Josh Clark's Blog, page 5

Change Blindness

A great reminder from Ethan Mollick of how quickly things have changed in AI generation quality in the last 18 months. AI right now is the worst that it will ever be; only getting better from here. Good inspiration to keep cranking!

When I started this blog there were no AI chatbot assistants. Now, all indications that they are likely the fastest-adopted technology in recent history.

Plus, super cute otters.

Change blindness | One Useful Thing

Introducing Structured Outputs in the API

OpenAI introduced a bit of discipline to ensure that its GPT models are precise in the data format of their responses. Specifically, the new feature makes sure that, when asked, the model responds exactly to JSON schemas provided by developers.

Generating structured data from unstructured inputsis one of the core use cases for AI in today���s applications.Developers use the OpenAI API to build powerful assistantsthat have the ability to fetch data and answer questionsvia function calling(opens in a new window), extractstructured data for data entry, and build multi-stepagentic workflows that allow LLMs to take actions.Developers have long been working around the limitationsof LLMs in this area via open source tooling, prompting,and retrying requests repeatedly to ensure that modeloutputs match the formats needed to interoperate withtheir systems. Structured Outputs solves this problemby constraining OpenAI models to match developer-suppliedschemas and by training our models to better understandcomplicated schemas.

Most of us experience OpenAI’s GPT models as a chat interface, and that’s certainly the interaction of the moment. But LLMs are fluent in lots of languages���not just English or Chinese or Spanish, but JSON, SVG, Python, etc. One of their underappreciated talents is to move fluidly between different representations of ideas and concepts. Here specifically, they can translate messy English into structured JSON. This is what allows these systems to be interoperable with other systems, one of the three core attributes that define the form of AI-mediated experiences, as I describe in The Shape of Sentient Design.

What this means for product designers: As I shared in my Sentient Design talk, moving nimbly between structured and unstructured data is what enables LLMs to help drive radically adaptive interfaces. (This part of the talk offers an example.) This is the stuff that will animate the next generation of interaction design.

Alas, as in all things LLM, the models sometimes drift a bit from the specific ask���the JSON they come back with isn’t always what we asked for. This latest update is a promising direction for helping us get disciplined responses when we need it���so that Sentient Design experiences can reliably communicate with other systems.

Introducing Structured Outputs in the API | OpenAI

Why I Finally Quit Spotify

In The New Yorker, Kyle Chayka bemoans the creeping blandness that settled into his Spotify listening experience as the company leaned into algorithmic personalization and playlists.

Issues with the listening technology create issueswith the music itself; bombarded by generic suggestionsand repeats of recent listening, listeners are beingconditioned to rely on what Spotify feeds them ratherthan on what they seek out for themselves. ���You���regiving them everything they think they love and it���sall homogenized,��� Ford said, pointing to the algorithmicplaylists that reorder tracklists, automatically playon shuffle, and add in new, similar songs. Listenersbecome alienated from their own tastes; when you neverencounter things you don���t like, it���s harder to knowwhat you really do.

This observation that the automation of your tastes can alienate you from them feels powerful. There’s obviously a useful and meaningful role for “more like this” recommendation and prediction engines. Still, there’s a risk when we overfit those models and eliminate personal agency and/or discovery in the experience. Surely there’s an opportunity to add more texture���a push and pull between lean-back personalization and more effortful exploration.

Let’s dial up the temperature on these models, or at least some of them. Instead of always presenting “more like this” recommendations, we could benefit from “more not like this,” too.

Why I Finally Quit Spotify | The New Yorker

Has the ���AI Edge��� Become a Dull Blade?

This essay is part of a series about Sentient Design, the already-here future of intelligent interfaces and AI-mediated experiences.

The book

Sentient Design by Josh Clark with Veronika Kindred will be published by Rosenfeld Media.

The talk

Watch Josh Clark's talk ���Sentient Design���

Need help?

If you’re working on strategy, design, or development of AI-powered products, we do that! Get in touch.

It’s already become a tired cliche: “AI won’t take your job, but someone using AI will.” The phrase is starting to feel overstated because AI, while helpful, isn’t proving to be as revolutionary as its proponents promised… but it is becoming an everyday technology.

At first it felt like AI gave us, the tech savvy, an edge. What happens now that recruiters, bosses, and peers have caught on and AI has become the expectation? In Fisayo Osilaja���s recent talk at the Rosenfeld Futures: ���Designing with AI��� conference, Osilaja discussed how she used AI to catapult her career from researcher to strategist���a ���super researcher.��� In Amy Webb���s 2024 SXSW talk, Webb talked about how AI could be used by attendees to generate SXSW roundup reports for their higher-ups. She even joked that attendees could use the time they saved to sip margaritas. But will 2024���s tequila time carry over to next year��� when everyone else is using the same AI time savers, too?

I was a college senior when OpenAI released ChatGPT in late November 2022. On campus, this meant that a powerfully accessible AI tool was unleashed just in time for finals. It could summarize the books you hadn���t read, write code you hadn���t bothered to learn, and write essays you hadn���t gotten to yet. December 2022 was a short but golden age of plagiarism for many of my classmates; teachers had already written or planned their final exams, and tools to detect if students had used AI were not yet widespread.

By spring 2023, the next finals season, the sun had mostly set on rampant AI plagiarism. Teachers were redesigning their exams to make it harder to cheat, and there were hosts of software platforms that promised to catch their students if they did. But, students still use AI. The typical student might not be turning in work straight as it���s generated (or maybe they are), but they���re certainly using it in their workflow. Etiquette is forming, and norms have been established. Most importantly, it���s not the students who adopted the technology first who benefit the most from AI in the post AI-plagiarism era, but those who found a good place for it in their workflow.

Are we in the golden age of sneaky workplace AI? The same cycle I experienced in school seems to be happening in the workplace, now complicated by an undercurrent of corporate anxiety. According to a May 2024 study by Microsoft, 75% of knowledge workers are using AI at work. Seventy-eight percent of those AI users are incorporating tools into their work that are not provided by their employers, a phenomenon the study���s authors have dubbed BYOAI. Just under half of those AI users started using AI at work in the past six months. These numbers are probably low: the same study shows that 52% of people who use AI at work are reluctant to admit to using it for their most important tasks. Fifty-three percent say they worry that using AI on important work tasks makes them look replaceable.

There’s an irony here: at a time when C-levels are actively trying to realize efficiencies with AI; individuals are tentatively doing so in their own personal practice���but they’re not sure if that’s okay. Is it cheating to use AI, or is it a savvy way to get an edge? As a result, there’s an unreported pocket of productivity that individual employees are silently mining���and not telling their bosses.

The unreported nature of this productivity will change as AI use in the workplace is normalized and as etiquette regarding AI���s use becomes formalized. Just as it happened on school campuses in 2022, hidden use of AI will yield to open use as norms are established. That normalization is already starting to happen. Those that benefit the most will be the ones who find appropriate places for the tool in their workflow. AI-powered tools are becoming everyday productivity tools like spreadsheets and word processors. That���s a good thing, by the way: technology tends to become more powerful as it becomes more ordinary.

So now what? The competitive edge of early adoption will fade quickly: Alas, when everyone’s special, nobody’s special. No sneaking off for solo margarita celebrations anymore. The AI advantages (and celebrations) that are more likely to emerge are at the team and organizational level.While AI might be giving the tech-savvy an edge for now, employers and peers will soon adapt. While my time at school showed me how individuals can benefit from AI integration, my research at Big Medium is showing that workplace adoption of AI is less about creating an individual competitive edge���that blade is getting blunter with every new adopter���but more about how adoption by a team can speed and elevate the work of everyone. So how can your team realize these benefits?

We���re finding that these elevations happen when:

Teams normalize the use of AI tools in everyday work. Teams who are open about their AI use and communicate their personal innovations will see the whole team progress faster. Openness, communication, and regular use breeds innovation.

Teams can adapt processes and workflow to accommodate AI. Thoughtful teams consider their own strengths and weaknesses as well as their tools���. How can AI fill in for parts of a process that the team is not good at? What is the team much better at than AI? When is the human in the loop?

Organizers establish clear guidelines and quality expectations for AI-supported work, including transparency and QA.

These are all themes that we’re following in adapting our own design and development processes at Big Medium. You can get a peek at how that plays out by watching our recent AI and Design Systems online event.

AI brings the opportunity to elevate the work of the whole workplace, not just the individual. If business is focused on efficiency, let���s realize those dividends in better results and higher revenues that benefit all of us. Just as it doesn���t help students to use AI to completely take over their homework, we can see that AI works best in the workplace as an enabler more than a replacement for critical human tasks. Indeed, the core principle of Sentient Design is that AI should amplify our agency and judgment instead of replacing them. AI may not be revolutionary, but it’s powerfully evolutionary in elevating the everyday work we all do���together.

Looking for help on the strategy, design, or development of AI-powered products and features? That’s what we do! Get in touch to learn more about our engagements, workshops, and Sentient Design sprints, or sign up for our newsletter to keep up with everything Big Medium is sharing about AI and Sentient Design.

The Shape of Sentient Design

This essay is part of a series about Sentient Design, the already-here future of intelligent interfaces and AI-mediated experiences.

The book

Sentient Design by Josh Clark with Veronika Kindred will be published by Rosenfeld Media.

The talk

Watch Josh Clark's talk ���Sentient Design���

Need help?

If you’re working on strategy, design, or development of AI-powered products, we do that! Get in touch.

The rapid evolution in machine intelligence has delivered a Cambrian explosion of user experiences. New ���species��� of interfaces roam our screens���copilots, agents, chatbots, assistants, tools, radically adaptive interfaces, and on and on. It���s noisy, chaotic, exciting… the start of something new, even if it���s not yet clear what that ���something��� will become.

What are the new design patterns, and which old ones fall away? How do you get your head around all the different AI-mediated experiences you might put to work? How do you name and organize those experiences? And how do you develop an intuition for how and when to use each interaction paradigm?

It helps to have a framework to get your bearings. In our intro to Sentient Design, Veronika and I describe Sentient Design like so:

Sentient Design refers to intelligent interfaces that are aware of context and intent so that they can be radically adaptive to user needs in the moment. Those are the fundamental attributes of Sentient Design experiences: aware and radically adaptive.

We supplement those core attributes with a handful of characteristics that travel with these experiences: collaborative, multimodal, continuous & ambient, and deferential. As a collection, we���ve found these to be helpful levers and dials to evaluate Sentient Design opportunities���and perhaps especially to develop design principles around a feature or product.

But this collection of characteristics isn���t exactly concrete, is it? While they describe the vibe of Sentient Design experiences, they don���t describe their form���their interaction, functionality, and posture. What shapes can different AI-mediated experiences take, and with what effect?

So, what���s the shape of Sentient Design? Turns out it���s a triangle:

A diagram of AI-mediated experiences across three attributes: grounded, interoperable, and radically adaptive. Thanks to Matt Webb. Mapping the landscape

Big thanks and huge props to Matt Webb for developing the visual framework behind this triangle diagram and sharing it here: Mapping the landscape of gen-AI product user experience. The read is worth your time���and so is a re-read. Highly recommended.

I made a few tweaks from Matt���s version that helped the diagram click a bit more for me, but most of the diagram shown above came directly from Matt’s giant brain and generous spirit. The triangle plots AI-mediated experiences based on how much each experience blends three characteristics:

Grounded. It has the info it needs to deliver reliable results. (Matt called this ���RAG or large context.���)Interoperable. It can share data and instructions with other systems. (Matt called this ���structured generation.���)Radically adaptive. It morphs in real time to user needs and context. (Matt called this ���real-time.���)

Of these attributes, ���radically adaptive��� is the one that���s particularly new and exciting for interaction design. It���s a core quality of Sentient Design experiences: the ability for intelligent interfaces to conceive and compile the experience in real time based on intent and context in the moment. But let���s not undersell ���grounded��� and ���interoperable.��� While those are hardly new, recent leaps in intelligence and capability have changed the nature of those characteristics. Generative AI has shown that when you give a system enough grounding in a specific domain���training in a sufficiently large and relevant data set, for example���it can faithfully create images, text, speech, music, etc., on demand. Same with interoperability: when you crank up a system���s smarts for interacting with other systems, you get autonomous agents. New things are happening, even in familiar areas.

Dialing these three attributes up and down yields a rich set of features, but when you pull up to a higher altitude, four fundamental archetypes emerge. Matt writes:

Tools. Users control AI to generate something.Copilots. The AI works alongside the user in an app in multiple ways.Agents. The AI has some autonomy over how it approaches a task.Chat. The user talks to the AI as a peer in real time.

At a medium altitude, four essential experience archetypes emerge: tools, agents, copilots, and chat.

Each of these (overlapping) archetypes suggests specific functionality, but just as interesting, they also present their own posture and manner to the user. Tools are controlled by the user through prompts or controls to get specific results (often through iteration that sculpts the result). Agents flip that script and do their work with little oversight or collaboration until they return with their results. Chat presents as a peer to the user with conversation that can go in any direction. Copilots stake out the middle of the diagram and blend attributes of all of these to provide collaborative support of your work in real time.

Descending to a lower altitude with increasing detail, specific nodes in the diagram light up to represent a cluster of AI-mediated experiences, each with its own emerging design patterns, opportunities, and constraints. (Matt provides lots of examples for each.)

The map is not the territory

This framework is hugely useful, but it remains (like all maps) a simplified representation that cannot capture every detail of the actual terrain. This diagram isn’t comprehensive���and it can’t be, thanks to a limitation of the triangle itself. In so-called ternary diagrams like this one, increasing the level of any two dimensions (grounded and interoperable, for example) necessarily reduces the third (radically adaptive). Mapping 3D attributes in a 2D chart means we lose some depth. That means this map can���t plot an AI experience with all three attributes maxed out. (Wait��� would that be artificial general intelligence???)

So the intent is not to catalog every possible AI interaction model���and that���s okay! Think of the diagram as more of a compass than a map. It provides orientation and suggests directions for exploration rather than a complete catalog of what’s possible. Matt describes it as a thinking tool instead of a solution engine: ���So it���s not a tool that gives me answers, it���s not that kind of map. But it helps me communicate, and it���s a decent lens, and it���s a helpful framework in a workshop context.���

I���ve found the map helpful in my own practice for developing ideas and concepts. I take the problem to solve and then roam the triangle to workshop how different AI postures could address it. I���ve also used it for product positioning by mapping clients��� competitors to identify market gaps or to reinforce our clients��� differentiating features.

Speaking of features, that���s the level at which this diagram operates. This is a map of features more than products. While some products might be single-feature, many more will include several at once. Even AI chat products like Claude or ChatGPT are more than ���just��� chat; both incorporate tools and agents to support a broad range of requests. Use the diagram to identify individual features to help the user overcome different frictions throughout the entire journey.

Tailored for Sentient Design

Inspired by Matt’s original framework, I played with some new labels for the three points to better suit the Sentient Design perspective, focusing more on experience attributes than technical capabilities:

Radically adaptive replaced ���real time��� in Matt���s version to emphasize the transformative nature of Sentient Design experiences, more than just their temporal aspect.Grounded replaced ���RAG / Large context��� to align with Sentient Design���s awareness of context and intent; grounded models understand context better.Interoperable replaced ���Structured generation��� to underline the resulting ability to work across systems and modalities, rhyming with Sentient Design���s collaboration.

This shift felt helpful to me, as it aligns with how Sentient Design experiences manifest rather than how the underlying tech is implemented. (Of course, the technical capabilities inform and shape the experience, but from a user perspective, they feel secondary.)

I also labeled the three sides of the triangle to call out the qualities that emerge in the experiences identified along the edges:

Collaborative = Radically adaptive + InteroperableAutonomous = Interoperable + GroundedIterative = Radically adaptive + Grounded

Note that these edge labels are not meant to suggest opposition to the point across the way. ���Collaborative��� is not the opposite of ���grounded,��� and ���autonomous��� is not the opposite of ���radically adaptive.��� Instead, the labels are meant only to call out a common characteristic that emerges across that cluster of features. But I love that working with the diagram brings up those kinds of thought experiments���are they opposites, and what would that mean? Again, a thinking tool more than a catalog.

Here be dragons

It���s also a thinking tool for exploring what���s not on the map. The diagram organizes what already exists, not what we���ve yet to imagine, but it can still work as a compass for helping us think our way there. What remains uncharted is just as intriguing as the territory that���s explicitly plotted.

Even within the mapped experiences, there���s more detail to explore than a ternary diagram allows. I���ve found it helpful to pluck these features out of the diagram and see how they might evolve if you dial from low to high along each of the different attributes. It���s an adjacent use of the diagram that helps to explore new opportunities.

For example, let���s consider how this works with the NPC experience. (NPC means ���non-player character,��� a term from the gaming world that refers to an automated character with pre-determined behaviors. In more general Sentient Design experiences, NPCs might appear as Slack bots, Figma users, or Miro sidekicks; they have a user account and some agency, but the system runs them.)

Here���s what happens when we pull NPCs out of the map and waltz them along the spectrum of each of our three attributes:

My sketch exploration of how NPCs change along each attribute’s spectrum. Explore and ask questions

The Sentient Design triangle is not a static map but a dynamic terrain for imagination and conversation. It invites us to question, explore, and redefine what���s possible in AI-mediated experiences. At a time when too many are rushing in with hurried solutions, devices like this encourage us to ask better questions and evaluate different postures to address the problem at hand. This may not be a complete taxonomy for our AI Cambrian explosion, but we���re at least developing the language to discuss the differences between these emerging UX species���and identify some weird platypus combinations.

Looking for help on the strategy, design, or development of AI-powered products and features? That’s what we do! Get in touch to learn more about our engagements, workshops, and Sentient Design sprints, or sign up for our newsletter to keep up with everything Big Medium is sharing about AI and Sentient Design.

AI Is Confusing ��� Here���s Your Cheat Sheet

Scratching your head about diffusion models versus frontier models versus foundation models? Don’t know a token from a transformer? Jay Peters assembled a helpful glossary of AI terms for The Verge:

To help you better understand what���s going on, we���veput together a list of some of the most common AI terms.We���ll do our best to explain what they mean and whythey���re important.

Great, accessible resource for literacy in fundamental AI lingo.

AI Is Confusing ��� Here���s Your Cheat Sheet | The Verge

Turning the Tables on AI

Oliver Reichenstein shares strategies for using AI to elevate your own writing instead of handing the job entirely to the robots. (This rhymes nicely with the core principle of Sentient Design: amplify judgment and agency instead of replacing it.)

Let���s turn the tables and have ChatGPT prompt us. TellAI to ask you questions about what you���re writing.Push yourself to express in clear terms what you reallywant to say. Like this, for example:

I want to write [format] about [topic]. Ask me questionsone at a time that force me to explain my idea.

Keep asking until your idea is clear to you.

Reichenstein is CEO and founder of iA, the maker of iA Writer. One of its features helps writers track facts and quotes from external sources. Reichenstein suggests using it to track AI-generated contributions:

What if the ChatGPT generated something useful that I want to keep? Paste it as a note Marked as AI. Use quotes, use markup, and note its origin. ��� iA Writer greys out text that you marked as AI so you can always discern what is yours and what isn���t.

It’s a good reminder that you can design personal workflows to use technology in ways that serve you best. What do you want AI to do for you? And as a product designer, how might you build this philosophy into your AI-mediated features?

Turning the Tables on AI

Doctors Use A.I. Chatbots to Help Fight Insurance Denials

In The New York Times, Teddy Rosenbluth reports on doctors using AI tools to automate their fight with insurance companies’ (automated) efforts to refuse or delay payment:

Doctors and their staff spend an average of 12 hoursa week submitting prior-authorization requests, a processwidely considered burdensome and detrimental to patienthealth among physicians surveyed by the American Medical Association.

With the help of ChatGPT, Dr. Tward now types in acouple of sentences, describing the purpose of theletter and the types of scientific studies he wantsreferenced, and a draft is produced in seconds.

Then, he can tell the chatbot to make it four times longer. ���If you���re going to put all kinds of barriers up for my patients, then when I fire back, I���m going to make it very time consuming,��� he said.

I admire the dash of spite in this effort! But is this an example of tech leveling the playing field, or part of an AI-weaponized red-tape arms race that no one can win?

Doctors Use A.I. Chatbots to Help Fight Insurance Denials | The New York Times

Still Trying to Sound Smart About AI? The Boss Is, Too

There’s a lot of “ready, fire, aim” in the industry right now as execs feel pressure to move on AI, even though most admit they don’t have confidence in how to use it. At The Wall Street Journal, Ray A. Smith rounds up some recent surveys that capture the situation:

Rarely has such a transformative, new technology spreadand evolved so quickly, even before business leadershave grasped its basics.

No wonder that in a recent survey of 2,000 C-suiteexecutives, 61% said AI would be a ���game-changer.���Yet nearly the same share said they lacked confidencein their leadership teams��� AI skills or knowledge,according to staffing company Adecco and Oxford Economics,which conducted the survey.

The upshot: Many chief executives and other seniormanagers are talking a visionary game about AI���s promiseto their staff���while trying to learn exactly what itcan do.

Smith also points to a separate spring survey of 10,000 workers and executives that cited AI as a reason 71% of CEOs and two-thirds of other senior leaders said they had impostor syndrome in their positions.

With limited confidence at the top, AI innovation is trickling up from the bottom. (This rhymes with our strong belief at Big Medium that to be expert in a thing, you have to use the thing.)

In fact, much of what business leaders are gleaning about AI���s transformative potential is coming fromindividual employees, who are experimenting with AI on their own much faster than businesses are buildingbespoke, top-down applications of the technology, executives say.

In a survey of 31,000 working adults published by Microsoft last month, 75% of knowledge workers said theyhad started using AI on the job,the vast majority of whom reported bringing their own AI tools to work.Only 39% of the AI users said their employers had supplied them with AI training.

Still Trying to Sound Smart About AI? The Boss Is, Too | WSJ

Master Design System Governance With This One Weird Trick

This article was originally published at bradfrost.com.

Looking for help? If your organization needs help refining your governance process or improving your collaborative culture, get in touch.

That one weird trick? TALK.

What do you do when someone doesn���t see what component they���re looking for in the design system?

TALK.��

What do you do when someone has questions about what a component does, if it meets their needs, or how they should use it?

TALK.��

What should happen when someone has a concern about the design system?

TALK.

What should happen when someone encounters a bug or issue with the design system?

TALK.

What do you do when someone has questions, comments, or feedback around any aspect of the design system?

TALK.��

It���s that simple

I���ve written before about the importance of establishing a successful design system governance model. In fact, I created and published a design system governance diagram that spells out all of the gory details that can go into governing and releasing design systems.

The details of a complete design-system governance process can seem intimidating at first glance.

Our team at Big Medium has worked with dozens of design system teams on their governance process, and when we get into the details, people sometimes get intimidated and overwhelmed. They look at the process and see complexity, which is understandable! But the good news is that this process can be broken down into a rough gist:

Zoom out to see the high-level view of healthy governance, and things get simpler.

Even better news is that the gist of that gist is: when in doubt, have a conversation. A healthy chunk of the whole governance workflow takes place as a quick chat on Zoom or Slack/Teams.

A little conversation goes a long way

A quick conversation can save hours, days, or weeks of work, and it���s absolutely magical when it happens. This is where the millions of hours/dollars of savings come from! The design system team can direct users to existing solutions, saving the user team from having to unnecessarily reinvent the wheel. We can���t assume that users will automatically know how to use the system properly just because components exist and even if they���re well-documented. A little direction or nudge via a conversation provides clarity and helps build rapport between the design system makers and users.

The conversation goes a little something like this:

What���s going on? What���s the question, comment, or concern?

Does any new work need to happen?

If work does need to happen, is it part of the core design system or a recipe?

If the work is part of the core design system, does the team have time to do it in the timeframe?

The flow chart details the decision tree, but in order to answer these questions in the first place you must make it clear how to have the conversation in the first place.

A culture of conversation

The Nord Health Design System does a fantastic job spelling out the various ways of connecting with the design system team. There are some great practices on display here:

Dedicated Slack/Teams channels

Email address

Office hours

FAQs

Detailed bug/support process

These are great tactics to consider, but like all things design systems the challenges around successfully implementing this has to do with humans. Creating a new Slack channel takes 10 seconds, but creating a culture of communication is where the real work lies! In our experience, simple advice like ���talk to each other��� can be difficult to implement in organizations that don���t have a history of open conversation and close collaboration. Sometimes relationships are frayed, teams are distrustful, or other forces are at play that keep people away from each other. That is the work of the design system: to dive headfirst into those cultural challenges and overcome them. Embodying a service-oriented mindset, proactively reaching out to teams, and being genuinely kind and curious are critical muscles for design system teams to devleop. The more you can do to make connections and demonstrate your willingness to help will make it easier for teams to reach out when they have questions.

So yeah, get out there and TALK!

If you need help with design system governance or with creating a more collaborative culture of design and development, get in touch. We offer workshops, executive sessions, and strategy/production engagements to elevate process and culture (all while delivering great products faster).