Jascha Goltermann: The Impact of AI on UX Design - Hatch Conference 2023
Summary
TLDRYasha 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
🎤 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.
🤖 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.
🗺️ 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.
🛣️ 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.
🔒 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.
🛠️ 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.
Mindmap
Keywords
💡Individual Contributors
💡Career Path
💡AI (Artificial Intelligence)
💡UX Design
💡Booking.com
💡Machine Learning
💡Deep Learning
💡Generative AI
💡AI Trip Planner
💡Prompt Engineering
💡Hyper Personalization
💡User Expectations
💡AI Proficiency and Trust
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
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