User Experience of AI - How to marry the two for ultimate success?

How to marry the two for
ultimate success?
UX AI
of

Introduction 01
Chapter 1 04
AI and User Experience –
How AI impacts User Experience
Chapter 2 10
Identifying and deﬁning the
right usage of AI in enterprise software
Chapter 3 19
Smart applications of
AI across businesses
Chapter 4 25
What does the future for
AI-infused UX look like?
Conclusion 31
About Koru UX Design 33
Index

Introduction
Artificial intelligence (AI), in the simplest of terms, is a part
of computer science concerned with building technology
that can perform tasks typically requiring ‘real’ (human)
intelligence.
Which tasks would these be?
It could involve weighing multiple alternatives against one
another, integrating new information into existing data
structures, deriving inferences from qualitative or
quantitative data, or estimating probabilities.
AI procedures vary in terms of the level of execution and
monitoring they require, that is, in the extent to which they
require human intervention or support before performing
an action.
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Examples of some prominent AI technologies include –
Big data analytics,
which is used to automate information gathering and
evaluation. What it does is retrieve voluminous data from
diverse sources and evaluate it to answer specific queries or
conduct statistical evaluations.
Machine learning,
which is a method of data analysis used to identify patterns
in unstructured data sets. This helps machines or systems to
make decisions based on those patterns.
Neural network-based deep learning,
which integrates and selects information across several
logical layers of an electronic information network. Artificial
neural networks comprise multiple layers of mathematical
routines which classify and arrange data into new sets to
find the most accurate parameters or solutions.
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It is through the application of technologies such as these
that AI executes complex tasks like Natural Language
Processing (NLP), for instance. NLP is the understanding
and translation of human language into other languages
and codes, or computer vision, i.e., the visual perception,
analysis, and understanding of optical environmental
information.
When we look at the phases of software design –
conducting research and defining requirements, to
designing it, testing it, developing it, followed by release and
maintenance – it becomes apparent that AI can be applied
to improve the process and eliminate many of the major
challenges that this process has been facing.
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AI and User Experience –
How AI impacts User
Experience
Chapter 1

Software design is the process of shaping technology based
on a deep understanding of human needs. And, there is a
commonality to be found between User Experience (UX)
design and AI.
If AI gains its learnings by continuously leveraging data, so
does UX design, which is an iterative process of optimizing
the product based on testing and feedback.
Here’s how the two fields can complement each other –
User experience design is crucial to the application and
delivery of AI-based experiences in software products;
whereas AI can augment UI-UX processes with automation,
research assistance, and personalized recommendations.
Today, AI is making its presence felt in product design
through various avenues such as –
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01
AI-based automation can help in reducing the load of
repetitive UI/UX design tasks such as transforming design
sketches into code automatically akin to basic-code
platforms that are able to convert wireframes into code.
This can result in time-saving, efﬁcient beneﬁts such as,
Quicker mockups with AI-powered prototyping using tools
such as Uizard, which applies AI, to generate native mobile
applications directly from sketches and even helps
generate code from the sketch.
Fewer errors, as AI helps maintain consistency across the
platforms and limit errors of human oversight, especially in
instances of taxing routine jobs in software development
such as debugging or data entry.
Build large-scale products with the help of preset
modules/templates. Test new ideas even with a budgeted
investment of time or money, such as conducting a
structured analysis of complex data pools to identify
patterns and meaningful information clusters.
Automating mechanical
tasks at great speeds
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02
User-centricity is non-negotiable in UX design. Delivering
experiences based on user needs that also align with the
business goals is the primary purpose of UX design.
AI technologies can assist UX design in this regard through
the following ways -
Analyzing voluminous data sets to
procure insights, identify patterns,
and customize user experiences
Quicker analysis of voluminous data to
come up with patterns, trends, and insights.
Dynamic testing of specific aspects of the
user experience based on user characteristics.
Hyper-personalisation through millions of
permutations and combinations that is truly
useful to users.
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03
Simulating cognitive processes to
enable decision-making
Automation can lend a helping hand to enhance the scope
of improving the design quality, resulting in better, more
sophisticated designs.
Some examples include -
Optimizing choices based on the user’s past behavior, AI
can shortlist options, enabling a better, more pleasurable
user experience. It can save the user from being
overwhelmed by a sea of options.
Predictive UX powered by AI can help anticipate user
actions based on an analysis of past behavior and help
create more intuitive user experiences.
With all these possibilities, it is also important to
acknowledge that AI algorithms are “mind tools,” not actual
minds. The successful application of AI hinges more on the
study of human psychology than big data and powerful
algorithms. AI applications must reflect realistic
conceptions of user needs and human psychology and not
just algorithmic outputs.
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In the words of design pioneer
Don Norman, AI needs to
“accept human behavior the
way it is, not the way we
would wish it to be.”

Identifying and defining
the right usage of AI in
enterprise software
Chapter 2

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AI and automation are transforming businesses across
industries, conjuring novel opportunities through intelligent
automation. For example, a majority of companies are
changing the way they deliver customer service by
deploying AI-powered chatbots. Salesforce Einstein makes
use of predictive intelligence, NLP, and machine learning to
power many Salesforce features such as answering
customer queries or routing and data-driven insights.
Ada is another AI-powered chatbot used by brands like
Shopify and MailChimp. It integrates with most messaging
channels and customer service software, which is a useful
feature to send customized content to your customers,
collect feedback, and report on your bots’ time, effort, and
cost savings. Ada’s chatbot has helped AirAsia to reduce its
wait time by 98% in 4 weeks.
Manufacturing units are making use of machine learning to
rapidly analyze fresh data to identify patterns and
anomalies. Any machine in the manufacturing plant
working at reduced capacity can be identified via a
machine learning algorithm and notify decision-makers to
take reparative action.
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Self-driving cars are contextualizing data gathered via their
sensors - for instance, the distance of peripheral objects,
their own speed, and using that to predict where they will
be in the next moments - all thanks to deep neural learning.
But for all its fascinating and revolutionary uses pushing the
rush to adopt AI-powered technologies, business owners
and decision-makers should first answer the questions,
‘Is it right for my business? Do we have a
problem that can be solved by AI?’
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“Look at how you are using
technology today during critical
interactions with customers —
business moments — and
consider how the value of those
moments could be increased.
Then apply AI to those points for
additional business value.”
- Whit Andrews
Distinguished VP Analyst, Gartner

https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G
https://www.technologynetworks.com/informatics/articles/the-hype-of-watson-why-hasnt-ai-taken-over-oncology-333571
AI does offer rich opportunities to introduce personalization
and differentiation by classifying data and making
predictions at a higher speed and volume than humans.
However, companies that focus solely on AI-powered
automation can face failure as compared to those that
apply AI to augment human decision-making and
interactions.
IBM’s Watson for Oncology was a software based on data
derived from hypothetical cancer patients rather than real
cases. Therefore, it ended up delivering erroneous, deeply
dangerous cancer treatment advice. Source
Amazon.com’s AI-based recruitment project was designed
with the intention of picking the best out of the thousands
of applications that came their way. The algorithm was
based on engineering job applicant résumés and
benchmarked against current engineering employees. The
results received were skewed heavily in favor of white, male
applicants based on the algorithm as it had “learned” that
candidates who seemed whiter and more male were more
likely to be good ﬁts for engineering jobs. Source
The benefits of AI: Is it necessary?
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It does not make sense to implement a relatively new
technology such as AI and automation just because it’s
there for the offing. Instead, businesses have to first identify
a problem that needs to be fixed, assess all options to solve
it and pick AI automation only if it makes absolute business
sense to use it.
A 2018 survey by Spiceworks reveals 50% of organizations
have not implemented AI as they did not find an
appropriate use case for it. Another 29% noted that security
and privacy concerns made them reconsider AI adoption,
with concerns around costs following close behind. Going
by this data, the challenge of integrating this technology
stems from a fear of the unknown, rather than anything
practical.
There are ample examples of AI-supported technology that
exists in consumer and niche cases - smart devices and
personalization, to name a few - but there aren’t as many
easily implementable solutions built for the enterprise yet.
Business leaders are now aware of the shortcomings of the
traditional models in a continuously disrupted business
environment.
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“Those in the C-suite must reengineer their operations in
a way that embraces AI,” says Frank Palermo, global head,
digital solutions, Virtusa. “A good starting point would be for
organizations to automate much of their ‘drudge’ work, and
focus instead on providing tailored, personalized products
and services that satisfy customers and meet growing
expectations.”
It is a gross fallacy of businesses to consider AI as a singular,
all-encompassing technology. On the contrary, the AI
technology of today is the result of several amalgamated
technologies (e.g. computer vision, Natural Language
Processing, and more).
There isn’t a single AI technology as such, but it exists in the
form of computers identifying images or processing voice
commands to deliver the best options for a customer, to
driverless vehicles.
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Our team at Koru recently bagged the prestigious
iF Design Award 2021 for our design of an integrated
telehealth Electronic Health Records (EHR) tool,
enhancing its existing capabilities to help providers deliver
top-notch healthcare in a convenient, stable, secure, and
timely manner. Clinician burnout has been a concern for
ages, with the Covid-19 pandemic aggravating it further.
Our telehealth integrated-EHR focused on eliminating
communication barriers by implementing AI-powered
Natural Language Processing (NLP). This helped in
simplifying accents and offered real-time translations with
enhanced accuracy. It cut down on lengthy hours spent on
documentation, as physicians would now refer to notes and
transcripts and not rely on memory, thus making the data
more authentic, reducing the potential for error.
With these pressures out of the way, providers became
more focused on the actual interaction and delivered a
better experience to the patient. Our entry scored highest
on impact from the jury as improving care standards and
clinicians’ burnout was our primary motive.

Thus, it is imperative that product leaders identify critical
business intersections where AI automation can be of true
help. Simply, AI and automation — like some other
emerging technologies — will allow businesses to cut costs,
boost productivity by freeing up workers from more
mundane tasks, increase agility and flexibility, and spur
innovation.
When done right - with high emphasis on the ‘right’ -
implementing this technology will allow businesses to
boost revenues, product lines and offer differentiated
customer experiences.
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Smart applications of AI
across businesses
Chapter 3

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Enterprise adoption of AI has been moving along rapidly
albeit in a cautious manner. As of now, AI is being put to use
in tasks such as planning, predictive maintenance, and
chatbots deployed for customer service.
With growing AI adoption, what are businesses doing to
limit risks and remain on top of their game? Here’s how AI
adoption is transforming the way business leaders think.
84% of C-suite executives want to leverage AI to achieve
their growth objectives. However, 76% reported facing
questions on how to scale.
Three out of four C-suite executives feel that AI needs to
be scaled in the next five years, or risk going out of business.
Among the companies featured, those that are strategically
scaling AI report nearly 3 times the return from AI
investments compared to those pursuing siloed proof of
concepts.
Source: Accenture’s AI: Built to Scale
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We have seen AI deployed across a wide spectrum of use
cases to solve business problems—like managing and
automating IT infrastructure, gathering new insights about
customers, identifying and responding to cyber threats,
guiding medical decisions, and actually improving the
hiring process. AI is increasingly being integrated into the
fabric of businesses.
AI adoption is fairly limited as of now as many businesses
are struggling to overcome the barriers and scale benefits.
But it is also apparent that the “early adopter” phase is
coming to a close.
Source
In which functional parts of the company are AI projects used?
(Select all that apply.)
% of all respondents
Research and Development 50%
34%
33%
22%
21%
16%
15%
15%
11%
9%
7%
5%
12%
Customer Service
IT
Operations, Facilities, Fleet Management
Marketing, Advertising, PR
Finance and Accounting
Manufacturing, Production
Sales
Purchasing, Logistics, Supply Chain
Human Resources
Distribution
Legal
N/A
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“Many pharma executives are
thinking about AI in terms of cost
savings and efficiency. When you’re
running a massive organization
that has hundreds of active clinical
trials costing millions of dollars,
there are low-hanging fruit that are
not necessarily scientifically
complex where AI can save the
organization hundreds of millions
of dollars.”
- Ron Alfa,
Senior VP of translational discovery,
Recursion Pharmaceuticals

https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/state-of-ai-and-intelligent-automation-in-business-survey.html#endnote-5
https://www.idc.com/getdoc.jsp?containerId=prUS46794720#:~:text=FRAMINGHAM%2C%20Mass.%2C%20August%2025,than%20%24110%20billion%20in%202024.&text=The%20compound%20annual%20growth%20rate,2024%20period%20will%20be%2020.1%25.
The IDC predicts that global spending on AI
stands to double over the next four years,
growing from $50.1 billion in 2020 to more than
$110 billion in 2024.
Two of the foremost causes driving AI adoption are
providing an optimized customer experience and
increasing employee productivity. In fact, the leading use
cases for AI which include automated customer service
agents, sales process recommendation and automation,
automated threat intelligence and prevention, and IT
automation reflect this.
These four use cases will represent nearly a third of all AI
spending this year. Some of the fastest-growing use cases
are automated human resources, IT automation, and
pharmaceutical research and discovery. Source
Novel uses for AI are growing—everything from creating the
rules for new sports to composing music to finding missing
children. Source
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Here are two real-world examples of companies pushing
boundaries and taking AI applications to the next level:
Recursion Pharmaceuticals is using AI as a microscope to
quickly and inexpensively ascertain the difference between
large data sets of healthy and diseased cells in order to
discover new compounds for drugs. Source
Florida Power & Light is deploying AI across its operations to
ensure a more reliable and efficient electric grid. The goal is
to better manage the utility’s increasingly complex
networks and incorporate more renewable energy sources.
Source. AI is the technology that will help businesses to be
agile, innovate, and scale.
The companies that become 'AI-powered' will have the
ability to synthesize information (using AI to convert data
into information and then into knowledge), the capacity to
learn (using AI to understand relationships between
knowledge and apply the learning to business problems),
and the capability to deliver insights at scale (using AI to
support decisions and automation).
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What does the future for
AI-infused UX look like?
Chapter 4

AI and UX design have evolved as disciplines that are quite
distinct from each other. However, there is encouraging
evidence pointing to the fact that AI technology can help
enrich a user interface in interesting, useful ways, as is evident
with the use of smart assistants such as Siri and Alexa.
Adaptive user interfaces (AUIs) such as these employ
elements of AI to enhance the user experience. AUIs help in
identifying and automating frequent tasks, for instance,
when an email recognizes a phone number and lets users
initiate a call with a tap on the number. These bits of low-risk
AI make things easy for users and perhaps deliver some level
of delight.
Similarly, enterprise users spend numerous hours using
business process applications, often having to perform
certain repetitive tasks day in, day out. For example, those in
procurement have to order the same supplies from the same
vendors, healthcare providers have to repeatedly retrieve
clinical information, and lawyers have to keep referring to
contracts they are drawing up based on incoming emails.
AUIs can take note of prior actions and automate the most
frequent, repetitive tasks, freeing users’ time and attention to
focus on decisions that require actual human intelligence and
efforts.
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The “adaptive system” in
computer science refers to a
process in which an interactive
system adapts its behavior to
individual users based on
information acquired about its
user(s), the context of use, and its
environment.

The idea of AUIs as a way to enhance user experience has
been talked about over the past 3 decades. Advances in AI in
the subsequent decade and the transition to online interfaces
have helped create the perfect environment for the concept
to manifest in the real world.
This technology is just now starting to spill over into
enterprise and business process systems. For example, users
of email technology are now able to enjoy actionable entities
in their messages, such as turning events as reminders via a
calendar entry, or phone numbers that can initiate a call
when clicked.
Specific features such as these have moved into the
enterprise through customer relationship management
(CRM) systems, which now also recognize and highlight
common entities like phone numbers and meeting
information, speeding workers' interactions.
Adaptive design is about paying attention to the context and
identifying user patterns. Consider the case of a supermarket
app that might identify the monthly shopping lists of
frequent customers and offer them discounts on
frequently-bought items or notify them when their favorite
items are on sale or are back in stock.
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The key to creating an adaptive experience lies in combining
smart device sensor data, network connectivity, and analysis
of user behavior. When these capabilities come together, we
not only understand the context of use but also anticipate
what the user needs at a particular moment.
Owing to the nature of their work, UX designers have to
always consider things from the users’ perspective. This
places them in the perfect position to identify use cases
where an AUI might be beneficial. They can identify situations
that affect a large number of users, those that may occur
frequently or may have to be performed repeatedly.
AUIs have another significant and distinctive advantage as
compared to other AI ventures, which is that the user is
always a part of those interactions.
AUIs are a confluence of a machine's capability to record
actions and automate tasks based on the user’s
understanding of context. Collectively, this combined
intelligence capitalizes on automation while at the same time
lowering the risk of the machine’s actions.
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A web browser is built in a way to correctly identify the user’s
personal data while filling online forms. The browser fills in as
many elements as permissible and leaves it to the user’s
discretion to identify any elements it may have filled in
incorrectly.
If the user corrects or edits any information that has been
auto-filled, it is registered as additional information to help
the AUI improve its performance.
Thus, AUIs are a complementary addition to other innovation
approaches which boost efficient and error-free information
processing. Easy execution is one of the more persuasive
advantages of AUIs, which uses a limited-risk version of AI to
have a significant impact.
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Conclusion
Across industries, enterprises are going through a profound
shift with regards to AI adoption. AI poses opportunities and
challenges in equal measure. Access to technology, data,
and information is common to all enterprises, but it would
be interesting to know how each one uses that
information—and to what effect. It is crucial for enterprises
to understand the parameters that will define their
individual and collective success.
Enterprise AI cannot be treated as a trend bandwagon
to jump on or something to be leveraged for standalone
projects and use cases. The will to become a true AI
enterprise can be realized by successfully scaling and
employing robust data methodology at all levels and treat it
as an organizational asset pivotal to the success of the
business, regardless of the industry.
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With over a decade of experience in the field of User Experience, Bansi
Mehta is the Founder and CEO of Koru UX Design. A self-driven leader,
she aspires to transform workforces through her work. Guiding,
mentoring, and leading a team of over 35 passionate UX, UI, and
front-end development professionals, she has empowered many
businesses.
“UX-ify workplaces” is her motto. She believes that the User Experience
for the workforces should be tailored to meet the needs of employees,
managers, and business owners. A better Enterprise UX will not only
result in a happier work environment, but also boost performances,
productivity, and subsequently the revenues.
Apart from work, her interests include traveling, fine dining, reading,
and socializing with like-minded people. An engineer from Bharti
Vidyapeeth, she is a Certified Usability Analyst from Human Factors
International (HFI) with an eye for detail.
A focused entrepreneur and a proficient UX consultant, Bansi is
passionate about disruptive ideas and technologies that can help people
be their best selves at work.
Bansi Mehta
CEO & Founder, Koru UX Design
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sales@koruux.com
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User Experience of AI - How to marry the two for ultimate success?

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