AI & Analytics Predictions of 2022. Infographic

AI & Analytics:
Trends 2021-2022 and
Market Research
from InData Labs

2
AI & Analytics: Trends 2021-2022 and Market Research
Before the pandemic, the path to AI adoption used to
be slow and steady. According to the survey of industry-
leading firms, only 48% of companies deemed AI and
Big Data a source of innovation. In 2021, this number has
climbed to almost 65 percent.
Today, companies are accelerating plans to implement
artificial intelligence as a response to the pandemic.
Indeed, the conditions of volatile demands, fleet and
resource efficiency, as well as rising costs have become
the breeding ground for automation. In fact, around 86%
out of 200 CEOs report AI to become a mainstream
technology at their company in 2021 and beyond.
‘We, at InData Labs, have seen first-hand how our smart systems assist companies in their
digital transformation. From predictive maintenance and image recognition to natural language
processing and pose estimation solutions, the versatile nature of AI is pervasive. What’s more
important, the field of cutting-edge technologies is being used as a tool to support the fight
against the viral pandemic. Companies spanning multiple industries are ramping up their use
of robots, chatbots, and autonomous vehicles to provide contactless services and automate
business functions.
Finally, a COVID-19-induced surge in automation was triggered by the pressing need to meet
the evolving business needs and ever-growing amount of Big Data. In these uncharted waters,
the problem-solving and predictive prowess of AI and analytics seems to be the main way for
businesses to navigate COVID-19 and make use of new data.
Through this comprehensive overview, we aim to explain the utmost importance of AI for long-
term business growth to an audience of business owners, opinion leaders, and enthusiasts.
We’ll also demonstrate the difference that AI has already made and its main benefits at an
enterprise level.’
Alexander Marmuzevich
CTO at InData Labs

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These days, the potential of intelligent systems is finding
traction in lots of use cases. According to McKinsey,
robotic process automation, computer vision, and
machine learning are among most commonly deployed
forms of artificial iIntelligence.
In this paper, we’ll also go over the main applications
of smart systems by industry, adoption challenges,
data security issues as well as the main prediction of AI
technology for 2022. In its final part, we’ll dwell on the
average costs of implementing AI and share time-tested
tips and tricks that will help you choose the right AI
vendor for your automation journey.
Key AI trends for 2021 and
beyond
40,2%
50%
of surveyed companies have
adopted AI in at least one
business function.
McKinsey
$10.9
billion
is spent on intelligent
process automation (IPA)
in 2021.
Statista
$15.7
trillion
increases in global GDP by
2030 generated by Artificial
Intelligence.
PwC
77%
of companies report AI use
in widespread or limited
production.
NewVantage
2020 2021
$62.35 billion
$93.53 billion
the GAGR of the global AI
market size from 2021 to
2028.
Grand View Research
The global AI market size value in 2020
and the forecasted value in 2021.
Grand View Research

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AI adopters have different levels of maturity
26%
of seasoned adopters
developed a high level of AI
expertise across the board.
47%
of skilled adopters have launched
several AI production systems
but lag behind the seasoned
enterprises.
27%
of starters get their AI adoption off the
ground and lack proficiency in building,
integrating, and managing AI solutions.
Deloitte
Companies deploying AI, by functional parts
In which functional parts of the company are AI projects used?
Confidence in AI technology has strengthened as
never before as during the pandemic companies had a
chance to reevaluate their ability to promptly respond to
challenges as well as to evidence the value driven by AI-
based solutions implemented providing the competitive
advantage to their business.
The statistics prove that artificial intelligence has gained
steam across companies and all business functions and
demonstrate the increasing dominance and importance
of this technology in the global landscape. Moreover,
the global market value of AI will continue to grow
exponentially, driven by the need for automation and
spearheaded by tech giants like Google, Amazon, etc.
O'Reilly

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The adoption of AI at companies, by industry O'Reilly
The percentage of the sample survey manifests the
omnipresence of business intelligence in virtually every
established industry. Finance, healthcare, and education
benefit the most from the use of AI. At the same time, the
pharmaceutical and chemical industries are looking into
the benefits of business intelligence.
However, the pandemic seems to tip the scales as the
world unites in the fight against COVID-19. Likewise, the
automotive industry has just uncovered the capabilities
of artificial intelligence in the production of self-driving
vehicles. Other industries are expected to join the walk
towards automation.
Business value of AI and Big Data (% of businesses)
49%
41%
39%
30%
24%
24%
Driving innovation with data
Competing on data and analytics
Managing data as a business asset
Establishing a data strategy
Forging a data culture
Creating a data-driven organization
Statista
As of 2021, we see a significant percentage of businesses
leveraging the combination of AI and Big Data. Most
respondents report the use of data in promoting
innovation across companies. The analytical capabilities
of smart systems underpin the business strategy
and help gain a competitive edge for organizations.
Also, data has changed its supplementary status and
become a prominent business asset for tech-focused
organizations. Overall, we can witness a growing
enterprise awareness that results in creating a solid and
unified data infrastructure and management practices.

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Retail And E-com
Price prediction, automated supply chain management,
visual search, product categorization, personalized
customer experience, real-time optimization.
Manufacturing
Quality checks, predictive maintenance, product
demand forecasting, robotics, inventory management,
prediction of failure modes.
Banking And Finance
AI chatbots, data collection and analysis, portfolio and
wealth management, risk management, fraud detection,
next-gen security.
Marketing And Sales
Demand forecasting, sales prediction, lead generation,
products recommendation, content personalization and
analytics, automating sales activities.
Agriculture
Precision farming, robotics, monitoring soil health,
market demand analysis, crop health monitoring, weed
detection, yield assessment.
Healthcare
Patient prescreening, diagnosis and medical imaging,
preventative healthcare, drug discovery, gene analytics
and editing, patient data analysis.
6 Industries where AI is
making the greatest disruption

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The majority of industries are on the path of digital transformation. Evolving consumer patterns and the changing
reality have also fast-forwarded the growing adoption of technology. In addition to the industries mentioned
above, we can witness the shifts in demand for tech solutions in the following areas:
Wellness & Sport has experienced a surge in requests
from mid-and large companies for developing exercise
tracking solutions that promote a safer wellness
experience and allow efficient workouts at home.
The global Internet usage is growing as a result of
stay-at-home practices. And as people are seeking
more entertainment online, the Entertainment industry
implements more intelligent technologies to attract
and retain users as well as offer an outstanding user
experience.
The sudden peak of online shopping has strained the
Logistics industry as well. Artificial intelligence is now
vital to optimize logistics and make transportation faster
and more effective.
We also expect an increase in AI adoption and demand
for Big Data expertise in Pharma, given the need for
fast and accurate vaccine development to meet global
demand and ongoing pharmacovigilance improvement.
AI adoption rates around the world
Deployed AI Exploring AI IBM
According to the industry report, China is at the forefront
of AI deployment, overtaking the US. Over the last few
years, China has announced billions in funding for start-
ups and AI-powered strategy for foreign relations. India
follows China in the race for Artificial Intelligence with
Italy and the United States closing the list of leaders.

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Business value generated by AI
Cost Reduction
• Automating low-value and repetitive
tasks
• Driving down operating costs
• Improving production output
Reduced Complexity
• Improving decision-making through
analytics
• Proactive course of action
• Discovering hidden patterns to solve
business problems
Improved Customer Experience
• Delivering personalized marketing efforts
• Automating customer journey through AI
chatbots
• Smarter customer experience
Business Process Automation
• Streamlining service delivery management,
order processing, billing, and others.
• Automatic processing of raw data for
valuable business insights
• Off-loading repetitive company
management tasks to smart systems
Data Security Improvement
• Detecting fraud attempts and unauthorized
access to personal data
• Predicting breach risks
• Faster incident response
Enhanced Performance of Products
• Recognizing user emotions
• Embedding recommendation systems for
increased revenue
• Automating adjusting to growing customer
needs through feedback

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AI and machine learning
top use cases
Nowadays, a growing number of companies are inclined to implement advanced machine learning solutions on
par with custom AI solutions to stand tall in the competitive market. Below, we’ll touch upon the most innovative
and recent applications of smart systems. Some of them have been ripening way before the pandemic hit. Others
owe their proliferation to the impact of COVID-19 and social distancing measures.
AI in healthcare and pharma
Pharmaceutical executives also experience the growing
need for automation due to the pandemic outbreak.
Machine-learning systems and computational analyses
have been essential in the vaccine quest. These systems
are aiding researchers in studying the virus and its
structure. As for vaccine creation, AI helps predict
an immune response of vaccine components. This is
something that has never been available before.
Moreover, intelligent systems and Big data help scientists
track the virus's genetic mutations over time. These
insights help evaluate the vaccine’s value over the longer
term.
Among established uses of Machine Learning in pharma
are also drug discovery and drug manufacturing.
Intelligent algorithms predict the 3D protein structure,
which then helps prognosticate the impact of a compound
on the target and evaluate safety. Artificial intelligence
can also help reduce the sample size in clinical trials,
reducing their duration and the conducting costs.
According to industry stakeholders, half of the
healthcare companies will implement AI by 2025
with an estimated value of nearly $100B annually
across the US healthcare system.

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Facial recognition to combat COVID-19
The pandemic has taken its toll on the medical sector
more than on any other. With the evolving technologies,
there is a silver lining in the long run. In particular,
facial recognition has proved to be a boon due to its
increased accuracy in identifying individuals. Also, facial
identification systems are becoming the preferred choice
of authentication to address the need for hygiene with
contactless applications.
The pandemic also advanced the proliferation of
biometric data collection tools for medical purposes.
Temperature checks, thermal imaging at public places,
face mask detection, and face scans at airports have
taken over each sphere of our lives.
Today, it’s clear that COVID-19 rather than hindering facial
recognition is being used as a reason to use it:
• In February 2021, Moscow deployed facial
recognition technology to monitor whether people
ordered to remain at home are complying with
quarantine orders.
• In 2021, a Chinese company developed the country’s
first facial recognition technology that can identify
mask-wearers.
• A group at Seoul National University in South Korea
has developed a facial recognition app to minimize
hospital mistakes in confusing patients.
• Face2Gene app allows clinicians to match facial
characteristics to rare genetic disorders.
Another promising area of medical identification software
includes gathering more data about a patient’s state
beyond COVID-19. For example, a University of South
Florida team combined facial analysis and pattern
recognition along with other data to assess pain levels in
neonates.
Furthermore, the burgeoning applications of face
identification are now widely used in biometric access
control and entry systems. It is currently the latest
authentication technology to be adopted for keyless,
hands-free access control and door entry.
With over $68 billion by 2025 vs $36 billion in 2020,
this year became a turning point in the expansion of
contactless pathways. Face recognition used for access
control allows for touchless and privacy-oriented access.
The latter means that any instance of patient or doctor
impersonalisation becomes impossible. Among other
things, it speeds up the identification process at hospitals
and simplifies patient check-ins.
the expected compound
annual growth rate of the
biometric authentication
market from 2019 to 2027.
14,6%
Statista

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Pose estimation for healthcare
Reliable posture labels in hospital environments have
also become possible thanks to intelligent systems. By
localizing human joints, pose estimation in healthcare
assists clinicians in analyzing human movement and
health conditions. In particular, detecting human
movements can prevent fall incidents. The latter often
becomes the reason for a serious injury or even death of
those who fell.
Therefore, to provide fall detection, the alarm system has
become an assistive telemedicine technology. Usually,
a person must wear a device. It triggers a fall detection
alarm if there's sudden downward movement followed by
no movement at all. In the case of pose estimation, the
system retrieves the locations of joint points and signals
the falling if the joint point location changes.
Patient-specific pose estimation can also simplify
monitoring patient activity, thus contributing to better
patient care. Movement detection can monitor the
unconscious/coma patients to assess their current state.
Home-based physical therapy is also among the most
mainstream applications of pose estimation. Real-time
virtual trainers augment physiotherapy treatments and
help restore a person’s movement. The systems track
patients' performance so that the physical therapist can
get comprehensive quantitative and qualitative data.
Smart sports and wellness experience
The AI craze has not spared the fitness industry. According
to the Research and Markets report, the digital fitness
market size is projected to stand at $27.4 billion by 2022.
Intelligent algorithms innovate fitness through fitness
equipment that makes at-home workouts smarter and
easier. Along with smart wearable, personal AI trainers
are also gaining momentum. With that said, pose
detection has permeated fitness applications to estimate
human body movements during workouts.
Prominent examples of applying pose detection in
fitness are Kaia, VAI Fitness Coach, Ally apps, and
the Millie Fit device. Smart wellness apps can guide
users in performing physical exercises in the right and
personalized manner. From a technical standpoint, the
system obtains an image of the user and visualizes the
coordinates of the specific key points on the human body.
This way, home exercises deliver maximum value with no
human trainer or gym classes.

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Predictive maintenance in manufacturing
To reduce the disaster recovery investments,
manufacturers began adopting predictive maintenance
practices en masse.
Deloitte Analytics Institute reports increasing equipment
uptime by up to 20%. Manufacturers could achieve such
results by predicting failures via advanced analytics.
Within this realm, Artificial Intelligence takes the form
of Machine Learning and amplifies predictive analytics
with intelligent algorithms. Together, these technologies
monitor the state of machinery and predict potential
malfunctions based on available sensor data. As a result,
organizations minimize the need for excessive frequent
maintenance and avert asset failures with little to no
disruption on daily operations.
Among other things, major improvements can be
achieved in spare parts inventory, and both direct and
in-direct overtime premiums. Machine life, production,
and operator safety are also among the acknowledged
benefits of predictive maintenance.
Oil and gas companies, including Shell and Aker BP,
deploy AI to monitor the production of fossil fuels. In
2019, a faulty well pump at an unmanned platform in the
North Sea halted production for Aker BP. The company
solved the problem by adopting a smart program that
monitors data from sensors attached to the pump.
The system then reports glitches before they cause a
shutdown.
These real-life use cases demonstrate the unrivaled
ability of smart systems to process enormous volumes
to uncover asset inefficiencies. The proactive course
of action further allows companies to save costs on
reactive maintenance and maintain their machinery in a
peak operating state.
The average equipment outage lasts four hours,
costing a company $2 million each time.
Machine Metrics

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AI in logistics and transportation
Today, logistics operations amount to a diverse
network of suppliers and customers. This patchwork
of players translates into the acute demand for
streamlining tasks and complex processes. Therefore,
the automation potential of AI-enabled infrastructure
augurs well for extensive supply chains.
In 2020, the lion’s share of smart systems found wide
application in inventory management.
In particular, computers helped companies avoid
overstocks and understocks with better demand
forecasting.
The rest of the applications included augmented
quality control, automated invoice processing,
customer care, and monitoring. Route optimization is
another eminent improvement area that benefits from
AI systems. The latter taps into historical trip sheets
and real-time statistics to estimate the delivery time
for each shipment. Data-laden reports are also used to
sequence deliveries and generate the best route.
But the power of AI goes well beyond enterprise-
grade implementations. The Automotive Coalition
for Traffic Safety (ACTS) announced the launch of the
first product equipped with new alcohol-detection
technology. The drunk driver monitoring system will
be available at the end of 2021.
Along with drunk-driving provisioning, computer vision
systems can detect drowsiness while driving to alert
the driver. This application can prevent about 100,000
police-reported crashes that involve drowsy driving.
The potential of AI applications in logistics holds
great promise - from on-site hardware to predictive
systems that embrace the power of Big Data. Logistics
companies will be able to make data-laden decisions
and improve their performance. At the same time, daily
uses of intelligent systems can prevent life-threatening
incidents and safeguard road users.
of respondents innovated
the stock count processes by
deploying inventory-tracking
drone solutions and advanced
analytics.
40%
Statista

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AI in marketing
Artificial intelligence is becoming a driver of marketing
development.
That is a huge advancement from 29% in 2018. Also,
companies report a median of seven use cases — up
from six in 2018.
One of such use cases is sales forecasting. The
unrivaled combo of data, analytics, and AI enables
companies to improve forecasts. In particular, the
advent of intelligent systems in sales helps marketers
underpin their outbound marketing strategies with
predictive models. The latter examine datasets and
uncover factors that impact a profit. And since sales
data is often complicated and variable, no other
method is fit for this application.
AI also overcomes the constraints of traditional market
segmentation. Artificial intelligence allows businesses
not only to unearth the habits and behavior of
consumers but also do cohort analysis to segment
them in clusters. Thus, smart algorithms analyze
customer data to discern more granular segments. It
means that companies can adjust campaigns to be
more personalized for each group of customers based
on their preferences.
On the same note, AI-enabled marketing campaigns
tend to fare well compared with conventional
marketing efforts. According to a survey of worldwide
marketers done in late 2020, 41% of respondents
noticed an improvement in revenue growth and
enhanced performance as a result of using AI in their
marketing campaigns.
A staggering 84% of marketers reported using
AI in their marketing activities.
Salesforce analysts

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Business outcomes realized throughthe use of artificial intelligence (AI)
according to marketers worldwide as of December 2020
41%
40%
38%
35%
34%
33%
Accelerating revenue growth / improving perfomance
Getting more actionable insights from marketing data
Creating personalizing onsumer experiences at scale
Reducing time spent on repetitive, data-driven tasks
Generating greater ROI on campaigns
Driving costs down / increasing efficiency
32%
29%
26%
21%
30%
Unlocking greater value from marketing technologies
Predicting consumer needs and behaviors with greater accuracy
Increasing qualified pipeline
Shortening the sales cycle
None of the above
Moreover, automation covers the whole campaign
lifecycle - from the creation and orchestration process
to analysis. AI systems can keep tabs on available
campaigns for each customer and suggest the next
marketing effort. Once your campaign is complete,
smart algorithms can dig deeper into outcomes to
determine human-level performance.
Statista

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Artificial Intelligence in E-commerce
Let alone the fact that use of machines for e-commerce
has skyrocketed by 600% over the last few years.
The defining factor behind massive automation is the
evolving needs of customers and excessive-choice
effect. Therefore, retailers have to pique customer
interest by knowing exactly what they need.
E-commerce giant Amazon achieves this prerequisite
by using item-to-item collaborative filtering. The latter
is an advanced form of recommender engines that
help predict users' interests and recommend product
items. As a result, the company owes 35% of its
revenue to the recommendation system.
Customer purchase behavior prediction is another
AI-fueled application to drive more sales. However,
traditional recommendation algorithms fall flat to
complete this task. Therefore, advanced predictive
algorithms take over. They monitor the associations
among products and assess customers’ motivations
behind purchases. Next, algorithms study customer
preferences for product features and suggest the
products most likely to be purchased.
But that’s just a sliver of AI possibilities. Advanced
chatbots, churn prediction as well as automated
inventory management, and price analysis - machine
intelligence has got it all in e-commerce.
PERSONALIZATION
• Website
• Search
• Recommendation
PRODUCT & SERVICES
• Pricing
• Image Recognition
• Product Description
• Conversion Rate
Optimization
• Retail Analytics
• Self-Checkout Systems
WEBSITE IMPROVEMENT
• Review and Forum Moderation
• Marketplace Moderation
CUSTOMER SERVICES
• Chatbots
MARKETING & SALES
• Lead Generation
• Task Automation
• Campaign Analysis
• Lead Identification
AI
IN E-COMMERCE
the revenue increase by 2022
compared to 2018 forecasted
for companies investing in AI
as reported by Accenture.
41%
SUPPLY CHAIN
• Inventory Planning
• Automated Warehouses

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AI in retail
Retail and e-commerce usually go hand in hand within
the realm of technological progress. However, AI
systems have also stepped out of the digital space to
enter physical stores. The global pandemic has fast-
forwarded this transition.
Therefore, part of the pandemic's new reality is
monitoring the maximum number of customers that
can reasonably follow social distancing guidelines
within the store. Hands-free entrance and temperature
detection devices are also a mandate with minimal
disruption to services.
In this regard, face recognition terminals can ensure
protective measures during COVID and promote
safer experiences for shoppers. Also, an increasing
number of brick-and-mortar retail stores are turning to
technology in order to prevent losses and run-of-the-
mill thefts.
The whole process takes seconds and flags a potential
robber:
• A facial image is captured from the live video
stream
• The system identifies facial features
• The system’s algorithms turn to a large database
for a match
• If a match is found in the database, the store is
notified of a potential shoplifter.
Aggression detection is also gaining traction in the
world. However, the whole potential of emotion
detection at stores is yet to be discovered.
of adults say they will only
go into shops with visible
Covid measures in place
while spending far more
time at selling areas with
the mentioned measures.
50%
over

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Computers in agriculture
Effective and accurate monitoring of crop and soil
health is one of the AI contribution fields that makes a
difference. Thus, the increasing proliferation of drone
and satellite technology ushered in aerial high-resolution
imagery of agricultural land. Now, farmers can monitor
crop health, irrigation equipment, herd, and wildlife, as
well as identify weeds timely and with no manual efforts.
Data-driven predictive decision-making also allows
governments, NGOs, and farmers to predict future yields
and food scarcity. Since farmers can make production
decisions beforehand, they can proactively manage
risk. Consumers also benefit from the availability of
reasonably priced food. Accurate data on forecast prices
also ensure that markets don’t balloon prices.
Let’s look at the bigger picture. From agricultural drones
to weed detection to predictive analytics, AI is the best
bet to feed the future sustainably. And while these
are just a snippet, the analytical capacity of machine
intelligence can solve the larger issues at hand, including
overconsumption of freshwater resources, overuse of
pesticides and biodiversity loss, and other legacies of
traditional farming.
The maturation of artificial intelligence is disrupting the AgTech sector as well.
Instead of being a low-tech sector, farming is now growing the global AI market
to reach $2,075 million in 2024. Field farming and livestock management
are among the most popular application areas of AI, with 61.15% and 18.1%
respectively.

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The future of animal care
Pet care isn’t the first thing that comes to our minds when
assessing the perspectives of AI. However, AI-enabled
facial recognition can help pet owners to locate their lost
animals.
According to The American Humane Association, 1 out of
3 pets become lost at some point in their lifetime. So why
not use technology to battle this missing epidemic? In
particular, the lost-and-found potential of AI crystallizes
in dog face recognition apps.
Thus, instead of combing through hundreds of listings,
pet owners can compare the image of their beloved
one against a database of found animals. For example,
InData Labs has embraced the recognition technology in
a mobile application with a deep learning approach for
dog face verification.
Furthermore, Artificial Intelligence can fetch the dog
breed for you. Microsoft Garage recently launched an
app called “Fetch!” that can determine a dog’s breed
from visual data. The app relies on AI for comparing the
image against its knowledge base of over 100 popular
pure breeds. So if you’ve ever been looking for on-
demand dog breed education, AI has got even that in
store.

20
AI in finance
The use of Machine Learning for complex analytics is
old news in the banking and finance industry. Today, this
sector has embedded intelligent systems at almost all
stages, including:
• At the prediction level: forecasting the demand for
banking products, predicting changes in demand,
automated risk assessment.
• At the production level: automating and optimizing
interaction with existing and potential clients,
automated document processing, and loan approval.
• At the marketing level: provision of personalized
offers and automatic adjustment of interest rates
based on the client's score.
• At the service delivery level: development of
automated systems and chatbots across all
communication channels.
Despite the nascent stage of AI, 80% of banks have
appreciated the potential benefits presented by machine
learning, as stated in an OpenText survey of financial
services professionals.
Fraud detection has been especially helpful for the
middle and back offices of financial institutions. Thus, J.P.
Morgan Chase and BBVA use ML-based techniques for
card fraud. Artificial Intelligence allows banks to trace the
steps of card usage and detect fraudulent transactions.
Machine Learning algorithms also provide a data-laden
solution for individualized credit scoring. By automatically
processing data on current income, employment
opportunity, recent credit history, and others, the system
can either approve or decline the application.
Face pay is another stamp of artificial intelligence that
could be set to replace credit cards. China has dived into
the facial terrain with over 100 million people signing up
for a facial recognition payment system set up across
100s of locations. The US is trying its hand in the first
payment system based on facial recognition, called
PopID.
All you need is to take a selfie for your profile and add
a preferred payment method. So when you need to buy
something, you just need to look into the camera.
Banks also ease the strain on their workers by
automating paperwork with OCR software. The Optical
Character Recognition technology allows for automatic
data capture software that eliminates the need for
manual data entry. Other benefits of OCR solutions
include data loss and misuse prevention and reduced
invoice processing time.

21
AI in gaming
Located at the intersection of disciplines, video gaming
is one of the most dynamic and tech-savvy industries in
the world. Artificial Intelligence is already being used to
solve several tasks in video game development.
Thus, Ubisoft Entertainment SA even spearheads an
innovative new initiative in Ubisoft Montreal called La
Forge. Its goal is to bridge the gap between the theory,
and the practical applications for AI in video games and
the real world.
If we look at more generic use cases, app smart
recommendations are the first to enter the head. In the
smartphone era, we can now choose between 3.48
million apps on Google Play and over 2 million apps
on the App Store. Existing mobile app recommender
systems often recommend popular mobile apps to
mobile consumers to make the choice of mobile apps
easier.
Intelligent technologies have also become the drivers
of consumers' loyalty in entertainment. Thus, innovative
applications of AI emotion recognition can discern the
player’s emotions and adjust the gameplay per player’s
personality with biometric facial recognition algorithms.
By collecting text data from forums and review sites,
companies can get real-time feedback from consumers.
AI-enabled sentiment analysis is used here to mine
opinions. Thereby, companies can better understand
the direction for further game development and
improvements.
With robust predictive analytics solutions in games, you
can also detect early symptoms of user churn and boost
a player's lifetime. The systems analyze log data and
player behavior to identify weak points.

22
Top challenges
of AI/ML
adoption
01 Data security
04
01 Expertise scarcity
03
01 Infrastructure
requirements
02
01 Lack of data or
data quality issues
01
01 Time-consuming
implementation
08
01 Legal concerns
and compliance
issues
07
01 Inappropriate
business use cases
06
01 Budget
constraints
05
Top challenges companies
face during AI/ML adoption

23
Along with its proven benefits, Aartificial Iintelligence also
comes complete with certain data privacy challenges.
A complicated AI or ML model is the result of feeding
significant amounts of data. The latter may often include
sensitive user data that is subject to an increasing array
of regulations.
This shifting regulatory landscape in the field of AI has
become even more intricate with GDPR compliance,
CCPA, and other federal legislation coming into effect.
The stringent scope of privacy-approved actions is also
reinforced by the monstrous costs of a data breach -
from ~$3.86M to ~$4.24M in 2021.
Moreover, ethical concerns of AI that result in model
misbehavior mount as well. For example, incomplete
data sets where a population is underrepresented can
deliver biased results or become unstable. To mitigate
this issue, organizations must pay due diligence to the
training data and make sure that all entities are equally
represented in the dataset.
Therefore, AI-friendly companies need to address
potential legal, compliance, and ethical risks in order to
avoid liability or fines. However, just like data privacy
can be the thorn side of AI, once used wisely, it can also
become the forte of this technology.
Advanced AI-enabled security measures techniques like
homomorphic encryption and differential privacy enable
data sharing through encryption or noise-induction.
Therefore, a growing number of companies use the AI
challenges to their advantage.
AI and data privacy:
compatible or at odds?
48% of the executives surveyed report using
AI in cyber threat detection, 34% embed it for
prediction, and 18% leverage AI for a response.
Capgemini

24
Instead of choosing between privacy and AI
innovation, McKinsey suggests the following risk
management practices when adopting AI:
• Shape the company’s ethical standards and vision
• Build the conceptual design
• Assign governance and key roles
• Implement transparency tools
• Hold awareness campaigns and regular training.
However, the main shortcut to responsible AI
and compliant technologies is the choice of the
right technology vendor. If your tech supplier can
demonstrate product maturity and calibrated data-
secured processes, then your AI journey won’t pose
any difficulties or put your data at risk.
How to choose the right
AI vendor
A targeted, well-trained AI system can render better ROI and performance provided you hire the right technology
vendor. The number of software companies that provide AI-focused services amounts to over 3,000 with 15 providers
leading the AI transformation. With the choice so wide, businesses often have a hard time separating the wheat from
the chaff.
Making the correct choice of a vendor can either make or break your company so here
are some points to consider when setting up the selection process.
Expertise
First and foremost, an ideal vendor should demonstrate
proficiency in intelligent technologies. Without true
and acknowledged expertise, it is impossible to
properly assess the core of the problem, thus failing to
deliver the set objectives.
We recommend assessing technical background from
a holistic standpoint. In particular, you should take
into consideration the set of programming languages
and platforms, target industries as well as specific
methodologies. Wordly-acknowledged certifications,
R&D centers, and the company’s corporate products
also attest to a solid technical background behind a
vendor.
Experience
The right AI supplier should also strike the right
balance between theory and practical experience.
Therefore, an AI software development company
should have a proven record of projects grounded
in various industries. Testimonials and success
stories also prove the acknowledged presence of the
company in the AI market.
The same goes for managing large-scale projects
with remote teams. A vendor that lacks experience
working with distributed teams would find it hard
to set up stable communication at the technical and
organizational levels.
1 2

25
Reviews
Social proof of a company’s high-quality services is
also important. Therefore, when selecting the right
vendor, scour the Internet for real customer feedback.
Popular review platforms like Clutch and GoodFirms
identify leading IT companies and rate companies
based on reviews and other essential factors.
Also, you can ask for references from existing or
previous clients. Social media platforms can also
provide valuable insights into a company's overall
digital presence and brand image. If the chosen AI
vendor has been awarded some reputable industry
awards or featured in ratings, it’s another sign of public
recognition and proven expertise.
Communication
Efficient distribution and processing during the
development process of AI software are only possible
when all systems communicate with each other
smoothly. The same goes for team collaboration.
To avoid misunderstandings and bottlenecks,
make sure your technical vendor offers a single
communication channel for you and your team. Ideally,
it should be a selection of team management tools so
that the client can choose the one they are familiar
with.
You can get a general idea of how your AI supply
manages interaction already at the screening stage.
If all your queries get addressed in a timely and
comprehensive manner then it’s unlikely you will have
any communication lags.
Clarity
Improved visibility and detailed reporting of the
process are core for the success of the project. At the
selection stage, the clarity and transparency of your
contract and vendor obligations should also be your
front and center.
In particular, the AI company should balance the
technical complexities of an AI project, and the
commercial terms with the need to have each
party’s rights vividly stated in the agreement. These
prerequisites have to be backed up with an agreed
timetable for delivery and implementation, as well as
an acceptance test procedure. Intellectual property
rights, project cost, and confidentiality provisions must
also be clearly stated in a contract.
3
4
5

26
Software development pricing is quite challenging
with lots of variables and requirements specific to
each business. Usually, the ballpark estimation of your
AI project depends on the following factors:
• The type of software you’re building (3D computer
vision, pose estimation software, predictive
solution, etc.)
• The complexity of your AI solution (cloud-fueled
backend, third-party APIs, and others)
• The nature and amount of data your project needs
and the data you actually have
• The quality of data available
• The team size and structure
• The overall scope of the project
• Timeframe, etc.
The AI implementation usually starts with a Discovery
stage or a PoC. This is a pre-development phase that
helps answer the questions that stakeholders face
and minimize vague requirements at the start of the
project.
This part of the AI development process is crucial
to have a holistic view of the final product, identify
the suitable technical approach as well as the most
cost-effective solution to the technical problem. To
get a more accurate estimation of future expenses,
we recommend requesting a quote directly from a
technology vendor.
How much does an AI
development project cost?
In most cases, the Discovery stage or PoC
requires a budget of around $20-30k to
deliver the initial piece.

27
From managing global supply chains to finding lost
animals, intelligent machines are influencing every
facet of our lives.
In a broad sense, all applications of smart systems
serve three types of business needs. They automate
business processes, fetch insights through data, and
interact with customers and employees. Moreover, by
automating low-value tasks and production output,
intelligence solutions can ensure higher profits
and lower operational costs. The business value of
Artificial Intelligence is even more tangible in analytics
that improves decision-making across the organization
and eliminates business problems.
With a span so wide, every organization can benefit
from AI-enabled capabilities in at least one business
function. Moreover, as the positive tendency in AI
implementation persists, we will witness the gradual
shift of automation into full-adopted business practice.
However, the long-term success of any tech-savvy
company relies on a stable and futureproof digital
strategy. As the market is evolving at lightning speed,
businesses should be ready to stand up to new
technical challenges and process increasing amounts
of incoming data for better decision-making.
At InData Labs, our mission is to drive the digital
transformation of global companies and help them
uncover new business opportunities with the help of
AI-enabled systems. Be prepared for the varieties of
markets with our computer vision, predictive analytics
and NLP solutions and other GDPR-compatible smart
systems.
Conclusion
Artificial intelligence is a central tenet for
disruptive changes in Industry 4.0 and a
driving force of modern businesses.

indatalabs.com
InData Labs is a leading data science firm and AI-
powered solutions provider with its own R&D center.
Having a mission to bring the power of AI to every
business, we help organizations of any size create
intelligent products and services or shape intelligent
business processes.
Since 2014, our solutions and consulting services help
our clients to get valuable insights into data, automate
repetitive tasks, enhance performance, add AI-driven
features, and prevent cost overruns.
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