Jascha Goltermann: The Impact of AI on UX Design - Hatch Conference 2023

Summary

Yasha Golderman's talk at a conference delves into the transformative impact of AI on UX design, using Booking.com's AI Trip Planner as a case study. He discusses the evolution from traditional UX design to incorporating generative AI, emphasizing the necessity for designers to understand AI's capabilities and limitations. Golderman highlights the importance of prompt engineering, setting AI guardrails, and maintaining user trust and control, while advocating for designers to stay empathetic and adapt to the changing landscape of AI-enhanced design.

Takeaways

• 🚀 The speaker emphasizes the growing interest in managing people and the nonlinear ways of managing a career in the tech industry.
• 🌟 Yasha Goldman's talk focuses on the impact of AI on UX design, specifically how AI is integrated into products rather than design tools.
• 🔍 AI's presence is exemplified by everyday applications like image recognition and search functionalities in smartphones, showcasing its accessibility and integration into user experiences.
• 🏢 Yasha's background in UX for over a decade and his role at Booking.com, where he manages designers and leads projects like the AI Trip Planner, sets the stage for his expertise on the subject.
• 🌐 Booking.com's scale, with 700 million monthly visitors and a strong data-informed approach, underscores the company's user-centric mindset and the potential for AI applications.
• 🤖 The distinction between AI, machine learning, deep learning, and generative AI (Geni) is clarified, highlighting the latter's role in creating diverse data types.
• 💬 The integration of Geni into everyday tools and platforms like Notion and Grammarly indicates a shift in user expectations and the need for designers to adapt.
• 🛍️ The AI Trip Planner at Booking.com is presented as a case study, illustrating how AI can enhance services like virtual travel agents with more natural and context-aware interactions.
• 🛑 The importance of setting guardrails for AI to ensure appropriate output and maintain brand values is stressed, especially in avoiding sensitive topics and malicious intent.
• 🔄 The need for designers to shift focus from UI to AI, considering the quality of AI output as a defining factor in user experience, is highlighted.
• 🔄 The talk concludes with a call for designers to understand AI, engage in prompt engineering, and embrace the concept of hyper-personalization to stay relevant in a rapidly evolving field.

Q & A

What was the main topic of Yasha Golderman's talk at the conference?

Yasha Golderman's talk focused on the impact of AI on UX design, specifically discussing how AI can be integrated into products to improve user experiences, using the AI trip planner at Booking.com as a case study.

What is the difference between AI, machine learning, and deep learning as mentioned in the script?

AI refers to the broader field of study of artificial intelligence. Machine learning is a subset of AI where computers are trained on large datasets to find patterns and make decisions. Deep learning takes this further by creating artificial neural networks with multiple layers to mimic the human brain, allowing the computer to learn more complex patterns.

What is the role of generative AI (gen AI) in the context of the talk?

Generative AI, or gen AI, is a type of AI that can create new data such as text, images, or video. In the talk, gen AI is highlighted for its potential to enhance user experiences in products, with examples of how it's already integrated into everyday tools and the Booking.com AI trip planner.

How has Booking.com utilized AI in its services prior to the development of the AI trip planner?

Booking.com had previously developed the Booking Assistant, which used machine learning models to help customers with trip-related questions. However, it was limited to specific topics and did not understand broader conversation contexts or apply real-world knowledge until the integration of gen AI.

What is the AI trip planner, and how does it differ from the previous Booking Assistant?

The AI trip planner is an enhancement of the Booking Assistant, integrated with open AI's GPT model, allowing it to discuss any travel-related topic in a more natural way, understand broader conversation contexts, and apply real-world knowledge to provide more personalized and accurate recommendations.

What is the significance of the number of visitors, listings, and reviews that Booking.com has in relation to AI?

The large volume of data from Booking.com's 700 million monthly visitors, nearly 30 million global listings, and over 250 million guest reviews provides a rich dataset for AI to learn from and offer personalized recommendations, enhancing the AI's ability to understand and cater to user needs.

Why is prompt engineering important for designers working with AI?

Prompt engineering is crucial as it involves instructing the AI on how to respond appropriately to user inputs. It helps in setting the AI's personality, defining guard rails, and ensuring the AI's responses align with the brand's values and user expectations.

How should designers approach the design process when working with gen AI?

Designers should shift their focus from UI to AI, mapping situations rather than steps, as gen AI interactions are nonlinear. They need to consider user flows that can adapt to various AI responses and ensure the AI's output is of high quality and meets user expectations.

What are some of the ethical considerations when integrating AI into a product?

Designers must consider setting guard rails to prevent inappropriate outputs, respecting sensitive subjects, and accounting for potential malicious intent from users trying to push the AI beyond its intended limits.

How can designers ensure users feel in control when interacting with AI-enhanced products?

Designers can ensure users feel in control by not using their data to train the AI models, allowing users to delete their conversations, and providing clear examples of how to interact with the AI, as well as managing expectations through disclaimers and welcome messages.

What are some resources recommended in the script for designers to learn more about AI and prompt engineering?

The script recommends Ben B's newsletter and the Invisible Machines podcast for staying updated on AI, the Prompt Engineering Guide for learning about instructing AI, and the Delo report 'Connecting with Meaning' for understanding hyper-personalization.

Outlines

00:00

🎤 Speaker Introduction and Career Paths

The speaker begins by discussing their preparation to introduce the next presenter, Yasha Goldman, and reflects on a podcast about career paths. They engage the audience by asking who are individual contributors and who aspires to manage people, highlighting the interest in career growth. The introduction segues into Yasha's role as a design manager at booking.com, focusing on strategic design projects that leverage AI. Yasha's background in UX for a decade and his current projects, including the AI trip planner, are briefly outlined, setting the stage for his talk on AI's impact on UX design.

05:01

🤖 Basics of AI and Its Impact on UX Design

This paragraph delves into the fundamentals of AI, machine learning, and deep learning, explaining how they work and their applications in generative AI. The speaker illustrates the prevalence of AI in everyday tools and its influence on user expectations. The talk then shifts to the speaker's work at booking.com, discussing the company's scale, user-centric approach, and commitment to data-informed design. The AI trip planner is introduced as a case study to explore AI's impact on UX, emphasizing the integration of AI into existing products and services rather than creating entirely new ones.

10:01

🗺️ The Evolution of the Booking Assistant to AI Trip Planner

The speaker recounts the history of booking.com's virtual travel agents, from the initial AI-enhanced Booking Assistant in 2017 to the current AI Trip Planner, which integrates with open AI's GPT model. The evolution is marked by a transition from keyword-based responses to a more natural, context-aware conversational ability, thanks to generative AI. The AI Trip Planner is described as a feature within the booking.com app, offering personalized travel recommendations by leveraging the company's extensive data on accommodations, guest reviews, and more.

15:02

🛣️ Designing for AI: Nonlinear User Experiences

The speaker discusses the challenges and considerations in designing for AI, emphasizing the nonlinear nature of AI interactions compared to traditional user flows. They introduce the concept of 'prompt engineering' as a new skill for designers, which involves instructing AI with examples and setting guidelines for its responses. The importance of setting guardrails for AI to prevent inappropriate outputs and maintaining brand consistency is highlighted. The speaker also stresses the need for designers to shift focus from UI to AI, considering the quality of AI output as a defining factor in user experience.

20:03

🔒 Managing User Trust and Control in AI Interactions

This paragraph focuses on the importance of maintaining user trust and control in AI-driven products. The speaker discusses the need for transparency about AI's limitations, managing user expectations, and providing examples of possible interactions. They also address the importance of user control, such as the ability to delete conversations and the assurance that user data is not used for model training. The speaker advocates for tight feedback loops to improve AI output quality and match user expectations.

25:04

🛠️ The Future of Design in the Age of AI

In the concluding paragraph, the speaker reflects on the implications of AI for the design profession. They assert that core design skills, particularly empathy, remain crucial but suggest that designers will need to become proficient in AI to understand its opportunities and risks. The speaker introduces resources for learning about AI and prompt engineering, highlighting the growing importance of these areas. They emphasize that AI will not replace the role of designers but will change it, necessitating adaptation and the acquisition of new skills to stay relevant in a rapidly evolving field.

🌐 Final Thoughts and Opportunities for Connection

The speaker wraps up with final thoughts on AI's role in enhancing rather than replacing the tasks of designers. They stress that the role of designers has always evolved and will continue to do so. The speaker invites the audience to connect with them on LinkedIn or ADP list and expresses openness to meeting for coffee in Amsterdam, signaling a willingness to engage in further discussions. They also look forward to any questions from the audience, indicating an interactive and open-ended conclusion to their talk.

Keywords

💡Individual Contributors

Individual contributors are professionals who focus on their specific tasks and do not manage a team. In the context of the video, the speaker uses this term to identify the audience members who are not in supervisory roles but are interested in potentially managing people in the future. The concept is tied to career progression and the desire to explore different paths within one's professional journey.

💡Career Path

A career path refers to the trajectory of professional development and job opportunities an individual may follow over their working life. The video discusses the idea that career paths are not always linear and can include various roles such as managing teams or being an individual contributor. The speaker encourages the audience to think about their long-term career goals and how they might incorporate different experiences.

💡AI (Artificial Intelligence)

Artificial Intelligence, or AI, is the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the video, AI is central to the discussion, particularly in its application to UX design and the development of the AI trip planner. The speaker explores how AI can enhance user experiences and is becoming more accessible and integrated into everyday tools.

💡UX Design

UX Design, or User Experience Design, is the process of designing products with a focus on the user's experience and interaction. The video emphasizes the impact of AI on UX design, showcasing how AI can be leveraged to create more personalized and responsive user experiences, as illustrated by the AI trip planner case study.

💡Booking.com

Booking.com is a prominent online platform for booking accommodations and is highlighted in the video as the company where the speaker works. The company's size, user-centric approach, and technological capabilities are mentioned to establish the context for the development and implementation of AI-driven projects like the AI trip planner.

💡Machine Learning

Machine learning is a subset of AI that involves training computers to improve at tasks based on data input without being explicitly programmed for those tasks. The video explains that machine learning is foundational to AI capabilities, with the speaker discussing how it has been used in the evolution of the Booking assistant into the AI trip planner.

💡Deep Learning

Deep learning is a more advanced form of machine learning that involves the creation of artificial neural networks with multiple layers, mimicking the human brain's structure to learn and make decisions. The video script touches on deep learning as part of the broader AI field, indicating its role in the sophistication of AI applications.

💡Generative AI

Generative AI, also known as 'gen,' refers to AI systems trained on data to generate new data, such as text, images, or sounds. The video discusses generative AI as a key component of current AI discussions, with examples of its integration into tools and services that designers and users interact with.

💡AI Trip Planner

The AI trip planner is a case study presented in the video, illustrating the practical application of AI in enhancing the travel planning experience. It is an AI-enhanced virtual travel agent developed by Booking.com that leverages machine learning and natural language processing to provide personalized travel recommendations and advice.

💡Prompt Engineering

Prompt engineering is the process of designing instructions or prompts for AI systems to guide their behavior and responses. In the video, prompt engineering is discussed as a critical skill for designers to master when working with AI, as it influences how AI interacts with users and provides personalized experiences.

💡Hyper Personalization

Hyper personalization refers to the tailoring of experiences or content to individual user preferences and behaviors at a very detailed level. The video mentions hyper personalization as a capability enabled by AI, allowing for a more nuanced understanding of user needs and the creation of highly customized user experiences.

💡User Expectations

User expectations refer to the standards or requirements that users have for a product or service, which are influenced by their experiences and the capabilities of technology. The video script notes that AI is changing user expectations rapidly, implying that designers must consider these evolving expectations when integrating AI into their designs.

💡AI Proficiency and Trust

AI proficiency and trust refer to users' ability to understand and rely on AI technologies. The video discusses the varying levels of proficiency and trust in AI among users, emphasizing the importance for designers to consider these factors when integrating AI into products to ensure a positive user experience.

Highlights

The speaker discussed the impact of AI on UX design, focusing on how AI can be integrated into products rather than as standalone tools.

AI's role in everyday life was exemplified through the use of image recognition and search in mobile applications, like iOS's swipe up or Google Lens.

The speaker's background in UX spans 20 years, with experience managing designers and leading projects at Booking.com.

Booking.com's scale was highlighted, with 700 million monthly visitors and a significant tech and design workforce.

The importance of a user-centric mindset and data-informed approach in product development was emphasized.

Basic terminology in AI, including machine learning, deep learning, and generative AI, was explained to provide foundational knowledge.

Generative AI's capability to enhance tools like Notion and Grammarly was mentioned, indicating AI's integration into everyday design tasks.

The case study of the AI Trip Planner at Booking.com showcased how AI can understand context and provide personalized travel recommendations.

The evolution from the Booking Assistant to the AI Trip Planner demonstrated the shift from keyword-based responses to context-aware conversations.

The AI Trip Planner's ability to provide real-time, accurate recommendations based on Booking.com's extensive data was highlighted.

Designers were encouraged to consider AI in the context of existing use cases to enhance user problems rather than creating entirely new products.

The importance of prompt engineering in instructing AI and setting guard rails for appropriate output was discussed.

The need for designers to understand AI proficiency levels and trust among users to create inclusive and user-friendly AI experiences was emphasized.

The concept of hyper-personalization in design was introduced, suggesting that AI can tailor experiences to individual user needs.

The speaker proposed that AI will replace tasks rather than roles, and the role of designers will continue to evolve and remain relevant.

Resources for designers to learn about AI, prompt engineering, and hyper-personalization were recommended for professional development.

The final takeaway was a reminder that designers should always serve the user's needs and be adaptable to technological changes.

Transcripts

yes okay so as I was getting ready to

present the next speaker I was listening

to the podcast that he registered with

Daman which I thought was fascinating

even if it was short and so because they

were talking about career path and I

wanted to ask how many of you are

individual contributors which means that

you do not manage

people yeah yeah good amount keep your

hand up keep your hand up now how many

of you are hoping ideally to manage

people in the next let's say five years

get your hand down put your hand down if

you do okay I think it was pretty much

everyone so this shows that uh we're

thinking of our career in the long term

of probably managing up but um jasha was

telling about the fact that there are

nonlinear ways of managing your career

as well and he's been exploring that a

lot from leading people managing people

being an individual contributor in all

sorts of directions and I think that's

very inspiring for for all of us but

this has nothing to do with the talk

that he came uh for and so as he's a

design manager at booking.com he's

working on strategic Pro um design

projects leveraging AI capabilities and

he's going to tell us all about that

please help me welcome on stage Yasha

golderman

thank

you thank you for the nice introduction

and thank you everybody for being here

it's a great conference and I'm really

happy and humbled to be here so like I

was just mentioned I'm going to talk a

little bit about Ai and the impact of AI

on ux design but just a quick disclaimer

I'm not talking about AI tools that you

can use to supercharge your workflow as

designers sorry to disappoint I'm going

to talk about how we can use AI in our

products and you might think I don't

think we will use AI in my product but

this might change soon because AI is

getting more available and more

accessible but before we get into it let

me start with a simple question when was

the last time you looked at a laundry

label and you're not quite sure what one

of those symbols actually mean or even

worse you get into your car and you're

not quite sure what one of those symbols

on the dashboards is about

well chances are you can just take a

photo if you have IOS you just swipe up

or if you have Android you use Google

lens and it's going to tell you what you

see and there are a few AI things at

play here it's doing image recognition

it's performing a search it's matching

information and I think it's a neat

little example of how AI is already

around us and improving our experiences

as

users so who am I and why am I talking

to you today I I've been in ux for about

a decade and my interest in design spans

roughly 20 years so I started designing

with micromedia fireworks and I built my

own little websites on front page and I

actually spent most of their time here

in

Berlin now I live in Amsterdam where I

manage designers at booking.com and I

also sometimes lead projects and one of

those projects is the AI trip planner

and I'm going to use that as a case

study today to talk about the impact of

AI on ux

design but let me tell you one or two

things about

booking.com as a product we have roughly

700 million visitors a month that's

about a million visitors at any given

time nearly 30 million Global listings

and over 250 million guest reviews and

I'm not just naming these numbers to

brag is relevant for the talk today

you'll see now as a company we're over

20,000 employees roughly 5,000 of those

in Tech more than 200 designers and we

have 140 offices in more than 70

countries and the one you're seeing up

here that's a new campass that was

opened in Amsterdam earlier this

year more importantly as a place to work

we have a user Centric mindset the user

is at the center of everything we do we

have a very data informed approach that

we're famous for just last year alone we

ran over 10,000 AB tests and we have a

machine learning organization that's

quite sizable and knowledgeable we have

more than 300 people working machine

learning roles we have a vice president

of machine learning we have a machine

Learning Hub in Tel Aviv and we have a

hackathon tradition we have hackathons

every year we have conferences and

events throughout the year and I'm

mentioning it here because some of the

Great Inventions originated in those

hackathons and the AI trip planner is no

exception so when we talk about

AI there's more to it than I could even

get into today and it's more complicated

than what you see here on the screen but

I want to just get into the very basic

terminology

so there is the field of study that is

artificial intelligence and within this

field of study there is machine learning

which is when you train computers on

vast amounts of data so that they can

find patterns and make decisions without

being specifically programmed for those

particular

tasks now deep learning is when you take

this to the next level not just more

amounts of data but those computers will

also generate multiple layers and create

artificial neural networks which are

supposed to mimic basically how the

human brain works so they can learn with

that data on their

own and within that there's the

application of generative AI or more

known as geni which is when you train

machines on data so that they can

generate data and this data could be

text images video sound anything that

you train the machines

on and today when people talk about AI

most of the time they actually talk

about

gen so when we hear about gen typically

one of these tools comes to mind we

think about cat GPT mid Journey Google

Bart we've probably played around with

these tools and I bet that within this

audience we also know that gen is

already built into some of our favorite

tools mro notion grammarly they're

already enhanced with Gen

capabilities but I want us to zoom out

from that and I want us to understand

that gen is already built into some

tools that we use on an everyday basis

it can transcribe a video call or create

live captions as we talk or help us

finish our sentence when we create a

memo or a

note and this means for us as designers

that user expectations are changing user

expectations are changing and they're

changing fast so think about that in the

context of your product or service that

you're working on as a designer because

like I said AI capabilities are becoming

more

available

so when we talk about gen specifically

like I said it can generate different

types of data at booking we're

experimenting with Gen to create audio

image numerical data or

text and just within the text based gen

we need to realize that there are vast

amounts of very diverse kinds of use

cases that we can apply it

to for example at booking we're using it

to create image descriptions which helps

with accessibility for users with screen

readers you can also use it for

translations for property descriptions

or summarizing reviews and we've also

used it to build a virtual travel agent

and that's the case study I will use

today to go deeper into this

topic now booking actually has quite a

history of virtual travel agents let me

take you back six years in

2017 long before the Advent of gen

booking released the booking assistant

and the booking assistant was already AI

enhanced it was trained with machine

learning models to help Travelers or

customers uh with questions about their

trip or they could find hotels all kinds

of things and it was not bad so in this

case you see you can ask about parking

information you could actually even

reserve a parking spot the way how it

was doing this it was trained on a

machine learning model for the topic of

parking it was Advanced over the next

coming years it was even available right

through the Facebook Messenger app so

this person here is asking if this is a

pet friendly hotel and it gave a

relevant answer and it was doing that

because it was trained on a machine

learning model for pet related questions

now think about that you needed a

machine learning model for a topic so

that it can identify keywords and give

you a relevant answer and why because it

wasn't really understanding the broader

context of the conversation it was

looking for keywords and matching that

with the probability of an answer that

might be relevant to your

question but what it wasn't able to do

was understand language it wasn't able

to understand how a previous question

was relevant to a question that you

asked now and it wasn't able to apply

let's say Real World Knowledge to the

topic but then came gen and at the

beginning of this year in a hackathon at

booking.com a group of people thought

what if we had gen capabilities to the

booking assistant what if we use open AI

API to basically plug in chat GPT into

the booking assistant and immediately it

became clear that now the booking

assistant would be able to talk about

any topic not just the ones that it was

specifically trained on and it was able

to do so in a much more natural way it

had a very much lower error rate and it

was able to understand that a question

you were asking may be related to a

question you asked earlier and it was

even able to apply real world context to

the question that you

asked and that's basically in a nutshell

how the AI trip planner was

born so the a planner is built right

into the booking.com app it's accessible

right now for audiences in the US and

it's expanding gradually to other

regions and what it can do is basically

anything that you would expect from a

real human travel agent so you can ask

about places to go or hotels to book or

some travel advice like to pack or what

to prepare or what to think about before

you uh travel before you start your

travels now that sounds like classic jet

GPT and it makes sense because we're

plugging open ai's GPT model into this

AI trip planner but we build a whole

wrapper around

it so basically the a planner is only

going to recommend places that actually

available that actually exists it's not

going to hallucinate places it's not

going to hallucinate activities or

things to do and it has information

about places beyond what you would

expect from any other available chatbots

because like I mentioned with those 266

million guest reviews booking.com has

knowledge about neighborhoods about

dining options about activities about

things to do it has vast amounts of

knowledge so when the a planner

recommends you a place to stay it knows

whether there are nearby restaurants

that are recommended by users for let's

say a romantic trip it knows if a

property is wheelchair accessible or pet

friendly basically it's the booking

assistant but capable of really

understanding the world similar in a way

how a human would and capable of uh

reacting and understanding the language

in a way how a real human would and all

of this information is live and accurate

you can see the prices the reviews the

availability you can book right through

the app then go back into the chat

continue your your conversation maybe

add something to your wish

list now for me this means that we as

designers should think about AI in the

context of existing use cases I know

it's very tempting to think that with

this novel technology there are all

these new kinds of products that we

could build that have never existed

before and to some extent that is true

but I would urge you to think about what

is your product or service trying to

solve for the users and how could AI be

used use possibly to solve that problem

in a better

way let me give you one more example in

the context of booking a trip let's take

this Persona Lisa and she's looking for

a mountain honeymoon in the USA so she

will go to Google and Google will take

her to a Blog and then she will research

some information on Wikipedia and she

will use social media and she will talk

to friends and family to sort of form

her trip intend and she goes through

various phases of inspiration and

research and narrowing down and finding

the best deals and all of that happens

in different places and not at the same

time and it's not a linear path she goes

back and

forth if she asked the a trip planner

for Mountain honeymoon in the USA is

going to give her relevant

recommendations and if you think about

the information that she has to provide

it's actually very little she just

mentioned a mountain honeymoon

USA But if you go to a real human travel

agent they would realize that likely

your duration is probably one to two

weeks you're looking for romantic

activities your budget is likely

elevated and all of this leads to better

recommendations because it understands

real world context and it can you know

understand the likelihood that those

pieces of information that you didn't

explicitly give are also

true this means that gen enables hyper

personalization ation this is something

we need to think about more as designers

in the future you can understand users

at the individual users level and don't

just think about it in the context of

this case study of the chatbot that

basically gives an individual

response think about it Beyond and think

about it more abstract and more

futuristic in theory the blocks of your

interface could assemble itself based on

what you know about that particular

single user at that moment in

time so when we built the a

planner we started by mapping the

experience and the skills you can apply

here is basically creating user flows

but it's not the same it's not the same

with Gen because gen interactions are

nonlinear so the interaction with Gen

can go in multiple different ways think

about the trip planner you ask it the

same question twice it's not going to

give you the exact same answer twice so

you map situations not steps you don't

say if this happens then that happens

you give examples of if this situation

occurs then I want this experience to to

get out of it and you need to let the AI

handle situations in multiple ways and

that's a bit difficult for designers to

wrap their heads around because we're

used to mapping out the exact experience

that we want the user to have so we're

losing a little bit of control when we

work with

Gen now what you need to do here or no

actually let me give you an example

first imagine you use the trip planner

to plan a road trip so it's going to ask

you if you already have a destination in

mind or if you're open to suggestions

now what happens if you just answer

hamburgers there are multiple ways how

it could handle this

situation in our case it's going to

understand it in the context of you

looking for travel advice so it's going

to ask you are you interested in

locations that are known for great

hamburgers or are you planning a road

trip that has Hamburg Germany as one of

the um places on your route so we

trained it to understand the question in

a travel context and the way how you

train the AI is with prompt

engineering so you need to design the

prompts prompts are how you instruct the

AI on what to do and you don't do that

by saying if this happens you need to do

this no you provide examples for the AI

to learn from and you need to create

guidelines and principles and you need

to Define things like like what's the

audience of this AI what's the goal of

the AI what's the goal of the audience

what's the role tone you need to infuse

it with a

personality think about it like a person

it's not a person but think about it

like a person if you had a if you had to

hire a travel agent and they have no

experience you need to train them and

the same thing with our virtual travel

agent we had to train it and give it a

personality let me give you another

example if you ask the AI trip planner

that you want to go elephant ride

writing is going to say that it cannot

recommend such activities because that

may result in animal exploitation and

their booking a com is committed to

sustainable and responsible

travel so it has a personality here it

applies that

personality now if you say I want to see

elephants that's great elephants seeing

elephants in the natural habitat that

can be a great experience so it's happy

to oblige and give you recommendations

there so this means that we need to set

guard rails we need to Define what our

AI should do and what it should not do

there's a huge potential for

inappropriate output we need to consider

sensitive subjects especially in the

context of our product and our brand and

we need to account for malicious intent

because chances are when users realize

they're interfacing with a product that

is outputting content with AI they're

going to try to find out what are the

limits of this Ai and they might try to

push it beyond the those

limits let me give you another tangible

example if you asked the a planner for

attractions in the US in the 1950s is

going to ask if you're interested in

attractions that date back to the 1950s

or if you would like to plan an

imaginary trip to the US of the 1950s

which are completely Val travel

requests but if you ask almost the same

question about attractions in Germany in

the 1940s it's just going to say that

I'm sorry I can't assist you with that

because likely in this case it's

somebody trying to trick the AI into

generating inappropriate output or

getting into a sensitive topic or

getting into a discussion so you don't

want to have to deal with that and what

you can see here is that it's not

explaining anymore why it's not getting

into the topic because that would only

fuel somebody who's trying to get it and

push it Beyond those uh limits that you

want to set and something that I think

is relevant for us to take away from

that is that we need to shift Focus from

UI to

AI don't get me wrong the UI is still

very important how things look how easy

they are to use all of that is still

relevant but it's equally important or

more more so what the quality of the

output of the AI in your product is

because that really defines the quality

of the experience that the users will

have with your product and it's not

abstract it's not the

future more companies and even smaller

companies will be able to apply AI into

their product relatively

soon something we had to account for and

I think this is a general learning for

anyone who's building AI into their

product is AI proficiency and Trust in

AI which varies greatly so some people

don't trust AI in general some people

don't have experience with it and they

might not know how to interact with it

just on this screen alone here I want to

give you three examples at the top there

is a disclaimer saying that the chat

includes machine generated content that

may contain errors so we are being clear

about limitations and risks then there

is a welcome message that says hey I'm

still learning but I'm happy to help you

so we're setting a friendly tone but

we're also managing

expectations and then we have those uh

interactions here that provide examples

of things you might ask because not

everybody who uses this has already

tried chat GPT in the past so they might

not know what they can expect from this

chat are they talking suddenly to

customer support all of this needs to be

clear so you need to account for

different levels of AI proficiency and

Trust in Ai and this also means keeping

users in control typically users have a

bit less control when I is generating

content for them and they know that and

you need to give them control back and

you need to make sure that they know

that they're in control and for us this

meant primarily two things first of all

the data that users provide in the chat

is never being used to train the model

it's never fed back into the model

second of all they can always delete

their conversations some users might

feel uneasy talking now about a

different kind of trip knowing that it

still has the context from the previous

conversation we're also resetting

conversations automatically after

certain session durations not while

they're chatting but if they haven't

used the app for a

while finally testing and feedback so

always important to improve the quality

of your product but even more so when

you have a little less control over what

the the product actually is which

happens when you add gen capabilities to

your product so you need to establish

tight feedback loops of course you still

need to do usability testing and

research and all those things but

whenever there is an output from AI in

your product you need to make sure that

users can give direct feedback about the

quality of this output and whether it

matches their expectations

so in our case those little helpful not

helpful interactions Whenever there is a

recommendation or whenever there is a

longer response from the AI

planner and I want to remind us that we

serve the user not the tech all of this

I think reminds us that sure we always

need to be uh mindful of our users goals

of their motivations of their time of

their privacy of their data even more so

when we work with new emerging maybe

experimental Tech in our

product but finally what does this mean

for us as

designers what does it mean for our

craft what does it mean for our skills

because like I said it's just a matter

of time until pretty much any product

has the ability to enhance their

experience with AI capabilities first

and foremost I think it means that all

the skills that are are relevant for

designers today are still relevant for

designers tomorrow and in a year from

now and a couple of years don't want to

look too far into the

future at the center of it obviously

empathy being able to understand our

users needs their motivations their

goals their

frustrations all of this is hugely

relevant even more so now when there is

new things that we can do new use cases

that we can unlock with a new

technology so what this means is our

role is uniquely positioned now in this

era where AI becomes more prevalent more

available more

accessible but we might want to add a

couple of new skills to the list first

and foremost I think we need to get

proficient at the field of study that is

artificial

intelligence artificial intelligence is

not a field of study for engineers or

data scientists alone designers need to

be involved in the discussion they need

to be able to have a say in the

direction in which it goes they need to

be able to understand it we need to know

which machine learning models are out

there what opportunities they have what

limitations they have what risks they

have and I want to leave you with two

resources here that are great to get

into the field of AI Ben B's newsletter

and the invisible machines podcast are

both resources that will give you on a

weekly basis bsz information so that you

know what's going on in the field of

AI something that I'm sure will be added

to the job descriptions of designers in

the future at least in some cases is

prompt engineering or now sometimes

called prompt

design because the way how you instruct

the AI in a proper way the way how you

give the AI of personality the way how

you set those guard rails like I

mentioned earlier today this is really

how you're going going to impact the

experience that your users will have

when your product is being enriched with

AI

capabilities so it's not an engineer

task it might happen in the code maybe

in the future not anymore maybe in the

future it's going to be part of your

figma workflow but it's the task that ai

ai designers need to be or ux designers

sorry need to be more proficient in so a

great resource for this is the prompt

engineering guide just Google that

you'll find this webbsite and it's an

extensive resource that has everything

you need to know about prompt

engineering long before your product is

ready to build AI into their uh into

their

service and finally the concept of hyper

personalization that I mentioned earlier

this is something we need to wrap our

heads around because it really unlocks

new ways new use cases new ways to

improve the experience for our uses in

our

product a great resource for this is the

Delo report connecting with meaning I

know it's very long and extensive but

it's free and openly available so if you

just get into the basics of this report

just scan through some of those chapters

and it will open your mind to a new kind

of thinking because you'll realize the

applications in your product in your

service with personalization at the

individual users level personalizing the

experience for each user

individually and this is going to be

more and more normal like I said earlier

user expectations are

changing my final takea away from all of

it is that I AI replaces tasks not roles

for us as designers yes AI might help us

build interfaces create wireframes and

whatnot but our

skills our responsibilities as designers

remain hugely relevant and the role of

designers has always

shifted what ux designers do today is

not what ux designers were doing 5 years

ago and it's certainly not what ux

designers were doing 5 years before that

if that title was even used back then

remember the time when we were basically

called web designers or visual

designers so the role of designers

change and we need to adapt but it's not

going

away thank you for your time if you want

to connect with me best way to reach me

is on LinkedIn you can also find me on

ADP list if you're ever in Amsterdam I

would be happy to catch up and a coffee

and I'm looking forward to your

questions