity.

Stop Chasing Design Tools. Start Building A Design Harness by Rex & Duckie and Bill Guo
As AI tools keep changing, teams get more value by building a reusable harness of context, workflows, skills and evaluation criteria, so good practice compounds across tools instead of resetting with every new release.

Product Direction

Product Roadmaps: How The Best Product Teams Plan For Uncertainty by Teresa Torres
Planning roadmaps around certainty levels instead of fixed features and dates helps teams preserve room for discovery while still giving sales, marketing and leadership enough visibility to coordinate around what is known, what is likely and what is still fluid.

Demand Mix, Shaping, And AI As (Dys)function Multiplier by John Cutler
AI does not fix broken product systems, it accelerates them, so teams need to understand their demand mix and shape work at the source rather than using faster prototyping and PRDs to push even more noise through the funnel. 

Continuous Research

What Gets Lost When UX Research Speeds Up by Dr Maria Panagiotidi
Speeding up UX research with AI can make outputs look sharper while quietly stripping out reflection, methodological rigor and interpretive depth, so teams need to protect the slower thinking that turns data into trustworthy insight rather than just faster deliverables.

How To AI UXR: A Map For Building AI-Augmented Research Operations by Kate Towsey
Scaling AI in research is less about individual shortcuts and more about building the operational systems, guardrails and evaluation practices that let teams make research faster, safer and more reusable without losing rigor.

Continuous Design

When AI Experiences Fail, Who Is Held Accountable? by Dolphia
AI products create more risk when teams make the system feel confident, final, and trustworthy without making responsibility equally clear behind the scenes. Designers, product teams, and companies need clearer escalation paths, stronger dissent before launch, and more deliberate choices about how AI outputs are presented so users are not left carrying the cost of organisational ambiguity.

Every Designer On My Team Ships The Same Quality Now by Hoang Nguyen
When AI can already produce system-compliant design output, teams create more value through judgment than execution alone. The harder and more valuable work becomes defining what should be built, where the system breaks down, and how design contributes beyond polished assets that now look increasingly interchangeable.

Continuous Development

The Machines Are Fine. I'm Worried About Us. by Minas Karamanis
Using AI to remove grunt work can also remove the struggle that builds engineering judgment, so the real risk is not smarter machines but development practices that optimise for output while quietly eroding the learning, intuition and independence that strong technical work depends on.

The Last Software Engineer by Kent C. Dodds
As AI makes implementation less scarce, engineering value shifts toward judgment about constraints, trade-offs, rollout risks and accountability, making product thinking a core part of modern software development rather than something that sits outside it.

Seen an interesting article online? Share it with us, and we might feature it in our next issue!
Click here to share an article

The First Agenda Is Now Live

Check Out The UXDX San Francisco’s Agenda

On November 5–6, UXDX expands to San Francisco for an exclusive 300-person flagship event at August Hall, focused on how companies are building AI-native product teams in practice.

The first confirmed speakers include leaders from Snapchat, ŌURA, Expedia, Target and Verizon, with more being added weekly. Across 20+ sessions, 4+ deep-dive workshops and a highly interactive format, the event is designed to feel more like a team offsite than a trade show.

See what’s on the agenda and grab your early bird ticket before we run out:
https://uxdx.com/sanfran/2026/agenda/

Use ‘NEWSLETTERSF26’ at checkout to get 10% off the price!

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Video Of The Week

Who Moved My Roadmap? Navigating the Unpredictable
Future of UX and Product in the Era of AI

Technology shifts can feel chaotic, but the teams that adapt best are not the ones that react the fastest. They are the ones who move with direction, stay curious, and make smart decisions when the ground is shifting.

In this talk, Dónal O’Mahony shares lessons from working through major industry disruptions, from the iPad’s impact on e-learning to generative AI in content management and 5G in connected vehicles. A useful watch for anyone navigating change in product, UX or leadership, especially if they want to turn uncertainty into opportunity without losing sight of their North Star.

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

The Science Of Social Loafing: Why Groups Kill Individual Effort by Joost Minnaar
Teams work better when individual contributions are visible, coordination stays manageable, and people can see how their effort connects to a meaningful goal. Small teams, clear ownership, transparency, and peer accountability help reduce social loafing before group size turns into hidden drag.

How To Build A Superteam That Keeps Getting Better by Ron Friedman
High-performing teams do not improve by accident. Research-backed team habits, leadership behaviors, and continuous improvement routines are what help good teams keep getting better over time.

Product Direction

5 Ways Product Discovery Breaks Down (Part 2) by Itamar Gilad
Product discovery breaks down when teams cannot measure impact, validate ideas too late, or ignore what experiments are telling them. Better infrastructure, earlier validation, and clearer success criteria help teams learn faster, make stronger product decisions, and avoid shipping ideas that should have been challenged sooner.

Thinking—Fast, Slow, And Artificial: How AI Is Reshaping Human Reasoning And The Rise Of Cognitive Surrender by Steven D. Shaw and Gideon Nave
AI can improve decisions, but it can also weaken judgment when people accept outputs too quickly and with too much confidence. Teams need stronger review habits, clearer accountability, and ways to challenge AI recommendations so speed does not turn into cognitive surrender

Continuous Research

Designers Will Never Have Influence Without Understanding How Organizations Learn by Pavel Samsonov
Prototypes create more value when teams use them to learn something they do not yet know, not just to win stakeholder approval. Qualitative research helps teams challenge weak metrics, make better sense of what the data is really saying, and turn evidence into decisions the organization can actually learn from. 

Provenance Matters: Why Citations Are The Missing Link In Insight-driven Decisions by Jake Burghardt
Research creates more value when teams can trace insights back to the source, understand the context behind them, and reuse them with confidence in future decisions. Clear citation patterns and better repository links help reduce cherry-picking, strengthen trust in evidence, and make research impact easier to prove over time.

Continuous Design

Collected Consciousness by Brandon Harwood
AI is more useful in creative work when it helps people explore ideas, challenge assumptions, and shape their own intent instead of trying to replace the creative act itself. Better products use AI to surface patterns, prompt reflection, and support co-creation while keeping human judgment, meaning, and authorship at the center.

Context Engineering: A Repeatable AI Workflow For Product Designers by Vadym Grin
AI works better when designers stop dumping in requirements and start structuring the right context in the right order. Treating context like a design problem can make outputs more reliable, reduce endless re-prompting, and turn AI into a more useful collaborator in the workflow.

Continuous Development

Who Will Be The Senior Engineers Of 2035? by James Stanier
Senior engineers do not appear overnight. Teams need real pathways for juniors to build judgment, make mistakes, and learn from experienced people, or they risk creating a future talent gap that AI alone will not fix.

Two 2026 Studies Just Proved AI Is Making Developers Lazy by Fran Soto
AI helps teams write more code, but that can come at the cost of understanding what they ship. Developers get better results when they use AI to explain concepts, challenge reasoning, and support review, rather than delegating the thinking and skipping the hard work of comprehension.

Seen an interesting article online? Share it with us, and we might feature it in our next issue!
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Price Increases This Week!

Order Your Tickets For San Francisco Now

UXDX San Francisco prices increase in 5 days. Planning to join us in San Francisco? Now is the time to book. UXDX brings together product, UX, design and engineering leaders to explore how teams are adapting to AI, improving collaboration, and building better products together.

Ticket prices increase in 5 days, so book now to secure your place at the current rate:
https://uxdx.com/sanfran/2026/tickets/

Use ‘NEWSLETTERSF26’ at checkout to get 10% off the price!

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Video Of The Week

Thriving as a Long-Term IC:
Lessons in Influence, Growth, & Fulfillment

Career growth does not always have to mean moving into management. Long-term individual contributors can create real organisational impact by staying close to the craft, building trust, and using their experience to shape better decisions over time.

In this talk, Cliff Seal shares lessons from his work at Salesforce on how senior ICs can influence strategy, navigate workplace dynamics, and keep growing without managing a team. A useful watch for anyone thinking about the next stage of their career, especially if they want to increase their impact while staying hands-on:

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

Redesigning Workflows For AI by Jakob Nielsen
AI creates more value when teams redesign the whole workflow instead of just speeding up one task at a time. Removing handoffs, automating the glue work, and shifting people toward higher-judgment exceptions can unlock bigger gains than isolated productivity wins.

Mockups Were Never The Hard Part by Luke Wroblewski
AI makes it easier for anyone on a team to generate mockups, but the real design challenge is still creating coherent products with clear intent, shared mental models, and strong cross-functional alignment.

Product Direction

My Proven Success-Metric Framework by Büşra Coşkuner
Success metrics get clearer when teams start with the user journey, identify the turning-point steps that matter most, and then define signals that show whether those steps are working. The GSM framework helps turn vague goals into measurable progress, making it easier to choose better metrics even in B2B or low-data environments.

Competitive Differentiation Is Poison by Ravi Mehta
Product strategy gets stronger when teams stop asking what makes them different and start asking what makes them better for customers. Real advantage comes from improving the things people value most and building strengths that compound over time, not from inventing differences that sound distinctive but do not improve the product.

Continuous Research

Your Best Work Won’t Speak For Itself: Tried-And-Tested Sales Tactics For Researchops Professionals by Glenn Familton
ResearchOps work gets more traction when teams explain it in terms that leaders care about, not just in terms of activity or effort. Framing the need, the solution, and the business benefit makes it easier to win buy-in, build champions, and get important infrastructure work understood before its impact is fully visible.

User Personas Of Consequence by Chris R Becker
Personas create more value when teams treat them as a rigorous representation of real people, not a fast template to fill in. Slowing down, adding more inclusive context, and resisting AI-generated shortcuts can help teams build personas that support better design decisions instead of generic user fiction.

Continuous Design

Designing Trust For Agentic AI In Enterprise Software by Brooke Bell, Shrut Kirti Saksena
Agentic AI works better when teams match the interface and level of autonomy to the risk and complexity of the task. Clear previews, visible reasoning, checkpoints, and reversible actions help users trust AI enough to use it without losing control when the stakes are higher.

The Last Interface by Joshua Leigh
As agents take on more of the navigation and execution, design work shifts from arranging screens to defining the rules, guardrails, and defaults that shape what users get. Teams will need to design simpler core interfaces, support outcome-first workflows, and make sure dynamically generated experiences still stay usable, accessible, and coherent.

Continuous Development

Harness Engineering For Coding Agent Users by Birgitta Böckeler
Coding agents become more useful when teams surround them with clear guidance, fast feedback, and checks they can use to self-correct before humans step in. Strong tests, linters, architecture rules, and review loops help reduce repeated mistakes, improve code quality, and make higher-autonomy AI delivery safer to trust.

From T-shape To Comb-shape: How Developer Knowledge Needs To Evolve In The AI Era by Luca Mezzalira
As AI takes on more of the routine implementation work, developers create more value when they can judge trade-offs across architecture, domains, and system constraints rather than relying on narrow technical depth alone. Building knowledge across several important areas makes it easier to direct agents well, catch bad assumptions earlier, and focus human effort on the parts of software that matter most.

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Always Wanted To Shine On Stage?

Apply Now To Become A Speaker For Our Next Events!

We’re always looking for real-world case studies from product, UX, design, and engineering leaders who are changing how modern product development works.

Share what you tried, what worked, what didn’t, and what others can learn from your experience. Apply to speak here: https://uxdx.com/speak/

UXDX San Francisco 2026

Click here and book San Francisco!
Use ‘NEWSLETTERSF26’ at checkout to get 10% off the price!

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Video Of The Week

AI in Learning & UX Design: Augmenting, Not Replacing

AI is already changing how teams work, but the biggest gains are not always coming from full automation. Often, the real value is in smaller process improvements that save time, improve quality and still keep humans firmly in the loop.

In this talk, Mike Brown, Head of Design at Barclays, shares how his team tested AI across internal learning workflows, what produced measurable gains, and where the tools fell short. A practical watch for anyone trying to adopt AI inside complex or regulated organisations, where speed matters, but accuracy, oversight and risk management matter even more:

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

Finally, The Handoff Is Dead by Luke Wroblewski
AI does not fix collaboration on its own. Teams work better when design decisions, development standards, and project context are carried in a shared system so people can contribute in parallel without losing alignment or creating handoff waste.

AI Layoffs: The Leadership Choice Behind Every Headline by Pim de Morree
AI layoffs dominate headlines, but IKEA proves there's another way. When AI could replace 57% of their work, they chose reskilling over firing.

Product Direction

Outcomes Vs. Outputs: What's The Difference And Why Does It Matter? by Teresa Torres
Product teams make better decisions when they measure the impact of what they ship, not just whether it shipped. Choosing outcomes that reflect real customer value helps teams avoid feature-factory thinking, focus discovery on the right problems, and stay accountable for results instead of delivery alone.

The AI Documentation Paradox by Florian Bonnet
AI makes it easier to produce polished documentation, but that does not mean teams are thinking more clearly or making better decisions. Shorter, sharper docs that lead with the decision and separate judgment from evidence are more useful than longer documents that signal effort without changing the quality of the thinking.

Continuous Research

The missing Seat At The Frontier Team Table by Martin Linde Danty, Rabeeza A., Anne Klærke-Olesen, Madhumay Sinha, and Michael O’Sullivan
AI evaluations work better when researchers help define what “good” looks like before teams start measuring it. Bringing qualitative insight into scenarios, assumptions, and success criteria helps teams catch unrealistic abstractions, avoid misleading metrics, and build AI features that perform well in real workflows instead of just looking good in evals.

The Research Operating System Too Few Are Building: Why “I-Me-Mine AI” Isn't Enough by Kate Towsey
Research teams will get more value from AI when they stop treating it as an individual productivity tool and start building shared systems around where it should be used, where it should not, and what good looks like. Clear standards, guardrails, and workflows help research scale more safely and make it easier for the function to create visible organizational value instead of isolated efficiency gains.

Continuous Design

What Designers Actually Struggle With On Product Teams by Laura Klein
Designers create more impact when they are brought in early, can connect user needs to business outcomes, and have clearer ways to work with product, engineering, and stakeholders. Alignment, influence, and role clarity matter as much as craft when teams are trying to shape better product decisions.

Google Says “Vibe Design” Is Here, But It Didn’t Pass My Vibe Check by Elvis Hsiao
AI can take on more of the making, but designers still create value through problem framing, taste, judgment, and deciding what is worth building in the first place. The pressure is real, but the stronger response is to use AI to extend your range while protecting the thinking skills that generic outputs still cannot replace.

Continuous Development

The Golden Rules Of Agent-first Product Engineering by Jina Yoon
Agent-first products work better when teams give agents full product capabilities, expose the right abstraction layer, front-load the context every session needs, and encode product judgment into the system. Treating agents like real users through dogfooding, trace review, and evals helps teams catch fragile experiences earlier and improve trust before bad behavior scales.

Explaining, Understanding, And Data Compression by Anton Zaides
Technical leaders are more effective when they explain complex thinking in a way others can actually absorb. Clearer communication, fewer unnecessary details, and checking what people understood can reduce confusion, speed up alignment, and make feedback more useful.

Seen an interesting article online? Share it with us, and we might feature it in our next issue!
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Managing Your Career In The Age Of AI

Have You Read My E-Book Yet?

AI is changing the shape of product, UX, design and engineering careers. Junior-level tasks are being automated, expectations are shifting, and the skills that help people progress are becoming less about output and more about judgment, influence and cross-functional thinking. My ebook, Managing your Career in the Age of AI, breaks down the 4 levels of career thinking, the shift from pyramid to diamond-shaped teams, and why the old T-shaped skills model is giving way to a more connected, comb-shaped approach.

Download the ebook to understand where you are today, what skills matter next, and how to position yourself for the AI-powered world of work.

UXDX San Francisco 2026

Click here and book San Francisco!
Use ‘NEWSLETTERSF26’ at checkout to get 10% off the price!

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Video Of The Week

From Cross-Functional To Cross-Purposes:
Why Collaboration Falls Apart Over Time

Cross-functional teams do not fall out of alignment because people stop caring about collaboration. More often, the problem is hidden in the structures, habits, and language teams use every day.

In this talk, Mihaela Draghici shares what she learned from embedding business stakeholders into hybrid product teams and why even strong collaboration frameworks can slowly break down. A practical watch for anyone trying to keep product, business, and engineering aligned beyond the first workshop, kickoff, or team restructure:

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

Who In The C-Suite Should Own AI? by Toby E. Stuart
AI initiatives work better when companies stop treating them as the responsibility of one function alone. Clear ownership, shared accountability, and tighter coordination across technology, operations, risk, data, and finance help organizations move faster without creating gaps in decision-making or control.

I’m An Introvert. This Is How I Get Myself To Speak Up. by Wes Kao
Prepare go-to phrases to insert yourself in meetings, ask a colleague to keep you accountable, and other tactics I personally use to get myself to speak up.

Product Direction

Messy Docs As Helpful Pattern by John Cutler
Teams often work better when they use messy, evolving docs to keep context visible, revisit assumptions, and connect signals that do not fit neatly into tickets. The trick is not forcing the mess away, but creating simple routines and shared language so others can understand the work without flattening how it really happens.

How To Close Your Confidence Loop by Tim Herbig
Product decisions get stronger when teams can clearly connect what they are building to the behavior change they expect and the business goal it supports. AI can help fill in the story faster, but confidence only becomes real when the team can explain and defend each link in that chain for themselves.

Continuous Research

Don’t Design For Average Users by Jakob Nielsen
Research gets more useful when teams look beyond the average user and study the needs of higher-value and more complex segments separately. Better segmentation can reveal where simple experiences matter most, where advanced workflows create disproportionate value, and why one-size-fits-all decisions often miss both groups.

Winning The Game Of Broken Telephone: A Blueprint For Evaluating AI Across The Research Pipeline by Lindsey DeWitt Prat
AI research workflows get more reliable when teams define what accuracy means for their context, compare tools side by side, and keep checking where outputs start to drift from the source. Better evaluation makes it easier to catch hidden errors early, stay closer to the participant's meaning, and stop polished outputs from creating false confidence.

Continuous Design

How I’m Dealing With The Pressure To Adopt AI As A Designer by Martin Wright
Teams do not need to adopt every AI tool at the speed of the hype cycle. Experiment widely, deploy narrowly, and use AI where it improves the work without outsourcing the judgment, synthesis, and craft that make the work valuable in the first place.

Something Big “Might” Be Happening by Elvis Hsiao
AI can take on more of the making, but designers still create value through problem framing, taste, judgment, and deciding what is worth building in the first place. The pressure is real, but the stronger response is to use AI to extend your range while protecting the thinking skills that generic outputs still cannot replace.

Continuous Development

Agents Don’t Know What Good Looks Like. And That’s Exactly The Problem. by Luca Mezzalira
AI agents can make code behave correctly against a spec while still making systems worse in production. Teams need guardrails around architecture, scale, resilience, and ownership so faster generation does not create software that passes tests but fails in the real world.

Every Layer Of Review Makes You 10x Slower by Avery Pennarun
Shipping gets slower when every extra layer of review adds more waiting time to the work. Teams move faster when they reduce unnecessary approvals, build quality into the system earlier, and create smaller, better-defined components that need less coordination to change safely.

Seen an interesting article online? Share it with us, and we might feature it in our next issue!
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The First Speakers Of UXDX San Francisco

5 - 6 November 2026

If you missed our announcement on stage at UXDX, here is the big news: UXDX San Francisco 2026 is locked in for November 5–6. Our core theme, Becoming AI Native, will tackle how product, design, research, and engineering teams can move past the hype cycle and embed AI deeply into their operational rhythm.

We’re launching with a stellar first look at our speaker lineup, featuring product and design leaders from Snap Inc., ŌURA, Expedia Group, Target, and Verizon. Early tickets and the first version of the agenda are now live. Be part of our West Coast event and grab your ticket today: Get your tickets for UXDX San Francisco 2026

UXDX San Francisco 2026

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Use ‘NEWSLETTERSF26’ at checkout to get 10% off the price!

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Video Of The Week

Bridging the Gap:
How Product, UX, and Dev Can Build AI-Native Products Together

AI-native products need more than strong models. They need product managers, designers, and engineers working from a shared understanding of what is technically possible, what users actually need, and how decisions should be made along the way.

In this conversation, Carsten Wierwille, Global VP of Product & Design at HTEC, and Jacobus Kok, VP of Product at Priceline, explore how teams can bridge the gap between bottom-up machine learning approaches and top-down product and UX thinking. A practical watch for anyone trying to build AI products without creating new silos in the process.

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

Demand Mix, Shaping, And AI As (Dys)function Multiplier by John Cutler
Orgs spend a lot of time talking about dependencies, intake, predictability, “discovery,” capacity, and so on. But far less time talking about the harder questions around the mix of demand teams are dealing with, and how they are actively involved in shaping that demand.

Positional Leadership: The Emperor's New Clothes Of Management by Joost Minnaar
Teams work better when authority comes from competence and trust, not just title or rank. Clear roles, psychological safety, and decision-making closer to the work help people speak up sooner, reduce distortion as information moves upward, and keep leadership grounded in reality. 

Product Direction

Product Management On The AI Exponential by Cat Wu
Product teams can move faster when they replace long planning cycles with short experiments, demos, and evals that make ideas tangible early. As models keep improving, teams need simpler implementations, faster feedback loops, and the habit of revisiting what is already built as new capabilities change what good looks like.

5 Ways Product Discovery Breaks Down by Itamar Gilad
Product discovery breaks down when teams fast-track “must-have” features and use plans as a substitute for clear outcomes. Stronger goals, better evidence, and fewer roadmap assumptions help teams validate ideas properly and focus on business and user results instead of just shipping. 

Continuous Research

Deprivation Studies: Take The Product Away To Reveal What Users Truly Need by Jakob Nielsen
Standard usability testing can show where users struggle, but deprivation studies reveal what they would actually miss if a product or feature disappeared. Watching the workarounds people create helps teams separate real value from habit, clutter, and lock-in, making it easier to cut bloat and protect the parts that truly matter.

A Practical Guide To Structuring ResearchOps Through Organizational Change by Carolyn Morgan
ResearchOps works best when its structure fits the organisation around it, rather than following a fixed model. Choose clear ownership, consistent standards, and the right mix of centralised and embedded support to help research scale through change without losing quality.

Continuous Design

Agentic UX: 7 Principles For Designing Systems With Agents by Alexandra Vasquez
Agentic systems work better when teams fix messy workflows, capture the right context, and keep automation inside familiar product patterns instead of bolting on separate AI surfaces.

Field Study: Prototypes Over Mockups by Édouard Wautier
Teams can make better design decisions when they test behaviour in real, runnable prototypes instead of debating static mockups. Working in code with shared components helps expose edge cases earlier, tighten feedback loops, and reduce the gap between what gets explored and what actually ships.

Continuous Development

Comprehension Debt - The Hidden Cost Of AI-Generated Code by Addy Osmani
AI-generated code creates hidden risk when teams can merge changes faster than they can truly understand them. Strong testing still matters, but teams also need clear intent, meaningful review, and enough system knowledge to judge whether the code is actually right, not just syntactically correct.

Engineering Managers Are Going To Hate Openclaw by Anton Zaides
Proactive AI agents can create real value, but they also raise the risk of teams shipping automation before they have thought through reliability, guardrails, or the blast radius of mistakes. Engineering leaders need to get involved early so hype does not turn into agent features that act too confidently and create more damage than value.

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Shoutout To Our Partners In Berlin!

UXDX EMEA 2026 Happens Next Week.
Don’t Miss Our Partners:

UXDX EMEA 2026 kicks off next week in Berlin, supported by partners helping teams research, design, test, and build better products: Optimal, UXIA, Baymard Institute, TestingTime, Leadium, and AccessiBe.

• Speak to one of Baymard Institute’s reps to get an exclusive UXDX discount on any Baymard plan subscription. Head to their booth at UXDX EMEA or book a meeting using this link: https://calendly.com/julie-baymard/30min?month=2026-05

• Outside of the event, UXIA is also running a UX Innovation Session. You can find out more here: https://www.uxia.app/ux-innovation-session

A huge thank you to all our sponsors and partners for helping make UXDX EMEA possible!

UXDX San Francisco 2026

Use ‘NEWSLETTERSF26’ at checkout to get 10% off the price!

Click here and book San Francisco!

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Video Of The Week

From Output to Outcome:
Driving Cultural Change in Product Teams

This week, we’re highlighting an insightful UXDX EMEA 2025 session focused on one of the hardest shifts in product management: moving from output to outcomes. Jay shares how Eneco successfully tackled this challenge by transforming their culture alongside their KPIs.

The session explored what it really takes to build a customer-focused culture across product, design, and engineering. Jay openly discussed the breakthroughs and lessons learned while implementing frameworks like ‘Opportunity Solution Trees’ to keep teams aligned on user value. If you’re trying to move your organization away from feature-factory thinking and toward true customer centricity, this talk is a must-watch:

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.

This Week’s Updates

Enabling the Team

Organizational Silos: What The Titanic Teaches Us About Information That Never Arrives by Joost Minnaar
Silos are usually created by structure, not bad intent, when teams optimize for local metrics instead of shared outcomes. Shorter paths between signals and decisions, more transparency, and cross-functional ownership help important information reach the right people before delays turn into bigger problems.

Technical Leaders Make These 3 Common Storytelling Mistakes by Wes Kao
Technical leaders lose people when they overload stories with details, backstory, and caveats instead of making the point clear first. Simpler stories, shorter examples, and a stronger hook make it easier to hold attention, build buy-in, and share the deeper details only when they are needed.

Product Direction

The “Be More Strategic” Trap by Stephanie Leue
Telling someone to “be more strategic” is not helpful unless teams make clearer what strategic thinking actually looks like in practice. People create more impact when they connect initiatives to business context, explain work in terms leaders care about, and are given the space to think beyond immediate delivery.

You Don’t Need Another Prioritization Framework: Just These 4 Components by Ant Murphy
Prioritization works better when teams stop searching for one perfect framework and start judging work through impact, effort, urgency, and confidence. The better decisions come from linking choices to goals, revisiting time-sensitive opportunities, and being honest about what you still do not know.

Continuous Research

From Gatekeepers To Enablers: The UX Researcher's New Role In 2026 | Sponsored Content by Optimal
Research creates more value when teams use it to answer clear decision-making questions, not to slow work down or create the appearance of rigor. Standardising what you need to learn, using existing data where possible, and matching the method to the problem can help teams learn faster and ship with more confidence.

How To Become An AI Super User: Your Complete Guide | Sponsored Content (Dovetail) by Emily Brogan & Peter Wooden
Teams get more value from AI when they treat it as support, not a replacement. Build enough hands-on experience to develop good judgment, use AI to speed up research and execution, and keep humans responsible for quality, decisions, and catching where the output falls short.

Continuous Design

What Design Leaders Must Unlearn To Lead In An AI-first World by Arin Bhowmick
AI adoption is not just about giving teams new tools. Design leaders need to strengthen fundamentals, blur rigid role boundaries where needed, and treat trust, clarity, and reliability as part of quality, so that faster work does not lead to weaker decisions.

The Hidden Cost Of AI Prototypes That Are Made To Die by Allie Paschal
AI prototypes save time early, but they create extra work later when teams cannot extend, hand off, or reuse what they have made. Choose prototyping tools based on whether the output needs to validate an idea quickly or carry forward into real product development.

Continuous Development

What We Wish We Knew About Building AI Agents by Ian Vanagas
AI agents create more value when teams start with the simplest useful architecture, give the system strong product context, and treat observability and evals as core product infrastructure from day one. Better adoption comes from reliability, transparency, and solving real user problems, not from piling on more agent capabilities.

Software Engineering Calories: What Would Cal.ai Say About Your Product? by Anton Zaides
Teams make better delivery decisions when they measure the real weight of what they are shipping, not just count PRs or assume more code means more progress. Tracking input more honestly can help uncover bottlenecks, surface wasted effort, and show when the better move is not to build more, but to improve adoption or remove features.

Seen an interesting article online? Share it with us, and we might feature it in our next issue!
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NEW: UXDX San Francisco 2026

We are expanding to the West Coast!

UXDX is heading to the West Coast. We announced it on stage last week at UXDX USA in New York, and now the newsletter community gets the first look: UXDX San Francisco 2026 is happening on 5–6 November. The sessions are around Becoming AI Native, focused on how product, design, research, and engineering teams move beyond AI experiments and start rebuilding the way they work.

The first speakers are already live on the website, and tickets are now available. If you want to be part of the first UXDX San Francisco event, you can check out the agenda and secure your spot here: https://uxdx.com/sanfran/2026/

UXDX EMEA 2026 Click here and book EMEA now!

UXDX San Francisco 2026 Click here and book San Francisco!

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Video Of The Week

How to Build an AI-Powered Product Development Process

This week, we’re resharing one of the most-watched sessions from UXDX USA 2025. Anthony Maggio, Head of Product at Airtable, shows how his team is using AI across the product development process, from interpreting user feedback at scale to improving strategic alignment and accelerating roadmap decisions.

The session gives a practical look at how AI can help teams turn complex data into clearer product choices, keep plans connected to business goals, and respond faster to changing customer needs. If you’re exploring how AI can move from isolated experiments into the operating rhythm of product management, this talk is well worth watching:

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.